1、The State of AI in Talent Management and Acquisition1The State of AI in Talent Management and APrivate&Confidential-Do Not Share Beamery Inc.All rights reserved2022The State of AI in Talent Management and Acquisition2ForewordAI is a complex and evolving technology,and the way forward with it while e

2、xciting will be difficult for talent practitioners unless they get the right foundations in place first.That means being strategic about using AI that drives outcomes at the business level not just buying AI for its own sake.According to McKinsey,“AI high performers”(those who see the highest impact

3、 of AI on the bottom line)are more likely to follow all AI best practices,many of which we will outline here.Ultimately,buying AI is not the primary goal of talent teams:hiring the right people and keeping them in the business is.Talent teams need to be crystal clear about what theyre trying to achi

4、eve,how their tech stack works,the right processes to have in place,and where to use AI(or not)in order to drive positive business outcomes and ROI.They also need to be ready to address the potential pitfalls of AI:answering important questions about bias,transparency and ethics.READ THIS REPORT TO

5、FIND OUT:What AI actually is:The fundamental concepts of AI and the technical foundations talent teams need in place to get started.What AI does for talent teams:How AI can help achieve KPIs around recruiter efficiency,candidate quality,workforce management,and Diversity and Inclusion.How AI can imp

6、rove the experience for candidates How to deploy AI solutions:Use cases and applications across the HR function and talent lifecycle.How to mitigate risk:Understanding and addressing the potential pitfalls of using AI,and complying with new regulations.How to evaluate AI providers:Which questions to

7、 ask in your RFP,and how to find the right fit for your business goals.The State of AI in Talent Management and Acquisition3Why Talent teams should care about AI AI adoption by talent teams is acceleratingAI adds massive value but only when applied strategicallyUnderstanding AIThe fundamental concep

8、ts of AILaying the foundations for AI adoptionDE&The role of AI in talent management and acquisitionRecruiter experienceCandidate experienceEmployee experienceRisks and rewardsSecurity and privacyExplainabilityEthicsEvaluating AI providersWhats next?44566731415161718Table of contentsThe S

9、tate of AI in Talent Management and Acquisition4Why Talent teams should care about AI AI ADOPTION BY TALENT TEAMS IS ACCELERATING AI is everywhere.Over the last few years,its revolutionized almost every industry and business function:56%of organizations globally have adopted AI solutions in at least

10、 one function,up from 50%in 2020.Now,it is coming for talent acquisition.To date,AI tools for HR and talent teams have been comparatively few,and uptake of them slow.But that tide is starting to turn,and fast:according to Gartner,17%of organizations were using AI for HR in 2019.Is predicted that ano

11、ther 30%will follow their lead in 2022.That acceleration in adoption has,at least in part,been driven by the pandemic.In the last 18 months,weve seen big shifts in the ways businesses manage their people.Theyve been forced to flex around mass layoffs,remote working and changed employee priorities.Ag

12、ility is a top priority,and talent leaders are turning to AI solutions for more efficient and effective ways to manage their workforces.Its no surprise that the global HR technology market is predicted to grow from$24.04 billion in 2021 to$35.68 billion in 2028,as companies prioritize investments in

13、 AI to optimize business processes and reduce costs.8%of those surveyed by McKinsey(who were using AI in their business)said they were using it for optimization of talent management.8%said it was being used for performance management.What HR and talent leaders are starting to recognize is the transf

14、ormative potential of AI in helping them manage their ever-increasing remit.With the right tools applied in the right ways,AI can make talent experiences more personalized,more valuable and more effective.Against the backdrop of a spiraling skills shortage and with HR teams under more pressure than

15、ever(78%of recruiters state that it is harder to find talent this year than last year,and 60%of companies say they do not have the skills in their workforce to be successful),that capability is critical.The State of AI in Talent Management and Acquisition5AI ADDS HUGE VALUE BUT ONLY WHEN APPLIED STR

16、ATEGICALLYBut AI isnt a silver bullet,nor a one-size-fits-all solution.Undoubtedly,the growth in tech solutions will open up a world of opportunity for HR leaders to add value to their businesses.But the proliferation of options means its imperative they think critically about the core principles of

17、 AI before investing.Implemented intelligently,AI-led talent management and acquisition tools can:Improve data-based decision making(and quality of hire)Improve the candidate and employee experience Automate burdensome manual tasks Cut operational overheadsBut AI also introduces complexity around bi

18、as and data security,as well as a whole host of as-yet unanswered ethical questions.Talent teams that fail to perform thorough due diligence at the evaluation stage could find themselves facing serious financial,legal and reputational risk.The State of AI in Talent Management and Acquisition6Underst

19、anding AI AI is a broad term and,to non-technical people,often a vague one.For talent leaders new to using AI-based solutions,it can be difficult to understand what it does and how it works.But its important to get to grips with the basics.Without understanding the difference between types of AI,and

20、 the functionality that they offer,its impossible to assess whether solutions will deliver the business outcomes HR and talent teams are looking to achieve.And there are more serious implications,too.Increasingly,regulators expect businesses to be able to explain how and why their AI works,and to pr

21、ove they are mitigating against the risk of bias or data breach.Those that cant might find themselves facing not only financial,but legal and reputational issues.So,what do we mean when we talk about AI?THE FUNDAMENTAL CONCEPTS OF AI Artificial Intelligence is any technique that allows machines to m

22、imic human behavior.It allows recruiters to do more of the same things,but also to do things better.AI can be split into 3 fundamental concepts:automation,machine learning and deep learning.Automation:Automation is rule-based.It applies the same rules(“if this,then that”)to every input,allowing proc

23、esses to be run faster and at scale,without the need for human intervention.For example,Chatbots.Rule-based chatbots would return candidates the right FAQ page or schedule based on specific keywords Machine Learning:Machine Learning(ML)is more intelligent than automation.Its not rule-based:ML is the

24、 ability for an algorithm to learn from a dataset how to perform a task,and then keep learning to do it better over time.One example could be in job/candidate matching.Automation would only group candidates based on explicit,pre-programmed rules.ML would be able to infer additional similarities,and

25、to adapt as the skills found in a job title changed over time,without algorithms needing to be re-written.Deep Learning:Deep Learning goes even further.It is able to look at a data set and figure out what is most useful and interesting about it in order to solve a task.CV parsing might be one exampl

26、e.Deep Learning can look at unstructured data(like a CV)and learn to identify the elements that might suggest a candidate is a good fit for a role,without being told what they are.Deep Learning can often identify important similarities between candidates that human recruiters might miss.The State of

27、 AI in Talent Management and Acquisition7LAYING THE FOUNDATIONS FOR AI ADOPTION Knowing what AI does is only part of the challenge for HR and talent teams.They also need to get some fundamental technical foundations in place to ensure theyre ready to adopt and effectively implement solutions.Data qu

28、ality All AI whether its rule-based or built on machine learning relies on huge volumes of high-quality data,which it uses to recognize patterns and learn to perform tasks like a human would.For that process to work properly,the data the AI learns from needs to be:Accurate,uniformly structured and u

29、p-to-date Complete and easily surfaced Accessible and explainablePoor quality data can be incredibly costly for businesses.Recruiters spend a lot of time performing manual tasks,including looking for data,cleaning up mistakes and checking for duplicates.And thats before you factor in the knock-on ef

30、fects of less efficient hiring practices,including poor team engagement and a longer time to hire.Potential non-compliance with legislation like GDPR adds further cost and risk.To ensure you are training your AI with high quality data,it is important to bring all your data together.The State of AI i

31、n Talent Management and Acquisition8Data platforms Data platforms make it much easier to maintain good data hygiene.A Talent Data Platform,such as the Universal Skills Platform from Beamery,is defined as:“An integrated technology solution that allows data located in databases to be governed,accessed

32、,and delivered to users,data applications,or other technologies for strategic business purposes.”In essence,data platforms are a single source of truth for disparate talent data points from across the business.They unify and standardize data in one place,so that talent teams can easily surface meani

33、ngful insights from it.These data platforms for talent teams will also contain a number of underlying technologies that help with cleaning,deduplicating,parsing and enriching data in a user-friendly interface.The State of AI in Talent Management and Acquisition9Data storage The quality of data for H

34、R and talent teams also depends on the way in which it is stored.Data can be stored in a number of ways,not all of which are a good fit for the specific needs of talent acquisition and management.Broadly speaking,data can be stored in tables(relational databases),documents or knowledge graphs.Tables

35、 and documents arent agile enough to deal with evolving organizational models,the growth in structured and unstructured data,or technologies like ML.Graphs are more useful for talent teams,especially when it comes to understanding exactly which data points are influencing outcomes.Integrations Talen

36、t teams rely on a diverse range of talent,recruiter,job,business and market data in order to do their jobs well.But this data usually exists in different systems and siloes,and is structured in different ways,making it hard to leverage and draw insights from.This means important data can fall throug

37、h the cracks,or lose meaning as it is migrated between systems.It also poses a challenge in terms of maintenance:if systems dont speak to each other,updates need to be made in multiple places,every time they are needed.To consolidate and synthesize data,good integrations between systems are instrume

38、ntal.Not all integrations are created equal.Some of them work in one direction only,or dont pull all the fields of information that are needed from one system to another,or are only updated intermittently.This can interfere with the quality of datasets,and by extension,the outcomes AI delivers.Again

39、,Talent Data Platforms perform better than integrated HR tech stacks in this regard.Using a tech stack comprising multiple different tools means that,no matter how good the integrations,there are still several gateways that data has to pass through.A talent data platform is a separate structure that

40、 receives and serves up data to these different tools,no matter how structured or unstructured it is,without losing any of its relational meaning.The State of AI in Talent Management and Acquisition10DE&I 70%of job seekers want to work for companies with an explicit commitment to diversity.Thats why

41、 many companies are increasing investment in diversity,even against budget cuts elsewhere.According to The Hackett Group,businesses on both sides of the Atlantic are planning to double their spend on diversity by 2025.The key capability for HR and talent teams is to be able to protect and promote DE

42、&I at scale.Its relatively easy to manage bias in one-to-one context,but gets much harder when candidate volumes expand.Avoiding Bias Using AI,models can be built to exclude bias in algorithms either explicitly(by removing identifiers like name,age and gender)or implicitly(by removing things like ad

43、dress,education or salary).AI can also be baked into candidate scoring systems to ensure consistent and equal weighting across data points,ruling out the possibility of over-reliance on a single factor.AI unlocks solutions to problems that would otherwise be impossible.Job descriptions are proven to

44、 be rife with unconscious bias,but rules engines,analytics and manual processes arent sophisticated enough to read data and understand which wording will ensure the most diverse slate of candidates.AI is able to analyze and respond to the changing market at speed.Explainability The risk of bias in t

45、alent management is high,and can have serious knock-on impacts not just for businesses,but for people.After all,hiring decisions determine the course of their career.The right AI can help talent teams provide transparency for candidates and employees about which data points have driven which decisio

46、ns,both positive and negative.That transparency can also promote inclusion by suggesting career paths that may not otherwise have been obvious,especially for minorities who may not be as well-represented at the senior level,or within particular teams.The State of AI in Talent Management and Acquisit

47、ion11The role of AI in talent management and acquisition For AI-powered solutions to generate successful results,theres one final but critical element that HR and talent teams need to consider.At least as important as laying strong technical foundations is ensuring that AI solutions are being applie

48、d to the right use cases.The addition of AI to the talent tech stack unlocks important business results,and directly impacts specific,identifiable talent metrics but only when its applied thoughtfully and strategically.AI might be becoming ever-more-ubiquitous,but theres no point in buying it for it

49、s own sake,or to keep up with what competitors appear to be doing.To be able to compete for the best talent,HR teams need to understand how to make the most of AI,and find the most valuable ways of implementing it.So far,the primary applications of AI in the talent sphere have focused around the bas

50、ics of candidate scoring,prioritization and sourcing automation.Now,solutions are starting to drill deeper to support the ever-growing mandate of HR and talent teams,including workforce planning and management,and Diversity,Equity and Inclusion(DE&I).There are 5 main areas that AI solutions can be i

51、mplemented to add value for HR and talent teams:recruiter experience,candidate experience,employee experience,workforce planning,and DE&I.RECRUITER EXPERIENCE AI can help recruiters become more productive and efficient,by enabling them to reach high volumes of candidates,and by helping them narrow t

52、he field of available options more accurately and efficiently.Used properly,AI can surface the ideal candidate for each role.Critically,these historically manual processes can be replicated automatically,with custom rules built to prioritize high-value tasks and roles.It means talent teams can put t

53、heir limited human resources to better use,improving their own performance and creating a positive business impact in a much shorter time.Sourcing:Intelligent sourcing tools can suggest candidates from outside recruiters usual talent pools,identifying relevant skill sets in people with roles or back

54、grounds recruiters might not think to consider.AI-driven talent tools can also automate data collection and parsing,automatically matching candidates to roles to create large,rich and compliant talent pipelines.Because the quality of data is better,so too is the predictability of hiring results:stro

55、ng candidates can be sourced at scale,reducing time-to-hire.Assessment:AI can be used to enrich candidate profiles with publicly-available supporting information,ready to hand over to interviewers.In addition to serving recruiters more sourcing options,the technology can also help recruiters narrow

56、down the options they have,or quickly select the best next steps in their workflows.Talent teams can use AI to create objective scoring and assessment mechanisms that simplify and speed up the assessment process,and make it easier to move candidates through the steps.The State of AI in Talent Manage

57、ment and Acquisition12CANDIDATE EXPERIENCE Delivering an excellent candidate experience both for those who win positions,and those who dont is critical to maintaining a good employer brand,and attracting talent in a competitive marketplace.AI can help personalize and streamline the talent experience

58、,so candidates feel desired,empowered and engaged from day one.Not only does this help deliver against key metrics like reduced time-to-hire,it can also have a knock-on effect to long-term employee retention.Personalization:AI can help talent teams understand what signals people are sending as they

59、move through the pipeline,so they can send customized content and targeted recommendations for job roles with every new visit.This helps candidates understand the why of the business and their fit within it,increasing engagement and conversion rates and filling roles faster.EMPLOYEE EXPERIENCE In or

60、der to fill skills gaps faster,some companies are starting to cut their recruitment budget and increasingly turning to internal investment on L&D,reskilling and upskilling,and moving to project-based cross-functional work to make better use of their existing workforce.Internal mobility:AI enables ta

61、lent teams to identify personalized internal mobility paths for employees.It can infer skills from roles and job titles,suggesting tailored growth paths for employees that improve retention.AI can also spot the valuable skills that already exist in-house,and that HR could invest in developing to mee

62、t future needs.Optimizing available skills within the existing workforce means businesses can execute on plans faster,and encourages retention.Thats important:our report with Aptitude Research notes that 78%of companies have lost talent due to a lack of career development opportunities.WORKFORCE PLA

63、NNINGPredictive analytics can analyze business goals and automatically translate them into forecasts and job openings.It can also provide insights into the costs and sticking points across the talent lifecycle,helping find areas for improvement.AI helps talent teams think more flexibly about the way

64、s they can activate human potential,too.Talent teams can often fall into copy/paste mentalities and end up hiring the same people in the same ways,rather than exploring different labor types.AI can help break down job specifications to find the most equitable and inclusive ways to hire,eg.reducing a

65、 job description from 5 years experience at a higher level to 3 years at a mid level,filling the gaps via the gig workforce and enabling the person with 3 years experience to continue their career development in-role.The State of AI in Talent Management and Acquisition13Risks and rewards Like any ne

66、w technology,AI brings with it new types of risk,and can even increase the scale of impact of existing ones.Globally,legislation is being created to help manage the use of AI and mitigate present and future risks.In the UK,the AI code was established in 2018 to try and influence the development of t

67、he AI industry,rather than“passively accept its consequences”.More recently,the EUs plans for AI regulation were leaked to the press,revealing their intention to limit some types of AI in order to protect individuals anre remove ambiguity.In the States,while no comprehensive federal legislation exis

68、ts,New York City passed a law in 2021(coming into force January 2023)that will prohibit the use of AI and algorithm-based technologies for recruiting,hiring or promotion without those tools first being audited for bias.Its the responsibility of HR and talent teams to ensure they keep abreast of new

69、legislation and stay compliant.While AI providers are accountable for their own governance,trying to pass the buck if issues arise wont cut it.Talent teams need to understand the risks posed by the technology they buy,and to be able to mitigate them.The only way to do that is to thoroughly understan

70、d their stack,train teams to use solutions responsibly,and constantly prioritize candidate and employee rights.There are 3 critical areas about which HR and talent teams need to stay vigilant.The State of AI in Talent Management and Acquisition14SECURITY AND PRIVACY Unsurprisingly where large volume

71、s of data are concerned,security and privacy are paramount.With the introduction of GDPR,and repeated headlines about breaches and losses,data security is already front of mind for most organizations.They know that data needs to be stored safely,and that they need thorough and provable due diligence

72、 processes to stay out of trouble with regulators.But AI brings in an additional level of risk.AI only works when its trained with large volumes of data.That means the onus is on businesses to think critically not only about how they store data,but how they use it.A core principle in the UKs AI code

73、,for example,is that“Artificial intelligence should not be used to diminish the data rights or privacy of individuals,families or communities”.Talent teams need to ensure theyre using data sensitively,appropriately and in ways that wont introduce unconscious bias.Important questions to consider are:

74、Why are specific data points used to train models?Is all the data being gathered relevant to the model?What sources are being used,and why?How is data checked for bias before it is used for training models?Are there any data points that might introduce bias?Is data representative of distribution in

75、the real world?What consent was obtained when the data was acquired?How is data per customer stored and used between other customers?What steps are being taken to ensure data is stored securely and cannot be stolen?Asking these questions is important to ensure data is stored and used in a safe and c

76、ompliant manner.But its also fundamental to ensuring that AI solutions actually work.If data sets are interfered with,it alters the behavior of models and their outcomes.Security is central to the functionality of AI solutions,and to talent teams confidence in and ability to explain results.There is

77、 also the question of consent and permission.The new rules in New York,for example,state that candidates must be given notice of the fact that an automated employment decision tool will be used in assessing them(as well as the job qualifications and characteristics that the tool will use in the asse

78、ssment).Candidates need to be made aware of the use of AI,and ideally be given the opportunity to set preferences as to how it is applied in their specific case.The State of AI in Talent Management and Acquisition15EXPLAINABILITY Increasingly,the legislative moves are being made away from opaque AI

79、to more transparent models.AI that HR and talent teams can clearly understand removes risks and improves outcomes for candidates and employees.Opaque models and systems produce outcomes talent practitioners cant clearly and thoroughly explain.Its less of an issue with rule-based algorithms,but those

80、 if this then that models dont work well for solving more complex problems.Deep Learning models with layers of neural networks can be incredibly effective,but because they learn without human input,it can be hard for non-technical practitioners to understand exactly how and why they work in the way

81、they do.That can cause serious problems,especially when it comes to bias.Its lower risk in other contexts.Take Google recommendations:it doesnt really matter too much why it returns the restaurants it does when you search a particular type of cuisine.It does matter why certain results are returned w

82、hen someones career is on the line.Bias can happen when the data AI learns from is skewed.Take email spam filters:they decide which emails to flag based on email provenance and metadata,as well as what users mark as spam.If users subconsciously spam emails from foreign-sounding names more often,AI-p

83、owered spam filters will eventually start removing them automatically.Its easy to see how the same could happen with candidate sourcing and assessment.Explainability means knowing what features and data points led to a recommendation,and having an audit trail to interrogate and justify them.HR and t

84、alent teams need to ask:Were all the data points used to evaluate candidates directly related to the job?Were those data points the same things that candidates were told they would be evaluated against?How much influence do users have over models?Are candidates and employees made aware of when AI is

85、 used?How do models work together and individually to deliver on use cases?How are outcomes explained to users?AI and its decision parameters need to be testable,auditable and documented to stand up to scrutiny.As discussed above,storing data in knowledge graphs,as opposed to tables or documents,can

86、 help.Explainable AI helps talent teams make fair,unbiased decisions.Indeed,it can lead to better decisions being made.Understanding how models are producing results means they can be taught,corrected and optimized.The State of AI in Talent Management and Acquisition16ETHICS As HR and talent teams g

87、et to grips with AI,they need to think about how it aligns with the broader ethics and values of their organization.That means being explicit and getting aligned around the standards they hold themselves to now,as well as thinking carefully about how theyll evolve as new technologies and use cases e

88、merge.Losing the human element in talent acquisition and management is a real concern for many.It opens up challenging ethical questions on 2 levels:Is the technology itself ethical?Are organizations using technology ethically?In the case of the technology itself,there are question marks around whet

89、her building a talent AI that can infer gender or age from names or resumes would be ethical.On the one hand,it could create shortcuts for some organizations to achieve DE&I goals.But on the other,it could make mistakes,or encourage other biases(like ageism)to creep in.An organization could use an i

90、nternal mobility tool to try to predict when employees might be ready for a new opportunity.But the flipside of this positive career pathing is that managers might use it to stop investing in employees who were considered a flight risk.That would constitute an unethical use of otherwise morally neut

91、ral technology.“AI opens up a huge amount of opportunity for talent teams,who are keen to capitalize on its many benefits.But because HR is concerned with peoples livelihoods and employment journeys,its high-risk.As regulation is developed globally it will help clear up some of the ethical gray area

92、s that currently exist,but talent teams should always ask themselves what data theyre collecting,whos using it and what for.Documenting what data is being used,and ensuring the process is transparent internally and externally,can help prevent issues.”Megan Butler PhD Candidate,Talent and HR AI exper

93、tThe State of AI in Talent Management and Acquisition17EVALUATING AI PROVIDERS With so much sensitivity surrounding the technology,talent and people teams should never go shopping for AI solutions.What they should be looking for are solutions to business problems.Having a clear understanding of use

94、cases and goals is the only way to find the right solution,and AI wont always be the answer.It shouldnt be implemented needlessly.Where AI solutions are available and appropriate,talent teams need to ensure they are asking the right questions to evaluate them effectively.A thorough understanding of

95、how AI works is fundamental to ensure it drives ROI against business goals and doesnt just add cognitive load,unnecessary expense and potential data,compliance and bias complications.The lists below summarize our recommended approach in assessing AI solutions,with a deeper dive into the three layers

96、 of any AI technology:data,models,and experiences.SuitabilityObjectivityExplainabilityValiditySecurityDataWhy are the chosen data points used to train the models?Is all of it relevant for the use case?What sources of data are used?Why?How is the data checked for bias before it is used for training m

97、odels?Are there data points that may potentially cause bias,such as gender,race and ethnicity?How are these handled and why are these there?Is the data representative of the distribution in the real world?How is the data segmented e.g.by geography,industry?How is the data stored and connected?Is it

98、in tables,documents or graphs?How do you maintain data quality?Is it complete,fresh and deduplicated?What consent was obtained when the data was used or acquired?How is data per customer stored and used between other customers?What steps are taken to ensure the data is securely stored and cannot be

99、amended or stolen?ModelsWhy were these models chosen over other models?What tests were done to choose these particular models?How are the models checked for bias?Are there humans in the loop?How often are they checked for bias?How does each model work individually and together to deliver on the use

100、case?What quantitative and qualitative methods do you use to check if the models are working optimally?Do you optimize for recall or precision?What about accuracy?Why?What steps are taken to ensure the models cannot be interfered with?Experiences How does the AI improve the user experience?Where doe

101、s it add value and how does it enable users to achieve their goals more effectively than without the AI?How much influence does the user have over the model?How does user interaction and feedback on the models impact the model training?How is the AI explained to users?Is it clear on why the AI made

102、recommendations?Is it easily understandable and reassuring?How is the AI tested as part of the user experience?How do you measure success?What steps are taken to ensure the AI experience is secure and cannot be interfered with?The State of AI in Talent Management and Acquisition18The HR technology l

103、andscape is evolving quickly,and AI opens up a huge amount of opportunity and potential benefit for talent teams.The capability AI offers will be critical over the next few years:the pandemic initiated a global transformation in the way we think about careers and workplaces,and what the future of wo

104、rk looks like remains in flux.When facing such profound changes,its imperative for talent teams to be strategic about the way they build moving forward.Getting the right foundations,tools and processes in place early will be critical in order to flex around continued disruption,while fulfilling the

105、talent needs of their organizations.AI can help talent teams stay on top of the fluctuating talent market and ahead of competition but success with AI starts with shopping for solutions,not technologies.For talent teams,aligning around business goals and understanding use cases are critical first st

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113、S PRODUCT OVERVIEW CAN BE SENT TO LEGALBEAMERY.COM.BEAMERY LIMITED 2021.ALL RIGHTS RESERVED.About BeameryBeamerys Talent Operating System allows enterprises to attract,engage,and retain top talent,and manage the entire talent journey through one unified platform.Beamerys mission is to help the world

114、s best companies acquire their greatest assets:their people.Founded in 2014,Beamery is trusted by the worlds most innovative global organizations to treat their candidates like customers.Beamery has offices in London,Austin,and San Francisco.For more information,visit the Beamery website,follow BeameryHQ on Twitter,or email us at .