Look Up: Your AI Voyage Depends On It

2026-07-27 · Source: Featured Blogs - Forrester · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Human Resources & Workforce Development · Depth: Fundamental Awareness, quick

Summary

Seventy-seven percent of AI decision-makers now report using agentic AI in production, indicating widespread adoption, yet many organizations struggle to demonstrate meaningful business value from these investments. The challenge stems from an inward focus on use cases, models, and costs, rather than an outward orientation towards customer value. High AI adopters, as identified in the "Accelerate Your AI Voyage" report, distinguish themselves through four key traits: a strong customer focus, CEO-driven AI vision, investment in AI-ready data and platforms, and high AIQ talent development. Companies like DBS Bank and Kraft Heinz exemplify this approach by using AI to create tangible customer value, rather than merely deploying technology. Reconnecting AI initiatives to customer outcomes and trust provides a reliable compass for navigating complexity and measuring impact over activity.

Key takeaway

For AI/ML Directors and VPs of Engineering seeking to justify AI investments, shift your focus from internal metrics to external customer value. Your teams should align AI initiatives with strategic business goals, ensuring every deployment directly enhances customer experiences or outcomes. Prioritize building AI-ready data foundations and developing talent to scale effectively. This approach will transform AI from a cost center into a measurable driver of trust and impact, moving beyond mere activity.

Key insights

High AI adopters prioritize customer value and outcomes over internal technology focus to achieve measurable business impact.

Principles

• AI success hinges on customer value.
• CEO leadership drives AI strategy.
• Data and platforms are foundational.

In practice

• Start AI initiatives with customer needs.
• Align AI investments with strategic goals.
• Build AI-ready data and tech foundations.

Topics

• AI Adoption
• Customer Value
• AI Strategy
• Data Governance
• Talent Development
• Agentic AI

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

Related on AIssential

• 4 paths to Frontier Transformation: From AI experimentation to real business value — Scaling AI from isolated experiments to enterprise-wide value requires strategic alignment with business outcomes, supported by intelligence and trust.
• How the Best Companies Use AI — Leading companies treat AI as a growth engine, building institutional systems that empower all employees, not just individual users.
• AI ROI is rising, but not where companies expected — AI ROI is rising, driven by insights and customer interaction, not cost savings, requiring strategic measurement and governance.
• Operational Excellence, Customer Intimacy, and Product Leadership in the Age of AI — AI-enabled enterprise systems require explicit optimization objectives defined by value disciplines, shifting strategy to governance.
• 7 issues impacting AI strategies — and how CIOs should respond — CIOs must strategically navigate ROI, cost, human, and data readiness challenges to drive AI transformation.
• Why AI Success Depends on More Than Just Quality Data — AI success hinges on data, product strategy, and marketing working together, not just data quality.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Featured Blogs - Forrester.