AI has moved from being a promise to delivering real value within technology teams. By 2025, organizations most commonly reported cost benefits in software engineering and IT, not just front-office applications. Leaders integrating AI into their processes (while keeping humans involved) are seeing significant advantages.

Why this is important now

Budgets are aligning with value. Gartner has observed strong enterprise spending connected to AI capabilities, especially in data centres and software sectors, reflecting a push to modernize systems and integrate AI into everyday IT tasks. Meanwhile, Canadian public sector experiments with Microsoft 365 Copilot demonstrated tangible, albeit self-reported, time savings, highlighting the pressure for mainstream adoption in enterprise IT.

Where AI Is Providing Value in Tech

1) Acceleration in software engineering

Code assistants aid developers in drafting, refactoring, and testing quicker. Independently reviewed tasks show developers using GitHub Copilot completed work approximately 55% faster than those without it, with organizations reporting better focus and reduced repetitive work. As teams responsibly scale usage, throughput and lead time see improvement.

2) IT operations and reliability (AIOps)

AIOps platforms link logs, metrics, and traces to detect anomalies, reduce noise, and automate fixes. Gartner anticipates rapid growth in automation—by 2026, 30% of enterprises will automate more than half of their network activities, up from less than 10% in mid-2023—reducing incidents and MTTR when combined with solid SRE practices.

3) Knowledge work within IT

Copilots integrated into productivity suites summarize meetings, draft documents, and reveal insights across service tickets and release notes. Forrester’s TEI on Microsoft 365 Copilot showcases substantial time savings and operational efficiencies in a composite enterprise.

4) Portfolio planning and governance

AI aids in demand shaping, dependency mapping, and risk analysis across backlogs. High performers are not just adding tools; they rework workflows and controls so AI outputs are reviewed, logged, and continuously improved.

What’s New in 2025–2026

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

• AI everywhere in IT: Gartner notes that AI will impact nearly all IT work by 2030, with mixed productivity at the team level today—emphasizing the need for training and process adaptation to unlock benefits.

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

Practical Steps: A 90-Day Adoption Playbook

1) Identify 3–5 high-impact use cases.
Focus on code assistance in the IDE, test generation, ticket summarization, and incident triage. Connect each to a target metric (lead time, PR cycle time, MTTR, ticket resolution). Set goals with baseline measurements.

2) Prepare data and safeguards.
Organize repositories, observability data, and knowledge sources. Apply the principle of least privilege access and data loss prevention. Ensure human oversight for code and runbooks. Document the “approved uses” and escalation paths.

3) Integrate tools into the workflow.
Start with a pilot group (10–20% of the team) using IDE copilots and AIOps correlations. Set up observability alerts → runbooks → automation. Log successes and challenges weekly.

4) Train for new working methods.
Teach prompt patterns, code review for AI-assisted changes, and incident management with automation. Canadian trials show that perceived time savings depend on adoption skills; invest early.

5) Demonstrate value with clear metrics.
Report changes compared to baselines: task duration in coding, defect rates, PR cycle time, incident volume/noise, MTTR, and % automated remediations. Align to financial metrics (run-rate OpEx, cloud usage, licence streamlining). For business stakeholders, provide weekly one-page updates.

6) Expand with a platform approach.
Create reusable components: prompt libraries, policy packages, evaluation frameworks, and established procedures. Extend to change-risk analysis and capacity planning once the foundations are solid.

Examples You Can Implement This Quarter

• Boost developer productivity. Deploy IDE copilots to a volunteer team; measure task completion times on a standard exercise (couple with quality checks). Expect significant speed improvements when combined with reviews and tests.

• Reduce operating noise. Use AIOps to eliminate duplicate alerts and link events across networks, apps, and infrastructure; build auto-remediation for the top five incident classes.

• Improve service desk operations. Automate ticket summarization and knowledge suggestions; ensure governance for privacy and PII. For cross-functional work, pilot Microsoft 365 Copilot to reduce meeting/administrative time.

Risks and How to Manage Them

• Avoid getting caught up in the hype. Many “agentic AI” projects are being rebranded and later discarded for poor ROI. Conduct value assessments early and avoid vendor “agent-washing.”

• Team-level inconsistency. Individual productivity might improve while team throughput stagnates. Standardize practices, training, and change management to transform individual advancements into system-wide impact.

• Data security & quality. Maintain human oversight, log prompts/outputs, and limit data by domain.

Summary & Next Steps

AI is now a reliable value enhancer in technology functions—especially in software engineering and IT operations—when tools are paired with process changes, training, and governance. To tailor this to your environment, contact Generation Digital for a targeted discovery and pilot plan.

FAQ

Q1. How precisely is AI adding value in tech functions?
By speeding up coding tasks, automating incident triage, and reducing routine documentation—delivering cost savings in software engineering and IT when embedded into workflows with the right safeguards. McKinsey & Company

Q2. Can you provide a concrete example of productivity improvements?
In controlled environments, developers using GitHub Copilot finished tasks about 55% faster than those who did not, while enterprises report time savings from Microsoft 365 Copilot in documentation and meetings. Visual Studio Magazine

Q3. Where should we begin?
Choose a few high-value use cases (IDE copilots, reducing AIOps noise, ticket summarization), set baselines, train teams, and measure monthly improvements. Gartner

Q4. Is AIOps truly valuable?
Absolutely—automation levels are increasing rapidly. Gartner forecasts that by 2026, 30% of enterprises will automate over half of their network activities, significantly up from under 10% in mid-2023. Gartner