Stop the Money Burn: Get Disciplined About Your AI Investments

Get total clarity on what business problem each AI initiative solves. Directly tie every project to organizational strategy and build an airtight business case before spending a dime.

Leading organizations follow these practices:

• Develop concise problem statements linked to enterprise priorities.

• Set realistic ROI metrics and track progress against them.

• Build cost models that include upfront build, operations, scaling and retirement.

• Make trade-offs explicit to enable resource shifts from lower-priority work.

Chasing popular use cases without this discipline risks uncontrolled spend, mounting change orders or projects that become sunk-cost traps. Anticipate all-in costs — including vendor price increases or retraining needs — before approving any major investment.

Don’t assume AI — or GenAI, specifically — is always the right answer. Pressure-test whether advanced analytics or business intelligence tools could deliver similar outcomes faster or cheaper. Skipping this due diligence is a costly mistake that locks organizations into long-term contracts with low ROI.

Before moving forward:

• Consider non-AI approaches.

• Decide whether to build, buy or blend based on evidence, not hype.

• Vet vendors for proven implementation experience and secure data integration.

• Link payments to measurable outcomes; pilot small projects before scaling up.

Treat vendor selection as a strategic decision — conduct reference checks, review performance proof points and build feedback loops. The best leaders make technology choices that fit both current needs and future ambitions.

AI fails without a strong data foundation. Confirm your organization has mapped relevant structured and unstructured data assets before production deployment.

To determine readiness:

• Assess cleanliness, accuracy, accessibility and legal compliance of all data sources.

• Formalize robust governance policies, automating quality checks wherever possible.

• Engage legal and compliance teams early to address privacy or jurisdictional risks and ensure regulatory issues don’t derail timelines or erode trust.

• Test model interoperability outputs before launch; establish security protocols.

Plan ahead for ongoing training investments — scaling up usually requires more data science talent, storage capacity and compute resources than anticipated.

Even perfect technology cannot save projects if employees aren’t ready for new workflows or decision-making processes. Invest in culture, skills and communication before deploying AI at scale.

Build alignment by:

• Developing clear change management strategies for users, employees and customers

• Setting a vision for the post-AI implementation org model

• Planning how hiring/staffing will evolve to support AI development

• Upskilling technical talent early

• Redesigning processes alongside technology rollouts

Treat change management as core — not support — and involve HR, operations and frontline leaders from Day 1. AI literacy programs help everyone understand capabilities, limitations and ethical considerations so your workforce partners with intelligent systems rather than working around them.