AI has shifted from promise to practical payoff inside technology teams. In 2025, organisations most commonly reported cost benefits in software engineering and IT, not just front-office use cases. The leaders rewiring processes around AI (with humans in the loop) are capturing outsized gains.

Why this matters now

Budgets are following the value. Gartner tracked strong enterprise spend linked to AI capabilities, particularly in data-centre and software categories, reflecting a push to modernise stacks and activate AI in day-to-day IT work. Meanwhile, UK public-sector experiments with Microsoft 365 Copilot found tangible, if self-reported, time savings, signalling mainstream adoption pressure for enterprise IT.

Where AI Is Delivering Value in Tech

1) Software engineering velocity

Code assistants help developers draft, refactor and test faster. Independent write-ups of controlled tasks show developers with GitHub Copilot completing work ~55% faster than those without, while organisations report improved focus and reduced “toil”. As teams scale usage responsibly, throughput and lead time improve.

2) IT operations and reliability (AIOps)

AIOps platforms correlate logs, metrics and traces to detect anomalies, cut noise and automate remediation. Gartner expects rapid automation growth—by 2026, 30% of enterprises will automate more than half of network activities, up from under 10% in mid-2023—reducing incidents and MTTR when paired with good SRE practices.

3) Knowledge work inside IT

Copilots embedded in productivity suites summarise meetings, draft documentation, and surface insights across service tickets and release notes. Forrester’s TEI on Microsoft 365 Copilot outlines substantial time savings and operational efficiencies in a composite enterprise.

4) Portfolio planning and governance

AI assists with demand shaping, dependency mapping and risk analysis across backlogs. High performers aren’t just layering tools; they rewire workflows and controls so AI outputs are reviewed, logged and improved over time.

What’s New in 2025–2026

• Value concentration in tech: The latest McKinsey survey highlights software engineering and IT among the top areas reporting cost benefits from AI.

• AI everywhere in IT: Gartner notes AI will touch virtually all IT work by 2030, with mixed team-level productivity today—underscoring the need for training and process change to unlock benefits.

• From pilots to platform: Public- and private-sector Copilot programmes are moving from experiments to enterprise rollouts, with documented time savings and guardrail patterns (RBAC, data boundaries, logging).

Practical Steps: A 90-Day Adoption Playbook

1) Pick 3–5 high-yield use cases.
Prioritise code assistance in the IDE, test generation, ticket summarisation and incident triage. Tie each to a target metric (lead time, PR cycle time, MTTR, ticket resolution). Ground ambition with baseline measurements.

2) Ready the data and guardrails.
Map repositories, observability data and knowledge sources. Apply least-privilege access and data-loss prevention. Require human-in-the-loop review for code and runbooks. Document the “allowed uses” and escalation paths.

3) Embed tools in the flow of work.
Start with a pilot cohort (10–20% of team) using IDE copilots and AIOps correlations. Configure observability alerts → runbooks → automation. Capture friction and success patterns weekly.

4) Train for new ways of working.
Teach prompt patterns, code review for AI-assisted changes, and incident command with automation. UK trials highlight that perceived time savings depend on adoption skills; invest early.

5) Prove value with transparent metrics.
Report deltas vs. baseline: coding task duration, defect rates, PR cycle time, incident volume/noise, MTTR, and % automated remediations. Align to financial levers (run-rate OpEx, cloud utilisation, licence rationalisation). For business stakeholders, summarise in weekly one-page updates.

6) Scale with a platform mindset.
Create reusable components: prompt libraries, policy bundles, evaluation harnesses and golden paths. Expand to change-risk analysis and capacity planning once the foundations are stable.

Examples You Can Deploy This Quarter

• Developer productivity. Roll out IDE copilots to a volunteer squad; measure task completion times on a standard exercise (pair with quality checks). Expect meaningful speed-ups when combined with reviews and tests.

• Noise-cutting in Ops. Use AIOps to deduplicate alerts and correlate events across networks, apps and infrastructure; build auto-remediation for top 5 incident classes.

• Service desk uplift. Automate ticket summarisation and knowledge suggestions; add governance for privacy and PII. For cross-functional work, pilot Microsoft 365 Copilot to reduce meeting/admin time.

Risks and How to Mitigate Them

• Over-indexing on hype. Many “agentic AI” projects are being rebranded and later scrapped for weak ROI. Run value tests early and avoid vendor “agent-washing.”

• Team-level variance. Individual productivity can rise while team throughput stalls. Standardise practices, training and change management to turn individual gains into system-level impact.

• Data security & quality. Keep humans in the loop, log prompts/outputs, and restrict data by domain.

Summary & Next Steps

AI is now a dependable value lever inside technology functions—especially software engineering and IT operations—when you pair tools with process change, training and governance. To map this to your environment, contact Generation Digital for a focused discovery and pilot plan.

FAQ

Q1. How exactly is AI adding value in tech functions?
By accelerating coding tasks, automating incident triage and remediation, and reducing routine documentation—delivering cost benefits in software engineering and IT when embedded into workflows with the right guardrails. McKinsey & Company

Q2. What’s a concrete example of productivity gains?
In controlled tasks, developers using GitHub Copilot finished ~55% faster than those without, while enterprises report time savings from Microsoft 365 Copilot in documentation and meetings. Visual Studio Magazine

Q3. Where should we start?
Pick a handful of high-yield use cases (IDE copilots, AIOps noise-reduction, ticket summarisation), set baselines, train teams, and measure deltas monthly. Gartner

Q4. Is AIOps really worth it?
Yes—automation levels are rising quickly. Gartner projects that by 2026, 30% of enterprises will automate over half of network activities, up from under 10% in mid-2023. Gartner