Best practice advice when building future-ready enterprise AI

2026-04-17 · Source: Tech Monitor · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

A Tech Monitor and AMD roundtable in Copenhagen, held on March 19, 2026, gathered senior IT professionals to discuss challenges and best practices for deploying generative AI (GenAI) applications in enterprises. Key issues identified included cultural resistance, siloed systems, and a reluctance to rethink legacy processes, all demanding active leadership and clear communication. Participants also explored strategies for selecting appropriate GenAI use cases, with suggestions ranging from aligning with core business objectives to using AI itself to score potential projects. The discussion highlighted the ongoing tension between governance and innovation, emphasizing the need to balance data privacy, sovereignty, and security with business needs, while acknowledging that governance can provide essential structure rather than being a blocker.

Key takeaway

For IT leaders and AI product managers struggling to move GenAI beyond pilots, prioritize active leadership to address cultural resistance and clearly communicate "the why" behind AI initiatives. Focus on use cases that directly align with core business objectives and consider using AI to evaluate potential projects, while integrating governance as a structural enabler rather than a blocker to progress. Your success hinges on balancing innovation with practical, people-centric implementation.

Key insights

Overcoming cultural resistance and selecting high-value use cases are critical for successful enterprise GenAI deployment.

Principles

• Active leadership drives GenAI adoption.
• Align GenAI efforts with core business missions.
• Governance provides essential project structure.

Method

One approach to use case selection involves creating an AI-powered weighted scorecard to assess hard factors (cost savings), soft factors (satisfaction), feasibility (tech, data, governance), and time to value.

In practice

• Use AI agents to test project merits.
• Build private models for sensitive data.
• Refine business processes with AI.

Topics

• Enterprise AI Strategy
• Generative AI Implementation
• AI Governance Frameworks
• Cultural Resistance to AI
• AI Use Case Selection

Best for: Executive, AI Product Manager, Director of AI/ML, Consultant, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Monitor.