Most organisations are experimenting with AI. Very few are getting value into production. In our work we see the same pattern: high usage, low impact, and a lot of shadow tools outside formal control. Roughly 80% of teams try AI, about 5% make it to production, and close to 90% of knowledge workers use personal AI without oversight. That is the gap we need to close.
BCG research in 2024 shows that “AI leaders” achieve ~1.5× higher revenue growth and ~1.6× higher shareholder return. The difference isn’t the model. It’s the operating model: governance, data health, smart pilots, selective scaling, and proven delivery disciplines.
In this blog, we will cover:
Set the rules before you buy tools. Establish an AI Steering Committee, publish simple policies (“what we will / won’t do”), and maintain a central inventory of pilots tied to business objectives. This reduces scattered spend, tackles shadow AI, and makes risk visible.
What to do this quarter
Stand up an AI Steering Committee with decision rights.
Launch “policy-in-a-box” guardrails (security, compliance, data residency).
Create a single pilot register linked to measurable outcomes.
AI performance follows data reality. Focus on four basics: data availability, accessibility, pipeline reliability, and augmentation. Don’t scale any AI until the right data can flow to the right process, at the right time.
What to do this quarter
Map the critical datasets for your top three use cases and expose them through APIs.
Fund the pipelines and skills to move and clean data reliably.
Use augmentation (including AI) to fill gaps you cannot close quickly.
Treat pilots as proving grounds, not mini-programmes. Keep scope tight, use off-the-shelf accelerators, and instrument from day one. Measure three things: efficiency gains, user adoption, and business impact. Scale nothing that cannot prove all three.
What to do this quarter
Baseline cycle time, hours saved, and accuracy before you build.
Run light pilots in weeks, not months; iterate prompts and workflow until adoption sticks.
Only expand what proves ROI in real work. Support winners with training, change management, and AI-Ops. Avoid the SaaS trap of buying through excitement instead of proving value first.
What to do this quarter
Gate “go-live” behind adoption and ROI thresholds.
Budget for enablement: training, guardrails, monitoring, and support.
AI is a product challenge, not a science project. Borrow from product management: discover needs first, pilot narrowly, iterate, and only then scale with proper change management. Start with process mapping and a portfolio of use cases that balance visible front-office wins with strong back-office ROI.
What to do this quarter
Map the target process end-to-end; remove friction before adding AI.
Build a portfolio view: quick wins, staged bets, and deprioritised ideas.
Days 1–30: Govern
Run an AI Governance sprint. Publish “policy-in-a-box” guardrails.
Build the pilot register and a standard business case template.
Kick off a Well-Architected-style review for your target workflows (security, reliability, cost).
Days 31–60: Data health
Identify two priority use cases and their critical datasets.
Expose data via secure APIs; harden pipelines and logging.
Prove access controls, audit trails, and data residency.
Days 61–90: Pilot smart
Deliver two light pilots using managed services and accelerators.
Instrument, measure, and prepare a scale/no-scale decision with evidence.
If scaling, stand up AI-Ops and training paths before broad rollout.
Pick one process where data is reachable, risk is manageable, and users are motivated.
| Function | Good first use cases | What to track |
|---|---|---|
| Operations | Demand forecasting, triage, scheduling, quality checks | Wait time, on-time %, forecast accuracy, rework, hours saved |
| Customer service | Assistive replies, summaries, intent routing, next best action | Reply time, handle time, CSAT, reopens, suggestions accepted %, edits made, self-serve rate |
| Finance | Faster close, account matching, contract checks | Days to close, % correct codes, exceptions, audit issues, time per invoice, cost per invoice |
| Sales/Marketing | Proposal drafts, lead notes, content reuse (with controls) | Time to first draft, redlines, cycle time, reuse %, win rate, cost per proposal |
| IT/Engineering | Code suggestions, test generation, knowledge search, incident summaries | Change lead time, review time, escaped bugs, MTTR, % AI-generated tests, incident reopens |
Tool-first buying: Start with a process and a KPI, not a vendor deck.
Skipping guardrails: Retro-fitting policy is slow and noisy. Publish it first.
Dirty or locked data: If data quality or access is weak, your pilot will mislead you.
Feature-chasing: New features arrive weekly. Your operating model must be the stable centre.
Scaling without enablement: Adoption collapses without training, change support, and clear ownership.
Decisions made in the next 12–18 months will lock in your AI architecture and vendor path for years. A disciplined playbook protects both speed and resilience. Do the basics brilliantly. Prove value in production. Scale what earns the right to grow.
If you’d like a copy of the detailed playbook and a practical starting plan for your team, email hello@CloudCombinator.ai. We’re happy to advise on the best way to start your next AI project.