For the last three decades, acquiring a tech services company (whether a consultancy, services provider or implementation partner) typically meant adding headcount: people billed by the hour, with growth driven by further hiring. AI is breaking this model: when work that once took weeks can be done in a fraction of time, the link between headcount and revenue starts to disappear, shifting value towards IP, automation, proprietary data, vertical, functional or workflow expertise and recurring business. So, what are you acquiring for in 2026? The answer is not simply a larger workforce, but a company that can scale value faster than it hires people.
A model that is changing faster than the multiples
The traditional tech services pyramid was built on the assumption that more people meant more revenue. The industry response shows a shift: McKinsey’s global headcount fell by c. 10% between end-2023 and mid-2025, with further cuts planned in support functions. Accenture booked c. US$6bn of gen AI work in 2025 and now embeds AI across its consulting and managed-services delivery. This becomes an integral part of their services net new bookings indicating not simply demand destruction but a reallocation of value, exposing which business models can adapt.
The key change is commercial, and it sits in how the work is priced. Once AI is embedded in delivery, T&M pricing starts working against the provider: the faster the work is done, the less the provider earns. The question is therefore who can capture part of that productivity gain rather than passing all of it through to the client, by packaging their know-how into IP and reusable assets (e.g. implementation accelerators, pre-built integrations, AI agents, industry templates, workflow libraries) and by pricing on outcomes or fixed scope rather than only hours. For management teams and investors alike, the question is not who is selling AI, but who can turn it into measurable delivery value and capture some of that value themselves.
In practice, value is migrating in four ways:
1. Revenue is moving from headcount towards value per person
2. Pricing from T&M towards IP, outcomes, subscriptions and managed services
3. Margins from utilisation and pyramid leverage towards reusable assets and automation, and
4. Competitive advantage from certifications and bench size towards industry and workflow expertise and proprietary data.
At the same time, a key risk is moving from losing a major client to having an entire service line disrupted by AI. These changes say more about the long-term value of a business than headline growth. Two companies growing at the same rate can be valued very differently, depending on how much of that growth relies on adding people.
Enterprise application ecosystems
The shift is visible across platforms such as Microsoft Dynamics 365, Salesforce, SAP and ServiceNow, where AI is increasingly embedded into the platform itself. Configuration and build work will compress, while the value moves towards data foundations, process redesign, change management, agent governance and continuous optimisation. The partner model is shifting from “implement and leave” to “run and improve”: ongoing optimisation, support and agent management are sold as recurring services rather than one-off projects, which tends to lengthen client relationships and make revenue more predictable.
The second dynamic to watch is the platforms acquiring capabilities themselves. ServiceNow deployed more than US$10bn in M&A capital across late 2025 and H1 2026, while SAP invested more than US$2bn in data, lake house and frontier-AI capabilities. For partners, this creates a larger market, but also a risk: today’s billable service can become native functionality tomorrow. Custom reporting, data migration, standard integrations and workflow configuration are examples of work partners bill for today that the platforms’ own AI agents and built-in tools are increasingly able to handle. Partners therefore need to understand which parts of their current offering are most exposed to platform automation, and where they can build higher-value services that are harder for the platform to replicate.
Market observations point to a widening valuation gap: businesses with genuine IP and vertical depth are commanding multiples up to 3x higher than generalist implementation partners with similar certifications.
Figure 1: The spread between archetypes is now wider than the spread between geographies.
Why the Nordics is interesting from an M&A lens
The Nordic Tech Services market offers a unique combination of advanced technology capabilities and enterprise digitalisation while the vendor landscape is relatively fragmented. Nordic organisations with access to approved AI tools rose from 37% to 56% over the past year, compared with c.25% across the EU. This gives local providers opportunities to develop practical experience and client references as businesses increasingly integrate AI into their operations.
Founder succession is bringing businesses to market, while corporates are reassessing their delivery models and portfolio focus. TietoEvry’s €300m Tech Services carve-out of BEKK Consulting in Sep-25 illustrates the potential for portfolio reshaping. Add a dynamic Nordic PE ecosystem, which accounted for c.16% of European PE/VC investment by value in 2025, and there is capital to execute buy-and-build strategies. The Nordic market may only grow around 2-3% a year, but what’s more interesting is how the mix of revenues, capabilities and business models evolves, and fragmentation creates opportunities for further consolidation.
Figure 2: Nordic enterprises are ahead of the EU average in AI adoption.
Five key investor questions to business leaders
This set of questions is today becoming central when investors assess a tech services business:
1. What is revenue per FTE, and where is it heading? Not simply how fast headcount is growing
2. What pricing has actually been achieved on recent new client wins? The rates and terms clients have actually agreed to (not the list rates in the pricing model)
3. What is gross margin by delivery type and who legally owns the IP and accelerators?
4. Can management show AI solutions running in production where the client can quantify the impact, such as cost saved, time reduced or revenue gained?
5. What happens if the current book of business can be delivered with 30-40% less effort? And what scenario can we build if the platform ships that functionality natively?
Certifications and partnerships can signal capability, but more increasingly the differentiator is demonstrated client impact i.e. what has been deployed, delivered outcomes and business repetition.
What this means for management teams, shareholders and investors
Investors are placing greater emphasis on revenue per FTE, gross margin by delivery type and recurring “IP-attached” revenue, while headcount growth and utilisation are becoming supporting metrics. They are also stress-testing for AI deflation: if 25-30% of the current time-based revenue base could be delivered with 30-40% less effort, would the investment case still hold? Businesses with deep industry specialisation tend to be more defensible than those relying primarily on platform partnerships and certifications. Meanwhile, founder succession and corporate carve-outs continue to create opportunities to build platforms at sensible entry points.
For strategic acquirers, the opportunity is less about adding capacity and more about acquiring capabilities that are difficult to build or hire quickly, such as data engineering, AI governance and deep vertical or workflow expertise. For management teams, the priority is to evolve the revenue mix before a transaction is on the table: build reusable IP, increase recurring revenue, demonstrate measurable client outcomes and understand which parts of the current offering are most exposed to platform automation. These changes take time to translate into reported financial performance.
Is AI bringing the era of labour arbitrage to an end?
The real question isn’t whether delivery becomes more efficient – it’s about who captures the value that efficiency creates. If AI simply reduces the hours needed to deliver the same work, clients may capture the savings while providers become more efficient, but not necessarily more valuable.
The opportunity lies in translating those productivity gains into proprietary IP, recurring revenue, measurable outcomes and deep workflow expertise. That is where AI can start changing the economics of a business, not just its cost base. For management teams, investors and acquirers, the question is therefore shifting from “How much AI does this company use?” to “What is AI allowing this company to own, earn and scale?”
Ultimately, the businesses with lasting value will be those that combine deep expertise with scalable and future-proof delivery models.