Nearly 80 years before Edison invented the lightbulb, electricity had already existed. What Edison did through the invention of the lightbulb was to bring about a practical and accessible way to convert electrical energy into light. This innovation paved the way for widespread adoption in homes, businesses, and cities, fundamentally altering how people lived, worked, and interacted with their surroundings.
Similarly, while AI has been around for several years, what we are witnessing today is its increasing accessibility thanks to recent advancements in GenAI. We recently had the opportunity to sit down with Cliff Justice, KPMG’s US head of Enterprise Innovation, to delve into the transformative power of AI and its implications for individuals and businesses. Our enlightening discussion shed light on this exciting frontier.
Here’s a breakdown of our key insights from our conversation….
From Edison’s lightbulb to GenAI’s brilliance, we stand on the brink of unimaginable transformation.
During our discussion, Cliff drew parallels between GenAI’s impact and the electrical revolution ignited by Edison’s invention. At that time, electricity had existed for decades, but its practical applications were limited. However, with the advent of the lightbulb, a transformative shift occurred. As Cliff put it, “Once the practical light bulb came along and the average middle-class home could illuminate their house, and factories could install them, enabling 24-hour productivity, investment in electricity generation and the transmission grid followed, enabling many subsequent innovations adjacent to the lightbulb, like electric motors, electronics, and air conditioning.”
Much like the lightbulb, the development we’re currently experiencing has been unfolding over the past 5-7 years, where AI has transitioned from specific, tightly controlled use cases to becoming a practical and cost-effective tool with broad accessibility. This shift has led to a “network effect,” attracting more data, investment, and excitement, resulting in rapid progress—the implications of this progress span across various sectors, from education to fueling entrepreneurial ventures. ” in 2022, not many people knew what GPT was, and now it’s in your kids’ vocabulary and on their phones.”
While we witness its growing productivity capabilities, AI’s transformative influence will extend far beyond productivity. Cliff contemplated in our discussion, “What’s after GPT 4? What’s after the diffuser model? What comes next? There are a lot of potential technologies that come next. The innovation dollars flowing into these technologies will accelerate that.” The horizon is filled with unimaginable possibilities. It’s “going to happen a lot faster because we already have the electrical grid – it’s called the cloud.”
Geopolitical realities and human adaptation could throw a wrench in AI’s advancements.
Amidst this promising landscape, Cliff noted several limiting challenges. However, these challenges are less about the technology itself and more rooted in human factors and the supply chains that underpin it:
Legacy operating models: Established companies with entrenched operating models may need help adapting to AI effectively. “Organizations that are very traditional and have a deeply entrenched operating model will face challenges in making the necessary changes to compete with AI-native businesses.”
Reskilling the workforce: Achieving AI integration demands a workforce skilled in using AI technologies effectively. Cliff highlights the importance of this by noting that companies must quickly reskill their employees to work confidently with AI tools while ensuring policies are in place to prevent potential risks. “Talent and skillsets in this area are one of the pillars that you have to pursue, and you have to pursue it aggressively…. The companies that can do that faster will have an advantage.”
Geopolitics and materials: Geopolitical tensions surrounding the competition for rare earth materials, crucial for advanced chips and green energy technologies, pose a significant challenge. As Cliff mentions, “You’re competing with the same rare earth materials that are needed for green energy and like solar panels and electric motors, and there are geopolitical tensions right now which are impeding the importation of those materials at the scale.” To continue progressing at the current pace, new mining operations and chip manufacturing facilities need to be established rapidly – not a simple task by any means.
Overcoming these hurdles is essential for sustaining AI advancement in the coming decade. While navigating the complexities of geopolitics, intricate supply chains, and human adaptation, the pace of AI development may experience occasional disruptions.
Cliff envisions a quantum leap in AI…alongside some disillusionment.
During our discussion, Cliff hinted at three predictions on the future of AI, highlighting a forthcoming AI leap, empowered by Quantum computing, alongside the possibility of disillusionment driven by resource constraints and inflated expectations:
Advancements in AI and Kurzweil’s Predictions: Cliff discussed the progress of AI and its convergence with human interactions. He aligns his prediction with Ray Kurzweil’s predictions that by the 2020s, we’ll have interesting conversations with AI, and by the late 2020s, we will form relationships with AI. “he’s dead on in terms of interesting; You can’t argue that our conversations now are not interesting.”
Convergence of Quantum Computing and AI: Cliff anticipates Quantum’s advancements to potentially increase in the next five years or more, indicating it may become “usable, productive and economical.” It will be a “gradual…. gradual…. all of a sudden, quantum is here.” Cliff predicts that as quantum computing becomes more accessible and converges with AI, “that’s when you see the Ray Kurzweil type of AI, where it’s indistinguishable, maybe even smarter than human intelligence.”
Disillusionment Crash in AI: Cliff acknowledges the potential for a disillusionment crash in AI due to inflated expectations and computing resource limitations. He warns, “I think there will be a disillusionment crash because, as amazing as this technology is, the expectations can always get inflated.” He emphasized the challenges related to data infrastructure and chip shortages that could impact AI development.
Cliff’s insights reveal both the promise of a quantum-boosted future and the looming disillusionment, underscoring the need for a balanced and realistic approach to AI’s transformative journey.
The Bottom-Line: As we enter the promising world of AI, it is imperative to maintain a tempered perspective to unlock the full potential of AI.
Just as Edison’s lightbulb changed the course of history, AI has the power to reshape our world. With tempered optimism acknowledging the challenges ahead, we can unlock the full potential to illuminate a brighter future.
COVID-19 shone a bright light on life sciences with unprecedented results—enterprises getting the new COVID-19 vaccine to market in less than a year. Life sciences enterprises’ ability to create new life-saving therapies, sophisticated medical devices, and other health vehicles continues to be astounding. That progress, however, has been possible because of an ecosystem approach that includes technology and service providers, academia, startups, and various other stakeholders.
The ecosystem approach will be particularly critical to managing pricing headwinds due to Medicare in the US, democratizing research with GenAI, and expanding hyperpersonalization.
In the HFS Horizons: Life SciencesService Providers, 2023 study, we evaluated 29 service providersfor their ability to address the cost (Horizon 1), experience (Horizon 2), and health outcomes (Horizon 3) for health consumers globally.
Exhibit 1:Eightproviders can address all attributes of the triple aim of care: reducing costs, enhancing the experience of care, and improving health outcomes
Note: Service providers within each Horizon are listed alphabetically.
Service providers bring a renewed level of sophistication unseen pre-pandemic
While life sciences get credit for the incredible successes of life-saving therapies or life-improving devices, the reality is those achievements are possible only because of partnerships with service providers and other stakeholders. Service providers have made significant investments in attracting expert domain, functional, and technical talent, adopting emerging technologies to develop critical accelerators and solutions, developing strategic ecosystem partnerships, and creating infrastructure capacity to be a service provider and force multiplier for the life sciences industry.
The combination has given most service providers the ability to address the entire value chain, including clinical research with wet and dry labs, clinical trials, smart manufacturing, intelligent supply chains, and a deep understanding of regulations at a global scale. One could argue that several providers can independently take molecules to market.
The key to differentiation and sustainable success is the ecosystem
The study unearthed the different levels of ecosystem maturity service providers have cobbled togetherand the rationale driving those partnerships. At one end of the spectrum, we have providers with the rationale that leveraging scale and brand recognition drives an ecosystem of enterprise platforms and hyperscalers. On the other end of the spectrum, the rationale to create differentiated offerings, accelerate outcomes, and be at the leading edge of changes drives ecosystems that include academia, startups, and life sciences–specific players buttressed by enterprise platforms. The efficacy of the different types of ecosystems will likely be reflected in the sophistication of offerings, quality of outcomes, and growth.
Despite progress in capabilities, there continues to be a bias toward selling services versusdelivering outcomes
Value-based constructs (VBC)on the front end (payer to pharma) are growing in Europe but not in the US. However, given that the US Centers for Medicare and Medicaid (CMS) has now been authorized to negotiate prices on select drugs starting in 2026, it’slikely VBC may not remain so foreign in the US. Despite that direction, there is still a very strong bias for life sciences and service providers tolargely engage in capabilities based on contracts rather than outcomes. This legacy orientation will continue to drive costs up without any true accountability. We need life sciences enterprises and service providers to step up and address outcomes in contracts to make a difference to the true cost borne by all those who pay.
The Bottom Line: Life sciencesenterprises’ desire to cure diseases fasteris only matched by the enmasse increase in the sophistication of service providers’ capabilities.
Climate change, political and societal polarization at home, and armed conflicts continue to make the world a dangerous place. The frequency of pandemics, increase in rare diseases, and prevalence of chronic conditions make the situation dire. In this context, a life sciences orientation combined with service providers’ capabilities give us more than hope that we will have some of the tools to keep us healthy.
Generative AI has become core to our very future and how we interact with the Internet. It is forcing us to unlearn the habits of our lifetimes, continually generate new ideas, and relearn new ways of doing things.
It’s what inspired us at HFS to trademark The Generative EnterpriseTM and rethink how value is being created across enterprise ecosystems as AI is increasingly able to mimic human behavior and make us so much slicker and smarter at what we do.
To this end, we are just so thrilled to announce Ray Kurzweil as our first confirmed keynote at the HFS Spring Summit 2024, scheduled to take place from May 8th to May 9th in New York.
Ray Kurzweil is one of the world’s leading inventors, thinkers, and futurists, with a 30-year track record of accurate predictions, including the age of mobile computing, digital books, wearables, self-driving cars, and high-speed wireless data transmission. Called “the restless genius” by The Wall Street Journal and “the ultimate thinking machine” by Forbes magazine, HFS is thrilled to welcome Ray to the stage this May.
Ray was the principal inventor of the first CCD flat-bed scanner, the first omni-font optical character recognition, the first print-to-speech reading machine for the blind, the first text-to-speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition software.
Ray Kurzweil, author of The Singularity Is Near (2005), and his forthcoming book, The Singularity Is Nearer, will be released in the summer of 2024
Ray has written five national best-selling books, including The Singularity Is Near (2005) and How To Create A Mind (2012), both New York Times bestsellers, and Danielle: Chronicles of a Superheroine, winner of multiple young adult fiction awards. His forthcoming book, The Singularity Is Nearer, will be released in the summer of 2024. He is a Principal Researcher and AI Visionary at Google, looking at the long-term implications of technology and society.
This summit promises to be an exceptional gathering of industry leaders and innovators across industries, providing valuable insights into the latest trends and technologies across emerging technology and IT/business services—a rare chance to network and meet face-to-face with fellow decision-makers and influencers.
Ravi Kumar, CEO of Cognizant and one of the leading voices in the global IT and business services industry for the last couple of decades has spent this year reshaping Cognizant as the firm surpasses the $20 billion services revenue milestone.
Talking with Ravi and hearing his developing thoughts, he has a deeper-than-ever focus on reversing the alarming commoditization trend of tech services by developing a talent strategy with a mindset grounded in creating new value through technology arbitrage value, not simply labor arbitrage.
Our most recent candid interview – as part of our GenAI Leaders Series – is to learn how Ravi is shaping things up at Cognizant in light of the opportunities and potential threats posed by GenAI…
Enterprises must do more with less and innovate in tandem
So, what are Cognizant clients doing to navigate the new business climate steeped in cost-cutting concerns alongside excitement and uncertainty about GenAI potential? Ravi described an accelerated pace of change that is occurring within the enterprise landscape, with technology as the foundation; all companies are now aspiring to become tech businesses and AI is becoming deeply embedded in business. Cognizant customers are looking to drive out costs and innovate in a hurry with equal determination. They are also focusing on the foundational elements of an AI-powered organization, including data readiness, privacy, security, and responsible AI.
This means firms like Cognizant must pivot their value proposition; getting stuck in Horizon 1 of cost-cutting and process optimization means continuing to live in the legacy world of commodity cost-savings-focused engagements. Cost savings are important, but ambitious providers have to keep striving for the co-creation of new sources of value to stay competitive in today’s era of rapid AI deployment. Many service providers have maximized the cost and efficiency levers in recent years, and now the focus is firmly shifting to genuine business transformation to provide faster, smarter data to drive rapid decision-making. That is the new lever that must be pulled in these engagements to deliver new thresholds of performance out of the business and support the growth agenda. Providers must support their clients to optimize and innovate at an equal pace in order to keep up with the competition and do the same themselves.
Gen AI creates a whole new workflow paradigm: enter, the jagged frontier
Ravi describes his view of the evolution of GenAI as a “jagged technological frontier.” This jagged frontier implies an uneven edge, where task allocation is irregular and the output quality is very disparate. Some operational tasks are easily done by AI, while other tasks of a seemingly similar difficulty are outside the current capability of AI. For a workflow designer, this means GenAI-capable tasks can fluctuate over the course of an employee’s workflow, with some tasks falling inside and others outside the frontier. You always need a human in the loop, at least with the AI we have today. The jagged edge GenAI creates for workflows is driving a whole new workforce dynamic that is both exciting and daunting. Let’s dive in on how this jagged frontier is playing out within Cognizant and for its clients….
GenAI is poised to amplify human production; the bottom half of workers will benefit most, but will overall improve productivity
There are two core areas where Ravi sees GenAI as having the most impact: the amplification of productivity within the delivery of services, particularly for developers and customer service staff, and augmenting customer ecosystems.
When we wrote about the new S-curve of value creation for GBS, we noted the rapid maturation of GenAI promises a significant productivity improvement (not just incremental) in voice-based work, coding, testing, and transactional processing. There’s also the promise of AI-driven operations to support autonomous decision-making, exception processing, and the capability to handle a more creative scope of work beyond mundane and boring activities.
This aligns with Cognizant’s view of the evolving GenAI capabilities, where human talent is both augmented and accelerated. Ravi sees a particularly significant impact on developers for equalizing performance; he cites a report that concluded that the bottom low-performing developers benefit much more from GenAI than high-performers when evaluated on the quality of output performance. This levels the playing field for talent across proficiency levels and offers a big opportunity to improve the developer productivity lifecycle.
“Every job can be upwardly mobile if you can use the power of technology, the power of AI, and make people do more value-added jobs and more prosperous jobs so that they see the value in embracing technology. So, while technology related to AI is going to eliminate jobs, it’s going to create upwardly mobile jobs.” –Ravi Kumar
The future workforce is driven by capability equalization and net new, different jobs
While Ravi also reminds us that any big disruptive opportunity is also a threat and cautious enthusiasm is the right tone to strike, Ravi firmly believes that the evolution of GenAI will create new jobs and impact the future workforce in a positive way. The shifting of agency to the end user brings a new opportunity for tech to be used to empower ourselves and our employees in their jobs. Generative AI still requires a human in the loop, and as a result, we will re-invent our workforce, and the types of jobs we need in the services industry will change. As Ravi puts it, “We will need more problem finders versus problem solvers; it will require more heuristic and creative skills, which will make this industry much more diverse.” That diversity of thought and people is something that services firms must embrace to refresh and reinvigorate the industry.
Moreover, in terms of the overall volume of jobs, Ravi is bullish on the GenAI shift as a catalyst for job growth in the services industry rather than the commoditization that has been threatened for decades every time a shiny new tech is introduced. “Looking back, technology actually created more jobs of the future than it’s taken away jobs of the past – so I’m very optimistic that it is going to create significantly more jobs for the future.”
AI first, machine + people is the future of work; services firms are the arbiters of change
Tech services firms are going through a transition fueled by these technology advancements and labor shifts. While the enterprise landscape is being completely reshaped by GenAI, and resistance to change is high, leading services firms will help connect their clients’ organizations to the broader GenAI ecosystem, and their partners must be in lockstep with them to navigate the changes.
Looking ahead, Kumar predicts that the world will become an AI-first, machine-plus-people endeavor. AI will lead to more tech intensity, more budget allocation due to higher productivity, and a greater need for creative and heuristic skills. This is an opportunity for the services industry to develop top talent for new and exciting tasks that shun the mundane and repetitive roles of the future and create fun, human things for people to do.
As technology becomes more central to enterprise landscapes, the traditional labor arbitrage value proposition will fade into the distance as technology arbitrage becomes the central role of the service provider.
“Technology arbitrage is the future of the services industry” – Ravi Kumar
The Bottom Line: Capability equalization of the jagged edge frontier will lead to the human amplification that the services industry desperately needs to deliver value.
There is still much work to do to bring the promise of GenAI to its potential in the services industry, and constant tech advancements and risks mean we cannot afford to take our eye off the ball for one minute. What is certain is that as enterprise operations and services leaders, we must be prepared for challenges, not least of which is a dearth of technology and business professionals prepared to ride the wave that an AI-powered enterprise requires, which is adopting a probabilistic mindset and an emboldened attitude to learn new methods and ways of doing things.
Bridging this divide we currently see ahead of us means better service partnerships, greater productivity as well as prosperity for workers, and greater job satisfaction leading to better outcomes across this new generative ecosystem.
“Gen AI is a catalyst for a new level of reinvention over the next decade” – Julie Sweet
As part of our GenAI Leaders Series, we got time with Accenture CEO Julie Sweet to talk about how the $64 Billion dollar corporation is approaching GenAI. With the services industry experiencing feverish excitement over AI and anxiety surrounding the demise of legacy labor arbitrage models and reduced technology expenditure, who better to listen to than the provider leading growth from the front and reinventing itself ahead of the market, with its formation of Accenture Digital in 2013, then Cloud First in 2020… and now its aggressive pivot to drive GenAI at scale.
Accenture’s Total Reinvention… can enterprises embrace change?
Just when you thought you couldn’t bear to hear the phrase “digital transformation” one more time, a fresh view of changing business dynamics is being ushered in by Accenture CEO Julie Sweet under the moniker “Total Enterprise Reinvention.”
Accenture claims this reinvention is fueled by technology, data, and AI and is already impacting how people work. While highlighting the responsibilities leaders must take to be successful during the next decade. As Sweet says, “Accenture clients are embracing change more than ever, and GenAI is the catalyst for embracing that change at a more rapid pace than we’ve ever seen.”
While enterprises are under pressure to innovate and cut costs at the same time, many service firms have been caught flat-footed when it comes to understanding the potentially revolutionary capabilities of LLMs and generative AI tools. And while others are defining their vision, Accenture is putting its money where its mouth is by declaring a $3bn investment in AI. Their goal is to embrace the workforce changes enabled by GenAI and proactively train thousands of staff, develop assets, and build solutions to help their enterprise clients adopt GenAI faster. Their big bet promises a big payoff for the $64bn services juggernaut, which announced in June it had secured $100m in GenAI projects, a number that’s surely climbing further since, claiming 40 of its enterprise clients are now experiencing GenAI at scale.
In our latest interview with Accenture’s top brass (see last month’s summary of Paul Daugherty’s insights here), Sweet describes the transformative potential of GenAI on the enterprise and the future of work. Here’s what we learned, and HFS’s take:
Spending constraints must not slow down innovation in the digital dichotomy of 2023. The economic climate this year is forcing companies to hunker down and cut costs, focusing on efficiencies, and making them do more with less. But at the same time, they are being tasked with increasing their innovation efforts critical to differentiation and growth. Accenture’s “total enterprise re-invention” is aimed at helping clients figure out where and when to invest. As Sweet says, “There’s going to be a significant amount of enterprise spend that’s unavoidable, and Accenture aims to help clients do that in the smartest way possible.” As enterprises add in the opportunities and risks inherent with GenAI, Accenture is bullish on helping clients embrace GenAI to drive the next decade of change.
GenAI is playing a role in the re-invention of the workforce and society at large. GenAI is leading to fundamental changes in how organizations operate and impacting how every part of the organization will have to work differently to compete and succeed. Mainstream discussions have mainly circled around whose jobs are in jeopardy and whether or not education is being stunted by the use of GenAI to “cheat.” (Sweet points out her teens have taught her that’s not happening – too many tools out there to detect when GenAI’s been used to cut corners!) For now, it seems the workforce is being augmented, and students are becoming savvy with GenAI to learn faster and, to improve the learning process rather than avoid it.
Knowledge workers will be affected most by GenAI-fueled re-invention. All knowledge workers will be using some form of GenAI to do their jobs better within the next five years. Just look at HFS Research’s own use of LLMs to transform our web search tool and allow greater access to research for our clients. In fact, we used GenAI tools on this blog itself to transcribe and summarize this interview and organize some of the key points. As knowledge workers, we must embrace the opportunity to use these tools to help us work more effectively. What remains to be seen is what parts of knowledge work jobs will be displaced versus those that will be augmented – there will be both in our future. It’s clear that (for now) the creative and provocative idea-focused tasks are still firmly in the human realm (though the return on the request below wasn’t terrible).
For Accenture, the evolution of GenAI in their workforce is underway as they are educating their workforce on prompt engineering, AI-centric risk assessment, and ongoing training for their global workforce. Under Sweet, Accenture is taking a stand that GenAI isn’t the enemy of knowledge workers; rather, it’s an infusion of capabilities as GenAI assists in how people get their job done more efficiently.
Responsible AI isn’t just a priority; it must be part of the foundation of your firm’s GenAI journey. You will put your company at risk by not thinking through the pitfalls of AI; companies must promptly establish a systemic foundation for responsible AI practices.Accenture is establishing a first-of-its-kind formal compliance program for responsible AI, including a framework providing guidelines for its teams and their customers on implementing AI projects. As it has been building AI solutions for over a decade, Accenture provides diagnostic tools to aid in the evaluation of risk levels related to implementing AI-based projects. This tool helps categorize AI applications into different levels of risk, allowing organizations to understand where they might be using high-risk AI. HFS agrees with Accenture that it is critical that while investment is going into GenAI, matters of bias, fairness, privacy, and security be addressed and protected from the start.
Adopting GenAI to augment how we work, its impact on people, processes, and tech will mean every part of the enterprise will work differently. As companies and employees apply data and AI into their everyday lives every part of how they work is going to change. If you only combine this seismic shift with what we know now, you’ll miss great opportunities during this change. For instance, people aren’t talking about how GenAI, combined with advancements in quantum computing will bring significant changes in fields like material sciences, security, and biotech. With this in mind, it is critical for organizations to adapt and work differently and do this with further change just on the horizon.
The Bottom-Line: GenAI’s potential is positive, but proceed with caution, education, and responsibility
We are at the tipping point of one of the most significant enterprise technology shifts ever. GenAI is maturing quickly, and whether it’s within two years or five years, our workforce and business environment will be completely different, and the services industry will be re-invented.
It is still early days, but it is clear that it is critical to invest in GenAI now to create future value; services firms must go all in on understanding and finding examples of effective use cases for GenAI. By doing so their enterprise clients will seek them out for guidance and collaboration as these investments in GenAI shape their generative enterprise aspirations. “When you embrace the shift, you win,” says Sweet, about each of the technology shifts she’s seen Accenture embrace in its past. GenAI promises to be a seismic shift, but moving forward carefully is necessary to avoid risk while maximizing value creation.
The biggest problem plaguing the world of business technology – ever since businesses started using technology – is the simple fact that most enterprises blow vast fortunes on expensive tech solutions and expect them to perform wonders for their businesses without redesigning and structuring their processes and data to achieve maximum value from the technology. It was the case back in the 1960s with MRP, then decades later with ERP, and these issues are even more exacerbated with the Cloud.
Most customers of Cloud just want “easy”… that is the problem.
At least ERP gave companies a standard set of processes to force themselves to follow, but moving your existing mess into the Cloud is an expensive disaster if you avoid changing how you operate, modernizing your hodgepodge of systems and applications, and redesigning your workflows so data can flow freely around your global organization, up and down your supply chain, and across your business ecosystem of customers and partners.
The Cloud is a whole new environment within which to operate your business, and you can’t shoehorn your legacy way of operating into this environment without addressing these (often painful) changes. “Clients just want easy; that is the real problem,” stated the leader of a major transformation practice. This is why our recent research of over 500 Cloud decision-makers from major enterprises (Global 2000) reveals that Two-thirds of organizations don’t fully achieve their strategic objectives for transformation enabled by the cloud (see Exhibit 1 below). Sadly many of those transformations clearly fail miserably if your Cloud transformation will fail if it is not grounded in business objectives. Yet, to keep the cloud bandwagon and spending going, hyperscalers and service providers are now peddling “Industry Cloud.” So let’s probe deeper into what “Industry Cloud” really means…Read More
HFS has just launched the industry’s first-ever Generative Enterprise™ Services Horizons report, covering the 35 leading service providers and advisors vying for what they expect to be an enterprise investment bonanza.
So we finally have real “low-code” technology for business people, and every company leader wants to make sure they aren’t caught flat-footed again like they were barely a year after that famous ChatGPT launch. What is clear is they all desperately need partners to help them, but which of today’s leading service providers have the real chops to provide GenAI value at scale? Who’s actively backing up their big rhetoric with real know-how and capability?
Well… after an exhausting process of researching the 35 market leaders, their customers, and partners, we can provide the industry’s first real view of how the GenAI services landscape is shaking out.
The next LLM update will create new business cases and just as easily destroy others
Generative AI (GenAI) seemed to land like an alien-bestowed gift at the start of the year, promising productivity step changes and transformed ways of working. It triggered a rush of announcements from service providers as they committed billions in investments, raced to train hundreds of thousands of staff in the new technology, and buddied up with the hyperscaler owners of so much of the critical technology. Enterprise leaders felt pressure from above and below to “do something” with this shiny new object.
This nascent market is very much subject to rapid change. Updates to the various versions of large language models (LLMs) on offer may reveal unexpected opportunities for new use cases at every turn—and they are just as likely to destroy other business cases. There’s never been a better time to be agile and to keep your options open.
Fix your data and cloud challenges now, or risk missing the GenAIrevolution
And yet, there is a growing realization that scaling the possibilities of GenAI will ONLY be possible with solid data designed to support GenAI tools and cloud maturity foundations. This realizationwill create a moment for a sharp collective intake of breath among those enterprise leaders who are still working to get their data and cloud capabilities up to scratch. Catching up won’t be cheap,but to join the GenAI revolution it’s going to be unavoidable.
Simply put, if we just layer GenAI onto what we already have, we’ll reach a ceiling very quickly with what we can achieve. If, however, we optimize how we collect data to be synthesized by GenAI tools, the opportunities are exponentially greater. We will have to change our habits and how we do things if we truly want to move to a new S-Curve of value.
This is where the smart service providers see the real gold – transforming enterprises to get them GenAI-ready, as opposed to their old habits of implementing software and hoping their clients actually use it effectively. Hence GenAI is a transform-first, then-implement scenario…
Our Generative Enterprise™ Services Horizonsreport covers the 35 leading service providers
Our report examines and assesses 35 service providers. It evaluates providers’ capabilities to understand the Why, What, How, and So What of their Generative Enterprise services offerings:
Note: Service providers within each Horizon are listed alphabetically.
HFS has called out the rise of the Generative Enterprise—the articulation of the pursuit of AI technologies based on LLMs like ChatGPT and GPT-4 to reap substantial business benefits for organizations in terms of continuously generating new ideas, redefining how work gets done, and disrupting business models steeped in decades of antiquated process and technology.
The report offers detailed profiles of each provider and places each in one of our three Horizons:
Horizon 1–Disruptors: Those best placed to help enterprises drive digital transformation by leveraging AI to drive predictive functional insights.
Horizon 2–Enterprise innovators: Those enabling the HFS OneOffice™ by leveraging AI to improve decision-making and driving unmatched stakeholder experience.
Horizon 3–Market leaders: Those enabling the Generative Enterprise by leveraging AI to generate new ideas to redefine how work gets done.
Five key dynamics this report reveals about the evolving Generative Enterprise services market
The GenAI gold rush is pursuing a $7 trillion prize We’ve never seen a technology adopted so fast. GenAI’s poster child, ChatGPT, reached 100 million users in two months. RPA took more than a decade to reach 15 million. Every boardroom is asking every CEO, “What are you doing with GenAI?” This bottom-up and top-down demand and promise of a $7 trillion prize has prompted a gold rush among service providers as they hurry to organize and claim a piece of the action. In months, leading systems integrators and consultancies have conjured new practices, divisions, platforms, and partnerships. They are scaling up, investing billions, training thousands of people, and recruiting thousands more—and this journey is only just beginning.
Point solutions dominate, but this is not where we will end up We are already witnessing a rapid diversion of AI budgets to GenAI projects. On average, this stands at 41% across the enterprises surveyed for this report. We expect that proportion to grow as enterprises move beyond their initial point solutions in proofs of concept (POCs) and pilots. Most are solving specific tasks. As the next budgeting cycle begins, we expect budgets to scale up to take GenAI deeper into end-to-end processes, shaping new ways of working. The next step will be more challenging but more rewarding. And if it doesn’t happen, there will be a lot of red faces among service provider leaders, many of whom have gone all-in on GenAI.
The disruption is coming first and fastest to CX, EX, and sales and marketing As part of our research for this report, we asked enterprise leaders about the functions they prioritize for applying GenAI. Customer experience (CX), employee experience (EX), and sales and marketing lead the way. This chimes with the case studies service providers shared. Transforming code has been touted as a leading use case by many service providers, and it features prominently in their internal use and service offerings. But in our research, it only shows up in around 10% of the case studies we’ve seen. One key thing to note regarding case studies to date is that few are coming with an ROI. At this stage, most enterprises are happy to see softer measures such as time-to-serve, CSAT, or time-to-market.
Knowing the tech is one thing, but helping to transform with it is quite another Enterprise customers see a gap between how well their service providers deliver on tech implementation and their ability to transform business. It’s an important gap as enterprises seek help on their journey to the Generative Enterprise beyond the initial point solutions. Knowing the tech is one thing; helping transform ways of working because of the tech is another altogether. We think this gap will close because many service providers are going all-in on GenAI, focusing on proving the effectiveness of applying it to their ways of working first. The lessons they learn through self-transformation will give them the credibility to help enterprises shape their journeys.
This revolution is personal, and you need to get down and dirty with it Using GenAI tools is where your personal experience and understanding begin. Using the tech yourself is your due diligence. The journey to the HFS Research Generative Enterprise is not easy, but it starts with your understanding. Leaders need to develop their GenAI muscle memory to begin seeing the future through today’s technology rather than persisting with a view constructed on their experience and knowledge of the technology of the past.
The Bottom Line: GenAI will start and finish with POCs if you haven’t already cracked your OneOffice digital transformation.
Much of the investment to date in GenAI has been in POCs and pilots. It’s likely to start and finish there for those organizations that lag behind the leaders in digital transformation. Those best placed to scaletheir capabilities in GenAI are already digitally savvy and have the cloud and data infrastructure to support GenAI’sdata-centric and cloud-computerequirements. We are headed for a critical decision point for leaders:Get very serious about yourOneOffice digital transformation, or watch your GenAI-augmented rivals leap beyond your grasp.
Generative AI is already making real impacts in the enterprise, but bad processes may create a false ceiling to hold back progress. That’s how Exponential View founder and esteemed independent AI researcher Azeem Azhar reads recently emerging research, where processes and data structures designed for GenAI can reap exponential rewards.
In a fireside conversation with HFS Research CEO and Chief Analyst Phil Fersht, Azeem offered evidence that LLMs were already helping people do their work quicker, at higher performance levels – and with greater employee satisfaction. So without further ado, here are Azeem’s keen insights….
LLMs boost performance among the majority of skilled workers
Azeem Azhar: “Phil, thank you so much for the opportunity to talk to you and the audience here. There was a Brynjolfsson study that looked at call center workers using LLMs before GPT-3.5 or 4, and they identified that these call center workers were 14% more productive, and after two months of using an LLM, a new worker was as well skilled as long-term employees who had not used an LLM.”
He explained that even uncodified knowledge and know-how not in the document was transmitted to these new workers over six months. In another survey by Noy and Zhang from MIT, higher-paid white-collar workers were provided with ChatGPT.
Azeem Azhar: “These were grant writers, or in HR, or marcomms, with an average salary over $100,000 yearly. They had a 40% improvement in the time taken and – I think – 15 to 20% improvement in the quality of the work.”
Poor processes constrain top performers and will limit the benefits GenAIcan bring
Recently, Azeem’s colleague Karim Lakhani at HBS and friend François Candelon at BCG looked at 800 Boston Consulting Group strategy consultants supported in their work using a GPT-4 application. Tasks got completed to a higher standard faster, and below-median employees improved the most.Azeem says these three studies show LLMs can be productivity enhancers across the board. But improvement at the top level is constrained.
Azeem Azhar: “I think, Phil, this falls right in the realm of the kind of strategic transformation work you have done with clients for years and years. The fact that the bottom three quartiles of the employees improve the most speaks to the limitations of the existing process flow.”
He says LLMs reveal the limitations that poor processes place on top performers.
Azeem Azhar: “It’s as if we have a high jump, and the bar never goes above 1.7m, and for me, that’s a stretch; for you,that’seasy, you could jump 1.9m, but we never raised it to 1.9m. And that’s the kind of thing that HFS helps firms think about. LLMs have shown that you must rethink your internal dynamics to get more performance.”
We must get to grips with a new‘jagged frontier’where performance can go either way
Azeem Azhar: “Where you (Phil) identify we may hit limits, that is what the AI researchers call the jagged frontier. On one side of the frontier, the LLM does better; on the other, it worsens things. The problem is we don’t know what that frontier looks like. It’s also a shifting frontier. It varies from task to task.”
Azeem thinks the arrival of LLMs triggers a moment to rethink how work is down.
Azeem Azhar: “You must be alert to where your existing systems or processes are so constrained they don’t allow you to perform at a GPA of 4.0 because you’ve never thought it was possible, and also how you manage for those tasks where working with an LLM might give you a worse result.”
Don’t blame the LLMs – it’s the shareholders and finance directors who are likely to be swinging the job cuts axe
Phil Fersht: So, do you feel white-collar jobs are under threat, Azeem? Or do you think this is another evolution, a new technology, and new jobs will be created…
Azeem Azhar: I think we can be reasonably expectant that new jobs will get created. I don’t believe the threat necessarily comes from LLMs. It probably comes from shareholders and finance directors, more than anything else, because there will be a lot of pressure for cost savings and, you know, “Can we deliver the same experience to our customers at a lower cost? And if we can, let’s do that.”
“There will undoubtedly be many processes that will be as efficient with fewer people. What a firm chooses to do at that point will depend a lot on its relationship with its workers, territory, and employee rights, the kind of direction, mission, and depth of capacity of the firm. Some big IT consulting firms managed to upskill and retrain hundreds of thousands of people in the face of automation. But they’ve done that against the backdrop of growing businesses.
Jobs created out of technical debt will be among the first to go
Phil Fersht:Yeah. Yes, very well put. And, you know, it’s interesting, the conversation I had yesterday with the academic was very much, “We need to keep reminding ourselves that AI is about ultimately improving human intelligence.” So…
Azeem Azhar: Yeah. But I think wehave to bear in mind that there was an assumption, when tasks were designed and processes were created, about who would do that job. Many jobs were framed so that they didn’t need to be done by humans; humans did them because it was a bit too complicated to get a computer to do them. Data entry is one. The whole of RPA exists because of poorly architected, monolithic computing frameworks, which meant we couldn’t move the ledger entry from the mainframe system into the minicomputer system, the client service system, or into the web–based system, so we had to do screen scraping and things like that. Now, that person has a job, but that job exists only because of technology debt.
They can’t reason, they can’t plan – but LLMs overcome limitations
Phil Fersht:Yeah. So, final question. You said it’s not all going to end here with LLMs. If you could look back in three years’ time, what do you think the world of enterprise tech will look like then, based on how fast things are moving now?
Azeem Azhar: LLMs are good at a bunch of things. But they can’t reason, reliably plan complex actions, and are not great at learning representations of the world. And this is really about how they’re designed, so it does appear like new science may be needed.However, these limitations that we see – for example, hallucinations– may get tackled through continual improvement in the LLMs themselves; GPT-4 is much less hallucinatory than GPT-3.5, but also by how they get productized by other tools, like vector databases, or RAG, retrieval-augmented generation, which is meant to anchor an LLM’s output to verifiable certified facts that it might find elsewhere. Because you’re starting to see technologies like that, and techniques like that, wrapped around the science, I think you’ll see many companies building SaaS and enterprise software with such solutions as an underlying model.
The Bottom-Line: Prepare to be surprised – just as you were surprised by ChatGPT
Azeem Azhar: I would expect the use of more and more open source, more sparse, more efficient models that are tuned to specific sub-verticals within industries. But at the same time, there will still be a constraint because if you are a customer of Salesforce, and you have the Salesforce GenAI chatbot helping you with this or that, there will still be things that it can’t see in your worldview, and you will then start to think about, “How do I bring that in, with my internal system?”
It’s an exciting time. We should be prepared to be surprised in the same way that we were surprised by ChatGPT. But I think there’s quite a lot of momentum in building these systems based around LLMs as a core orchestrator and reasoning engine, even though it doesn’t do any of that stuff particularly well. Still, it does it well enough, which looks like a framing for the next few years.
Phil Fersht:Very well put, Azeem, and thank you very much for your time today – always good to hear from you.
HFS is back with its analysis of the finance and accounting (F&A) service providers, but now in the Horizons framework!
With the rush to remain relevant in the new AI era, there should be no more important function than finance as the repository for all the data that is needed to support rapid and smart business decisions. Yes, it’s pivotal in helping organizations transform to respond to changing market forces. Traditional finance remains the backbone, but strategic finance is gaining importance. Key F&A service providers are moving beyond the usual F&A functions and trying to act as strategic advisors to finance organizations.
Data is driving this change, and most enterprises seek help from F&A service providers due to the lack of in-house advanced knowledge and capabilities specific to data requirements. Real-time insights and future-proof actionability are the needs of the hour, and enterprises unable to quickly make that shift will be left behind. This is where we see rising interest in the financial planning and analysis (FP&A) and finance transformation levers of finance.
The F&A basics remain important, but newer areas are catching up
Cost efficiency, speed, and industry expertise remain the most important selection criteria for service providers in F&A, but innovation, emerging tech, and environmental, social, and governance (ESG) prioritization are catching up. F&A service providers are integrating generative artificial intelligence (GenAI) modules into their service portfolio to minimize business risk and bring more efficiency and accuracy into the processes. At the same time, ESG KPI reporting is also making headway in bringing visibility into the ESG agenda, with accountability moving toward the finance function.
Data-driven finance combined with agility, predictive analytics, and tech-savvy talent is needed to push the boundaries toward making an impact spanning enterprises, partners, and clients.
Opportunities abound for service providers, with F&A taking on a pivotal role for enterprises at large
We conducted an exhaustive research exerciseinto15 of the key service providers (Exhibit 1) in the latest Horizons report,HFS Horizons: F&A Service Providers, 2023,by divingdeep into the capabilities of each and how they have contributed tothe changing F&A landscape over the last year.
Exhibit 1: We see more and more enterprises willing to outsource finance functions to service providers for a more holistic F&A transformation
Note: Service providers within each Horizon are listed alphabetically.
A few glimmers of Horizon 3 are making its impact felt, courtesy of the market leaders
Market leaders like Accenture, Capgemini, Genpact, IBM, Infosys, and TCS are doing the right things, creating an ecosystem of differentiation and client impact—but it is still very early days. These service providers use data, analytics, AI, emerging tech, and strategic partnering to deliver beyond the usual F&A services. They lead the way, bringing a wind of change to how we look at F&A and where we expect F&A to go over the next couple of years. ESG is another area where some service providers are working to bring about socially responsible accounting practices.
Horizon 2 providers are well on the way to achieving multi-level outcomes
Cognizant, EXL, Sutherland, Wipro, and WNS have taken the enterprise innovator Horizon in the 2023 edit. Some of these F&A players are moving into consulting, some focus largely on enabling data-driven finance to become a reality, and others focus on strengthening digital IP and partner ecosystems to push the boundaries further and continue to drive outcomes that span enterprises.
Horizon 1 disruptors focuson getting the basics right andfinding solutions that drivemultiple business outcomes
The disruptors—Conduent, Datamatics, HCLTech, and TechMahindra—are doing a great job either catering to the mid-market with agility and efficiency and winning multi-tower tech and operational improvements or building internal IP to support F&A advancements. While this is great for clients still at the nascent stage of their F&A transformation journeys, we would love to see them move beyond Horizon 1 outcomes to deliver more synergistic outcomes.
The Bottom Line: Finance is no longer a boring,back-office function, and the CFO role is evolving as a strategic partner across the organization. Each service provider is doing something differentto drive new sources of value beyond the usual.
TheHFS Horizons: F&A Service Providers, 2023reportincludes detailed profiles of each service provider supporting the F&A ecosystem, outlining the service provider’scapabilities, strengths, provider facts, and development opportunities. Each service provider has a unique approach to enabling organizations to become more predictive with data and insights.
