From People to Tech arbitrage: Can we really survive this Great Services Transition?

The IT and business services world has entered a crucial phase where the winners and losers will become clear in the next few months.  Many are already getting left behind in the legacy services world of shopping low-cost labor, while the smarter ones are vying to become strategic partners to their enterprise clients, helping them write off decades of people, process, data, and technology debt to forge the path to the brave new AI world.

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We are firmly along this S-Curve evolution from people to technology arbitrage that the Generative Enterprise demands. Welcome to this Great Services Transition, where the entire financial construct of services relationships is being reinvented to capitalize on the complex ecosystem of AI platform players, hyperscalers, data integration products, automation tools, LLM builders, and so on.

For example, services powerhouses like TCS and Wipro are digging deep into their tried and trusted past glories to (at least) restore some of the old energy and verve into their teams, but they won’t be able to rest on their laurels by simply placing popular leaders at the helm.  They have to embrace the complex shift from people-based arbitrage to technology-based arbitrage if they are truly going to make it through to the other side – which we are calling this Great Services Transition.

Can today’s services firms really make the painful changes to reinvent their business models, or have their owners made so much money off the old model they simply aren’t motivated to grapple with painful change?

All these major providers, from Accenture, IBM, and Capgemini to the plethora of Indian heritage services firms and technology consultants such as Deloitte, EY, and KPMG, have to change their financial construct with their clients to one of shared risk, shared learning, and ultimately shared reward; otherwise, they face a race to the bottom.

This means changing the habits of a lifetime.  You only need to look at the likes of Kodak, Nokia, Yahoo, Xerox, and even JC Penney, which simply failed to innovate with the times and were too late to play catch-up once they had woken up to the new reality.  One could argue that many of the services firms in today’s spotlight are already too embedded in their legacies to turn things around.  The continued cycle of providing people-based services will yield a modicum of modest growth as enterprises seek continued cost savings and invest in AI  build-out initiatives. But as the model transitions to AI-led technology arbitrage, those left with hundreds of thousands of resources requiring decent utilization rates will see margins further degrade. The people-based arbitrage model is plateauing.

When your leadership is fat and happy, and the stock still holds up, why go through the aggravation of painful change when you can quietly ride off into the sunset with your cash pile?  When your board and stockholders only care about your quarterly numbers, and you don’t have the time or trust to drive a long-term plan, what can you really do beyond chasing ever-decreasing deals and focusing on cutting costs to the bone?  Sadly, it’s not always the fact that leaders fail to see the change coming; it’s more the casino that is Corporate America’s stock market that dictates which companies will survive or add themselves to the list of innovation failures.

However, as analysts who’ve covered this market for nearly 30 years, we steadfastly refuse to give up because many of today’s IT service leaders are too greedy, too risk-averse, or just too ignorant to find a path for survival and renewed prosperity.  So let’s break down this Great Services Transition into four simple problems to solve:

To survive The Great Services Transition, there are Four Problems to Solve:

Solving problem 1).  Enterprises and service partners must be aligned on the change mandate

What service partner has a culture you want to work with that will blend well with yours? Ambitious enterprises and their service partners are both striving to be effective in the emerging world of AI-driven business models and operations. This means this transition only works when there are two parties ready to tango and change together. To this end, service providers must become partners of change for their clients to help them understand the sheer noise of technology change going on around them.  Clients need internal alignment to ensure that its time to make the move.

Solving problem 2).  Services must provide access to affordable talent with real expertise

The shift from labor to technology doesn’t take away the need for people; it actually necessitates experts who can shepherd their clients along to help them change. They must provide continuous education on how to manage organizations’ fast-moving technology ecosystems and work with them to create business roadmaps based on emerging tech to make them slicker, smarter, more efficient, and less bloated.

Solving problem 3).  Determine the people, process, data, and technology debt to address

In the Great Services Transition, enterprises are buying services solutions that improve performance, drive speed to market, reduce cost, and create new content and data.

You must address your debt in these four areas which your firm has likely collected over the last 30+ years:

1. Fixing your skills debt: Develop new skill sets that can support the transition to embracing emerging technology and AI-driven business models.

2. Fixing your process-debt:  Recreate new processes process to determine what should be added, eliminated, or simplified across your workflows to support your slicker AI-led operating model.

3. Fixing your data debt:  You must align your data needs to deliver on your AI-centric business strategy. This is where you clarify your vision and purpose. Do you know what your customers’ needs are? Is your supply chain effective in sensing and responding to these needs? Can your cash flow support immediate critical investments? Do you have a handle on your staff attrition?

4.  Fixing your technology debt: IT spending just keeps increasing and only keeps swelling with each new platform and coding change. Stop buying tech for the sake of tech—this has been the failure of so many previous investments, such as the two-thirds of enterprises left struggling with their cloud migration journeys signed during the pandemic. The Great Services Transition is where you proceed through steps one to three before making bold decisions on your technology investments of the future.

Solving problem 4).   Restructuring your services engagements to shift from labor arbitrage to technology arbitrage

Enterprise leadership has always been – and still is – obsessed with cost reduction.  This is what they understand more than anything, and they view innovations such as GenAI as another lever to justify investments based on yet more cost take-out. The best approach is to reduce overall delivery costs by 20-30%, apportioned over 3-5 years.  This is offset by the increased value and reduced labor costs driven through effective investments in change, processes, data, and technology.  Clients MUST sign up for process reinvention and data transformation as part of it.  Clients need to TRUST their partners to get them there.  Providers need the TALENT to work with their customers, or the whole thing simply erodes to the bottom.

The Bottom Line:  Change the habits of a lifetime, or crawl away, as this S-Curve is the biggest people and technology challenge we’ve ever faced

As human beings we’ve already grown comfortable with what is familiar to us and avoided doing things differently until we have literally no choice.  This is the case with the services industry, which has ballooned in growth and home comforts for three decades.  The stark reality today is that enterprises do not need to keep spending on low-cost people-based services – they have what they need, and there is so much supply they can look at many providers to get it.  What enterprises desperately need are partners to work with them who share similar desires to learn new methods, unlearn old habits, and to teach them to exploit new technologies and new data methodologies and work with them to attack new markets with these capabilities.

This is how to survive the Great Services Transition. The big question now is whether enterprises and their services partners have the appetite to fix their skills, processes, data, and technical debt? Can they really learn new ways of operating, change their cultures, and embrace emerging technologies? Everyone needs to dig deep and decide whether they want to be a footnote or the future

Posted in : Analytics and Big Data, Artificial Intelligence, Automation, Autonomous Enterprise, Business Data Services, Business Process Outsourcing (BPO), Buyers' Sourcing Best Practices, Cloud Computing, Digital OneOffice, Digital Transformation, GenAI, Generative Enterprise, Global Business Services



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  1. Can we really survive this Great Services Transition (from People to Tech arbitrage) is indeed an apt question. The coming years will provide clarity on whether this will become another Blockbuster’s existential crisis scenario due to advent of Netflix.

    Conventional IT companies have the task ahead to reinvent their business models and not get caught in the trap to not make this painful change which would impact revenues in the short term. As outlined in the post, these changes will entail refining the skills, process, data & technology debt and make the major shift from labour arbitrage to technology arbitrage.

    This will entail shifts from the traditional services efficiency model to conventional AI to Gen AI based operations. The typical metrics, measures of SLAs, KPIs, response time, resolution time … will need to be reinvented.

    Core LLMs integrated with organisations knowledge systems, ITSMs will make the shift towards the adage of ‘the best incident is the one which has not been created’. The shift will be towards self-help, self-heal, proposed design & code changes to meet the regulatory changes from co-pilots and many more.

    The conventional unit based, FP based estimation model may need to be turned around. The focus will be towards gauging ‘experience’ (customer, employee, partners …). It is a good time to rewrite the ‘delivery methods’, revisit controls, some or many of the conventional IT software engineering & delivery management roles. The other dimension to revisit is the changes in the ways of working for hyper scalers, SaaS solution providers and conventional Technology services players. Interesting days & months ahead.

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