AI and the Future of Managed Services Contracts: Lessons from MSP Earnings Calls

“AI is no longer a niche. It’s becoming essential to how businesses operate at scale…we see AI as a force reshaping industry and amplifying human potential.” – Srini Pallia (CEO Wipro)

The managed services industry is undergoing a significant AI-driven transformation. As evidenced in recent earnings calls, the leading Managed Services Providers, including Accenture, Capgemini, Cognizant, HCL, Infosys, TCS, and Wipro, are embedding Generative AI (GenAI) and Agentic AI into their offerings.

For CIOs and procurement teams, recognizing shared themes and vendor-specific strengths is essential to negotiate value-oriented, future-ready contracts. Understanding where the providers’ AI focuses are is critical as organizations navigate these new conversations and negotiations in managed services contracts.

Most of the major MSPs referenced above have drastically increased the amount of time centered around highlighting their AI capabilities with public releases, particularly within their quarterly earnings reports. Other than directly asking your MSP for input, this is a great way to gain intelligence on how MSPs will approach the AI discussion in your next managed services negotiation.

Below are six strategic takeaways and common themes based on MSP commentary during recent earnings releases.

AI as a Productivity Lever vs. Revenue Cannibalization

One of the biggest debates in the managed services industry is whether AI’s ability to streamline operations will ultimately reduce vendor revenues or reshape them into higher-value opportunities. Most providers concede that AI adoption eliminates repetitive tasks and lowers manual effort, leading to potential revenue cannibalization. At the same time, MSPs are signaling that AI creates space for larger, more strategic AI-driven managed services transformations and opens the door to expanded wallet share.

• HCL CEO, C Vijayakumar:
  “It will mean some reduction in revenue for us. And we are okay with that… we have been able to get a little more wallet share, which more than offsets some of the productivity benefits that we have given.”
• Infosys CEO, Salil Parekh:
  “With Enterprise AI, now there are areas where there’s good productivity benefits and especially as we’re deploying agents or setting up whole enterprise AI platforms for clients using foundation models. And then there are some areas where we are seeing new opportunities for revenue. Typically, there are productivity gains, and those are shared between clients and ourselves.”

Procurement Takeaway: When negotiating with MSPs, ensure that you are appropriately sharing in the benefits driven through Gen AI implementation by explicitly defining how productivity benefits will be measured, reported, and shared. Push for cost-reduction clauses that guarantee a percentage of AI-driven efficiency savings are passed through and your organization is truly sharing in the productivity gains with the MSP, as Infosys called out. Additionally, building continuous improvement commitments into contracts will ensure gains persist after initial automation.

Flexible AI Commercial Models

AI adoption is forcing a rethink of how managed services agreements are priced. Traditional contracts have leaned heavily on FTE-based or ticket-based pricing, but as AI reduces manual effort and automates resolution, those models start to break down. Providers are experimenting with hybrid commercial structures.

One example is layering outcome-based measures like, SLA improvements or ticket deflection rates), on top of standard base fees. Another is building pricing corridors that flex as AI tools take on more work. The underlying theme is clear: MSPs want to protect revenue while organizations push for measurable, shared savings.

• TCS CEO, K. Krithivasan:
  “This is an evolving space…There are some [AI solutions] where we do based on the outcome. There are some customers that expect… T&M. Because as it is evolving, they also want to see how they are able to benefit from the results. And then after a period of time, move towards the fixed-price model.”

Procurement Takeaway: When embedding AI into managed services, do not let providers default on project-style pricing alone, as your MSP should be flexible in adjusting to your organization’s desired model based on TCS’s quote above. As an example, it may make sense to push for contracts that blend base managed services fees with AI-linked outcome metrics such as automation rates or mean time to resolution. Ultimately, building in transparent step-down clauses will ensure costs automatically decline as AI reduces the level of human intervention over time.

MSP Differentiation Through IP and Platforms

With the major MSPs rapidly expanding their AI capabilities, MSPs are looking for ways to stand out and their strategy is increasingly built around investing in proprietary AI platforms, accelerators, and IP. These platforms not only help them market differentiated services but also influence pricing power and client stickiness.

For clients, the risk is that IP ownership could lock them into a single vendor, limiting future flexibility and negotiation leverage. Providers, however, view these assets as central to their competitive positioning.

• Cognizant CEO Ravi Kumar Singisetti:
  “tThe differentiation for the IT services sector…is no longer about just building capability, it’s actually about interdisciplinary capability layered with IP-on-the-edge…this is the last-mile infrastructure needed to get enterprise-grade AI or agentic.”

“It certainly gives us the opportunity to make it sticky, as well as to create some premium attached to it.”

Procurement Takeaway: Clarify in writing who owns AI models, training data, and derived insights. Avoid contracts that grant MSPs sole ownership of intellectual property developed during your engagement. Push for data portability rights so you can migrate AI models to other vendors if necessary.

Upskilling and Change Management

Even the best AI platforms fail if users don’t adopt them effectively. MSPs are increasingly acknowledging that change management, reskilling, and user training are critical enablers to the success of AI programs. Providers like Accenture are embedding training into their AI delivery models, reflecting a recognition that adoption challenges are often the biggest barriers to realizing value from AI investments, rather than the technology itself.

• Accenture CEO Julie Sweet:
  “Through our LearnVantage services, we are training the company’s digital employees on leveraging agentic AI to drive operational efficiencies, foster joint ownership, and enable seamless adoption.”

Procurement Takeaway: AI adoption depends on workforce enablement. Include mandatory training, adoption milestones, and user satisfaction KPIs in your contracts. Negotiate bundled AI change management services so the vendor shares responsibility for driving real organizational uptake, not just delivering technology.

Provider Consolidation and Pricing Dynamics

The managed services industry is consolidating, with M&A activity reshaping the vendor landscape. While consolidation creates larger, more capable providers, it also introduces pricing volatility and margin pressure. Clients often benefit from upfront discounts as MSPs compete for share, but once the deal stabilizes, vendors typically look to claw back margins over time. Capgemini’s commentary highlights how procurement needs to anticipate both the near-term pricing benefits and the longer-term risks of industry consolidation.

• Capgemini CEO, Aiman Ezzat:
  “If you’re small, you probably get squeezed out. If you’re big, you stay in and you win.”
  “Every time there is consolidation, you basically give upfront savings. It takes eighteen to twenty-four months to try to recover some of these margins.”

Procurement Takeaway: Push for multi-year pricing protections so providers cannot raise rates once market consolidation takes effect – particularly with larger providers as they may use their proprietary AI platforms to justify premium pricing. Add benchmarking clauses to compare rates against peer deals annually and negotiate exit provisions if pricing or service quality is impacted by consolidation.

Timing of AI Tool Introduction

A critical theme across providers is not if they will bring AI into client engagements, but when. Some organizations expect AI enhancements to arrive only at renewal, but many MSPs are proactively embedding AI capabilities into live contracts.

This means that even if you’re mid-term, your provider may already be layering AI into delivery to improve outcomes and lower costs. The risk for clients is missing out on these gains by not proactively pressing providers for specifics.

• TCS CEO, K. Krithivasan:
  “To be honest, we don’t wait to infuse AI for the renewal in most of our clients.”

“Whenever we see an opportunity where an AI-infused solution can give better productivity and a better outcome for our customers, we proactively do that… in almost all kinds of projects.”

Procurement Takeaway: Don’t wait until renewal to discuss AI. Ask your providers today what AI-infused solutions they’ve already deployed within your contract and request hard metrics on realized productivity improvements. If the gains are material, consider renegotiating early to reset pricing and delivery terms around AI-enabled value, rather than waiting for the next contract cycle.

The Bottom Line

AI is already baked into how MSPs price, deliver, and scale their services. Expect MSPs to be aggressive in capturing as much margin in future engagements as possible, especially considering many MSPs have made multi-billion-dollar investments in building out AI capabilities.

To ensure organizations are sharing in the efficiencies able to be captured by leveraging AI tools, procurement leaders need to be pushing vendors to show where AI is already improving outcomes, how those savings are being shared, and what flexibility exists to adjust pricing models.

Negotiating full transparency, coupled with strong commercial term frameworks, will enable organizations to capture the upside of implementing AI solutions to improve support of application and infrastructure environments.

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