AI ROI Is Stalling. Here's How Managers Can Fix It
Workforce Engagement Management - News Analysis
Two large surveys send a clear message: most companies have AI in the stack, but few see meaningful ROI. IBM found 62% of UK enterprises aren't using AI to its full potential. Teradata reported that while AI is deployed in some form across nearly all respondents, only 9% have fully advanced its place in the workforce.
The gap isn't tools. It's preparation, skills, data, and governance. If you lead a team, the path to returns starts with those four levers.
What the Studies Actually Show
IBM (EMEA, 3,500 leaders): Adoption is high, but value is uneven. 62% haven't achieved full transformation with AI. Internal resistance is real (67% see pushback on pilots). Training is thin: 45% offer company-wide or function-focused AI training; only 38% upskill all employees. Just 27% of UK senior leaders report clear cost or financial benefits so far.
Yet productivity is moving: 66% of leaders report strong productivity gains, and 63% see higher operational efficiency.
Customer Sentiment Is Ahead of Most Firms
IBM's UK consumer survey shows rising acceptance. 74% are comfortable with AI assistants in their experience. 79% believe chatbots can deliver trustworthy results; 72% are satisfied using them. Customers value speed (40% cite convenience) and expect strong privacy (37%).
Translation: the demand signal is clear. The question is whether your org can deliver AI that's fast, useful, and safe.
Teradata's Reality Check on Agentic AI
Teradata surveyed 500 AI-relevant executives (100+ employees, $500M+ revenue). Implementation is the blocker, not interest. Over 50% want to move fast, but more than a third prefer to wait until trends are proven (up from 22% last year). 91% haven't fully adopted AI agents, but among those that have, 81% report high confidence in CX impact.
The hard parts: only 4% have consistent, accurate data access; 96% experience delays or inconsistency. Top concerns: data accuracy (34%) and security (32%). Governance is a headache for 93% of firms, and it's worse at scale (42% of $20B+ companies report major governance challenges). Skills gaps persist across internal teams (73%).
Why ROI Lags: The 3 Big Constraints
- High upfront costs: 37% say spend is too high to show short-term ROI.
- Attribution issues: 35% struggle to isolate AI's impact from other changes.
- Skills gaps: 31% point to missing knowledge that blocks gains.
Yes, There's Progress-If You Invest in People
Leaders credit AI for freeing up time for higher-priority work. IBM leaders in the UK report two-thirds seeing productivity gains and nearly as many reporting efficiency lifts.
As one executive put it: the bigger gains come from workforce transformation and upskilling. Another industry voice stressed making AI reskilling a core part of employee development. That theme is hard to ignore-especially as some firms restructure around reskilling and exit roles they can't retrain.
What You Should Do This Quarter
- Set a hard business target for each AI use case. Pick metrics you already trust (AHT, CSAT, FCR, cost-to-serve, NPS, revenue per agent).
- Run one well-scoped pilot per function. Keep it to 6-10 weeks. Define success criteria, budget, and exit ramps on day one.
- Fund skills before scale. Train the teams that will touch the model output: operations, quality, compliance, and product.
- Fix the data path. Inventory sources, access, latency, and quality rules. If the data isn't reliable, neither is the ROI.
- Stand up governance early. Risk owners, approvals, audit trails, and model monitoring. Write it down. Follow it.
- Plan attribution. Use pre/post baselines, A/B groups, or switchback tests. No test plan = no credible ROI story.
Build the Operating Model That AI Needs
- AI Steering Group ("AI Board"): Approves use cases, risk ratings, and launch gates.
- Product-style delivery: A product owner, data lead, and risk lead per use case. Treat AI like a product, not a project.
- FinOps for AI: Track unit costs (per API call, per interaction) and tie them to business outcomes.
- Reusable components: Patterns for prompts, policies, red-teaming, and human-in-the-loop that every team reuses.
Solve the Data Problem First
- Data access: Resolve ownership and permissions for the top 3 use cases.
- Quality: Define accuracy thresholds and freshness SLAs; set automated checks.
- Security: Decide what can leave your boundary. Default to minimal exposure.
- Observability: Log prompts, outputs, and user feedback. Build a feedback loop into model updates.
De-risk With Clear Guardrails
- Use-case risk tiers: Low, medium, high-each with matching controls.
- Human controls: Human review for high-impact actions (refunds, credits, escalations).
- Model governance: Versioning, drift checks, and incident response procedures.
- Compliance and privacy: Data minimization, retention limits, and vendor due diligence.
Prove ROI With Measurement That Sticks
- Baseline before you ship. Capture at least four weeks of pre-data.
- Run controlled tests. A/B, phased rollout, or switchback to isolate impact.
- Measure both cost and quality. Time saved is useful, quality preserved is crucial.
- Reinvest smartly. If a pilot hits target, scale the narrow slice that worked, not the entire idea.
Where Training Fits (And What to Teach)
- Foundations for everyone: Basic AI concepts, prompt skills, privacy, and acceptable use.
- Role-based depth: For managers (KPIs, change management), for analysts (data prep, evaluation), for compliance (risk testing), for engineers (integration patterns).
- Hands-on labs: Real tasks, your data, your workflows-no generic demos.
If you need a structured path, explore role-based programs and certifications that accelerate adoption: AI courses by job and popular AI certifications.
Watch Outs That Kill ROI
- Tool-first thinking: Buying a platform without a scoped use case and a data plan.
- Hobby pilots: No baseline, no control group, no owner.
- Shadow AI: Teams launching agents without governance or auditability.
- Training theater: One-off workshops with no on-the-job application.
The Manager's Takeaway
The studies are consistent: adoption is broad, value is patchy. Customers are ready. Your returns hinge on skills, data reliability, governance you can enforce, and measurement you trust.
Set targets. Fund people. Start small and measurable. Scale what works. That's how AI produces ROI you can defend in the next budget cycle.
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