AI News
21 Feb 2026
Read 9 min
AI strategy for IT services companies 3 ways to grow revenue
AI strategy for IT services companies helps scale legacy systems faster, boosting revenue and hires.
An effective AI strategy for IT services companies can grow revenue by turning new tools into end-to-end work. Focus on strategy and data readiness, brownfield integration and modernization, and trusted AI operations. These plays meet client demand for speed, security, and results—and expand hiring, not shrink it.
Recent AI launches from Anthropic and others spooked markets, and IT stocks fell 15–20%. Some fear fewer engineers and smaller deals. Infosys CEO Salil Parekh sees the opposite. AI can write parts of code, but large clients still need design, testing, security, deployment, and care in live systems. Most run older systems built over years. That is where services win. He pegs the AI services prize at $300–$400 billion by 2030 and says hiring and reskilling are rising, not falling.
Writing code is only one part of enterprise work.
Design, integration, testing, security, and support drive large engagements.
Enterprises pay for reliable change at scale, not just code speed.
Systems span ERP, data warehouses, custom apps, and mainframes.
AI must fit rules, controls, and workflows built over decades.
Industry knowledge matters: banking, healthcare, and public sector each have strict needs.
AI lifts engineer output, but software demand grows faster.
Clients want more features, more automation, and more integration.
This expands the need for skilled talent and managed services.
Set the AI vision by function: sales, service, finance, supply chain.
Run use-case discovery sprints with business owners and agree on outcomes.
Fix data first: audit sources, clean, label, catalog, and set governance.
Stand up secure patterns for RAG, fine-tuning, and prompt libraries.
Move from pilot to production with a standardized path and guardrails.
Commercials: fixed-fee roadmaps, discovery sprints, then scale programs with milestones.
Proof points: time-to-first-solution, data quality scores, adoption rates, payback period.
Wrap AI around core systems (ERP, CRM, core banking) via APIs and events.
Use code assistants to modernize legacy apps with tests and refactoring.
Build golden datasets and feature stores that serve many use cases.
Harden solutions: security reviews, performance tests, and audit trails.
Deploy with MLOps/AIOps for versioning, monitoring, and rollback.
Offer managed services for uptime, drift control, and cost optimization.
Proof points: faster release cycles, defect reduction, lower incident rates, higher system throughput.
Create policy frameworks for safety, privacy, copyright, and compliance.
Set human-in-the-loop for high-risk steps; define clear escalation paths.
Add model evaluation, bias checks, red-teaming, and prompt security.
Monitor hallucinations, drift, and cost per query; tune regularly.
Map controls to industry rules (e.g., banking KYC, healthcare privacy).
Commercials: governance setup projects plus ongoing assurance subscriptions.
Proof points: audit pass rates, risk event reduction, SLA adherence, and unit-cost control.
Hire and reskill at scale; certify on key platforms and toolchains.
Teach coding fundamentals first; then introduce copilots and agents.
Blend engineers with domain experts to meet industry needs.
Use reusable accelerators: data connectors, prompt packs, test suites.
Standardize pipelines for pilots, hardening, and production rollout.
Document patterns so new teams can deliver fast and safely.
Start with fixed-fee discovery; move to outcome-based milestones.
Offer managed services with SLAs for uptime, quality, and cost-to-serve.
Share productivity gains where measurable to build long-term deals.
Cut time-to-pilot and time-to-production by 30–50%.
Pre-built integrations and templates speed delivery.
Solutions that work inside current systems and respect controls.
Industry-grade workflows and reports that teams can use today.
Clear KPIs: cycle-time cuts, cost per ticket, conversion lift, NPS gains.
Trust metrics: fewer incidents, audit-ready logs, and stable unit costs.
Clients also expect tighter integration and measurable impact. Set baseline metrics up front, agree on dashboards, and review results every sprint. This builds confidence and opens the door to more work in adjacent functions.
With pressure from markets and rapid AI tool launches, the winners will turn hype into operations. Craft an AI strategy for IT services companies that begins with data and governance, connects to legacy estates, and runs with strong delivery and skills. Do this, and revenue will grow as software demand surges.
Why many investors misread AI’s impact on IT services
Code is not the whole lifecycle
Most clients live in “brownfield” environments
Demand can outpace productivity gains
AI strategy for IT services companies: 3 revenue plays
A strong AI strategy for IT services companies turns point tools into programs that deliver business results. These three plays match where clients spend and where service firms add clear value.1) Strategy and data readiness
2) Brownfield integration and modernization
3) Trusted AI operations and governance
Execution playbook: people, delivery, and pricing
People
Delivery
Pricing
What clients will pay for now
Speed
Fit
Proof
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FAQ
Q: How can AI tools expand opportunities for IT services firms?
A: AI tools can automate parts of software development, but integrating, testing, securing, deploying and maintaining that software in complex legacy “brownfield” environments requires services firms’ capabilities. An AI strategy for IT services companies focuses on turning point tools into end-to-end programs that handle data readiness, modernization and trusted operations.
Q: Why did IT services stocks fall after recent AI tool launches?
A: Share prices of leading IT services companies dropped 15–20% after Anthropic announced new tools as investors worried these tools might reduce the need for large teams and shrink deal sizes. Infosys CEO Salil Parekh argued investors may be misreading the impact because code is only a slice of the enterprise lifecycle and services remain essential for integration and live operations.
Q: What are the three main revenue plays in an AI strategy for IT services companies?
A: The three plays are strategy and data readiness, brownfield integration and modernization, and trusted AI operations and governance. These plays convert point tools into programs that deliver measurable business outcomes, managed services and scalable engagements.
Q: What does “brownfield integration” mean and why does it matter?
A: Brownfield integration refers to embedding AI into existing enterprise estates built over decades—ERP, CRM, core banking, data warehouses and mainframes—that carry rules, controls and workflows. Services firms add value by wrapping AI with APIs and events, modernizing code, building golden datasets and feature stores, and hardening solutions with security reviews, tests and monitoring so AI runs reliably in production.
Q: Will AI reduce hiring in IT services companies?
A: Parekh and the article argue AI will expand software demand and the IT services workforce rather than shrink it, noting Infosys is on track to add about 20,000 college graduates this year and hire a similar number next year. The shift emphasizes different skills and reskilling at scale, with thousands of employees being certified on AI tools, platforms and delivery methods.
Q: What do clients pay for when buying AI services now?
A: Clients pay for speed, fit and proof—faster time-to-pilot and time-to-production (the article cites 30–50% reductions), solutions that work inside current systems and respect controls, and measurable KPIs and trust metrics like fewer incidents and audit-ready logs. Vendors win work by setting baseline metrics, agreeing dashboards and reviewing results every sprint to show measurable impact.
Q: How should IT services companies price AI projects?
A: Start with fixed-fee discovery sprints, move to outcome-based milestones for scaling, and offer managed services and governance subscriptions with SLAs for ongoing assurance. Sharing measurable productivity gains where evident helps align incentives and build long-term deals.
Q: What skills will be most valuable in the AI era for service firms?
A: The most valuable talent will blend strong engineering fundamentals with deep domain understanding, and freshers should learn to program without AI tools first so they can assess machine output. Reskilling at scale and certifying employees on platforms, toolchains and delivery methods are core elements of an AI strategy for IT services companies.
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