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1、 MARCH 2024 Do you have an“early days”generative AI strategy?Organisations at the forefront of generative AI adoption address six key priorities to set the stage for In a recent conversation about generative AI with one of our colleagues,the CIO of a major healthcare company laid out a wide range of

2、 issues that concerned her:risk protocols,use case development,cybersecurity,ethics and bias,training and development,and many more.After a few minutes,our colleague asked the client to take a step back:“How clear are you on what you are trying to accomplish,and why?In other words,do you have a stra

3、tegy?”These questions stopped the CIO,leading her to call a series of meetings with key leaders,and ultimately the board,to create a sharper set of objectives.What emerged was a group of priorities that collectively formed what might be termed an“early days”AI strategy.Early days,becauselets face it

4、thats exactly where we are with generative AI.It was only in November 2022 that the consumer release of ChatGPT captured the worlds imagination.Since then,organisations have been struggling to keep up with the pace and potential they see in this new,general-purpose technology application.Some organi

5、sations are doing better than others,and its not too soon to start taking stock of early leaders that are leveraging generative AI to capture value and pull ahead.Across industries,were seeing these leaders tackling a number of critical priorities:Theyre navigating tensions between the need for prud

6、ence and risk mitigation,and the importance of moving quickly to grab emerging opportunities.Theyre aligning their new generative AI strategy with their existing digital and AI strategies,building on these foundations to guide their thinking rather than starting from scratch.Theyre thinking bigencou

7、raging experimentation across their organisations,with a focus on identifying use cases that can scale.Rather than simply looking for ways to improve productivity,theyre looking strategically at their options for putting productivity gains to use.Relatedly,theyre considering impacts on workers,roles

8、,and skills-building,determining how best to both prepare employees to take advantage of the new tools available and include employees in shaping the companys generative AI journey.Theyve realised that with such a potentially disruptive technology,teaming up and collaborating with their ecosystems c

9、an be a truly transformative route to a radical rethink of their value chains and business models.In many cases,these priorities are emergent rather than planned,which is appropriate for this stage of the generative AI adoption cycle.Leaders and organisations are learning as they go.Priority 1:Manag

10、e the AI risk/reward tug-of-war Theres a fascinating parallel between the excitement and anxiety generated by AI in the global business environment writ large,and in individual organisations.At the same time that surging market capitalisations for early AI leaders are providing financial evidence of

11、 the opportunity investors and markets see in generative AI,a number of experts in the field are voicing existential angst about the potentially significant unintended consequences that could emerge as the reach of AI grows.Similarly,in many companies we know,theres a tug-of-war going on between the

12、 executives and managers seeking to rapidly tap the potential of generative AI for competitive advantage and the technical,legal,and other leaders striving to mitigate potential risks.Although such tension,when managed effectively,can be healthy,weve also seen the oppositedisagreement,leading in som

13、e cases to paralysis and in others to carelessness,with large potential costs.Achieving healthy tension often starts with a framework for adopting AI responsibly.At PwC,we developed such an approach several years ago,and we continue evolving it with the changing nature of AI opportunities and risks.

14、PwC played a pivotal role in the early days of generative AI,collaborating with a leading technology company in China to develop a responsible AI application framework and action guide to ensure ethical and responsible use of generative AI technology.In addition,PwC published a white paper in collab

15、oration with the Hong Kong Monetary Authority focusing on fostering AI adoption in the banking sector.Building on their success,PwC further contributed their expertise by crafting a national-level AI strategic plan for a Middle Eastern country,enabling the country to leverage AI for economic growth,

16、innovation,and societal development.Practical safeguards and guidelines help organisations move forward faster,and with more confidence.Open-minded,agile leadership also is critical:risk-minded leaders deliver better,faster guidance as they internalize the momentous significance of the generative AI

17、 revolution.Opportunity-seekers are well-served by spending time immersing themselves in what can go wrong to avoid costly mistakes.And both groups need a healthy dose of appreciation for the priorities and concerns of the other.One company we know recognised it needed to validate,root out bias,and

18、ensure fairness in the output of a suite of AI applications and data models that was designed to generate customer and market insights.Given the complexity and novelty of this technology and its reliance on training data,the only internal team with the expertise needed to test and validate these mod

19、els was the same team that had built them,which the company saw as an unacceptable conflict of interest.The near-term result was stasis.Another company made more rapid progress,in no small part because of early,board-level emphasis on the need for enterprise-wide consistency,risk-appetite alignment,

20、approvals,and transparency with respect to generative AI.This intervention led to the creation of a cross-functional leadership team tasked with thinking through what responsible AI meant for them and what it required.The result was a set of policies designed to address that gap,which included a cor

21、e set of ethical AI principles;a framework and governance model for responsible AI aligned to the enterprise strategy;ethical foundations for the technical robustness,compliance,and human-centricity of AI;and governance controls and an execution road map for embedding AI into operational processes.F

22、or this company,in short,addressing risk head-on helped maintain momentum,rather than hold it back.In Chinese mainland and Hong Kong,the adoption of generative AI in the business environment has generated both excitement and anxiety.Similar to global trends,there is a tug-of-war within individual or

23、ganisations in Chinese mainland and Hong Kong between executives eager to leverage generative AI for competitive advantage and technical,legal,and other leaders concerned about the potential risks.Observing the Chinese mainland and Hong Kong market,it appears that most enterprises are open and willi

24、ng to embrace generative AI for a competitive advantage.Very often they start with small-scale pilot projects to validate outcomes,proactively address potential risks,and assess the return on investment(ROI)before committing to full-scale deployment.This approach furnishes decision-makers with a fou

25、ndation for evaluating the appropriateness and advantages of integrating generative AI within their respective organisations.Of course,we cannot underestimate the challenges and complexities in applying generative AI.For example,in the last year or so,Chinas large models have flourished,which has bo

26、th accelerated the evolution of local large models and posed technical challenges to organisations in selecting the most suitable ones.Additionally,the cost and source of computing power required for deploying such models are also important factors that might hinder organisations from taking the fir

27、st step.Priority 2:Align your generative AI strategy with your digital strategy(and vice versa)If youre anything like most leaders we know,youve been striving to digitally transform your organisation for a while,and you still have some distance to go.The rapid improvement and growing accessibility o

28、f generative AI capabilities has significant implications for these digital efforts.Generative AIs primary output is digital,after alldigital data,assets,and analytic insights,whose impact is greatest when applied to and used in combination with existing digital tools,tasks,environments,workflows,an

29、d datasets.If you can align your generative AI strategy with your overall digital approach,the benefits can be enormous.On the other hand,its also easy,given the excitement around generative AI and its distributed nature,for experimental efforts to germinate that are disconnected from broader effort

30、s to accelerate digital value creation.To understand the opportunity,consider the experience of a global consumer packaged goods company that recently began crafting a strategy to deploy generative AI in its customer service operations.Such emphasis has been common among companies.The chatbot-style

31、interface of ChatGPT and other generative AI tools naturally lends itself to customer service applications.And it often harmonizes with existing strategies to digitise,personalize,and automate customer service.In this companys case,the generative AI model fills out service tickets so people dont hav

32、e to,while providing easy Q&A access to data from reams of documents on the companys immense line of products and services.That all helps service representatives route requests and answer customer questions,boosting both productivity and employee satisfaction.As the initiative took hold,leaders at t

33、he company began wondering whether generative AI could connect with other processes they had been working to digitise,such as procurement,accounts payable,finance,compliance,HR,and supply chain management.It turned out that similar generative AI models,with refinement and tailoring for specific busi

34、ness processes,could fill out forms,as well as provide Q&A access to data and insights in a wide range of functions.The resulting gains,in total,dwarfed those associated with customer service,and were possible only because the company had come up for air and connected its digital strategy and its ge

35、nerative AI strategy.In this case,the alternative would have been a foregone opportunity to turbocharge existing digital efforts.In the extreme,siloed digitisation and generative AI efforts might even work at cross-purposes.Given how much companies have already invested in digitisation,and the signi

36、ficance of generative AIs potential,theres no substitute for the hard work of bringing the two together.A fringe benefit of connecting digital strategies and AI strategies is that the former typically have worked through policy issues such as data security and the use of third-party tools,resulting

37、in clear lines of accountability and decision-making approaches.Such clarity can help mitigate a challenge weve seen in some companies,which is the existence of disconnects between risk and legal functions,which tend to advise caution,and more innovation-oriented parts of businesses.This can lead to

38、 mixed messages and disputes over who has the final say in choices about how to leverage generative AI,which can frustrate everyone,cause deteriorating cross-functional relations,and slow down deployment progress.These disconnects are easily avoided,though.At another financial services company we kn

39、ow that was seeking to exploit generative AI in the HR function,the CHRO,the CIO,and the CISO came together quickly to assess the new opportunities against the companys existing data,tech,and cybersecurity policies,providing helpful guidance that maintained momentum.Priority 3:Experiment with an eye

40、 for scaling The C-suite colleagues at that financial services company also helped extend early experimentation energy from the HR department to the company as a whole.Scaling like this is critical for companies hoping to reap the full benefits of generative AI,and its challenging for at least two r

41、easons.First,the diversity of potential applications for generative AI often gives rise to a wide range of pilot efforts,which are important for recognising potential value,but which may lead to a“the whole is less than the sum of the parts”phenomenon.Second,senior leadership engagement is critical

42、for true scaling,because it often requires cross-cutting strategic and organisational perspectives.Experimentation is valuable with generative AI,because its a highly versatile tool,akin to a digital Swiss Army knife;it can be deployed in various ways to meet multiple needs.This versatility means th

43、at high-value,business-specific applications are likely to be most readily identified by people who are already familiar with the tasks in which those applications would be most useful.Centralised control of generative AI application development,therefore,is likely to overlook specialised use cases

44、that could,cumulatively,confer significant competitive advantage.Certainly,our experience at PwCwhere internal hackathons have identified value creation opportunities comprising 1 to 2%of revenue in some of our service lineshas underscored the importance of engaging individual workers and department

45、s in experimentation and exploration.Powerful as pilots like this are for spotting business-specific trees of opportunity,they run the risk of missing the forest(at best)or(at worst)veering toward the“pilot purgatory”state in which many corporate advanced data analytics efforts found themselves a fe

46、w years ago,with promising glimmers generating more enthusiasm than value.The above-mentioned financial services company could have fallen prey to these challenges in its HR department,as it looked for means of using generative AI to automate and improve job postings and employee onboarding.Fortunat

47、ely,the CHROs move to involve the CIO and CISO led to more than just policy clarity and a secure,responsible AI approach.It also catalysed a realisation that there were archetypes,or repeatable patterns,to many of the HR processes that were ripe for automation.Those patterns,in turn,gave rise to a l

48、ightbulb momentthe realisation that many functions beyond HR,and across different businesses,could adapt and scale these approachesand to broader dialogue with the CEO and CFO.They began thinking bigger about the implications of generative AI for the business model as a whole,and about patterns unde

49、rlying the potential to develop distinctive intellectual property that could be leveraged in new ways to generate revenue.This same sort of pattern recognition also was important to scaling at the consumer packaged goods company we mentioned earlier.In that case,it soon became clear that training th

50、e generative AI model on company documentationpreviously considered hard-to-access,unstructured informationwas helpful for customers.This“pattern”increased accessibility made possible by generative AI processingcould also be used to provide valuable insights to other functions,including HR,complianc

51、e,finance,and supply chain management.By identifying the pattern behind the single use case initially envisioned,the company was able to deploy similar approaches to help many more functions across the business.As leaders make such moves,they also need to take a hard look at themselves:What skills d

52、oes the organisation need to succeed at scale with AI,and to what extent do those capabilities already reside somewhere in the company?Whats the plan for filling skills gaps,and on what time frame?Failure to pose questions like these can lead to problems down the roadand theyre much better answered

53、in the context of early experiments than in the abstract.Priority 4:Develop a productivity plan Generative AIs ability to find relevant information,perform repetitive pattern tasks quickly,and integrate with existing digital workflows means the increased efficiency and productivity it can deliver ca

54、n be almost instant,both within individual departments and organisation-wide.Such opportunities arent unique to generative AI,of course;a 2021 s+b article laid out a wide range of AI-enabled opportunities for the pre-ChatGPT world.Generative AI has boosted the awareness and interest of many leaders

55、in AI-enabled productivity gains,which companies can do three things with:Reinvest them to boost the quality,volume,or speed with which goods and services are produced,generating greater output,broadly defined,from the same level of input.Keep output constant and reduce labour input to cut costs.Pur

56、sue a combination of the two.PwC firms in Chinese mainland and Hong Kong followed the first approach in small-scale pilots that have yielded 30%time savings in systems design,50%efficiency gains in code generation,and an 80%reduction in time spent on internal translations.When generative AI enables

57、workers to avoid time-consuming,repetitive,and often frustrating tasks,it can boost their job satisfaction.Indeed,a recent PwC survey found that a majority of workers across sectors are positive about the potential of AI to improve their jobs.Generative AIs ability to create contenttext,images,audio

58、,and videomeans the media industry is one of those most likely to be disrupted by this new technology.Some media organisations have focused on using the productivity gains of generative AI to improve their offerings.Theyre using AI tools as an aid to content creators,rather than a replacement for th

59、em.Instead of writing an article,AI can help journalists with researchparticularly hunting through vast quantities of text and imagery to spot patterns that could lead to interesting stories.Instead of replacing designers and animators,generative AI can help them more rapidly develop prototypes for

60、testing and iterating.Instead of deciding that fewer required person-hours means less need for staff,media organisations can refocus their human knowledge and experience on innovationperhaps aided by generative AI tools to help identify new ideas.PwC China has forged partnerships with several vendor

61、s to explore the potential of combining digital people with generative AI.In the past,digital people were limited to repeating pre-designed content and providing predefined answers.However,with the integration of generative AI,digital people can now engage with individuals more intelligently by answ

62、ering questions,engaging in reasoning,and making associations,thereby fostering a broader commercial imagination.This collaboration between digital people and generative AI opens up new commercial possibilities for enhanced interaction and innovation.Its also important to consider that when organisa

63、tions automate some of the more mundane work,whats left is often the more strategic work that contributes to a greater cognitive load.Many studies show burnout remains a problem among the workforce;for example,20%of respondents in our 2023 Global Workforce Hopes and Fears Survey reported that their

64、workload over the 12 months prior frequently felt unmanageable.Organisations will want to take their workforces temperature as they determine how much freed capacity they redeploy versus taking the opportunity to reenergize a previously overstretched employee base in an environment that is still tal

65、ent-constrained.Other companies may focus more on cost savings,which can be substantial,but which also carry with them risksfor example,worker unrest(as we saw in Hollywood),or the hollowing out of the capabilities that companies need to differentiate themselves from competitors.Some organisations m

66、ay decide these risks are worth taking;the right approach will obviously vary from industry to industry,company to company,and even department to department.Whats crucial is to have a plan:What is the relative importance of speed,quality,and cost improvements?What time horizon are you solving for?Wh

67、at will you do with employees whose skills have become redundant as a result of new generative AI capabilities?Getting clarity on the answers to questions like these is an important starting point for focusing your plan.Priority 5:Put people at the heart of your generative AI strategy Regardless of

68、the productivity path you choose to pursue,considering its impact on your workforce and addressing it from the start will make or break the success of your initiatives.Our 27th Annual Global CEO Survey found that over the next year,about half of CEOs expect generative AI to enhance their ability to

69、build trust with stakeholders,and about 60%expect it to improve product or service quality.Yet our 2023 Global Workforce Hopes and Fears Survey of nearly 54,000 workers in 46 countries and territories highlights that many employees are either uncertain or unaware of these technologies potential impa

70、ct on them.For example,few workers(less than 30%of the workforce)believe that AI will create new job or skills development opportunities for them.This gap,as well as numerous studies that have shown that workers are more likely to adopt what they co-create,highlights the need to put people at the co

71、re of a generative AI strategy.CEOs anticipate greater impact ahead,but most workers arent sure what that means for them Source:PwCs 27th Annual Global CEO Survey and Global Workforce Hopes and Fears Survey 2023 To ensure your organisation is positioned to capitalise on the promise of generative AI,

72、prioritise steps to engage employees in the creation and selection of AI tools,invest in AI education and training,foster a culture that embraces humanAI collaboration and data-driven decision-making,and support innovation.To this end,we suggest several key strategies:Engage your people early and of

73、ten.Continually communicate why AI is important and how it fits into the companys goals.Explain how AI can make employees jobs better and not replace them,and highlight that amassing AI skills will be critical for workers to succeed in their careers going forward.But remember that communication shou

74、ld be a two-way street.Provide mechanisms to gather feedback from employees about their AI experiences,and use it to refine tools and training programs and address any concerns or challenges.Offer customised training and upskilling.Assess your employees current AI skills and knowledge,and provide ro

75、le-specific training programs,learning resources,and certifications to address the gaps.Consider teaming up with educational institutions or AI training providers to offer these programs.Create mentorship opportunities that give employees guidance on their AI journey,and provide a way for them to ge

76、t advice and feedback from AI experts within your company.And although its still difficult to predict many of the new roles that generative AI could give rise to,we know theyll materialise.Preparing employees for these roles and highlighting the opportunities can energize those looking for career gr

77、owth and tamp down workers fears of replacement.Prompt engineering is a much-discussed role,though it may prove to be a short-term one as generative tools advance.Many other emerging roles involving AI ethics and training will become more prevalent,along with unforeseen roles.Promote a growth mindse

78、t.Create a workplace where learning and trying new things with AI is encouraged by recognising and rewarding those who do so.And,importantly,make it clear that,with proper guardrails and protections in place,failures mark innovation and are expected,and even celebrated.One financial services firm we

79、 know,for example,highlights at least one instance of failure on a weekly stand-up call among its designers to make visible that these occurrences are acceptable and incur no punitive measures.Unfortunately,this organisation remains in the minorityin our 2023 Annual Global CEO Survey,53%of responden

80、ts said leaders in their company dont often tolerate small-scale failures(and employees think that figure is closer to two-thirds).Fostering a growth culture also includes encouraging employees to share their learnings with each other as they begin working with these tools.Some companies we know are

81、 establishing prompt libraries,for example.Advocate and enable ethical AI use.Provide clear guidelines that articulate how your organisation defines the ethical use of generative AI,and ensure that employees understand the importance of fairness,transparency,and responsible AI practices.At PwC,for e

82、xample,weve created an internal microsite articulating the generative AI tools approved for employee use,acceptable business use cases,restrictions on the nature of information employees can input into these tools,requirements for human oversight and quality checks,and more.Measure impact.Knowing wh

83、ats working and what isnt requires not only worker feedback but also measurement.Implement key performance indicators to assess the impact of AI on productivity,innovation,and customer satisfaction;and actively promote the results.Some companies we know are conducting controlled experiments,such as

84、by having software engineers use coding assistants,to measure productivity improvements.Establish feedback loop.Setting up mechanisms to gather continuous feedback from employees and end-users regarding their experiences with generative AI tools and solutions.Regularly solicit feedback through surve

85、ys,focus groups,or user testing sessions which helps identify areas for improvement,address challenges,and ensure that generative AI systems align with the business needs.By following these strategies,organisations can systematically equip and empower their workforce to position themselves,and the o

86、rganisation,for success in an AI-driven world.Priority 6:Work with your ecosystem to unlock even bigger benefits Recent PwC analysis has found that companies with a clear ecosystem strategy are significantly more likely to outperform those without one.Its important,as you experiment with AI,to look

87、outside the four walls of your company:Do you know how your suppliers,service providers,customers,and other partners are planning to leverage this technology to improve their service proposition?What implications does their use of AI have for your early days strategy?Will it impose new conditions an

88、d demands?Could closer collaboration on AI lead to fresh opportunities to develop stronger propositions?The holy grail of healthcare and pharmaceutical firms,for instance,is the ability to access patient records at scale and identify patterns that could uncover routes to more effective treatments.Ye

89、t information sharing between organisations has long been restricted by privacy issues,local regulations,the lack of digitised records,and concerns about protecting intellectual propertyall of which limit the scope and power of ecosystem collaboration.Meanwhile,the use of AI has already become wides

90、pread across the industry.Medical institutions are experimenting with leveraging computer vision and specially trained generative AI models to detect cancers in medical scans.Biotech researchers have been exploring generative AIs ability to help identify potential solutions to specific needs via inv

91、erse designpresenting the AI with a challenge and asking it to find a solution.This AI-supported treatment discovery approach is already being used for both precision medicine(via genetic and healthcare record analysis to identify the best treatments given an individuals specific circumstances)and d

92、rug development(via protein and chemical model synthesis that can create custom antibodies).Until recently,the true potential of AI in life sciences was constrained by the confinement of advances within individual organisations.Today,organisations can combine generative AIs ability to help create an

93、d manage records with its capacity for creating statistically reliable,yet fully anonymized,synthetic datasets to enable safe,secure,large-scale data-sharing and data-pooling among healthcare organisations and their partners.That larger pool of information increases the opportunity for medical break

94、throughs by helping researchers identify commonalities that can reveal more effective treatmentsas well as new opportunities for collaboration between organisations,new business models,and new ways to capture value along with improved patient outcomes.Use cases have come up several times as weve des

95、cribed these priorities.That makes sense,because generative AI is a general-purpose technology,suitable for an enormous range of business activities;its hardly surprising that emerging leaders are emphasizing the search for smart,targeted applications.Here again,though,its important to underscore th

96、at its still early days.To understand how early,consider another general-purpose technology:electricity.Beginning with lighting in the 1870s,electricity began permeating a range of industrial settings and applications,bringing with it a variety of productivity improvements in the decades that follow

97、ed.Electricity was the force behind a key feature of Henry Fords automated assembly linethe overhead monorail conveyor system that made it possible to move parts and materials smoothly throughout the plant.Looking back,no one talks about Fords“electricity strategy.”Rather,the focus is on the moving

98、assembly line.We suspect the same will be true with generative AI,which will give rise to revolutionary business innovations that are beyond our imagination today.That makes early days AI strategies and priorities like the ones weve described even more important.They wont just yield near-term busine

99、ss benefits;theyll also build muscle and generate valuable experience that sets up todays leaders to achieve much bigger breakthroughs to make product,process,and service innovations that represent the assembly lines of the future.Authors Scott Likens Global AI and Innovation Technology Leader PwC U

100、S Nicole Wakefield Global Advisory Clients and Markets Leader PwC UK Contact us Wilson Chow Global TMT Industry Leader China AI Leader PwC China Horatio Wong Mainland China and Hong Kong Digital and Technology Consulting Leader PwC China Chinese mainland Harry Zhang Partner Financial Services&AI Sol

101、utions Advisory PwC China Timeus Lin Associate Director AI Solutions Advisory PwC China Hong Kong James Lee AI&Emerging Tech Consulting Leader PwC Hong Kong Sonia Chung Senior Manager AI&Emerging Tech Consulting PwC Hong Kong 2024 PwC.All rights reserved.PwC refers to the PwC network and/or one or m

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