How Enterprise Leaders Should Measure the ROI of AI - with Darko Todorovic of HTEC

2026-06-12 · Source: The AI in Business Podcast · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Operations & Process Management · Depth: Intermediate, extended

Summary

Darko Todorovic, CTO at HTEC Group, highlights that enterprise AI investments frequently falter at scale despite successful pilots, primarily due to organizational conditions rather than technological failures. He explains that poor problem definition, inadequate change management, and undefined success metrics are common causes for ROI gaps. Leaders must establish cost per unit baselines, build robust ROI measurement frameworks, and cultivate organizational readiness by treating AI agents as co-workers. The discussion emphasizes assessing technological and organizational maturity, avoiding common POC-to-production pitfalls, and selecting appropriate AI tools for specific business contexts. Real-time ROI tracking is achievable through well-architected solutions that monitor token consumption and efficiency gains.

Key takeaway

For Directors of AI/ML overseeing enterprise deployments, recognize that AI project success at scale depends less on technology and more on organizational readiness. You must prioritize defining clear business problems and establishing measurable KPIs and cost baselines before implementation. Invest in change management and educate your teams to treat AI agents as co-workers, ensuring a supportive environment for human-AI coexistence to achieve sustainable ROI.

Key insights

Enterprise AI ROI hinges on organizational readiness and clear problem definition, not just technology.

Principles

• Treat AI agents as co-workers, not just tools.
• Organizational maturity is key to AI scaling success.
• Define clear KPIs and cost baselines pre-implementation.

Method

Assess organizational maturity (tech, data, change management) and define cost per unit baselines. Start with process owners to identify AI-augmentable workflows, then select appropriate tools.

In practice

• Monitor token consumption for real-time cost tracking.
• Educate staff to view AI as junior assistants.
• Prioritize AI for well-defined, cost-per-unit processes.

Topics

• Enterprise AI
• AI ROI Measurement
• Organizational Readiness
• Change Management
• AI Adoption
• HTEC Group

Best for: Director of AI/ML, VP of Engineering/Data, Executive

Related on AIssential

• Rewiring Systems to Scale AI From Demos to Deliverables - Nina Edwards of Prudential Insurance — AI pilot failures stem from misaligned pre-AI metrics that obscure true velocity and value gains.
• RAND studied 2,400 AI projects. Only 19.7% succeeded, and the failure pattern is almost identical every time — AI project success is primarily driven by organizational readiness, clear goals, and sustained leadership, not just technology.
• Exploring AI’s Value and Profitability — AI ROI is about augmenting human work and fostering innovation, not just incremental productivity.
• Economist Enterprise: How Leading Firms Make AI Deliver — Successful AI adoption requires organizational change, disciplined execution, and continuous governance beyond initial deployment.
• 2026 AI in Professional Services Report: AI adoption has hit critical mass, but now comes the tough business questions — AI adoption in professional services is widespread, yet strategic integration, ROI tracking, and client communication lag significantly.
• My own Dos and Don’ts of Enterprise AI Adoption — Effective AI adoption hinges on change management, dedicated resources, and strategic integration, not just technology access.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.