3 Ways to Ensure Your AI Project Succeeds
▼ Summary
– Global AI spending is forecast to reach $2.52 trillion in 2026, but boards are now asking tougher questions about the return on these investments as generative AI enters a “Trough of Disillusionment.”
– Gartner analyst John-David Lovelock suggests this period of lowered expectations is an opportunity to refocus AI strategies away from over-ambitious “moonshot” projects.
– A key priority through 2026 should be capacity building, focusing on investments in AI-optimized infrastructure like servers and data centers to support future models and applications.
– Organizations should prioritize creating strong partnerships with established technology providers in their existing stack rather than attempting extensive in-house AI development.
– Success requires avoiding random explorations and instead focusing on co-development with partners, clear business outcomes, and aligning supplier rewards with project success.
The significant financial commitment organizations are making towards artificial intelligence continues to grow, with global spending projected to hit $2.52 trillion by 2026. Yet, as the initial excitement around generative AI cools, corporate boards are demanding clearer proof of value. This shift presents a crucial opportunity for technology and business leaders to refine their strategies and ensure investments translate into tangible results. Moving beyond experimental pilots requires a deliberate focus on foundational elements that support sustainable success.
Industry analysis indicates that several AI domains have entered a phase of recalibration, where early experiments often fail to meet inflated expectations. This isn’t necessarily a setback. Instead, it’s a chance to move away from speculative “moonshot” projects and concentrate efforts where they will have the most impact. With research indicating a high rate of projects that don’t deliver value, a more pragmatic, focused approach is essential. Experts emphasize three priority areas for the coming years to navigate this landscape effectively.
First, a strategic focus on capacity building is non-negotiable. A substantial portion of AI investment is flowing into the underlying infrastructure, servers, data centers, and computing power optimized for AI workloads. This foundational build-out by technology providers creates the essential capacity organizations will need. The critical decision lies in determining how your company accesses this capacity. Options range from building proprietary systems and leveraging major cloud platforms to utilizing specialized APIs. The key is to align your capacity strategy with your core resources and strategic differentiation. Ask pointed questions: How much of this technology must we own and control internally? What can we treat as a standardized service? Your answers will define a sustainable operational model.
Second, cultivating strong, strategic partnerships is a major determinant of return on investment. For most enterprises, the most viable path to AI integration in the near term will come through their existing technology partners and software vendors. Rather than attempting to develop everything in-house, which demands immense resources and expertise, forging deeper collaborations with proven providers is a smarter play. These partners can guide the journey, whether the goal is implementing straightforward AI tools or building toward more advanced, autonomous operations. The objective is to find allies whose roadmap and capabilities complement your business objectives, allowing you to leverage their innovation while focusing on your unique value proposition.
Finally, organizations must avoid scattered, random explorations and instead channel resources into co-development with clear outcomes. The era of broad, unfocused experimentation is giving way to targeted initiatives with defined business goals. Success hinges on a tight alignment between technology, data, internal processes, and, critically, line-of-business stakeholders. Ensure every project is directly tied to a measurable business outcome from the start. Furthermore, structure partnerships to incentivize mutual success. Move beyond simple time-and-materials contracts toward models like value-based or outcome-based pricing. When a provider’s reward is linked to your project’s success, it creates a powerful shared interest in delivering real results. While this model requires careful negotiation and relationship management, it transforms vendors into true collaborators invested in your long-term achievement.
This disciplined approach, centered on robust infrastructure, strategic alliances, and outcome-focused development, turns the current period of scrutiny into a powerful catalyst. It provides a clear framework for transforming AI investments from cost centers into engines of tangible value and competitive advantage.
(Source: ZDNET)
