Why successful AI implementation requires a focus on long-term value creation

AI’s evolving landscape in 2025: The artificial intelligence revolution continues its rapid growth, but businesses are beginning to reassess their approach to AI implementation and investment strategies.

• Forrester’s State of AI Survey, 2024 reveals that two-thirds of respondents would consider their AI investments successful with less than 50% return on investment, indicating a shift in expectations.
• Enterprises are recognizing the need for a comprehensive, long-term business strategy that incorporates effective data management practices alongside AI initiatives.
• The focus is shifting from quick wins and immediate ROI to building a sustainable AI strategy that addresses business impact, governance, talent, operations, and appropriate use case activation.

Key predictions for AI in 2025:

1. Premature scaling back of AI initiatives: Many enterprises fixated on immediate AI ROI will reduce their efforts sooner than they should.
   • This retreat risks stifling long-term growth and innovation as leaders realize that AI ROI often unfolds over a more extended period than initially anticipated.
   • To avoid this pitfall, AI leaders need to develop differentiating use cases and a solid strategy that aligns with business aspirations while balancing immediate gains with sustained ROI.
   • Successful early AI projects can fuel a virtuous cycle of reinvestment into future AI initiatives.
2. Convergence of data and AI governance: 40% of highly regulated enterprises will combine their data and AI governance frameworks.
   • This shift is driven by the increasing complexity of AI governance, rapid technological innovation, and the absence of universal standards or certifications.
   • The implementation of stringent AI regulations, such as the EU AI Act in February 2025, is accelerating this trend.
   • This convergence goes beyond compliance, representing a move towards a more integrated, transparent, accountable, and ethically responsible approach to AI.
3. Challenges in building agentic AI architectures: Three out of four firms attempting to build aspirational agentic architectures independently will fail.

• Agentic AI is emerging as the next significant development in artificial intelligence.
• However, these architectures are highly complex, requiring diverse models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and specialized expertise.
• Mature companies will recognize these limitations and opt to collaborate with AI service providers and systems integrators to build cutting-edge agentic solutions.

The importance of strategic partnerships: Enterprises are increasingly realizing the value of collaborating with external partners to achieve excellence in AI implementation.

• AI service providers and systems integrators offer specialized expertise that many companies lack internally.
• Partnerships can help overcome the challenges associated with building complex AI architectures, particularly in emerging areas like agentic AI.
• Collaboration allows businesses to leverage external knowledge while focusing on their core competencies and strategic objectives.

Balancing short-term gains and long-term vision: Successful AI implementation requires a delicate balance between achieving quick wins and maintaining a long-term perspective.

• While immediate ROI is important, businesses must not lose sight of the potential for AI to drive sustained growth and innovation over time.
• A well-crafted AI strategy should include a mix of projects with varying time horizons for returns, allowing for both short-term successes and long-term transformational initiatives.
• Regular reassessment and adjustment of AI strategies will be crucial to ensure alignment with evolving business goals and technological advancements.

The role of data in AI success: Effective data management and governance are becoming increasingly recognized as critical components of successful AI initiatives.

• The convergence of data and AI governance in highly regulated industries highlights the growing importance of data quality, accessibility, and ethical use in AI applications.
• Enterprises must invest in robust data infrastructure and management practices to support their AI ambitions and ensure compliance with emerging regulations.
• Data strategy should be closely aligned with AI strategy to maximize the potential for insights and value creation.

Navigating the AI talent landscape: As AI continues to evolve, the demand for specialized AI talent remains high.

• Enterprises must develop strategies for attracting, retaining, and developing AI talent to support their initiatives.
• Collaboration with external partners can help fill skill gaps and provide access to specialized expertise, particularly in emerging areas like agentic AI.
• Continuous learning and upskilling programs will be essential to keep internal teams up-to-date with rapidly evolving AI technologies and best practices.

Preparing for the AI grind: The path to long-term AI success requires persistence, strategic thinking, and a willingness to adapt.

• Enterprises must recognize that there are no shortcuts to AI success and prepare for a potentially lengthy journey of implementation, learning, and refinement.
• A culture of experimentation, continuous improvement, and resilience in the face of setbacks will be crucial for organizations looking to maximize the benefits of AI.
• Regular assessment of AI initiatives against business objectives will help ensure that efforts remain aligned with overall strategy and continue to deliver value.