1、Tech Trends 2024Deloittes 15th annual Tech Trends report helps business and technology leaders separate signal from noise and embrace technologys evolution as a tool to revolutionize business.Trend LinesThe future is already here,albeit unevenly distributedOur technology case studies form a collage

2、of how pioneering leaders and organizations are building distinct facets of the future,today,through emerging technology Trends 2024Trending the trends:Last decade of researchNote:To learn more about past Tech Trends,go to analysis.20242023202222020201820162015Rebooting the digital workpl

3、ace Intelligent interfacesInternetof ThingsAmbient computingThrough the glassBespoke for billionsHuman experience platformsBeyond marketingDigital realityMixed realityAR and VRgo to workDimension-al marketingData-sharingmade easyMachine data revolution Enterprise data sovereigntyDark analyticsIndust

4、rial-izedanalyticsOpening up to AIMLOps:Industrial-ized AI DigitaltwinsAI-fueled organiza-tionsMachine intelligenceAmplified intelligenceAbove the cloudsCloud goes verticalNoOps in a serverless worldAPIimperativeEverything-as-a-serviceBlockchain:Ready for businessBlockchain to blockchainsTrust econo

5、myDemocra-tized trustAPI economyFlexibility,the best abilityDEI tech:Tools for equity Finance and the future of ITNo-collar workforceIT unboundedRight-speed ITIT workerof the futureStrategy,engineeredCIO as chiefintegration officerThe tech stack goes physicalSupply unchainedArchitec-ture awakensConn

6、ectivity of tomorrowInevitable architectureAutonomic platformsReengineering technologySoftware-defined everythingIn uswe trustCyberAIZerotrustEthical technology and trustDevSecOpsand the cyber imperativeConnect and extendIT,disrupt thyselfCorerevival The new coreReimagining core systemsCore renaissa

7、nceBUSINESS OF TECHNOLOGYCYBER ANDTRUSTINFORMATIONINTERACTIONCOMPUTATIONGenie out ofthe bottleSmarter,not harderDefending realityFrom DevOpsto DevExCoreworkoutInterfaces in new places Tech Trends 20241Table of contentsTech Trends 24:Executive summaryINTRODUCTION Generative AI:Force multiplier for hu

8、man ambitionsINTERACTIONInterfaces in new places:Spatial computing and the industrial metaverse INFORMATIONGenie out of the bottle:Generative AI as growth catalyst COMPUTATIONSmarter,not harder:Beyondbrute force computeBUSINESS OF TECHNOLOGYFrom DevOps to DevEx:Empowering the engineering experienceC

9、YBER AND TRUSTDefending reality:Truth in an age of synthetic mediaCORE MODERNIZATIONCore workout:From technical debt to technical wellnessAcknowledgments02.06.10.16.26.34.42.48.55.2Three elevating forces(interaction,infor-mation,and computation)and three grounding forces(business of technology,core

10、modernization,and cyber and trust)continue to be the bedrock upon which we build Tech Trends,Deloittes annual exploration of the impact of emerging technologies.In Tech Trends 2024,our 15th annual foray,we highlight the stories of pioneering organizations that are ahead of the curve in using new tec

11、hnologies and approaches that stand to become the norm within 18 to 24 months.We also project where the trends could be headed during the coming decade.Elevating forcesThe history of IT has been defined by pioneering advances in interaction,information,and computation,which together serve as an endu

12、ring source of innovation.Interaction Interfaces in new places:Spatial computing and the industrial metaverse Augmented and virtual reality for consumer applica-tions have garnered a lot of attention,but these tech-nologies are making their biggest impact in industrial settings.Companies are using t

13、he industrial metaverse to power things such as digital twins,spatial simulation,augmented work instructions,and collaborative digital spaces that make factories and businesses safer and more efficient.Factory workers,designers,and engi-neers are benefiting from immersive 3D interactionthrough teste

14、d devices such as tablets and experimental ones such as smart glassesin ways that traditional knowledge workers havent yet.Accessible,high-fidel-ity 3D assets are paving the way to an operationalized spatial web,where a digital layer atop reality accelerates ways of working.Eventually,autonomous mac

15、hines,advanced networking,and even simpler devices can lead to breakthrough spatial web applications,such as remote surgeries or entire factory floors being overseen by a single well-connected worker.Tech Trends 2024Executive summary3Tech Trends 2024InformationGenie out of the bottle:Generative AI a

16、s growth catalystPhilosophers have long debated whether machines are capable of thought,but generative AI makes the question moot.The underlying operation of these models shares much in common with earlier machine learning tools,but thanks to enhanced computing power,better training data,and clever

17、coding,generative AI technology can imitate human cognition in a number of ways.Regardless of whether it possesses intelligence in the philosophical sense,it does in a practical sense,creating the opportunity for huge productivity and efficiency gains in enterprise settings.Now that machines can beh

18、ave,comprehend,and narrate like humans,the question becomes how this will impact business and the world broadly.Computation Smarter,not harder:Beyond brute force computeAs technology has become a bigger differentiator for enterprises,businesses have built ever-more complex workloads.Typical cloud se

19、rvices still provide more than enough functionality for most business-as-usual operations,but for the cutting-edge use cases that drive competitive advantage,a new need for specialized hardware is emerging.Training AI models,performing complex simulations,and building digital twins of real-world env

20、ironments require different kinds of computing power.Leading businesses today are finding new ways to get more out of their existing infrastructure and adding cutting-edge hardware to further speed up processes.Soon,some will be looking beyond traditional binary computing entirely.Grounding forcesEx

21、isting systems and investmentsrepresented by the business of technology,core modernization,and cyber and trustwill need to integrate well with pioneering innovations so that businesses can seamlessly operate while they grow.Business of techFrom DevOps to DevEx:Empowering the engineering experienceAs

22、 emerging technology is increasingly viewed as a differ-entiator and crucial part of the business,tech talent is becoming more important than ever.Yet,ways of work-ing are far from efficient:In most companies,developers only spend 30 to 40 percent of their time on feature development.But now,a new f

23、ocus is emerging for companies that are dedicated to attracting and retaining the best tech talent:developer experience,or DevEx,a developer-first mindset that aims to improve software engineers day-to-day productivity and satisfaction by considering their every touchpoint with the organiza-tion.In

24、the years to come,DevEx can lead to a future of integrated,intuitive tools that enable citizen developers across the business to drive tech value.Cyber and trustDefending reality:Truth in an age of synthetic mediaWith the proliferation of AI tools,its now easier than ever for bad actors to impersona

25、te and deceive their targets.Were seeing deepfakes being used to get around voice and facial recognition access controls.Theyre also 4being used in phishing attempts.Security risks are multi-plying with every new content-generation tool that hits the internet.However,leading organizations are respon

26、d-ing through a mix of policies and technologies designed to identify harmful content and make their employees more aware of the risks.Core modernizationCore workout:From technical debt to technical wellness After years of investments in once-cutting-edge technol-ogies,companies are grappling with a

27、n expanded set of core technologies,including mainframes,networks,and data centers,that are in dire need of moderniza-tion.Those that want to lead in the future need to forgo piecemeal approaches to technical debt for a new holis-tic frame of technical wellness.Preventative wellness assessments,root

28、ed in business impact,can help teams prioritize which areas of the tech stack need treatment and which can continue serving ITs needs.In the years to come,companies are likely to develop a highly custom-ized and integrated wellness plan across the tech stack,including investments in self-healing tec

29、hnologies that reduce tomorrows modernization needs.5Tech Trends 20246Generative AI:Force multiplier for human ambitionsINTRODUCTIONLast year,our team of futurists and research-ers decided to use generative artificial intel-ligence(AI)to create the cover and chapter art in Tech Trends 2023.The resul

30、t was nothing short of spectacular.Yet our exact-ing design standards required significant human collaboration and intervention in the genera-tion process.On the heels of that successful experiment and the subsequent launch of ChatGPT and ensuing generative AI mania,we decided to explore the use of

31、AI-generated text to help write the introduction of this years Tech Trends.As with last years artwork,substan-tial human intervention was required,supporting our point that in the era of artificially intelligent machines,humans are more important than ever.As someone whos spent a quarter-century up

32、to my eyeballs in all things newfangled,I want to provide some additional perspective on the current excitement around generative AI and frame this breakthrough technology within the context of our enduring macro technology forces.Tech evolution,business revolution First,while generative AI feels at

33、 once unprecedented and revolutionary,the technology itself is actually a surprisingly straightforward evolution of machine intel-ligence capabilities that weve been tracking and chron-icling since Tech Trends inception.Organizations have employed mechanical muscles(industrial robotics)for nearly 70

34、 years,and mechanical minds(machine learning systems)for the last 25.That our inorganic colleagues can now paint a picture,write a product description,or sling Python is neither random nor unexpectedtheyre the next page in a book that future computer scientists might one day call Cognitive Automatio

35、n:The Early Years.Indeed,the best companies have been engaged in this quest to reduce the cost of decision-making for at least the last 15 years(figure 1).Technologically,generative AI is simply the next chapter in the ongoing history of information.But on the busi-ness side,the hyperbole is very mu

36、ch warranted.Make no mistake:The newfound opportunity to augment productive professionals with silicon-based intelli-gence is indeed a generational business opportunity.Its a full-on paradigm shift that is poised to unlock the doors to altogether-new business opportunities and fundamentally change h

37、ow the enterprise itself orga-nizes and operates.You cant shrink your way to successIn my recent experience,far too many business leaders see generative AI as a mere weight loss pilla quick and dirty means to simply reduce costs by automating and,in turn,eliminating jobs.Nipping and tucking at busin

38、ess cost centers is a short-term approach to pleasing share-holders,taxpayers,and other key constituentsbut in the final calculus,you cant shrink your way to success.To be sure,B-school textbooks are rife with cautionary tales of once-great organizations that,seduced by the allure of automation and

39、outsourcing,found themselves leaner,meaner,and,as a result,squarely in the crosshairs of competitors or acquirers.7Tech Trends 2024Figure 1A brief history of informationSource:Deloitte Technology Futures Report 2021.TIME(years)t175t75t50t25t10tt+10t+nFirst computer designFirst digital computerMid-20

40、th centuryLate 20th centuryEarly 21st centuryTodayHorizon nextFurthest starsInformationEndgameStoreArithmeticcalculationRelationaldatabasesDescriptiveanalyticsPredictiveanalyticsCognitiveautomationExponentialintelligenceGeneral-purpose AIIntelligenceInstead,generative AI should be considered rocket

41、fuel for elevated ambitions.Virtually every C-level leader I meet tells me,in their own vivid way,how the intensity of their present demands precludes them from paying as much attention as theyd like to future ambitions.“Operations eats innovation for lunch,”one chief tech-nology officer(CTO)told me

42、,in a spin on the famous Peter Drucker-ism“Culture eats strategy for breakfast.”AI(traditional and generative alike)can free up precious human cycles from mundane operations and allow people to focus,finally,on higher-value work that better aligns with tomorrows business imperativesnamely,new and im

43、proved products,services,experiences,and markets(in other words,the time-tested keys to profit-able growth).Wanted:Generative humansMany worry that generative AI reduces the need for(or perhaps more accurately,diminishes the worth of)human creativity.Ive observed the opposite is true:In an age of cr

44、eative machines,creative humans matter more than ever.For example,late last year,I gathered with a room full of C-suite executives to demonstrate a then-new gener-ative AI tool that painted unique images based on text prompts.One of the attendees asked the tool,“Show me a sunset.”The resulting pictu

45、re was fine but unre-markable;the attendee shrugged and dismissed it as“just a sunset.”Undeterred,another participant took her turn,prompting the tool,“Show me a war between 8pretzels and cheeseballs on Mars where the pretzels have nunchucks and the cheeseballs have squirt guns.”The image generator

46、produced an absurd,delightful image that made the room full of executives applaud and marvel.Most(understandably)celebrated the“mirac-ulous machine”that rendered the image,but I couldnt help but quietly acknowledge the clever human with the magical mix of mind and moxie to even ask for such a thing.

47、With generative AI as a force multiplier for imag-ination,the future belongs to those who ask better ques-tions and have more exciting ideas to amplify.As generative machines continue to find purchase in the many nooks and crannies of our professional lives,people will determine whether these tools

48、scale with magic or mediocrity.With mindful and imaginative guid-ance,generative AI stands to unlock a world of magical new business possibilities.Without it,we run the risk of scaled mediocrityor worse.As my friend and Deloittes global CTO Bill Briggs likes to say,“Good does not come from making ba

49、d things faster.”Eyes to the skies,feet firmly on the ground Finallyand this is a big onenone of this works with-out a solid technology foundation.We geeks(ahem,professional technologists)are typically well aware of the old trope“garbage in,garbage out.”Our early forays into our shared AI future sug

50、gest that going forward,the experience will be more akin to“garbage in,garbage squared.”Small biases in training data can beget cata-clysmic biases in AI outputso get your enterprise data in order first.And remember:Information is just one of the six macro technology forces that drive business(figur

51、e 2).A creaky core in desperate need of modernization will buckle under tomorrows AI-fueled workloads.An undifferentiated computation strategy will increasingly break the bank.Cumbersome interaction modalities will muddy your message,to say nothing of disengaged talent,or worse,cyberthreats.If you t

52、ake anything from this years report,its this:Dont become so blinded by the buzz around generative AI that you neglect the five other fundamental forces.Indeed,AI matters more than ever,but this does not mean that everything else youve been working on suddenly doesnt.Onward,!mb(with a little help fro

53、m generative AI)Mike Bechtel Chief futurist,Deloitte Consulting LLP9Tech Trends 2024Figure 2Six macro forces of information technologySource:Deloitte analysis.SPECIALIZED COMPUTINGDECENTRALIZED PLATFORMSCLOUDCYBER AND TRUSTCOREMODERNIZATIONBUSINESS OF TECHNOLOGYINTERACTIONINFORMATIONCOMPUTATIONAMBIE

54、NT EXPERIENCEEXTENDED REALITYDIGITAL ENGAGEMENTEXPONENTIAL INTELLIGENCEMACHINE LEARNINGDATA AND ANALYTICSAMBIENT EXPERIENCEEXTENDED REALITYDIGITAL ENGAGEMENTELEVATING FORCESGROUNDINGFORCES1011Tech Trends 2024More often than not,novel technol-ogies spark excitement with early adopters and consumers b

55、efore quietly receding from the public eye,only to reappear months or years later as productive business tools.Some see this pattern as a movement along research firm Gartners technology hype cycle,1 while others understand it as a move from tech to toy to tool,as we discussed in Tech Trends 2023:In

56、 last years trend“Through the glass:Immersive internet for the enter-prise,”we projected that the metaverse,or the immersive internet,would soon graduate to a full-blown enterprise tool as companies discover and build around new inter-action capabilities such as augmented and virtual reality(AR/VR)s

57、imulations.2 This year,weve seen some of those metaverse capabilities progress in new directions,toward the broader realm of spatial computing.As theyve turned the corner from consumer toy to enterprise tool,spatial technologies are especially taking hold in industrial applications,where companies a

58、re focused on digital twins,3 spatial simula-tion,4 augmented work instructions,and collaborative digital spaces5 that make factories and businesses safer and more efficient.The opportunities are promising:Revenue driven by the industrial metaverse is projected to reach nearly US$100 billion by 2030

59、,far outpacing the consumer(US$50 billion)and enterprise(US$30 billion)segments.6 Whether through time-tested devices such as tablets or experimental ones such as smart glasses,factory workers,designers,and engineers are benefiting from immersive 3D interaction in ways that traditional knowledge wor

60、k-ers havent yet experienced.The industrial metaverse is defined by real-world physics,using spatial data and arti-ficial intelligence to render immersive visualizations that exactly replicate real-life processes.Imagine line workers using smart glasses to call an expert at a plant across the countr

61、y,or engineers prototyping new equipment in physics-based,photorealistic digital twins.Where orga-nizations have the opportunity to build new facilities,many are adopting a“simulation first”strategy before construction.Improved and accessible high-fidelity 3D assets and hardware for extended reality

62、(an umbrella term for immersive technologies such as AR,VR,and mixed reality)can pave the way to an operationalized spatial web,where a digital layer atop reality accelerates ways of working across industries.Eventually,this progress can lead to a simplified era of operations,where autonomous system

63、s,instant 3D models,and Interfaces in new places:Spatial computing and the industrial metaverse As the industrial metaverse transforms to enterprise tool,spatial technologies are taking hold in industrial applications,using data and AI to replicate real-life processes.INTERACTION12quantum computing

64、are paired with optimized human involvement for applications such as remote surgeries.Or imagine an entire factory floor staffed by a single well-connected worker.Now:Simulating the enterpriseOver the past few years,advancements in technology have been building the scaffolding for the industrial met

65、averse.Investments in digital twins,5G enablement,cloud,edge,and AI have driven significant value and addressed long-standing pain points.Thats why 92%of manufacturing executives surveyed in a recent Deloitte study said that their company is experimenting with or implementing at least one metaverse-

66、related use case,and,on average,they are currently running more than six.7 These executives already expect a 12%to 14%improvement in areas such as sales,throughput,and quality from investing in industrial metaverse use cases in the coming years.The most common use cases highlighted by executives wer

67、e process simulation and digital twins.8 In indus-trial settings where operations are complex,pricey,and exact,robust simulations are a lifesaver.When connected to real-time data and models through the Internet of Things(IoT)and advanced networking,simulations can increase the chances of successfull

68、y building a new operation or optimizing an existing one.Its no surprise,then,that some analysts believe the global market for digital twins could grow from US$6.5 billion in 2021 to US$125.7 billion in 2030.9 The optimal way to interact with these full-scale digital twins is through AR,a medium tha

69、t can overlay the physical world with a digital layer to create a shared,three-dimensional immersive internet.As a result,the global market for AR devices has been estimated at US$38.6 billion in 2022,with an annual growth rate of 36%through 2030 for related software and hard-ware.10 While industria

70、l and manufacturing applica-tions currently make up the largest market share for AR,health care applications(such as training,surgical simulation,and vein visualization)are expected to grow by a compound annual growth rate of 44%through 2030.Consumer applications,catalyzed by the e-com-merce boom of

71、 the pandemic,also abound,proving that the use cases for digital twins extend beyond just the enterprise.11Spatial operations are just beginning,and enabling tech-nologies continue to improve.Imagine powerful satellite networks combined with IoT sensors in a remote factory,processing real-time data

72、on output and performance.12 As technologies advance,a new era of digital twins is on the horizon,where simulations could be photorealistic,based on physics,and enabled by AI,13 all while linked to company ecosystems,such as BMWs Omniverse platform.14 This evolution is poised to affect multiple area

73、s of the enterprise,from space planning to design to operations.New:The spatial web is under construction The impending spatial web(also known as Web 3.0)promises to eliminate the boundary between digital content and physical objects,effectively blending these two realities into one.15 Through next-

74、gen interfaces such as smart glasses,the spatial web can allow us to interact with real-time information prompted by our physical environment,through geolocation,computer vision,or biometric commands like voice and gestures.Given the possibilities,the market for spatial computing is poised to dwarf

75、previous estimates for the metaverse,with some projections estimating upward of US$600 billion by 2032.16 While the true potential of the spatial web is still years away,innovators are building its infrastructure now.In the next 18 to 24 months,companies should pay attention to the value opportuniti

76、es for adopting spatial operations and arming their employees with tech that supercharges their work.US$600 billion by 2032Given the possibilities,the market for spatial computing is poised to dwarf previous estimates for the metaverse,with some projections estimating upward of US$600 billion by 203

77、2.13Tech Trends 2024Augmented workforceAs workers in industrial settings continue to adopt AR/VR tools,companies are reaping the benefits of efficiency and effectiveness across a few key areas:Increased monitoring.As AR devices and spatial immersion allow employees to be in multiple“places”at once,f

78、ewer experts could monitor a greater number of facilities.For instance,Nokias real-time eXtended Reality Multimedia provides 360-degree views,3D audio,and live streaming to allow human operators to immerse themselves in a physical space many miles away.17 This can bolster preemptive maintenance,secu

79、rity,and qual-ity control.Reduced onboarding time.New employees can follow standard operating procedures that are built into simulations,along with visual cues that help them learn while in the flow of work,instead of having to separate learning from practice.For example,new employees at a global ca

80、rmakers manufacturing plants use AR devices to collaborate in real time with experts across the United States.Sharing the same vision and sound,the experienced line workers can instruct exactly where and how to strike a hammer on a door.18 Reduced safety risk.As we discussed last year,companies can

81、arm workers with AR/VR to better prepare them for risky settings.Stanford Medicine is piloting a VR system that combines images from MRIs and CT scans,among others,to create a 3D model of a patients body prior to surgery.Surgeons can see and manipulate this anatomical digital twin,not only in traini

82、ng settings but in the operating room itself,as a more detailed guide to the body than 2D images.Doctors are already seeing benefits in improved accuracy and safety of some of the most complex procedures in medicine,such as brain surgeries.19Product design,development,and sales Use cases for spatial

83、 operations are not just limited to improving the bottom line;AR technologies can improve top-line revenue growth as well.For example,leading AR companies are enabling clothing retailers to integrate AR technology into their apps,websites,and physical locations to further differentiate their offerin

84、gs.With generative AI,these retailers can soon use AR technol-ogy to create 3D models from 2D images,increasing the availability of digital assets for customer engagement in a spatial web.Such AR technology can do much more than superim-pose an image of clothing over a shopper.For example,it can sim

85、ulate how fabric will fall on a customer or how different lines in the stitching create shadows.And the results are clear:Some retailers have seen an increase in revenue per visitor of more than 50%after building in AR technology.20 As brands aim to stay relevant in spatial computing,AR companies ar

86、e envisioning impact beyond retail,in sectors like education,entertainment,and travel.Another way to take advantage of spatial operations is in design and testing of products under simulated conditions,which can lead to major improvements in agility,time to market,and even sustainability.For instanc

87、e,instead of automakers subjecting their vehi-cles to hundreds of crash tests,they could use an initial set of data to simulate thousands of such tests and even consider events like natural disasters that cant be easily replicated in the real world.Pharmaceutical giant GSK applied these principles t

88、o employ simulations for vaccine production,enabling it to cut its time to run experiments from three weeks to a few minutes.21 And in heavy asset industries such as mining,simulations can help fine-tune machine movements for efficiency and reduce emissions while preparing for the move to more renew

89、able energy.Space planning and simulationThe old adage of“measure twice,cut once”takes on new meaning in the age of spatial computing.Companies can employ spatial computing to visualize,simulate,and test layouts of facilities before undertaking costly invest-ments:Measure 3,000 times,cut once.Archit

90、ects can design an exact replica of a factory or hospital,replete with predictions of how many humans and machines will be present and how theyll interact and move.For instance,a busy hallway for triaging ER patients may need to be expanded after a hospital simulates its usual intake numbers.Or an a

91、uto manufacturer may want to predict how a planned factory will handle a surge in demand for electric vehicles in the years to come.14Thats exactly what Hyundai Motor had in mind when partnering with Unity to build a pioneering full-scale factory simulation.The automaker plans to test the factory vi

92、rtually to calculate an optimal method of operations and spacing,as well as one day enable plant managers to assess issues remotely.22 Similarly,Siemens,a pioneer in the industrial metaverse field,has announced a new factory in Germany that will be entirely planned and simulated in the digital world

93、 first.23 Only after adjusting its blueprints based on digital insights does the company plan to build the real-world campus.Apart from the use cases for designing new spaces,spatial computing can also optimize a companys use of existing physical locations.For instance,the retail planning team at GU

94、ESS planned out in-store updates digitally and moved forward only after virtual testing,resulting in a 30%cost reduction and a lower carbon footprint from reducing travel to make in-store updates.24Next:Lets get digital The impending release of the Apple Vision Pro has made the term“spatial computin

95、g”more mainstream than ever.25 While some may wonder if this latest trend may be a passing fad,we would not bet against simplic-ity.The history of technology has proven that simpler interaction modalities have reliably unlocked massive step changes in the accessibility,and in turn,use of technologie

96、s.26 Spatial computing may be another such step changewhere our natural gestures and ways of interacting with the physical world can be mapped onto the digital world,creating an ideal match between biol-ogy and technology.As interaction technology continues to expand beyond computer science into the

97、 natural sciences(as we discuss in xTech dimensions27),brain-computer interfaces(BCIs)represent the furthest star of progress for simplic-ity.While todays BCI functionality is concentrated in restoring human capabilities(such as the ability to walk),future endeavors may augment human capabili-ties,e

98、nabling us to accomplish digital and physical tasks at a speed and scale that were previously unimaginable.For that to take place,well need enabling technologies such as 6G networking and IoT.Through high-speed connectivity and massive machine-type communications,machines of the future may be able t

99、o coordinate with each other seamlessly.28 And the World Economic Forum has already predicted that omnipresent IoT sensors can one day digitize physical human work,enabling a higher degree of automation.29 Such advancements could pave the way for our interactions with machines to be much simpler as

100、they become smarter at communicating about their environment and status.Imagine a future of interaction where BCIs enable us to start,monitor,and modify an interconnected series of machines on an assembly line.Industrial work could also become remote work,carried out from a desk.And language could f

101、eel like a bottleneck compared with the efficiency of human thought.While the possibilities are exciting,companies are at a crossroads:They need to move beyond the buzzwords if they want to be early moversor find themselves trying to catch up with innovators.Beyond hiring or training their engineers

102、 on computer vision,sensor tech,and spatial mapping algorithms,they should also get ahead of the potential risks.Opening up the physical world to digital manipulation comes with its fair share of privacy issues(as computer vision expands),cyberse-curity issues(as the physical world becomes hackable)

103、,and data protection issues.30 Fortunately,the progress of digital twin technologies and early 3D models offers valuable lessons for steps forward.Once the initial benefits of spatial operations are under-way in industrial settings,enterprises should be prepared:The natural evolution of spatial comp

104、uting may radically change the way we interact with consumer and enterprise applications in the years to come.15Tech Trends 20241.Gartner,“Gartner Hype Cycle,”accessed October 2023.2.Deloitte Insights,Through the glass:Immersive internet for the enterprise,December 6,2022.3.Aaron Parrott,Lane Warsha

105、w,and Brian Umbenhauer,Digital twins:Bridging the physical and digital,Deloitte Insights,January 15,2020.4.Deloitte,“Unlimited Reality for operations,”accessed October 2023.5.Deloitte,“Unlimited Reality for the workforce,”accessed October 2023.6.ABI Research,Evaluation of the enterprise metaverse op

106、portunity,September 20,2022;Transparency Market Research,Industrial metaverse market outlook 2031,June 2023.7.Paul Wellener et al.,“Exploring the industrial metaverse,”Deloitte and Manufacturing Leadership Council,accessed October 2023.8.Ibid.9.J.Pankaj,M.Neha,and V.Vitika,Digital twin market size,s

107、hare and trends analysis by 2030,Allied Market Research,July 2022.10.Grand View Research,Augmented reality market size and share report,2023.11.Ibid;Markets and Markets,Augmented reality market report,August 2021.12.Deloitte,xTech Futures:SpaceTech,2023.13.MIT Technology Review Insights and Siemens,

108、The emergent industrial metaverse,March 29,2023.14.Deloitte,“Connect and extend:NVIDIAs vision for modernizing legacy applications,”Deloitte Insights,November 9,2022.15.Allan V.Cook,Siri Anderson,Mike Bechtel,David R Novak,Nicole Nodi,and Jay Parekh,The spatial web and Web 3.0,Deloitte Insights,July

109、 21,2020.16.Market.us,Global spatial computing market report,August 2023.17.Nokia,“Real-time eXtended Reality Multimedia,”accessed October 2023.18.Jack Siegel,“HoloLens 2 brings new immersive collaboration tools to industrial metaverse customers,”Microsoft,December 20,2022.19.Mandy Erickson,“Virtual

110、 reality system helps surgeons,reassures patients,”Stanford Medicine News Center,July 11,2017.20.Deloitte interviews.21.Deloitte,“Unlimited Reality for operations.”22.Hyundai Motor Company,“Hyundai Motor and Unity partner to build Meta-Factory accelerating intelligent manufacturing innovation,”press

111、 release,January 6,2022.23.Siemens,“Siemens to invest 1 billion in Germany and create blueprint for industrial metaverse in Nuremberg metropolitan region,”press release,July 13,2023.24.Deloitte,“Unlimited Reality for operations.”25.Tech Trends is an independent publication and has not been authorize

112、d,sponsored,or otherwise approved by Apple Inc.26.Deloitte,Tech Trends 2023 Prologue:A brief history of the future,Deloitte Insights,December 6,2022.27.Deloitte,Tech Trends 2023 epilogue,Deloitte Insights,December 6,2022.28.Charles McLellan,“What is the state of 6G,and when will it arrive?Heres what

113、 to look out for,”ZDNET,February 17,2023.29.Francisco Betti,Thomas Bohn,and Cathy Li,“The industrial metaverse and its future paths,”World Economic Forum,January 19,2023.30.Wellener et al.,“Exploring the industrial metaverse.”Endnotes1617Tech Trends 2024Starting around 2015,people began refer-ring t

114、o almost any application of machine learning as artificial intelligence.Some pundits and industry experts pushed back.These applications were pattern matchers,they said.1 Given an input,they return an output.The models didnt think,but rather computed probabilities,so how could they be intelligent?Ge

115、nerative AI makes moot the question of whether machines can be intelligent.The underlying operation of these models shares much in common with earlier machine learning tools,but thanks to accelerated computing power,better training data,and clever appli-cations of neural networks and deep learning,g

116、enerative AI technology can imitate human cognition in a number of ways.More and more often,machines that possess intelligence in at least a functional,practical sense create the opportunity for huge productivity and efficiency gains in enterprise settings,as well as the opportunity to bring innovat

117、ive new products and services to new markets.In plenty of instances,AI tools perform at least as well as,if not better than,human counterparts in tests of cognitive capabilities.ChatGPT recently scored a 5“extremely well qualified”on the notoriously challenging Advanced Placement biology test.2 The

118、Dall-E 2 image generator was able to solve Ravens Matrices,a test given to measure a subjects visual IQ.3 Anthropics Claude 2 chatbot scored above the 90th percentile in the verbal and writing sections of the GRE test,which is used by many graduate schools in the United States and Canada as part of

119、their admissions standards.4 In fact,AI tools now consistently outperform humans on measures of handwriting,speech,and image recognition;reading comprehension;and language understanding.5 The question is no longer whether AI tools are intelligent.Today the question is more about how to deploy these

120、cognitive tools in ways that provide real business impact.Now:Generative AI interest and adop-tion soar,promising disruptionGenerative AI captured the publics imagination when it burst onto the scene in the second half of 2022 and first few months of 2023.Few technologies have ever debuted to such f

121、anfare.Adoption and use of genera-tive AI have been sudden and rapid among the public.OpenAI reported reaching 100 million users within 60 days of releasing ChatGPT to the public;in compari-son,it took TikTok nine months to reach that milestone(figure 1).6 Midjourneys image generator has around 16 m

122、illion users.7 There are 1.5 million daily users of Dall-E 2.8 Googles Bard chatbot had 10 million page views in July.9 Growth in the use of generative AI in enterprise settings has been no less impressive,according to Deloittes 2023 CEO Priorities Survey(figure 2).10 Genie out of the bottle:Generat

123、ive AI as growth catalyst Since gen AI technology exploded on the scene,many enterprises have been scrambling to determine how their businesses might benefit.The answer might be simpler than they think.INFORMATION18Figure 1Generative AI interest and adoption soarNote:Graphs are figurative and do not

124、 depict actual rates of growth.Source:Krystal Hu,ChatGPT sets record for fastest-growing user base-analyst note,Reuters,February 2,2023.2 months 9 months Public launch100 million usersChatGPTTikTokFigure 2Business leaders are increasingly using generative AI in enterprisesSource:Fortune/Deloitte CEO

125、 Survey Insights,summer 2023.Generative AI will increase efficiencies in their business80%My business is evaluating or experimenting with generative AI55%Generative AI will increase growth opportunities52%My business is implementing generative AI to some degree37%Percentage of business leaders who a

126、greed with the following statements 19Tech Trends 2024Whats made generative AI so impactful is a conver-gence of factors.First,advanced hardwareprimar-ily specialized AI chips used in training modelshave helped produce more advanced models such as large language models(LLMs).These tools have gone ma

127、in-stream due to a seamless user experience,enabling even nontechnologists to engage with very advanced models.All this attention has kicked off a gold rush among investors(figure 3).Investors are pouring money into startups that have generative AI technology at their core,betting that were witnessi

128、ng the dawn of a new para-digm for business technology,one where insights are surfaced automatically,contracts review themselves,and a never-ending stream of content is generated to keep brands in front of their audiences.While theres been plenty of talk about how AI may threaten jobs,theres no real

129、 indication that business leaders are planning on using it to automate knowledge jobs at any kind of scale.In a survey of leaders,improv-ing content quality,driving competitive advantage,and scaling employee expertise were the most common reasons for deploying generative AI.Reducing headcount was on

130、e of the lowest priorities.11 It looks more likely that AI will liberate workers from rote,repetitive tasks and free them to focus on more creative aspects of their jobs.The picture thats emerging is that AI is coming,and for some,its already here.But,as the saying goes,leading businesses know they

131、cant shrink their way to growththat is,minimize risks or costs as a path to growth.12 This means the most productive uses of generative AI wont be about replacing people but instead will focus on arming employees with tools that help them advance and enhance their productivity,knowledge,and creativi

132、tywhich,in turn,will help drive innovation in the enterprise.Executives are increasingly under pressure to speed this transition and stay ahead of their competitors.According to one survey,64%of CEOs say theyre facing significant pressure from investors,creditors,and lenders to speed the adoption of

133、 generative AI.13 But just as leaders know they cant shrink their way to growth,they also know the importance of leading with need.14 Shoehorning gener-ative AI into any and all processes just because its a shiny new thing is unlikely to deliver meaningful gains.Instead,businesses may benefit from a

134、 more strategic Figure 3Investment in generative AI has explodedAnnual cumulative venture capital money invested in generative AI technologies20232022Q1Q2Q3Q4Q1US$4.5BUS$12BNote:Graphs are figurative and do not depict actual rates of growth.Source:Jacob Robbins,The most active investors in generativ

135、e AI,News&Analysis,June 15,2023.20approach to implementation that focuses on leveraging generative AIs unique capabilities to solve existing prob-lems and help businesses differentiate themselves from competitors.Thats the approach innovative enterprises are taking today.New:Enterprises aim for scal

136、-ability and domain expertise The true value of generative AI is likely to be unlocked when organizations can use it to transform business func-tions;reduce costs;disrupt product,service,and innova-tion cycles;and create previously unachievable process efficiencies.To get there,business leaders may

137、consider more of an evolutionary approach to their enterprise data and technology strategy.Becoming an AI-fueled organization takes careful disci-pline and a focus on maintaining systems and algo-rithms.15 Just as a rocket needs a launch pad and flight controls to reach its destination,generative AI

138、 tools need infrastructure and control systems to succeed in enterprise settings.The good news is a lot of the muscle memory businesses have been developing over the past several years while building up data analytics and machine learning capabilities also applies to generative AI,though some practi

139、ces may require subtle retooling.Generative AI typically requires terabytes of data on graphics processing unitenabled high-performance computing clusters.Since few businesses have this infra-structure,most will access it as a service.Via application programming interfaces,engineers can weave genera

140、-tive AI capabilities into their existing software with-out needing to build out new infrastructure.16 While AI vendors are prioritizing ease of use in their products,its still important for enterprises to keep these engineering requirements in mind.Additionally,its important to pick your use cases

141、wisely.AI can be used to reduce costs,speed up processes,reduce complexity,transform customer engagement,fuel innovation,and build trust.17 The specific application of generative AI will vary from business to business,but looking for projects that drive improvements in one area is a good place to st

142、art.Here are some additional considerations from businesses that have already adopted the technology.Data is the fuel that drives the generative AI engine Businesses need to ensure their data is architected prop-erly and accessible to AI applications to enable model training as well as next-generati

143、on use cases.This was one of the learnings for Enbridge,the larg-est natural gas utility in North America.Several years ago,when it began an ambitious cloud migration jour-ney,it didnt set out to pioneer new generative AI uses.The primary goals were to modernize its infrastruc-ture and eliminate tec

144、hnical debt by reducing the size of its on-premises data centers.Along the way,it built a centralized data repository that collects data from across the enterprise,including regulatory,marketplace,HR,and other data.This centralized data marketplace replaced what used to be hundreds of silos.Once gen

145、erative AI arrived on the scene,Enbridges lead-ership knew this centralized data marketplace was the perfect engine to drive new AI-fueled efficiencies.The technology team rolled out a generative-AI-based copilot tool that helps developers quickly and more efficiently build out code.It also supplied

146、 the companys office staff with a copilot tool to help them navigate productivity applications.The goal,says Joseph Gollapalli,director of cloud,IT ops,and data at Enbridge,is to“accelerate our delivery and drive innovation and efficiency.These AI solutions have the potential to enhance our operatio

147、ns,improve safety,elevate the customer experience,and enhance our environmental performance.”18Governance is more important than ever Without effective governance guardrails,AI cant scale.A governance framework should define the businesss vision,identify potential risks and gaps in capabilities,and

148、validate performance.19 These types of consider-ations not only safeguard the business but can also help scale projects beyond the proof-of-concept stage.At CarMax,the largest used car retailer in the United States,effective use of generative AI is predicated on a systematic,enterprise-wide approach

149、 that embraces 21Tech Trends 2024the power of the technology while also putting in place guardrails to ensure employees are using it effectively.One of CarMaxs most prominent applications is a tool that adds AI-generated content to research pages for vehicles.These pages summarize information from t

150、hou-sands of actual customer reviews to let shoppers quickly see what other buyers had to say.Shamim Mohammad,executive vice president and chief information and technology officer at CarMax,says these kinds of use cases deliver the most business value when they are done in a controlled manner.20 Car

151、Max has prioritized governance,which may not feel like the most exciting aspect of generative AI but is key to scal-ing it.The company has created an AI governance team dedicated to ensuring teams across the organization are using AI appropriately.The key is that this team is not charged with simply

152、 saying no to new use cases.Part of its mission to help scale impactful applications across the enterprise by standardizing how models are trained and used.The goal is that generative AI is used beyond just technology or product teams.“Weve done a lot of cool things through machine learn-ing and AI,

153、”says Mohammad.“What Im focused on now is ensuring were using it in a responsible manner and making sure that,as a company,whatever we deploy,its being done in ways that are consistent with our core values.”Make sure you have the(copy)right Generative AI has altered the copyright landscape.Now anyon

154、e can create images,video,text,and audio with a few clicks.However,some models have been trained on content that comes from third parties.One US court recently ruled that this makes AI-generated content inel-igible for copyright protection.21 Additionally,training models on copyrighted material scra

155、ped from the web may present legal risks,including intellectual property infringement.22 However,these dont have to be problems.The content provider Shutterstock,for one,has shown that it is possible to use generative AI in ways that both respect the rights of the original copyright holder and ensur

156、e that AI-generated content can be used for commercial purposes.Shutterstock recently unveiled an image-generating tool that creates visuals based on users prompts.Like other image generators,the tool was trained on images created by third-party artists.However,unlike other image generators,every ar

157、tist whose work was used in training the model agreed ahead of time to participate.Participating artists are also paid when their work is used to train a model and when a user licenses an image gener-ated on the platform.Shutterstock licenses its content as data,which allows it to offer added legal

158、protections to end users.“Everyone is creating content,from CEOs to folks who work in retail,”says Michael Francello,director of innovation at Shutterstock.“The need to create content was absolutely exploding.We saw an early opportunity to look at our content as data that could train generative AI m

159、odels.Its about protecting the core of our business,but also respecting the core,which is the artists and the contributors.”23 Crawl,walk,run,fly This approach has for years been an effective way for enterprises to scale up their use of service offerings.24 Generative AI is no different.In the crawl

160、 stage,appli-cations may be ad hoc and require lots of manual effort.These eventually graduate to the walk stage,in which processes become more defined at the foundational level and automated.In the run stage,use cases get standard-ized and become pervasive at the enterprise level.When its time to f

161、ly,the organization leverages the work it has already done to embrace next-generation capabilities.That approach helped chemical company Eastman begin developing generative AI-based internal services.The company has a long track record of using data and analytics in an industry that isnt typically k

162、nown for it.For example,it has an advanced intelligence service(with proprietary thermal stability measures)that will predict when a heat transfer fluid used in its customers industrial processes is likely to degrade,allowing engi-neers to maintain optimal fluid quality,forecast predic-tive maintena

163、nce needs,and avoid costly downtime on manufacturing lines.Building on this experience,the company is now exper-imenting with how generative AI can enhance its sales processes.It built an AI-enabled tool that can read 22through natural language text files.Still in the develop-ment stages,the tool is

164、 being tested on extracting insights from notes from sales calls.These documents are gener-ated by sales teams after every call but rarely get read by anyone,even though they hold significant intelligence.Now,with the help of generative AI,the company is starting to unlock those insights.“It lets us

165、,a chemical company,bring a digital service layer to the table to differentiate ourselves in the market and create a competitive advantage,”says Aldo Noseda,chief information officer at Eastman.25 Given the pace with which generative AI is progressing,it may be wise to apply this kind of framework t

166、o new enterprise use cases.Let proof-of-concept projects lead to standardized practices that become standard operating procedures across the enterprise.Once a business has achieved this kind of maturity,the sky is the limit.In the near future,it may become even easier for busi-nesses to reap the ben

167、efits of generative AI within their industries thanks to the emergence of models that are trained on more specific data.Today,most enterprises that are using generative AI are using tools built on foun-dational models that were trained on general-purpose data.That tools with such a general knowledge

168、 base can be used in very specific subject-matter areas shows the power of LLMs.But the next generation of LLMs is likely to be more hyper-focused and tailored to busi-nesses specific needs.26 This is a trend thats already begun to emerge.NVIDIA has introduced a tool called BioNeMo,an LLM aimed at t

169、he biotech sector.27 Googles Contact Center AI is a tool trained to handle customer service interactions.28 BloombergGPT is a chatbot designed to answer finance industryrelated questions.29 ClimateBERT is a model trained on climate change research and can advise busi-nesses on their climate-related

170、risks.30 As businesses realize the benefits of models trained specif-ically for their sector,were likely to see more demand for these types of services.More than one-third of enter-prises are already planning to train and customize LLMs for their business needs in the future.31 Private LLMs are like

171、ly where the true potential of generative AI lies for businesses.They are developed and maintained by organizations that keep underlying code proprietary and closed to the public.These LLMs are purpose-specific,hosted securely,and trained on private data,and they can offer tremendous competitive adv

172、antage to organi-zations.This is likely the next wave in the generative AI journey.23Tech Trends 2024Next:Imaginative executives wantedThe motivational poster has gone from mere corporate clich to its own category of meme,but one overused aphorism may reclaim its stature as an enterprise imper-ative

173、:Were only limited by our imagination.While you might have heard the saying before,teams and organizations have always been bound by limiting factors.They dont have enough data or the right data.Leadership is skeptical.Or,most dreaded of all:“That wont move the needle.”But in a generative AI world,i

174、magination truly is the only limit.Its now possible to create constant streams of content,identify new operational efficiencies,or scan regulatory filings or customer reviews in minutes.Now the only question is,what do you want to know?Asking better questions will become a crucial skillset in enterp

175、rises that have adopted generative AI.This trend may create demand for a new type of leader,one that is driven more by creativity than weve seen in the past.The past 20 years or so have seen leaders rewarded for steering their organizations based on data and insights,rather than gut and instinct.But

176、 the next few years could see more imaginative leaders leap ahead.Give an image generator a boring prompt,and it will produce a boring picture.The same is true of generative AI applications at the enterprise level.Unimaginative use cases produce limited impact.As more businesses attempt to differ-en

177、tiate themselves from their competition,leaders who can find creative new applications for generative AI may separate themselves from their peers who are busy just following data.This isnt to say that data-driven decision-making will become pass.In fact,it will be as important as ever,if not more so

178、.But the definition of what it means to be data-driven may change because the range of data that leaders can access will increase,thanks to generative AI.So much of an enterprises data is buried in natural language text files,machine logs,and,increasingly,intel-ligent products.32 Generative AI gives

179、 organizations the ability to make sense of this digital exhaust.The creative leader will understand what these oft-overlooked data sources have to say about their business and will use generative AI to ask intelligent questions of the data sources.And they will ask these questions at the speed of t

180、hought,rather than waiting for their weekly report.But all that barely scratches the surface of generative AIs full range of likely impacts.Were pretty sure its going to be seismic.We just dont know exactly where the ground will shift the most.241.Michael I.Jordan,“Artificial intelligencethe revolut

181、ion hasnt happened yet,”Harvard Data Science Review,July 1,2019.2.Tom Huddleston Jr.,“Bill Gates watched ChatGPT ace an AP Bio exam and went into a state of shock,”CNBC,August 11,2023.3.Saliha Malik,“How will the Open AI products DALL.E and DALL.E 2 change the face of augmented reality?,”Medium,Marc

182、h 1,2023.4.Anthropic,“Claude 2,”July 11,2023.5.Douwe Kiela et al.,“Dynabench:Rethinking benchmarking in NLP,”Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,June 611,2021,pp.411024.6.Krystal Hu,“ChatGPT set

183、s record for fastest-growing user base analyst note,”Reuters,February 2,2023.7.Rob Krivec,“Midjourney statistics(How many people are using Midjourney?),”Colorlib,September 14,2023.8.Leigh McGowran,“OpenAI unveils Dall-E 3 art generator with ChatGPT support,”Silicon Republic,September 21,2023.9.Simil

184、arweb,Google Bard overview,accessed October 2023.10.Deloitte,“Summer 2023 Fortune/Deloitte CEO Survey insights,”accessed October 2023.11.IBM Institute for Business Value,“Enterprise generative AI,”July 2023.12.Mike Bechtel,Khalid Kark,and Nishita Henry,“Innovation Study 2021:Beyond the buzzword,”Del

185、oitte Insights,September 30,2021.13.IBM Institute for Business Value,“Enterprise generative AI.”14.Bechtel,Kark,and Henry,“Innovation Study 2021:Beyond the buzzword.”15.Nitin Mittal,Irfan Saif,and Beena Ammanath,State of AI in the Enterprise,5th edition report,Deloitte,October 2022.16.Deloitte AI In

186、stitute,“Generative AI is all the rage,”2023.17.Deloitte AI Institute,The AI Dossierexpanded,2022.18.Interview with Joseph Gollapalli,director of cloud and IT operations at Enbridge,June 13,2023.19.Beena Ammanath et al.,“Trustworthy AI in practice,”Deloitte AI Institute,2022.20.Interview with Shamim

187、 Mohammad,executive vice president and chief information and technology officer at CarMax,August 3,2023.21.Trishla Ostwal,“Judge rules GenAI content does not have copyright protection,”Adweek,August 22,2023.22.Gil Appel,Juliana Neelbauer,and David A.Schweidel,“Generative AI has an intellectual prope

188、rty problem,”Harvard Business Review,April 7,2023.23.Interview with Michael Francello,director of innovation at Shutterstock,May 12,2023.24.Jagjeet Gill,Deepak Sharma,and Anne Kwan,“Scaling up XaaS,”Deloitte,September 29,2019.25.Interview with Aldo Noseda,chief information officer,Eastman Chemical C

189、ompany,October 11,2023.26.Deloitte AI Institute,A new frontier in artificial intelligence,2023.27.Ibid.28.Google Cloud,“Contact Center AI,”accessed October 2023.29.Bloomberg,“Introducing BloombergGPT,Bloombergs 50-billion parameter large language model,purpose-built from scratch for finance,”press r

190、elease,March 30,2023.30.ChatClimate,“ClimateBert,”accessed October 2023.31.expert.ai,“Nearly 40%of enterprises surveyed by expert.ai are planning to build customized enterprise language models,”press release,May 11,2023.32.Jagjeet Gill et al.,“Analytics operating models,”Deloitte,2020.Endnotes25Tech

191、 Trends 20242627Tech Trends 2024Smarter,not harder:Beyondbrute force computeBusinesses are getting more out of their existing infrastructure and adding cutting-edge hardware to speed up processes.Soon,some will look beyond binary computing entirely.As technology has become a bigger differentiator fo

192、r enterprises,busi-nesses have built ever-more computa-tionally complex workloads.Training artificial intelligence models,perform-ing complex simulations,and build-ing digital twins of real-world environments requires major computing resources,and these types of advanced workloads are beginning to s

193、train organizations existing infrastructure.Typical cloud services still provide more than enough functionality for most business-as-usual operations,but for the cutting-edge use cases that drive competitive advantage,organizations now require highly optimized and specialized computing environments.

194、1 Optimizing code bases for the hardware they run on is likely the first step toward speeding up business appli-cations.An area thats long been overlooked,this opti-mization can provide significant performance gains.Beyond that,emerging hardware geared specifically for training AI and other advanced

195、 processes is becom-ing an enterprise mainstay.Graphics processing units(GPUs),AI chips,and,one day,quantum and neuro-morphic computers are beginning to define the next era of computing.1Most advances in computing performance have focused on how to get more zeros and ones through a circuit faster.Th

196、ats still a fertile field,but,as were starting to see,it may not be for much longer.This is leading research-ers and tech companies to look for innovative ways to navigate aroundrather than throughconstraints on computing performance.In the process,they could be laying the groundwork for a new parad

197、igm in enterprise computation in which central processing units(CPUs)work hand in hand with specialized hardware,some based in silicon,others,potentially not.Now:Past performance not indicative of future returnsThe last 50 or so years of computingand economicprogress have been shaped by Moores Law,t

198、he idea that the number of transistors on computer chips,and therefore performance,roughly doubles every two years.2 However,chipmakers are increasingly running into physical constraints.At a certain point,there are only so many transistors a piece of silicon can hold.Some observers believe Moores L

199、aw is already no longer valid.3 This is contested,but at the very least,the end of the runway may be coming into view.Chips are getting both more power-hungry and harder to cool,which hampers performance,4 so even as chip manu-facturers add more transistors,performance doesnt necessarily improve.All

200、 this comes at a bad time:Businesses are increas-ingly moving toward computationally intensive work-loads.Industrial automation is ramping up,with many COMPUTATION28companies developing digital twins of their real-world processes.Theyre also increasingly deploying connected devices and the Internet

201、of Things,both of which create huge amounts of data and push up processing require-ments.Machine learning,especially generative AI,demands complex algorithms that crunch terabytes of data during training.Each of these endeavors stands to become major competitive differentiators for enterprises,but i

202、ts not feasible to run them on standard on-premises infrastructure.Cloud services,meanwhile,can help bring much-needed scale,but may become cost-prohibitive.5 The slowing pace of CPU performance progress wont just impact businesses bottom lines.NVIDIA CEO Jensen Huang said in his GTC conference keyn

203、ote address that every business and government is trying to get to net-zero carbon emissions today,but doing so will be difficult while increasing demand for traditional computation:“Without Moores Law,as computing surges,data center power use is skyrocketing.”6 After a certain point,growing your da

204、ta center or increasing your cloud spend to get better performance stops making economic sense.Traditional cloud services are still the best option for enabling and standardiz-ing back-office processes such as customer relationship management,enterprise resource planning(ERP),enter-prise asset manag

205、ement,and human capital manage-ment.But running use cases that drive growth,such as AI and smart facilities,in traditional cloud resources could eventually eat entire enterprise IT budgets.New approaches,including specialized high-performance computing,are necessary.7New:Making hardware and soft-war

206、e work smarter,not harderJust because advances in traditional computing perfor-mance may be slowing down doesnt mean leaders have to pump the brakes on their plans.Emerging approaches that speed up processing could play an important role in driving the business forward.Simple When CPU performance in

207、creased reliably and predict-ably every year or two,it wasnt the end of the world if code was written inefficiently and got a little bloated.Now,however,as performance improvements slow down,its more important for engineers to be efficient with their code.It may be possible for enterprises to see su

208、bstantial performance improvements through leaner code,even while the hardware executing this code stays the same.8A good time to take on this task is typically during a cloud migration.But directly migrating older code,such as COBOL on a mainframe,can result in bloated and inefficient code.9 Refact

209、oring applications to a more contemporary code such as Java can enable enterprises to take advantage of the modern features of the cloud and help eliminate this problem.The State of Utahs Office of Recovery Services recently completely a cloud migration of its primary case manage-ment and accounting

210、 system.It used an automated refac-toring tool to transform its code from COBOL to Java and has since seen performance improvements.“Its been much faster for our application,”says Bart Mason,technology lead at the Office of Recovery Services.“We were able to take the functionality that was on the ma

211、inframe,convert the code to Java,and today its much faster than the mainframe.”10Situated Using the right resources for the compute task has helped Belgian retailer,Colruyt Group,embark on an ambi-tious innovation journey that involves automating the warehouses where it stores merchandise,using comp

212、uter vision to track and manage inventory levels,and devel-oping autonomous vehicles that will one day deliver merchandise to customers.One way to manage the compute workload is to lever-age whatever resources are available.Brechtel Dero,division manager at Colruyt Group,says thanks to the prolifera

213、tion in smart devices,the company had plenty of computation resources available.11 However,many of these resources were in operational technologies and werent tied to the companys more traditional digital infrastructure.Developing that connective tissue was initially a challenge.But Dero says Colruy

214、t benefit-ted from a supportive CEO who pushed for innova-tion.On the technical side,the company operates a 29Tech Trends 2024flexible ERP environment that allows for integration of data from a variety of sources.This served as the backbone for the integration between information and operations tech

215、nology.“Its about closing the gap between IT and OT,because machines are getting much smarter,”Dero says.“If you can have a seamless integration between your IT envi-ronment,ERP environment,and machines,and do it so that the loads and compute happen in the right place with the right interactions,we

216、can make the extra step in improving our efficiency.”12 Specialized Smarter coding and better use of existing compute resources could help enterprises speed up many of their processes,but for a certain class of problems,businesses are increasingly turning to specialized hardware.GPUs have become the

217、 go-to resource for training AI models,a technology that is set to drive huge advances in oper-ational efficiency and enterprise innovation.As the name suggests,GPUs were originally engineered to make graphics run more smoothly.But along the way,developers realized that the GPUs parallel data-proces

218、s-ing properties could streamline AI model training,which involves feeding terabytes of data through algorithms,representing one of the most computationally intensive workloads organizations face today.GPUs break prob-lems down into small parts and process them at once;CPUs process data sequentially

219、.When youre training an AI algorithm on millions of data points,parallel processing is essential.13 Since generative AI has gone mainstream,the ability to train and run models quickly has become a business imperative.Large tech and social media companies as well as leading research,telecom,and marke

220、ting companies are deploy-ing their own GPUs on their premises.14 For more typical enterprises,however,using GPUs on the cloud is likely to be the most common approach.Research shows cloud GPUs reduce AI model training costs by six times and training time by five times compared with training models

221、on traditional CPUs on the cloud(figure 1).15 Most lead-ing chip manufacturers are offering GPU products and services today,including AMD,Intel,and NVIDIA.Figure 1GPUs can reduce AI model training time and costsSource:Deloitte analysis.Cloud CPUsCloud GPUsTimeCost-6X-5XTraining30However,GPUs arent t

222、he only specialized hardware for training AI models.Amazon offers a chip called Inferentia,which it says aims to train generative AI,including large language models.These chips are built to handle large volumes of data while using less power than traditional processing units.16 Google also is in the

223、 AI chip game.It offers a prod-uct it calls Tensor Processing Units,or TPUs,which it makes available through the Google Cloud service.These processors fall under the category of application-specific integrated circuits,optimized to handle matrix opera-tions,which underlie most machine learning model

224、s.17 Specialized AI chips are likely to continue to gain prom-inence in enterprise settings in the coming months as businesses realize the value of generative AI.Increased adoption of AI may strain most organizations existing data center infrastructure,and the higher performance of custom chips comp

225、ared with general-purpose resources could become a major competitive differentiator.This doesnt mean enterprises will reap these benefits overnight.Historically,theres always been a lag between the wide availability of specialized hardware and the development of standards and ecosystems necessary fo

226、r using hardware to its fullest.It could be years before enterprises move at pace to adopt these innovations.Enterprises can develop ecosystem partnerships to prepare for emerging technologies and have ready the skills needed to take advantage of these innovations as soon as the business case is rip

227、e.Next:Beyond binaryThe beauty of the CPU has always been its flexibility.It can power everything from spreadsheets to graphic design software.For decades,enterprises could run just about any application on commodity hardware without having to think twice.But researchers and tech companies are devel

228、oping new approaches to processing data and building entirely new worlds of possibilities in the process.One of the most promising new paradigms may be quantum comput-inga technology thats been discussed for years and whose impact is becoming clearer.Quantum annealing is likely to be one of the firs

229、t enter-prise-ready applications of quantum computing,promis-ing a new route to solving optimization tasks such as the traveling salesperson problem.18 These types of problems have traditionally been attacked using machine learning.But due to the complexity of optimization problems,the underlying ma

230、th,and therefore computation,gets incredibly intricate,while still delivering less-than-per-fect answers.But quantum annealing uses the physical attributes of quantum bits to find an optimal solution,enabling quan-tum computers to find solutions to notoriously complex problems that involve a high nu

231、mber of variablessuch as space launch scheduling,financial modeling,and route optimization.19 Quantum annealing can find solutions faster while demanding less data and consuming less energy than traditional approaches.Quantum annealing may be the first widely available application of quantum compute

232、rs,but its not likely to be the last.The technology is maturing rapidly and could soon be applied to a range of problems to which classical computers are poorly suited today.Quantum computers process information in fundamentally differ-ent ways than classical computers,which allows them to explore c

233、hallenges from a different perspective.Problems involving large amounts of data over long periods of time are potentially a good fit.For example,IBM recently worked with Boeing to explore how quantum computing could be applied to engineer stronger,lighter materials and find new ways to prevent corro

234、sion.“It is time to look at quantum computers as tools for scientific discovery,”says Katie Pizzolato,director of theory and quantum computational science at IBM Quantum.20“In the history of the development of clas-sical computers,as they got bigger,we found amazing things to do with them.Thats wher

235、e quantum is today.The systems are getting to a size where theyre compet-itive with classical computers,and now we need to find the problems where they provide utility.”Quantum computers represent an entirely new way of performing calculations on data compared with our current state of binary comput

236、ation,but its not the only new approach.Another promising field is neuromorphic computing.This approach takes its inspiration from the neuron-synapse connections of the human brain.Rather 31Tech Trends 2024than a series of transistors processing data in sequence,transistors are networked,much like b

237、rain neurons,and computing power increases with the number of connec-tions,not just transistors.The major benefit is the poten-tial for increased performance without increased power.21 Better AI applications are the most likely use case for neuromorphic computing.While its still early days for this

238、computing approach,its easy to see how a computer that is modeled on the human brain could give a boost to cognitive applications.Natural language understand-ing,sensing,robotics,and brain-computer interfaces are all promising use cases for neuromorphic computing.The field is still relatively new,bu

239、t it has the backing of computing heavyweights such as IBM,which is devel-oping a neuromorphic chip called TrueNorth,22 and Intel,which just introduced the second generation of its research-grade chip,Loihi.23Optical computing is another promising approach.Here,processors use light waves to move and

240、 store data,rather than electrons crawling across circuit boards.The advan-tage is that data is literally moving at the speed of light.This field is less developed than quantum and neuro-morphic computing,but research is underway at major technology companies,such as IBM and Microsoft.24The common a

241、dvantage to all these paradigms is using lower power than CPUs or GPUs while achieving simi-lar,and potentially better,performance.This is likely to become even more important in the years ahead as businesses and nations as a whole push toward net-zero carbon emissions.Demand for faster and more per

242、vasive computing is only going to increase,but simply spinning up more traditional cloud instances isnt going to be an option if businesses are serious about hitting their targets.This doesnt mean these technologies are going to be a panacea for tech-related climate worries.There are still concerns

243、around cooling and water use for quantum,and,as with any form of computing,bulky code could drive up energy requirements for technologies such as neuromorphic computing.The need for simplified code will persist,even as new computing options unfold.These innovations arent likely to replace CPUs at an

244、y point.Traditional computing resources remain the most useful and trustworthy tools for the vast majority of enterprise workloads,and thats not likely to change.But businesses may be able to advance their most inno-vative programs by incorporating some of these tech-nologies into their infrastructu

245、re in the future.And just as were seeing cloud services that combine CPUs and GPUs in one product today,future offerings from hyper-scalers may add quantum,neuromorphic,or optical capabilities to their products,so engineers may not need to even think about what kind of hardware is running their work

246、loads.Our informational worlds today are defined by zeros and ones,and,without a doubt,this model has taken us far.But the future looks ready to lean into the near-limitless possibilities of not-only-digital computing,and this could drive a new era of innovation whose outlines were only just beginni

247、ng to see.32Endnotes1.Shankar Chandrasekaran and Tanuj Agarwal,The secret to rapid and insightful AI-GPU-accelerated computing,Deloitte,2022.2.Brittanica,“Moores law:Computer science,”accessed October 31,2023.3.David Rotman,“Were not prepared for the end of Moores Law,”MIT Technology Review,February

248、 24,2020.4.A16Z podcast,“AI hardware,explained,”podcast,July 27,2023.5.Ranjit Bawa,Brian Campbell,Mike Kavis,Nicholas Merizzi,Cloud goes vertical,Deloitte Insights,December 7,2021.6.Jensen Huang,“NVIDIA GTC 2024 keynote,”speech,NVIDIA,accessed October 31,2023.7.Christine Ahn,Brandon Cox,Goutham Ball

249、iappa,and Tanuj Agarwal,The economics of high-performance computing,Deloitte,2023.8.A16Z podcast,“AI hardware,explained.”9.Stephanie Glen,“COBOL programming skills gap thwarts modernization to Java,”TechTarget,August 10,2022.10.Interview,Bart Mason,technology lead,Utah Office of Recover Services,Jul

250、y 28,2023.11.Interview with Brechtel Dero,division manager,Colruyt Group,August 18,2023.12.Ibid.13.Ahn,Cox,Balliappa,and Agarwal,The economics of high-performance computing.14.NVIDIA,“NVIDIA hopper GPUs expand reach as demand for AI grows,”press release,March 21,2023.15.Ahn,Cox,Balliappa,and Agarwal

251、,The economics of high-performance computing.16.Amazon Web Services,“AWS inferentia,”accessed October 31,2023.17.Google Cloud,“Introduction to cloud TPU,”accessed October 31,2023.18.Cem Dilmegani,“Quantum annealing in 2023:Practical quantum computing,”AIMultiple,December 22,2022.19.Deloitte,“Quantum

252、 annealing unleashed:Optimize your business operations,”video webinar,August 3,2023.20.Interview,Katie Pizzolato,director of theory and quantum computational science,IBM Quantum,October 16,2023.21.Victoria Corless and Jan Rieck,“What are neuromorphic computers?”Advanced Science News,March 13,2023.22

253、.Filipp Akopyan et al.,TrueNorth:Design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip,IBM,October 1,2023.23.Intel Labs,“Neuromorphic computing and engineering,next wave of AI capabilities,”accessed October 31,2023.24.Bert Jan Offrein,“Silicon photonics,”IBM,accessed Octob

254、er 31,2023;Microsoft,“AIM(Analog Iterative Machine),”accessed October 31,2023.33Tech Trends 20243435Tech Trends 2024From DevOps to DevEx:Empowering the engineering experienceA new focus is emerging for companies that are dedicated to attracting and retaining the best tech talent:developer experience

255、.BUSINESS OF TECHNOLOGYWith emerging technologies domi-nating the news,tech talent remains as important as ever to businesses.The worldwide developer population is projected to reach nearly 29 million worldwide in 2024,1 outpacing the entire population of Australiayet barely keeping up with the pace

256、 of demand,as we discussed in Tech Trends 2023.2 Despite this growth,developer productivity is far from optimized at most organizations:Developers typically spend only 30%to 40%of their time on feature development.3 Shifts to Agile,DevSecOps,and cloud engineering have all become mainstream in recent

257、 years because they enhance speed,quality,and cross-functional collabora-tion.Now,a new focus is emerging for companies that are dedicated to attracting and retaining the best tech talent:developer experience,or DevEx,a developer-first mindset that considers each touchpoint software engi-neers have

258、with the organization to improve day-to-day productivity and satisfaction.4 Leaders agree that a good developer experience results in better end-user and customer experiences,increas-ingly shifting focus from measuring speed and quantity to providing the proper tools,platforms,and feedback mechanism

259、s and ultimately creating a culture that works for developers.Metrics around speedsuch as lines of code or story points per developerare giving way to more holistic measures such as time-to-first pull request(how long it takes for a developer to publish their first major batch of code),backlog chang

260、es,and defect ratios.5 Decentralized teams and fragmented tool sets are giving way to pod structures that formalize collaboration across engineering,user experience,cyber,risk,quality management,and product teams,in addition to tailored performance management and streamlined architecture and tooling

261、.The upside of all these changes?Eighty-one percent of companies have realized a moderate or significant impact on profitability from their investments in developer experience.6 Improving engineering experience can lead to a future state in which newly hired software engineers are produc-tive from d

262、ay one on the job,and a companys internal technology landscape is thoroughly integrated with its business strategy.Looking forward,companies may look to the benefits of integrated intuitive tools and realize that the investments made for developer experience may enable other aspects of the business

263、to drive tech value.Now:Engineers are in high demand but hinderedDigital transformation was kicked into high gear by the recent COVID-19 pandemic.Eighty-five percent of global CEOs agree that their organizations have signifi-cantly accelerated transformation after 2020,7 and global spending on digit

264、al transformation is expected 36to reach US$2.51 trillion in 2024,nearly double the amount spent in 2020.8 This increased investment has led to an elevated role for tech leaders and employees,as discussed in our 2023 Global Technology Leadership Study.9 Organizations across industriesnot just techar

265、e adding software to their core offerings and operational infrastructure.Think of,for example,auto manufacturers autonomous driving algorithms and vehicle connectivity platforms that enable new mobil-ity services;industrial manufacturers use of connected equipment like turbines and generators to col

266、lect perfor-mance data,identify issues before failures,and optimize maintenance scheduling;and consumer brands virtual try-on apps that use augmented reality to let shoppers digitally try on clothes.Software engineering excellence,and the developers who bring those capabilities,is criti-cal for comp

267、anies to capitalize on these transformation opportunities.As a result,the demand for developers has skyrocketed.Jobs in software development are expected to grow by 25%within the next decade,compared with an 8%average growth rate for other professions.10 And thats not just within the tech industry.I

268、n fact,only 10%of new software developer roles are concentrated in tech companies,as the need for digital goods and services across industries is only poised to grow in the years to come.11 Companies across government and commercial sectors are following the developer experience practices estab-lish

269、ed by tech-forward companies to attract and retain developers.For example,the implementation of contin-uous integration and continuous deployment pipelines drives more frequent code releases;the notion of“shift-ing left”calls for adopting automation and testing earlier in the software development pr

270、ocess;12 and full-stack engineering exposes tech talent to the full spectrum of development on a product(front end,back end,web,and so on)through simulations,apprenticeship models,and rotations.13 Yet,despite the demand for software developers,many companies have not cleared the roadblocks to develo

271、per Figure 1Software developers suffer from productivity challengesSource:Stripe,The developer coefficient,September 2018.Maintenance issues17 hours Annual opportunity cost of US$85BBad code4 hours Average work week41 hours37Tech Trends 2024productivity and satisfaction(figure 1).14 Time spent on co

272、nfiguration,tool integration,and debugging takes away from time spent building new features and appli-cations that can grow revenue.15Moreover,developers typically deal with a notoriously homogenous and noninclusive culture that hinders job satisfaction.16 And on top of this,the proliferation of low

273、-code and no-code platforms such as Appian,Outsystems,and Zoho Creator has lowered the barriers to software development,enabling the“citizen devel-oper”movement.While this brings new opportunities,such as enabling faster innovation by decentralizing soft-ware development across the business,it could

274、 also pose potential risks around governance,security,and tech debt accumulation.The problems that engineering leaders face when design-ing leading developer experiences are multifaceted.Instead of making one-off changes,a holistic change in engineering experience can help attract and retain the bes

275、t talent by arming them with the tools,performance measures,and processes to succeed.New:The DevEx differenceWhile the shift to DevOps focused on productivity tools and frameworks,developer experience consists of a range of mutually reinforcing capabilities that an organization provides to maximize

276、developer productivity and satisfaction,which operate in a virtu-ous cycle.Developers empowered with the right tools,processes,and culture typically perform better.In fact,according to the Harvard Business Review,employees are 230%more engaged and 85%more likely to stay beyond three years in their j

277、obs if they feel they have the technology that supports them at work.17 In turn,devel-opers who are satisfied are able to move fast,deploy code frequently,and collaborate in ways that enable efficiency.To enable this virtuous cycle,organizations need a new,thorough framework that considers all aspec

278、ts of impact to developers,not just tooling or talent(figure 2).Shifts in DevEx could then manifest in improvements in prod-uct performance and customer experience.Figure 2Organizations can improve engineers effectiveness and experience through standardized capabilities Source:Deloitte analysis.Plat

279、forms and toolsWays of working and flowTalent experienceArchitectureMaximizing polyglot modularity and maintainability to promote scalability,reusability,and reliabilityMeasurement toolsGarnering data on platform health,product usage,and developer efficiencyEnablement toolsCreating tools to ensure c

280、ollaboration and knowledge-sharing among engineersDevelopment acceleratorsEnhancing efficiency and reducing friction in day-to-day development activitiesService ownershipOwning and integrating responsibilities across the life cycle to reduce riskWorkflow management and DevSecOps Increasing organizat

281、ion and coordination of activities to drive consistent outcomesCommunity and culturePromoting a fun,productive,and diverse workplace environmentContinuous learningDeveloping education pathways for engineers throughout their careersCareer progression and developmentMobilizing advancement opportunitie

282、s for engineers at all stages of their careers38Platforms and toolsOne aspect of establishing an effective developer expe-rience is providing standardized platforms and tools.Though the notion may seem simple,it is far from simple in action.Developers today often wrestle with an average of more than

283、 250 software as a service applications and other technical environments that are poorly integrated and cause fragmentation of knowledge across teams.18 Companies can address this inefficiency through three key capabilities:ArchitectureMaximizing polygot modularity and maintainability to promote sca

284、lability,reusability,and reliability.Measurement toolsGarnering data on platform health,product usage,and developer efficiency.Enablement toolsCreating tools to ensure collab-oration and knowledge-sharing among engineers.Leading organizations are acting on this trend by creat-ing a one-stop platform

285、 for developers,where they can access a source code repository,onboarding informa-tion,documentation,tools,software development kits,and more.Only 37%of developers have access to such a portal today,19 but Gartner estimates that by 2025,75%of organizations with platform teams will provide self-servi

286、ce developer portals to improve developer expe-rience and accelerate innovation.20 Ways of working and flowOnce the ideal technologies(that is,platforms and tools)are in place,the second aspect of DevEx is building clear,continuous processes for developers so they can accom-plish tasks in a flow,wit

287、hout facing friction from discon-nected systems or poor governance.Organizations can focus on three capabilities here:Development acceleratorsEnhancing efficiency and reducing friction in day-to-day development activities.Service ownershipOwning and integrating responsibilities across the life cycle

288、 to reduce risk.Workflow management and DevSecOpsIncreasing organization and coordination of activities to drive consistent outcomes.The ideal developer experience would likely entail a single process and pipeline across the organization for code validation and testing,performance measurements,and s

289、afe rollbacks of code without causing outages.While cutting-edge tech organizations are working to approach this scenario,enterprises across industries are making progress on maturing their developer experience across the capabilities outlined above.For instance,CarMax,the largest used car retailer

290、in the United States,has found clear success in modernizing development processes.21 The technology team replaced a project-based operating model with a product-based model made up of cross-functional teams.Instead of measuring developers on projects completed,CarMax began setting transparent quarte

291、rly objectives for more frequent delivery.It also placed a huge focus on rapid testing of products with associates and customers so that it could gather feedback and iterate before rolling out a new feature.In a similar vein,after Etsy invested 20%of its engineering budget in developer experience,it

292、 was able to scale its organization from 250 people to almost 1,000.22Talent experienceFinally,for process and technology changes to be accepted,the culture must be conducive to a more modern engineering experience.Developers in many companies still specialize in traditional mainframe languages and

293、ways of working,but others are eager to spend their time on innovation toward a purpose that resonates with them.Companies looking to attract and retain such talent can build out these capabilities:Community and culturePromoting a fun,produc-tive,and diverse workplace environment(much needed in most

294、 technology divisions).2339Tech Trends 2024 Continuous learningDeveloping education path-ways for engineers throughout their careers.With more tech talent learning skills from a wide variety of resources and methodologies(including blogs,online coursework,books,and formal education),it is more impor

295、tant than ever for organizations to standardize onboarding and training.24 Career progression and developmentMobilizing advancement opportunities for engineers at all stages of their careers,as discussed in last years Tech Trends.25 For example,Citibank has defined career paths for engineers who wan

296、t to keep building their technical skills,allowing them to stay current with coding trends.By prioritizing technical expertise,the organization facilitates enduring careers in deep technical roles,providing technologists with diverse and compelling avenues for progression.26Most importantly,a shift

297、in culture can help companies realize that developers shouldnt be measured in the same way as other employees.Because developers are often asked to build new features and work in an experimental capacity,standards of velocity and quality wont always be accurate measures of learning or growth.Rather,

298、tech talent needs an avenue to collectively brainstorm,learn from others,and feel connected to end goals.CarMax paid close attention to talent experience,not just process,when undergoing its own transformation.On top of physically moving employees to sit in cross-func-tional teams so IT wouldnt be i

299、solated,it organized product showcases.Every two weeks,engineers would present on technology capabilities in development,along with outcomes and lessons learned,to increase trans-parency and hear feedback from senior leadership.To further signify the elevated role of the technology team,the IT depar

300、tment was also formally renamed CarMax Technology,with a focus on business outcomes over traditional IT requirements and deadlines.Next:Every employee is a tech employeeCompanies often hope to hire“10 x”engineers,those who are 10 times as productive as the average devel-oper.But searching for unicor

301、ns in the talent market is rarely a winning strategy.Instead,with the right platforms,process,and culture in place,10 x engineers could become much less rare.Especially as generative AI continues to bolster developer productivity and opens up a future of increased workplace automation,many of todays

302、 hindrances may not be relevant in the next five to 10 years.As we mentioned in last years trend on“serial specialists,”engineers who are interested in challenging themselves can use productivity enhancements to free up their time and work on new and interesting projects and technologies over the co

303、urse of their career.27Crucially,the work organizations do in the next few years to set up a new developer experience wont be contained to the technology division.As technology itself continues to become more and more central to the business,technology tasks and required talent will likely become ce

304、ntral as well.Standardized tools and plat-forms,like the ones discussed above,as well as advanced low-or no-code tech,may one day enable all employees of a business to become low-level engineers.Instead of transforming from a 1x to a 10 x engi-neer,employees outside the tech division could be going

305、from zero to one.These citizen developers are likely to be empowered by a future where the most common programming language is not Python or Java but English,or whatever natural language they choose.Depending on how quickly automation improves,more employees should carry out basic technology tasks i

306、n the years to come or simply oversee automated digi-tal processes.Expanding the pool of technologists then allows experienced engineers to focus on the highly complex tasks and novel builds on which theyre excited to work.By all accounts,the opportunity to focus on cutting-edge innovations and chal

307、lenging problems is likely to bolster both productivity and satisfaction for the next generation of developers.40Endnotes1.Statista,“Number of software developers worldwide in 2018 to 2024(in millions),”2023.2.Deloitte Insights,Flexibility,the best ability:Reimagining the tech workforce,Tech Trends

308、2023,December 6,2022.3.Jacob Bo Tiedemann and Tanja Bach,“Why should you invest in good developer experience today,”Thoughtworks,May 10,2021.4.Deloitte,“Accelerating developer experience(DevEx),”accessed October 2023.5.Nolan Wright,“Three engineering performance metrics the business can understand,”

309、Forbes,August 5,2019.6.Carrie Tang,“Forrester snapshot:Platform engineering is key to reducing time to market,”Humanitec Blog,March 17,2023.7.Deloitte Insights,How digital transformationand a challenging environmentare building agility and resilience,April 29,2021.8.Statista,“Spending on digital tra

310、nsformation technologies and services worldwide from 2017 to 2026(in trillion US dollars),”October 2022.9.Deloitte Insights,“Global CIO and technology leadership survey collection,”accessed October 2023.10.Bureau of Labor Statistics,US Department of Labor,Occupational Outlook Handbook,accessed Octob

311、er 2023.11.Will Markow,Jonathan Coutinho,and Andrew Bundy,Beyond tech:The rising demand for IT skills in non-tech industries,Burning Glass Technologies and Oracle Academy,September 2019;Steve Rogers,Kasey Lobaugh,and Anthony Waelter,The rise of digital goods:Opportunity over threat,Deloitte Insights

312、,January 23,2023.12.Mike Kavis,“DevOpsshift everything left,”Deloitte,February 28,2018.13.Deloitte,Technology Skills Insights report,accessed October 2023.14.Stripe,“The developer coefficient,”September 2018.15.VMware Tanzu,“Developer experience:Optimizing DevOps UX,”accessed October 2023.16.Wiley E

313、dge,Diversity in tech:2021 US report,accessed October 2023.17.Brad Anderson and Seth Patton,“In a hybrid world,your tech defines employee experience,”Harvard Business Review,February 18,2022.18.Deloitte,“Accelerating developer experience(DevEx).”19.Stack Overflow,“Developer experience:Processes,tool

314、s,and programs within an organization,”accessed October 2023.20.Gartner,“Gartner identifies the top 10 strategic technology trends for 2023,”press release,October 17,2022.21.Deloitte Insights,Technology transformation revs up CarMaxs business,accessed October 2023.22.DX,“Inside Etsys multiyear DevEx

315、 initiative|Mike Fisher(Etsy,PayPal),”podcast,April 19,2023.23.Deloitte,“Accelerating developer experience(DevEx).”24.Statista,“How did you learn to code?,”June 2023.25.Deloitte Insights,Flexibility,the best ability.26.Interview with Colin Heilman,global functions CTO at Citibank,October 11,2023.27.

316、Ibid.41Tech Trends 20244243Tech Trends 2024Defending reality:Truth in an age of synthetic mediaWith the proliferation of AI tools,its now easier than ever to impersonate and deceive,but leading organizations are responding through a mix of policies and technologies.CYBER AND TRUSTYou may have recent

317、ly seen an ad with Tom Hanks pitching a dental plan.The actor himself didnt participate in the shoot.Someone simply used his likeness,together with deepfake technology,to make it appear as though he had.1Take it as a sign of the times,when anyone can be made to look as though they said or did anythi

318、ng.Artificially generated content,driven by rapid advances in generative AI,has reached a point where its almost impossible for people to separate whats real from what was conjured from the depths of computers.Its not just celebrities in the crosshairs.With the prolif-eration of artificial intellige

319、nce tools,its now easier than ever for bad actors to impersonate others and deceive their targets.Many are using deepfakes to get around voice and facial recognition access controls,as well as in phishing attempts.AI applications themselves,which demand huge amounts of data,are rich targets for hack

320、-ers.The security risks are multiplying with every new content-generation tool that hits the internet.But leading organizations are responding through a mix of policies and technologies designed to identify harmful content and make their employees more aware of the risks.The same generative AI tools

321、 used by bad actors to exploit organizations can be used to identify and predict attacks,allowing enterprises to get ahead of them.Now:The next generation of social engineering hacks Social engineering hacks have always relied on convinc-ing a person to hand over data or access to systems for illegi

322、timate purposes.Though the strategy can be very effective,it also requires a lot of personal interaction between the bad actor and the victim.Artificially gener-ated content enables attackers to create that personal touch with a much lower time investment.A wave of artificially generated content is

323、now targeting enter-prises,exploiting vulnerabilities by impersonating trusted sources.The problem is accelerating rapidly.2 Currently,theres a large gap between AIs ability to create realistic-sounding content and peoples ability to recognize it.A majority of people say they can tell the difference

324、 between AI-and human-generated content,but another 20%arent sure.3 However,the first group is likely being overconfident.Few people can reliably distinguish between the two precisely because AI content generators are trained on human-created content and developed to replicate it as closely as possi

325、ble.4 People may expect artificially generated content to look or sound robotic in some way,but more than ever,it feels human.Bad actors are likely to use artificially generated content to attack businesses in several ways(figure 1).44Improved phishing:Phishing is the most common type of cyberattack

326、,with 3.4 billion spam emails sent every day.In 2021,cybercriminals stole an estimated US$44.2 million through phishing attacks.5 Phishing attacks typi-cally succeed not because theyre high quality but because theyre sent out in massive volumeout of billions of emails,eventually a few will achieve t

327、heir goal.Most recipients are generally able to identify phishing attempts because of the use of poor grammar and spelling,or because the sender clearly doesnt know the recipient.But generative AI tools allow fraudsters to craft convincing,error-free messages quickly and easily and to provide releva

328、nt context,which enables them to tailor messages to each recipient,making the messages harder to ignore.The problem is likely to get worse as the quality of publicly available models improves.6 Deepfakes:Deepfakes have been around for years,but until fairly recently,they havent been convincing enoug

329、h to be used in cybercrimes.Now,were starting to see them used to attack businesses.For example,the CEO of a UK-based energy firm was conned out of US$243,000 by scammers using deepfake AI voice technology to imper-sonate the head of the firms parent company.7 Deepfake Figure 1Bad actors are likely

330、to use artificially generated content to attack businesses in several waysSource:Deloitte analysis.PhishingDeepfakesPrompt injectionMisinformationObtaining information by falsely communicating as a trusted sourceManipulation of a subject through AI-generated videoFeeding false data to a targets AI a

331、lgorithmsGeneration of deliberately misleading information about a targetMethods of attackPermutations of attackBusiness points of entry45Tech Trends 2024tools have advanced significantly since this incident and are likely to continue improving rapidly,making it harder for people to know with confid

332、ence with whom they are dealing.Prompt injection:Web browsers and email clients with virtual assistants could be leveraged by bad actors who leave malicious prompts in webpages or emails that instruct the assistant to forward data such as contact lists,banking information,and health data.8 Most type

333、s of social engineering hacks have historically worked by tricking people into handing over data or access to systems.But with prompt injection,hackers dont even need to bother with this step.The prompts execute auto-matically,without the victims knowledge.Misinformation:Social media campaigns against busi-nesses are nothing new,but artificial content is adding fuel to the fire.AI tools can be use