In my last blog, I explained that when your CMDB is a mess, AI makes that mess happen faster. The same principle applies when your Managed Service Provider (MSP) gets there first. MSPs are now moving fast. AI is now embedded across service delivery operations. IDC research shows how AI is shifting IT services away from labor-led delivery toward platform-led, outcome-based models. The smarter MSPs get to know your environment, the harder it becomes to challenge their pricing.
When MSPs deploy AI across service delivery, their per-unit costs fall. But contract rates rarely follow. Buyers who maintain an independent, cost-enriched CMDB can verify MSP-reported usage, identify idle resources, and negotiate from evidence rather than estimates. The good news is that the same data foundation that protects you in IT Service Management can also protect you commercially.
What MSPs are actually deploying
Every major MSP now has an AI platform. The table below shows what the leading providers are running and what it means for buyers.
| Provider | Platform | What It Does |
| Accenture | AI Refinery for Industry | Builds and deploys industry-specific AI agent solutions using NVIDIA’s AI stack, now targeting over 50 solutions across telecommunications, financial services, and insurance. |
| Atos | Polaris AI | Delivers agentic AI capabilities for IT engineering and business functions. |
| Capgemini | GenAI Operations | AI-powered service desk and infrastructure intelligence. In 2026, Capgemini joined OpenAI’s Frontier Alliance to build and scale agentic AI workflows across enterprise operations. |
| Deloitte | Zora AI | Agentic platform for business finance functions, leveraging NVIDIA AI. |
| DXC Technology | OASIS | Connects the entire IT estate into a single trusted view, using AI agents and human expertise to anticipate issues and act before they affect the business. |
| EY | EY.ai Agentic Platform | Leverages NVIDIA AI to enhance tax, risk, and finance operations. |
| Fujitsu | Kozuchi AI Agent | Enhances operational productivity across managed infrastructure contracts. |
| IBM | Consulting Advantage / watsonx Orchestrate | Deploys AI agents across HR, finance, and IT workflows, with a catalog of over 150 pre-built domain-specific agents and multi-agent interoperability across SAP, AWS, and other enterprise platforms. |
| Infosys | Agentic AI Foundry (part of Infosys Topaz) | Published case data shows 30–40% reductions in ticket volume for specific engagements. |
| KPMG | KPMG Velocity | AI-powered business transformation platform built for consultants. |
| Kyndryl | Agentic Service Management | Combines a maturity model, structured assessments, and implementation blueprints to help enterprises move from traditional service operations to autonomous, intelligent workflows at scale. |
| McKinsey | LegacyX | Accelerates legacy infrastructure modernization using agentic AI. |
| PwC | Agent OS | A new operating system for orchestrating AI agents across enterprise functions. |
| TCS | WisdomNext | Enterprise AI platform delivering automation across managed services delivery with integrated AI orchestration capabilities. |
| Wipro | WeGA Studio / WEGA | Provides pre-built accelerators, domain frameworks, and agentic AI toolkits across software engineering, cloud, data, and enterprise applications, with NVIDIA AI Enterprise integrated throughout. |
This list is not intended to be exhaustive.
Agentic AI refers to AI systems that can autonomously plan and execute multi-step workflows. They do not just generate responses; they act on them. IDC research confirms that leading platforms now share a common architecture covering agent orchestration, model services, knowledge management, enterprise integration, and governance. This is no longer experimental. It is becoming the standard delivery model.
For buyers purchasing the same services under existing contracts, the commercial question at renegotiation is straightforward: if your MSP’s delivery costs are falling, are your contract rates falling with them? In most cases, the answer is no. MSPs are actively working to demonstrate additional value to justify maintaining or increasing their rates, and the ones doing it well have the data to back it up. Buyers who lack independent data do not.
What this means for managed services pricing
IDC survey data shows that 55% of IT buyers expect AI-powered managed services to cost more than traditional providers, while 37% expect prices to fall. Both sides of that picture matter.
Factors pushing prices up
Agentic platforms are enabling always-on, outcome-based service models, which support premium pricing for higher-value outcomes, particularly in business process services. Demand is also growing for orchestration, governance, and multi-agent operations across IT and business functions. MSPs are also expanding into new customers and workloads, including mid-market segments that were previously too expensive to serve.
Importantly, not all customers will adopt premium agentic services. MSPs need to recoup their platform investment costs across a broader base, which adds further upward pressure on pricing across the board.
Factors pushing prices down
AI agents now handle monitoring, triage, root cause analysis, and remediation within defined boundaries, reducing reliance on people for routine tasks. Platform efficiencies compress the cost per unit of work, and performance in predictable scenarios is becoming more consistent.
Net impact for MSPs
AI platforms are compressing MSP delivery costs while enabling premium pricing for outcome-based services. The net result is stronger margin for MSPs, and a real commercial case for buyers who can prove what they’re actually using.
Important context for IT buyers
AI platforms are not free for MSPs to build and run. Infrastructure, integration, specialist skills, and ongoing maintenance all carry significant costs. Buyers should find out whether AI investment is listed as a separate line item or is absorbed into base rates at renewal.
The commercial model is also shifting.
IDC research projectsthat by 2029, 30% of all service provider contracts will be outcome-based, driven by the changes agentic AI is introducing.
Traditional time-and-materials and consumption-based pricing do not map well to how agentic services are actually delivered, which is one reason outcome-based models are gaining ground. Buyers who understand this shift and write contracts that reflect it will be better placed to share in the productivity gains, rather than fund them.
Where IT buyers are still losing ground
Three problems consistently arise at contract renewal.
1. Accepting the MSP’s usage data
Most organizations have no independent, current view of their own environment. IDC PeerScape research found that organizations regularly discover assets MSPs keep billing for even after they leave active use. Old virtual machines, unallocated storage, and devices that were never decommissioned are common examples. When the MSP’s AI platform generates usage reports, most buyers have no external check, so they accept the numbers.
2. Paying for unused infrastructure
In one financial services organization I worked with, a pre-renewal audit found 50 virtual machines switched off for more than three months, and 15 terabytes of storage with no active application attached. The MSP was billing for all of it. Removing those items from scope saved 48,000 euros a year.
3. No basis to challenge per-unit pricing
MSP contracts priced per virtual machine, per device, or per terabyte are only negotiable if the buyer can independently verify what is actually in use. Without that, the MSP’s numbers go unchallenged. This is also one of the reasons outcome-based pricing deserves serious consideration. When delivery is driven by AI agents rather than headcount, per-unit pricing often fails to reflect the true cost or value of the service.
The answer is still the same
IDC’s research on agentic AI in services concludes that the providers who win will be those who prove value transparently and build trust through governance, portability, and accountability. Buyers have a role to play in demanding exactly that.
Build an independent source of truth. A well-maintained, cost-enriched CMDB is your foundation.
IDC TechBrief research confirms that accurate, continuous discovery supports contract oversight, SLA validation, and audit readiness. Organizations with mature configuration data achieve service management costs 10 to 15% lower than comparable organizations that do not, and they get 2.5 times more value from their AI investments.
Run a pre-renewal audit. Start 90 to 120 days before contract expiry, using independent discovery tools such as ServiceNow Discovery or Dynatrace. This lets you verify MSP-reported usage, identify idle resources, and challenge billing for unused infrastructure before you reach the negotiating table.
Get the contract language right. Include the right to conduct your own audits, tie billing to verified active use, and build in scope reduction mechanisms. Ask directly how AI deployment costs and efficiency gains will be shared. Providers who cannot answer that question clearly are worth watching.
Push for outcome-based models, and define outcomes broadly. Buyers who request transparent pricing, clear ownership terms, and outcome-based commercial structures will be better placed to capture value rather than pay for it. But make sure the outcomes you measure go beyond technical metrics. An MSP can meet every SLA target and still fail to deliver real business value. Build business outcomes, not just service desk targets, into the contract from the start.
The bottom line
IDC forecasts cumulative AI economic value of $22.5 trillion between 2025 and 2031. The MSPs listed here are investing heavily to capture their share of the market. The platforms they are building are not just delivery tools. They are becoming the primary way MSPs own the workflow and outcome layer in managed services.
IT buyers with accurate, independent data about their own environments are in a much stronger position to participate in that value rather than fund it. An accurate, cost-enriched CMDB is not an IT housekeeping exercise. In the age of AI-driven managed services, it is a commercial imperative.
