Nothing has disrupted the cozy status quo of big enterprise technology transformations more than GenAI. It has been a true leveler across the industry where suddenly everyone is operating on a level playing field, trying to convince the world they have a better GenAI story than their competitors.
Up until November 30th, 2022, the Big 4 consultants dined on the ineptitude of large enterprises to move bad processes into the cloud, while the Indian heritage outsourcers fed on the tasty scraps of supplying armies of low-wage talent at scale to keep these hulking institutions somehow functioning.
The legacy software companies sold their licenses through these services firms to maintain the flow of insane amounts of money and maintain the veneer of competence as these clunking enterprises kept up with the latest versions of SAP, Oracle, Workday, and Salesforce, with smatterings of UiPath bots to knit together broken workflows and poorly integrated systems.
And to cap it off, the hyperscalers profited from everyone as they sought to force a cloud narrative for CIOs to desperately follow to sound credible. In short, everyone has been on the enterprise technology gravy train, and it’s taken a genuinely credible new technology that business leaders can understand to redirect the train.
As Henry Hill famously said in the classic movie Goodfellas: “We ran everything. We paid off cops. We paid off lawyers. We paid off judges. Everybody had their hands out. Everything was for the taking. And now it’s all over.”
Our industry has been equalized, and a new set of winners are going to emerge
A senior partner in a Big 4 consulting org literally declared to a major enterprise leader this past week: “GenAI is just another technology tool and nothing more”. Many of these (previously) highly respected and handsomely paid consultants have been caught flatfooted and are dismissing GenAI because they don’t understand it and can’t divert extra millions of dollars out of their clients’ budgets to “help” them. The harsh reality is smart clients can smell the bullshit and aren’t going to get burned like they have so many times in the past with consultants wielding shiny new tech. And you only need to look at the sheer scale of layoffs in these firms to comprehend that the gravy train is screeching to a halt.
Net-net enterprise perceptions are changing fast, and our latest pulse survey of 425 global 2000 enterprises shows the Big 4 did no better than other IT and business service providers when rated as strategic partners for emerging technology capabilities.
Why the legacy Big 4 approach and mentality will fail with GenAI
Services partners need to win their clients’ hearts before they can attack their wallets. No one budgeted for GenAI, and two-thirds of major enterprises are still recovering from overspending on bad cloud migrations. However, most ambitious C-Suite leaders are infatuated with the potential of GenAI to make their companies more competitive and improve their own capabilities to be smarter and slicker at their jobs. Partners who understand their clients’ institutional issues and are willing to invest time at no cost to figure out a GenAI roadmap will reap a lot of fruit next year.
Enterprises want to explore solutions that are fast and uncomplex. The big challenge with GenAI is to clean up enterprises’ messy data so they can benefit from the tools. Otherwise, GenAI becomes lipstick looking for a pig. Enterprise leaders want no-nonsense partners which can understand the business context behind their data needs, as opposed to teams of highly expensive technical and domain consultants who’ll charge $2 million just to show up and document the problems.
The winners will be the partners which can quickly understand what needs to be done to fix and scale the data without charging the earth, with the ability to work fast and smart. When you look at the deep institutional relationships the likes of Cognizant, HCL, Infosys, TCS et al. have with their clients, many of whom are into 4th or even 5th-generation contracts, surely these firms have a huge opportunity to convince enterprise leaders to take a risk with them to make the painful changes necessary to capitalize on GenAI tech?
Tech spend is rebounding in 2024, with AI as the main driver. The battle is on to partner with ambitious enterprises
As the HFS Pulse study showed this year, tech spending plummeted from 11% growth in 2022 to barely 2% in 2023. While a lot of the pullback has been a result of difficult economic conditions earlier this year, there has also been a backlash as our research has shown only 32% of enterprises consider they have achieved their strategic priorities with their Cloud investments. Net-net, enterprises are being careful shelling out more millions on new technologies after such heavy disappointment with Cloud.
However, the good news is that our latest Pulse data of 600 Global 2000 enterprises reveals 2024 tech budgets are rebounding and the core driver is AI (both Machine Learning and GenAI). So the big question now is which services firms enterprises will choose to partner with to embed GenAI into their data and processes:
AI-driven technology spending is expected to increase by 10% in 2024
The Bottom-line: The old way Big 4 operated is over, and the smart ones are focused on re-winning their clients’ hearts
The Big 4 need to practice what they preach to get back their competitive edge. We have three recommendations for them:
1. Double-down on the business narrative for technologies. The Big 4 have stronger relationships with the business compared to their IT services counterparts (with the exception of Accenture), who continue to struggle beyond the CIO / CTO. Our latest pulse survey indicates that IT controls just about half of the tech-related spending (see graphic below). The other half of the tech spending is with the business – that is where the Big 4 can win with their relationships if they can create a compelling business narrative for emerging technologies. They need to simplify technology, not complicate it. Make it solve business problems and make it easy to use and adopt!
2. Align advisory with managed services to create real value. The days of charging top dollar for slick PowerPoint decks are gone. The Big 4 need to get their hands dirty and be a part of the solution. In fact, managed services can protect the advisory business. Firms with operational relationships with clients can make the case for end-to-end services relationships. Managed services and ongoing operational relationships offer client stickiness and prevent client defections. The Big 4 should structure services around some elements of risk, trust, and compliance. This can also include LLM model evaluation and monitoring for Gen AI. Establish trust as a core value and marketing principle for managed services. This approach aligns with their branding and is presently not fully exploited by the competition.
3. Move beyond hourly consulting fees to performance and purpose-driven pricing. We don’t see clients paying $500-an-hour rate for advice at scale for long. The continued problem with consulting is the lack of skin in the game. But this is possibly the hardest challenge facing the Big 4 with a partner-led model where each partner thinks of their book of business, and while there is a lot of money for scoring the goal, there is no incentive for passing the ball. Managed services provide a foundation to shift away from a time-and-materials model, but this requires a fundamental transformation of the Big 4 operating model. EY tried to change things with Project Everest (splitting its audit and consulting business), but its legacy audit partners voted it down… too afraid to change their traditional model.
Large Action Models (LAM) are the most exciting development in AI evolution since ChatGPT was launched. Having an AI assistant not dependent on islands of apps that do not integrate with each other is everything we’ve been crying out for… But the future potential of LAMs is a lot bigger than addressing this burning problem plaguing our smartphone lives – it also has significant implications for the future of enterprise tech. It’s just incredible that while Google, Meta, Microsoft, et al. are all working on the evolution of LLMs toward actions and problem-solving, a startup like Rabbit is allegedly ahead of them.
Welcome rabbit.tech and step forward Jesse Lyu, who could well be the new Chinese Steve Jobs.
Source: YouTube, 2024
His introduction of a proprietary LAM makes it possible for AI systems to see and act on apps in the same way humans do. They learn through demonstration – watching what a person using an interface does to replicate the process – even if the interface changes. LAMs learn the interfaces from any software. They solve the problem of islands of apps that would not otherwise integrate.
The most impressive launch since Steve Jobs revealed the iPhone in 2007
Models for controlling computer actions are significantly less mature than language models, which is likely why Rabbit.tech caused such a stir at the recent 2024 CES event. What Lyu is developing with his firm Rabbit could totally disrupt the app store in a similar fashion to how ChatGPT is disrupting web search. This is super impressive in our view (except for Lyu’s love of Pizza Hut and Rick Astley). Even Microsoft CEO Satya Nadella has described the Rabbit’s launch of its R1 hardware as the most impressive since Jobs’ historic iPhone launch in 2007.
Real innovations are driven by consumer needs to begin with – which eventually find their way into the enterprise. The web and the smartphone are just two clear examples. Both have created the world in which we now live – a world of wanting instant gratification from our tech. And this next wave of AI is all about… making things work here and now.
LLMs understand what you say, LAMs get things done
We like the premise that “ChatGPT is great at understanding your intentions but can be better at triggering actions.” LLMs understand what you say… LAMs get things done. We have yet to produce an AI agent as good as one in which users simply click the buttons. We must go beyond a piece of complex software. In the case of Rabbit and its R1, a large language model (LLM) understands what you say, and the LAM actions your request. This model understands and enacts human intentions on computers and any user can teach it new skills.
Rabbit has applied this to the many applications sitting on your smartphone. The R1 device attaches to your phone and uses a camera and GPS to provide context for its decision-making and actions. You can use voice to ask questions and get voice and text responses. With the support of the LAM, you can ask ‘for a ride home,’ and Rabbit will use your preferred smartphone app to make the booking – understanding where you start the journey from.
Source: HFS and DALL-E 2024
Rabbit delivers outcomes through dialogue – in an ‘out-loud’ conversation, the likes of Siri and Alexa cannot match. However, the real breakthrough happens when you need a range of apps to solve your challenge – such as booking a vacation. Rabbit can respond to and fulfill complex requests such as ‘Book me a vacation in London for two adults and a child and find us a great hotel in a central location.’
And you can enter into a dialogue with it. At this point, you are effectively in conversation with it, having a conversation to hone – for example – your vacation itinerary. This implies a level of sustained memory more akin to that found in ChatGPT than in voice interfaces to date – including Alexa and Siri.
Once that vacation itinerary is honed to your liking and reported back, it takes just one human click-to-confirm to trigger the tech to complete all the bookings required – and pay for them.
The R1 ideal could sound the death knell for today’s smartphone, app stores, and even RPA… anything with needless complexity that prevents getting things actioned at the click of a button or simple voice action.
R1 wants to be everything to the user across IoS, Android, and desktop. The issue is that all these apps have a user interface. Its LAM can learn interfaces from any software. Though LAMs are not designed to replace your phone – they will eventually make it obsolete in its current form. R1 is aiming to kick off a whole new generation of native AI-powered devices and is just getting started; for example, this year, we can also expect the Humane AI Pin and the Tab AI Pendant.
LAMs effectively make it simpler to get stuff done, cutting through the needless complexity legacy applications have saddled us with. Robotic Process Automation (RPA) may allow us to stitch software together to form a process to complete an outcome, but RPA breaks the moment you change one app or interface in that process. With a LAM, the idea is you can just teach the new process through demonstration, and you get to continue getting stuff done.
Can it really be that simple? A note of caution before we pop the champagne
Should we really be ready to celebrate so soon? HFS’s Tom Reuner sounds a note of caution. “The big claim for LAMs is that they can action things. My suspicion is that LAMs require a high level of standardization for their actions. Therefore, we remain some distance away from objective-driven AI and automation that future large models may yet bring.”
In addition, while we believe that LAMs will eventually be a game-changer, specific to R1, we have a healthy dose of skepticism about whether another device is required for this functionality and whether or not consumers will appreciate carrying another device in their pockets just to save a couple of taps on their phones. The mobile revolution has been about device convergence all along. And what will prevent Google Assistant and other established assistants from improving their NLP and getting plugs for apps for similar functionality so we can just use our existing devices?
The Bottom-Line: Even if this bunny turns out to be a turkey – you need to prepare for the impact of Large Action Models
Like other AI there are risks to consider – will it comply with data and privacy rules and concerns? How many eggs do you want to put (to mix our metaphors) in the Rabbit basket? Is the device even going to show up and work (if not, there’s a bunch of HFS analysts who will be wanting their $199 bucks back). The answers for Rabbit will only come when the first consumers start getting their hands on the device. That’s expected to be early April (or, as Rabbit quips, ‘in time for Easter’).
And, let’s face it, enterprises are decades behind waking up to the need for actions so that AI can then actually do a better job, such as documenting a process, mapping a process (mining) automatically, reusing assets, securing them, and ultimately, solving the API/RPA conundrums. But when we start experiencing the end of application dysfunction in our consumer lives, surely this mindset will eventually trickle into the enterprise as we embrace all the wonders and anxiety of today’s emerging AI technologies.
But even if the device is a failure, the LAM genie is out of the bottle. Rabbit’s iPhone moment will inspire more investment to drive forward the maturity of models for controlling computers at an ever-increasing rate. And if the arrival of the R1 device does define the moment the great leap forward happens, then it will have ramifications for how work gets done in every app, in every process, in every enterprise. Either way, this is not a moment you can afford to ignore.
We have to stop using “ethics” as an excuse to avoid investing in AI. Ethical standards are something enterprise leaders must lay out for their enterprises regardless of technology investments.
For example, what DEI standards are acceptable, and what biases does a company want to set in stone? This dictates how AI can ultimately be governed and also which partners in the ecosystem a company should work with, as most enterprise leaders want to be aligned with other like-minded enterprises. For example, if a company deems it important to create a genuine gender balance within its management ranks, it will likely prefer to work with partners who share and practice those values.
Ethical fear-mongering threatens to kill off the commercial gains of GenAI at birth in three-quarters of global enterprises
Three in four CEOs believe those with the best GenAI will obtain a competitive advantage. Yet the data – presented at an IBM event in London covering GenAI and HR – also shows three in four CEOs are willing to forego the commercial benefits of GenAI over ethical concerns.
These ethical concerns are regularly cited as the cause of delays in the implementation of AI and GenAI projects. We hear this often from service providers. Many report an uptick in the volume and value of GenAI projects in Q4 of 2023 – but they also lament how many enterprises are dragging their feet over governance concerns.
Lumping ethics in the same governance bucket as accuracy and transparency has confused enterprise buyers
But many tech firms and service providers have done themselves no favors by lumping ethics in the same ‘AI governance’ bucket as accuracy and transparency. In doing so, they have muddied the waters. Ethics, accuracy, transparency, and openness are fundamentally different.
Ethics are a reason for governing—the why. Ethics are standards by which an enterprise chooses to be held accountable.
Transparency is what is required to understand how well those standards are met.
Accuracy is a measure of AI performance.
Yet we throw these three together and then wonder why the enterprise stands back, confused.
AI can’t be intrinsically ethical or empathetic
Transparency and accuracy are intrinsically machine capabilities.AI can be accurate. AI can be transparent (this is more an ambition than a reality currently). AI can’t be intrinsically ethical like a car, a washing machine, or a gun can’t be ethical. AI can be no more ethical than it can be empathetic (automatically firing out soothing phrases because you have been trained to do so is NOT the same as being empathetic.)
Only humans are capable of devising and living by ethics.
Ethics are not set in stone either. They are highly context–dependent. Context is another reason whyleaders should separate ethics from things that can be built into the machine (such as accuracy and transparency). Today’s ethics are not the same as those of 50 years ago, and no doubt not the same as those of 50 years in the future. Hard-coding ethics into AI could prove an extraordinarily arrogant and risky thing for any human to attempt today.
Ethics remains the C-suite human concern it always was – don’t use it as an excuse to delay a tech project
As discussed in a LinkedIn article in 2018: “The challenge when trying to set rules for behavior is the huge cultural weight shaping our view of wrong and right. That view varies from culture to culture and through time.”
Ethics are not for sale. They should not be sold as part of AI governance. The enterprise owns them.
Separating the two reveals ethics is much less an AI concern and much more the C-suite human challenge it always was. Leaders should certainly NOT use ethics as a reason to delay benefiting from the 10-20% boost to business performance our report GenAI will re-shape business economics, identifies.
The Bottom-Line: Separate ethics from legal and regulatory compliance to fast-track your GenAI route to better business performance.
Enterprise leaders should own ethics. They should not leave goal setting and targets for this to third parties – machine or supplier. Leaders should assess the outcomes of using AI against enterprise-owned targets. But AI can only ever be ethical by rote, meaning ethics is one loop humans must continue to own.
Legal and regulatory obligations aren’t ethical concerns; they are compliance issues. Service providers can and should help with these – building in accuracy to measure compliance and transparency to show how compliance is met.
Enterprise leaders should separate ethics from legal and regulatory compliance to fast-track their GenAI route for better business performance.
Generative AI (GenAI) has been top of mind for leading executives globally since ChatGPT first stole headlines in November 2022, but enterprises have yet to realize anything close to its true value. Organizations must understand that GenAI and large language models (LLMs) cannot act alone; they are only as good as the data built and designed to power them.
Celonis Co-Founder and Co-CEO Alex Rinke believes that process intelligence tools could hold the key to unlocking faster and better innovation with GenAI, and every enterprise should be keen to learn why.
In a recent Fireside Chat, I connected with Alex Rinke to understand his perspectives on GenAI and how Celonis will shape the future of the technology.
For GenAI to reach its full potential, it needs complete insight into your business—and that’s no easy task
While UHG is the world’s largest commercial healthcare enterprise with ~$360B (Sep 30, 2023) in revenues, it is also amongst the largest enterprises by workforce of some 440,000 clinicians, technologists, and market-facing professionals. An enterprise of this size with even greater implications for the health and well-being of over 150 million people must be deliberate when exploring new technologies. That is precisely the approach that is being considered while the world is abuzz with GenAI.
The ease of using ChatGPT has driven the rapid excitement of GenAI for enterprises—something Alex admitted has taken him by surprise. Despite this, many enterprises don’t yet understand how to deploy it effectively in their organization. The technology needs complete insight into a business to deliver accurate, efficient, and reliable results. Traditionally, this means tackling siloed and unstructured data to create a mature data foundation—a time- and cost-consuming task. For example, developing HFS’ first-of-its-kind large language model (LLM) is the result of a six-month process that required a complete restructure of data and a fresh approach to how we work.
That’s exactly where Alex believes tools like Celonis can help, accelerating the speed of innovation by unleashing the synergy of GenAI and process intelligence.
Enterprises should put process intelligence at the heart of their operations, serving as an engagement layer for LLMs
Technology is best leveraged in combination with other technologies. Throughout the conversation, Alex highlighted how he believes Celonis and other process intelligence tools will be key enablers for GenAI, helping address adoption challenges and delivering more intelligence-data-driven insights and rich process context.
He explained that process intelligence tools provide a unified view of a business, pulling together all the pieces of fragmented systems and providing a single view for AI models. He called this the engagement layer. This unified view drastically reduces the need for extensive infrastructure changes, accelerating innovation. It’s important to note that this doesn’t eliminate the importance of maintaining a high data quality; ultimately, the garbage in, garbage out adage still applies!
To bring this concept to life, a large bank is working with Celonis to build an LLM-fueled customer service chatbot. To deliver it effectively, the model needs access to internal business processes, the ability to collect data from multiple sources, and an understanding of everything. Celonis had already been deployed across the business, which allowed data to be pulled directly from Celonis with deeper process context, such as where an order is and when it will arrive. This allowed the bank to implement the chatbot faster and deliver more intelligent responses, improving customer experience.
The Bottom-Line: Enterprises should consider process intelligence’s role in better connecting their data with GenAI.
If GenAI is set to be as impactful as the Internet, as Alex believes, organizations must ensure they are using it to its full potential, or they might just give their competitors an edge. To fulfill its potential, an LLM must have a deep understanding of the business and its data to deliver intelligent answers.
When enterprises define their GenAI adoption roadmap, they must consider the value process intelligence can deliver if they want the technology to reach its full potential. Celonis understands the power of ecosystems, and Alex teased the idea of partnerships between Celonis and the likes of LLaMA and Hugging Face, so it might be about to get even easier to infuse process intelligence with your Generative AI plans.
Am very flattered by how leading creative marketing strategy firm Antics Marketing Solutions has positioned HFS Research. Our simple goal is to empower your organization to be the disruptor in your industry 💪
As we collect our thoughts for the year and prepare for the next, one area I never want to compromise on is my desire to speak the truth and never be muted by corporate propaganda and pay-to-play bribery. Let’s just call it what it is, folks.
When I founded HFS 14 years ago, all we had was our honesty and reputation for calling a spade a spade. The more we kept true to this reputation, the more valuable our brand became, and the more money companies were prepared to pay for our insight and expertise. This was a terrific way to do business and feel proud of our work.
HFS does not do business with a handful of software and services businesses, which “canceled” us because we put out research that did not make them look as amazing as they were trying to portray themselves or we just called them out for poor practices. We also turn away business from several suppliers as we do not want money purely for puffing up brands with no proven research and customer evidence to back up the claims. These firms choose to work with other firms that are clearly more flexible to bend to their dollar bills. Like has anyone ever received an “award” in this industry they didn’t have to pay for? Like ever?
I won’t embarrass some of these firms here, as I do not want to play that game, but they know who they are…
In 2024, I will push my team even harder to be brave and speak the truth in this world of bullish*t marketing, relentless hype, blatant lies, and swirl of nonsense. We have more than doubled HFS since 2019, so there is one lesson to take away from this: Truth sells!
Sandeep Dadlani, Executive VP and Chief Digital and Technology Officer at UnitedHealth Group (UHG), the world’s largest healthcare enterprise with diversified businesses, has a long and deep technology experience across multiple industries on both the supply and demand side. His early days at UHG coincided with the explosion of GenAI on the global stage and has been shaping some of the thinking and doing for him.
In speaking with Sandeep, it is clear about the methodical and structured approach he is driving at UHG could define how healthcare leverages the latest technology miracle.
Our most recent candid interview – as part of our GenAI Leaders Series – is to learn how Sandeep fashions GenAI’s use and ways to realize its potential in healthcare and potentially beyond.
Pragmatic, excited, and responsible: the steps to get it done
While UHG is the world’s largest commercial healthcare enterprise with ~$360B (Sep 30, 2023) in revenues, it is also amongst the largest enterprises by workforce of some 440,000 clinicians, technologists, and market-facing professionals. An enterprise of this size with even greater implications for the health and well-being of over 150 million people must be deliberate when exploring new technologies. That is precisely the approach that is being considered while the world is abuzz with GenAI.
A pragmatic approach is to find the problem(s) to solve as the first critical step in being able to address it with any new technology. At UHG there is a bottom-up effort at identifying use cases that have led to piloting some 500 use cases while the top-down identified some 14 use cases. The approach to identifying the top-down use cases was an enterprise celebratory event called Tech-Tank involving tens of thousands of employees. While ideation and spitballing are part of the effort, UHG took a hard look at the business case and the ability to scale those use cases in their selection. Given the size of UHG, scaling means very different, and early indications are very encouraging.
The use cases are generally in the administrative realm of the value chain, which historically has accumulated suboptimal processes and is a rich target for technology transformation. These low-hanging fruits include processes used by thousands of call center agents to summarize their interactions with United’s members.
“…call summarization is a simple thing but has eluded the industry for a while but really eases the work for our call center advocates and has them focus on caring for the person who is calling” – Sandeep Dadlani
Never mind the cape GenAI wears, just focus on its superpowers
“…great synthesis and data extraction from structured and unstructured fantastically well, content generation very well and automates code writing…” Sandeep Dadlani
UHG’s selection of use cases keeps clinicians in the loop to ensure that they can practice at the top of their license and not replace them. This extends to all processes that may or may not include clinicians, that a human is always in the loop to help improve the outcomes and it is done responsibly.
And so, the notion of responsible AI does not have a stronger motivator than the use of GenAI in healthcare. In the context of life and death implications, be it for diagnosis, choice of therapies, or care delivery, responsible AI must become table stakes in action vs. narrative. There must be added urgency to ensuring fairness, eliminating bias, and clear explanations of results.
GenAI’s iPhone moment is more impactful than the Kodak moment
IT services are experiencing a flat revenue trajectory in 2023 after a quarter of a century of sequential growth. As a result, most of them are investing in GenAI to fuel the next era of growth. However, the philosophy of investments in healthcare could have long-term implications. There are two schools of GenAI investments in the context of the triple aim of care (reducing the cost of care, improving health outcomes, and enhancing the experience of care);
Positively improving the tripleaim of care by empowering clinicians to practice at the top of their license, incorporating ambient tech to be virtual caregivers, or accelerating drug discovery. This philosophy will take longer to pay off but will be sustainable and result in strong growth.
Maintaining the status quo by following legacy paradigms, including labor arbitrage, could see an immediate improvement but is unlikely to be sustainable.
The potential of GenAI is like the launch of iPhones in 2007 and the realization that it could not only replace the 36 pictures of a Kodak film role, but one could store thousands of pictures on the device. The notion of experimentation became common because one did not need to be precise in the shooting of a picture, photography expanded to everyone with a smartphone, and functionality expanded beyond pictures. In a similar vein, expect GenAI to deliver more technology faster with better outcomes.
Yet before IT service providers run the idea to the banks, it is important to address the improved productivity and how that will be shared. Early indicators suggest that we should expect 30-70% productivity gains, and enterprises expect that the productivity gains will be shared with them by service providers. Providers who figure out how they realize productivity gains and find an equitable way to share them with their employees and clients will likely prosper.
The Bottom-Line: A future of elevating work beyond the mundane, learning continuously and faster, while GenAI becomes a copilot aiding in better decision-making and improving outcomes many times over.
GenAI opens the door to interrogating data differently and smartly, leading to using data (structured, unstructured, images, audio, etc.) in ways perhaps only imagined. In a future where we are going to experience an acute shortage of clinicians, GenAI, being an able aid to a clinician, will help with speed and accuracy of diagnosis, reduce administrative burden, ensure gaps in care are addressed by engaging with health consumers, and the list goes on. The sky is the limit with GenAI, and that has some extraordinary possibilities in healthcare…assuming we make the right choices and deploy GenAI against the right problems.
DXC Technology’s latest play is to bring Raul Fernandez off the bench as the new interim chief and move on from a difficult four years under Mike Salvino, who’s passing the torch. But when you look at the challenges facing this firm, you might just come to the conclusion that not even God can turn this one around.
Let’s not kid ourselves; this isn’t your usual passing of the baton – it’s more like handing off a ticking time bomb. The company’s market value is literally running on fumes at barely a third of its revenue numbers. And that’s not just a hiccup – it’s a full-blown identity crisis:
Let’s not forget our trip down memory lane from four years back, where we laid out the gauntlet of challenges and opportunities for DXC (remember this blog?).
So, where did Mike Salvino go wrong?
DXC made zero acquisitions under Salvino and gave all their money back to stakeholders to prop up the share price. He could have made acquisitions to bolster strengths in growth areas such as cloud migration, AWS services, analytics, Azure, etc., or double down on industries where DXC could have real differentiation, such as insurance, private healthcare, energy, and manufacturing. The Luxoft analytics business had real potential, and little was done to build on the firm’s insurance software and IP.
Sold a lot of pieces but didn’t build new capability fast enough. For example, its US State and Local Health and Human Services Business (Medicaid) was sold to Veritas Capital for $5 Billion, but that money was never reinvested.
Stabilizing delivery on infrastructure doesn’t mean people will buy transformation. Just look at the similar price-to-sales ratio to Kyndryl, another firm struggling to sell transformational services tied to its commodity infrastructure business.
Very limited diversity on the leadership team. DXC’s leadership is almost all US men… diversity wins deals, and many enterprises want to work with firms with a strong gender and cultural mix.
Very limited stability on his leadership team. Salvino hired and fired at least 15 senior leaders and churned through 3 CFOs in 4 years, one of whom publicly sold off his stock.
What challenges face Raul Fernandez?
Fight back in a cut-throat market. We’re in an IT services market that is suffering from flat to negative growth, and even the most successful IT service providers are reporting low single-digit growth at best (Accenture reported barely 3% growth yesterday). What Hail Mary can Fernandez conjure up to convince enterprise leaders to take a bet on this train wreck of a company? When you have aggressive outsourcing juggernauts, such as Accenture and TCS, to contend with, where can you realistically play when you’re this far behind?
Find some way to survive the GenAI revolution. Then there’s GenAI, the Chicxulub meteor that will result in wiping out the dinosaurs in the IT services industry. Will DXC dodge this extinction-level event, or will they be left behind like the dinosaurs? With Fernandez at the helm, it’s do-or-die time, and we are watching closely to see if DXC can pull a phoenix and rise from the ashes.
Find a raison d’être for DXC to reinvent itself. DXC has not been able to create a true brand association and find its mission. Financial restructuring to bring it back to life is also going to be hard. It has not even been able to find a buyer for its BPO business that it has wanted to divest for several years now. Simply put, there is no strategy, and investors have little confidence left in the firm. Maybe Fernandez will find a transformation acquisition or two to redefine exactly what DXC is and create a path forward to long-term survival.
The Bottom-line: Raul may not be God, but he needs to find a saviour
Raul Fernandez is only interim chief, so his task is most likely to search drastically for a path to salvation for the firm and install a dynamic leader to take them there. This may be the toughest tech CEO turnaround task since Steve Jobs returned as interim Apple CEO in 1997, faced with the task of making Apple profitable again after losing over $1bn in 1996. How did he do it?
1) Rebuilding the core products and value,
2) Prioritizing the customer experience,
3) Collaborating with rivals, and
4) Reinventing the company culture.
Perhaps these four areas are the best guide to follow…
In a recent Fireside Chat, I had the pleasure of sitting down with Nigel Vaz, the visionary CEO of Publicis Sapient, to delve into the burgeoning world of generative AI (GenAI) and its profound implications for the business landscape and the broader world at large. As we unpacked the complexities and potential of AI, Nigel’s dual sense of excitement and caution resonated with the current sentiment in our industry, as the GenAI hype and fear are reaching a fever pitch. Companies like Publicis Sapient are thoughtfully leading the GenAI charge by developing toolsets and talent, evolving their mindsets to embrace a culture of learning and unlearning, and being mindful of the risks and downsides so they can help their clients do the same.
From predictive to generative: GenAI is enabling a new wave of creativity to drive business transformation
Nigel’s enthusiasm was palpable as he described how GenAI goes beyond traditional predictive AI—potential outcomes based on data—and adds creation. This means, Nigel says, you get a powerful combination of decision-making and creativity, which will be a powerful catalyst for enterprise transformation. It could dramatically affect enterprise efficiency and growth, particularly how companies create new products, services, and experiences.
His excitement is tempered by a healthy dose of anxiousness fueled by ethical and regulatory issues and concerns related to how companies think through re-inventing business in the context of GenAI. Much like the advent of the internet and the mobile and social waves that followed, enterprises won’t get everything right with GenAI right off the bat. It’s creating a need to consider the risks and downsides as companies examine how this will play out within their organizations.
GenAI is here and now, and production at scale is on the horizon
At HFS, we’re cutting through the GenAI noise to understand what action is truly happening and what’s hype. Nigel tells us the difference between now and one to two years out is seeing GenAI move from primarily experimentation and prototyping to production at scale. He currently sees very real GenAI progress, particularly in content generation, software development, language translation, data augmentation, chatbots and conversational AI, and product design. These experiments are becoming increasingly optimized, meaning that proofs of concept get into production much faster. For example, in data augmentation, synthetic data based on an organization’s internal inferences can be used to train AI models to eliminate problems inherent in generic language models, meaning they can move to prototyping much faster. Publicis’ software developers now have tools such as a proprietary code library for all to contribute, significantly speeding up coding time. All of this means we’ll have a greater scale of GenAI examples in production at an increased level of scale in the not-too-distant future.
Perpetual evolution requires constant learning
To embrace these new tools and ways of working, Nigel urges a culture of perpetual evolution within organizations. Leaders must champion a cycle of learning, unlearning, and relearning to stay relevant. The strategic vision is paramount, but so is the seamless integration of AI into the business fabric. It’s about preparing for an AI-infused future where technology and strategy dance in lockstep. From a talent standpoint, everyone needs to learn the tools, accept that the tools are constantly changing, and get ready to learn again. Taking pride in being able to adopt new tools and learn quickly versus having knowledge is a cultural mindset shift. As Nigel says, Gen AI demands people who are “learn-it-alls” rather than “know-it-alls.”
So, how do you influence your people’s mindsets to embrace change? First, says Nigel, it is important to look at GenAI in the light of digital transformation, which means it is about way more than implementing new technology. You have to start with a vision of how you want things to look in the future to re-imagine how your business looks in the context of Gen AI.
This clear vision, which Nigel likens to a “North Star,” must be clearly articulated to employees so that they have the impetus to develop a common set of platforms and toolkits for people to experiment with. Publicis Sapient is creating these “sandboxes” of platforms and toolkits while being clear about the priority focus areas. The goal is to create a culture that embraces iterations as technology advances and goals and outcomes are achieved or missed. This is a constant evolution without a beginning or an end. At Sapient, Nigel and his team know that if they don’t embrace this change in mindsets and way of working themselves and embody the transformation, they won’t be able to help clients do the same.
Developing the right strategies to mitigate ethics, data privacy, and security risks is critical to success
While it’s easy to get excited about potential opportunities and cultural shifts GenAI offers, the risks are very real. Not having a robust strategy to evaluate and address these risks is a recipe for failure or even disaster. Ultimately, we must control AI in a way that is responsible, fair, and aligned with societal values. Nigel outlined the following key issues to consider:
Develop oversight and understanding of ethical and legal considerations. Companies need a framework to think about data privacy and security to address data protection regulations and ensure responsible AI guidelines and transparency in decision-making. This is a fundamental cornerstone for a safe and effective AI strategy.
Think about data in the context of bias and fairness. There’s a big issue around bias and fairness because the training models rely on potentially biased data. Particularly for hiring or bank lending, it’s essential to consider the implications for potential bias and carefully examine the training data for potential bias to eliminate it.
Enable transparency in AI systems. The “black box” nature of some AI systems can make it difficult to understand their decision-making processes. Ensuring transparency in AI models is essential for accountability, regulatory compliance, and ensuring you are building on datasets you want to propagate versus potentially biased data to avoid.
The future of tech and humanity is a platform effect fueled by increased computing power.
While GenAI is a transformative tool, like many other emerging technologies we’ve seen, it is only optimized in combination with other tech; Nigel sees an emerging “platform” effect. Technology such as 5G, cloud, and big data act as one layer of the platform, building on the internet and mobility, and now GenAI is an added layer for greater value. He predicts that the third wave to further expand AI to its full potential will be exponentially greater computing power, such as quantum. Adding this tremendous power will allow us to process and train models significantly faster, enabling changes such as the ability to focus on bigger problems, develop hyper-real augmented environments, and advance physical robotic capabilities.
Nigel’s future view of the platform effect is very optimistic for humanity in general; first, he mentions the increased ability to tackle big planetary and social issues like climate change and food security. He also thinks the platform will supercharge virtual and augmented reality, characterized by a significant increase in immersiveness and interactiveness, making the distinction between physical and digital experiences increasingly seamless. Lastly, he anticipates advancements in robotics and automation to extend the digital automation we’ve seen into the physical world, overcoming current physical technological limitations.
The Bottom-Line: Design your future state “North Star” and align your firm to navigate to it efficiently
Inevitably, GenAI’s evolution will not just change business; it will change how we live and work. It could also significantly impact our lives and the planet, but we have to shift our mindsets and be wary of the pitfalls to get it right. GenAI has ushered in the potential for us to become more efficient—and more creative and impactful. Nigel reminds us that we must develop a culture that embraces adopting new and changing tools, applies that new knowledge appropriately to realize the “North Star” vision, and remains willing to evolve once we get there.
Cognizant has made a significant investment in ServiceNow capabilities with the acquisition of Thirdera, one of the much-admired deep consultancies in the space. Eleven months into his leadership, Ravi Kumar has made his first major acquisition, which sets his stall out for large transformations, of which so many are built on a ServiceNow foundation. The big question now is whether Cognizant can absorb the new talent at its disposal to paint a real picture of technology arbitrage for ambitious clients wanting “more for the same” as opposed to “more for less.”
So, who better than our resident ServiceNow guru, Dr Tom Reuner, to share his viewpoint on the acquisition…
Despite the headwinds, ServiceNow continues to outgrow the market
There is an infectious enthusiasm across the ServiceNow ecosystem that has few bounds, even at a time when many organizations are grappling with the Digital Dichotomy of driving our cost while innovating at the same time. Looking at ServiceNow’s earnings (stock is up 65% this year), it is not difficult to see why the mood continues to be so bullish.
Despite the headwinds, ServiceNow continues to outgrow the market. If anything, it provides the building blocks to enable the Big Hurry to transform. Those engagements are around next-generation GBS, industry solutions, ERP modernization, and beyond – no longer just IT-centric implementations. (for details on the trends in the ServiceNow ecosystem, see: ServiceNow can become the digital foundation of the Generative Enterprise™.
Thirdera has the second-largest number of Certified Master and Technical ServiceNow Architects after Accenture
This provides the backdrop as to why we are hearing so much of late on potential M&A in that ServiceNow ecosystem. After months of rumors and speculation, Cognizant made the most significant move in the ecosystem to date by acquiring ServiceNow pureplay Thirdera. While not a household name Thirdera is the largest independent consultancy that adds significant muscle in the US but also expands Cognizant presence in Europe and APAC.
Perhaps the most important factor is that Thirdera has the second-largest number of Certified Master and Technical Architects after Accenture. This is crucial for being considered for complex transformations. On the other hand, its last acquisition of a ServiceNow pureplay has been a moderate success, and we are being polite here. Linium was meant to be a change agent for Cognizant, but not much is left of its culture. Thus, the key challenge for Cognizant is to learn the lessons and maintain some of the culture that has made Thirdera an attractive option in the first place. To assess the potential strategic impact, let’s peel back the onion. Let’s start with Thirdera, whose whole history tells the story of the development of today’s thriving ServiceNow ecosystem.
Thirdera was created in 2021 by PE Sunstone Partners by merging three acquisitions: Evergreen Systems, Cerna Solutions, and Novo/Scale, all focused on the North American and Latin American markets. Perhaps it shows my age, but the next move sounded like the Blues Brothers in the eponymous movie trying to “put the band back together.” Because Sunstone installed a management team that included CEO Jason Wojahn, who was the President of the ServiceNow Business Unit at Cloud Sherpas (formally Navigis, ServiceNow’s first services partner) – and after the acquisition of Cloud Sherpas by Accenture, the ServiceNow Senior Managing Director and Global practice leader there. Marc Talluto took over as Chairman of the Board, and he was the co-founder and CEO of Fruition Partners, acquired by CSC in 2015 (now DXC Technology). Many of the other leading executives have a history with these two companies.
Thirdera gives Cognizant global ServiceNow depth and breadth
Fast forward to 2023, Thirdera boosted its ambition to create a global ServiceNow pureplay through more acquisitions and achieved a presence in all key geographies. Thus, it maintained the cultural attributes of pure play whilst being able to compete with GSIs on scale. This is what sets them apart from peers like GlideFast, Plat4mation, Cask, or NewRocket. Furthermore,Read More
