For decades, Technology Services firms built their growth models around a relatively predictable equation: more projects required more delivery capacity, and expanding capacity largely meant adding people.
AI is beginning to change that equation. As productivity improves, customers are reassessing the effort required for existing work and what they should pay for it. For Services providers, this creates pressure on established contracts and revenue streams.
At the same time, enterprises have significant technology work waiting to be addressed. Legacy systems need modernization. Data foundations need improvement. New business priorities continue to compete for investment.
This creates a difficult transition for Technology Services firms. Existing revenue faces pressure, while new opportunities are opening up, with no guarantee that the same providers will capture them.
Zinnov estimates that approximately USD 250 Bn of the USD 1.1 Tn outsourced Services market could face compression by 2030. The scale of this potential disruption makes the question of where new revenue will come from increasingly important.
The challenge is how firms navigate that transition, strengthen their position with customers, and build new sources of growth as AI changes how Services are delivered and bought.
Consider a large enterprise approaching a contract renewal.
AI has improved software engineering productivity and reduced the human effort required for parts of delivery. The customer now expects those improvements to be reflected in the next contract.
For the provider, the choice is difficult. It can try to preserve the existing contract value or proactively introduce a more efficient delivery model that may reduce revenue from the account.
Yet holding on to the old economics may protect revenue only temporarily. If one provider is unwilling to offer productivity improvements, another may be prepared to do so.
Proactively improving existing delivery can therefore help protect the customer relationship, even when it puts current revenue under pressure. However, providers also need to consider whether the resulting savings will create opportunities for additional business or simply reduce the value of the existing contract.
The next decision concerns the capacity created by these improvements.
Greater delivery efficiency can free up budgets and capacity for modernization, data engineering, and other priorities that have remained in the backlog.
Customers may use the savings to reduce technology spending, address additional work within existing budgets, or invest in new priorities. Each decision has different implications for the provider’s revenue.
This also means large Technology Services deals may evolve rather than disappear. Lower spending on certain activities can be accompanied by investment in new capabilities.
But released capacity does not automatically become new revenue for the provider. The firm still needs to understand the customer’s next priorities and demonstrate why it is equipped to address them.
This creates two considerations for Technology Services firms: how much revenue could come under pressure as existing work becomes more efficient, and what will determine their ability to win the new business that emerges.
Imagine two providers competing for an enterprise’s next transformation program.
Both have access to capable AI models. Both have trained their teams and established technology partnerships. Both can demonstrate improvements in software engineering productivity.
What would make the customer choose one over the other?
As AI models improve and become more accessible, access to a particular model may offer less differentiation. Enterprises can choose different models for different workloads, including smaller or open-source alternatives where appropriate.
The advantage increasingly depends on understanding what the technology needs to accomplish within a particular business.
An enterprise’s operations reflect years of decisions about processes, systems, data, and organizational responsibilities. A provider may understand the technical architecture without fully understanding why the business operates as it does.
Consider an enterprise planning to modernize a legacy application that supports several business functions. A provider familiar with the application may understand its architecture and dependencies. But shaping the modernization program also requires understanding which processes the application supports, where operational constraints exist, and what the business expects to improve.
Established Technology Services firms may have an advantage here. Longstanding customer relationships can provide an understanding of which approaches have worked, which initiatives have failed, and where operational constraints exist.
However, familiarity with a customer’s systems does not automatically qualify a provider to lead the next transformation. It must combine that understanding with the ability to adopt new technologies and rethink how work gets done.
For providers, the opportunity is to use this knowledge to identify new customer priorities, shape the next engagement, and demonstrate capabilities that competitors may not have developed within that account.
Understanding the customer’s business creates an opportunity for Technology Services providers to participate in broader transformation programs. But turning that understanding into measurable business outcomes remains challenging. Much of the AI work currently being undertaken is still focused on productivity, particularly in areas such as software engineering.
The reason is understandable. Productivity improvements are relatively easy to observe through reduced effort and faster execution. Broader business transformation requires more difficult decisions.
An enterprise may want to introduce AI agents across its operations, but its underlying processes may still contain unnecessary approvals, duplicated activities, or outdated ways of working.
Automating those processes could make them faster without addressing why they are inefficient in the first place.
This requires providers to engage with questions that extend beyond technology implementation.
What should the enterprise eliminate before introducing automation? Is its data ready to support the intended use case? Does the workload require the most advanced AI model, or would a smaller, more specialized model be sufficient?
These decisions change where Technology Services providers can contribute.
A provider engaged primarily to execute a defined technology requirement may have limited influence over the underlying business problem. A provider involved in understanding the process, selecting the appropriate approach, and shaping the intended outcome can contribute to a broader set of decisions.
The commercial model may eventually change as well.
Outcome-based engagements, co-investment, and gain-sharing are beginning to feature in discussions between enterprises and providers. However, much of the current AI work remains productivity-focused, and the shift toward business outcomes is still developing.
These models also introduce commercial risks. Outcomes may depend on customer decisions, data readiness, and organizational changes beyond the provider’s control. Firms therefore need to establish how results will be measured, which responsibilities they will assume, and whether the expected returns justify the risks involved.
Providers therefore need to deliver measurable improvements in existing operations while building the capabilities required for broader transformation.
The opportunity is to expand the scope of what providers can deliver and demonstrate the value of that work through measurable business improvements.
The immediate challenge is to make deliberate choices about existing revenue and future opportunities.
Providers need to identify which services face the greatest pricing pressure, which customer accounts have significant modernization or transformation requirements, and where their existing capabilities offer a credible advantage.
These decisions also have implications for how firms invest in talent, technology, and partnerships. Opportunities that require entirely new capabilities may demand a different level of investment and carry different commercial risks from work that builds on existing customer relationships.
As AI changes the relationship between delivery effort and revenue, firms may also need to reconsider how they measure growth. Revenue productivity and the business outcomes delivered to customers could become increasingly important alongside traditional measures of scale.
The central leadership question is becoming more specific: Can we create enough new value for our customers to offset the revenue pressure on the work we already perform?
There is no automatic transition from productivity gains to new growth. Some customers will retain the savings, others will invest in new capabilities, and some will change the mix of providers they work with.
For Technology Services leaders, the priority is to identify where revenue is most exposed, where customer spending is likely to move, and which opportunities warrant investment. Sustaining growth will depend on how effectively firms translate these decisions into new capabilities, stronger customer relationships, and profitable engagements.
As AI changes Technology Services revenue models, firms must rethink how they protect existing business and capture new growth opportunities. To explore what this means for your business, connect with us at info@zinnov.com.
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