Bridging the AI Value Gap: Strategies for Successful Transformation in Businesses
This heading captures the essence of the content, reflecting the need for actionable strategies to realize the promised benefits of AI in business operations.
Closing the AI Value Gap: Navigating Successful Transformation
Artificial Intelligence (AI) is swiftly reshaping the operational landscape of businesses across sectors. With Gartner® predicting that by 2028, at least 15% of daily operational decisions will be made autonomously through agentic AI, the push for AI investment continues to grow. Notably, 92% of companies are amplifying their AI spending, as reported by McKinsey. However, a significant discrepancy persists between investment and tangible outcomes, leaving many organizations grappling with the realization of AI’s potential benefits.
The Current State of AI Investments
An alarming trend has emerged: according to S&P Global Market Intelligence, the proportion of companies abandoning AI initiatives has surged to 42% in early 2025, up from 17% the previous year. Gartner further estimates that over 40% of AI projects will be scrapped by 2027. This gap—between significant expenditures and lackluster results—underscores the necessity for companies to transition from ad-hoc experiments to comprehensive, enterprise-wide AI strategies.
As McKinsey emphasizes, organizations that cultivate a genuine, transformative approach to AI can unlock competitive advantages that fundamentally reshape business models, cost structures, and revenue streams. To bridge the AI value gap, businesses must consider a multi-faceted strategy that encompasses leadership alignment, incentivization, governance frameworks, and outcome measurement.
Practical Considerations for Closing the AI Value Gap
In this blog post, we explore essential implementation considerations for driving meaningful AI transformation across organizations.
1. Incorporate Leadership Across Functions
AI transformation is not solely the domain of technical leaders; it requires a unified approach featuring leadership from multiple business functions. Involving roles like Chief Revenue Officers and line-of-business leaders from the outset ensures alignment between AI initiatives and organizational goals. For instance, a global institutional investment organization successfully launched a dedicated data and AI organization by engaging all leadership levels early in the process. This collective focus not only enhanced product development but also facilitated better customer service and monetization of data assets.
2. Redesign Incentives for AI Adoption
To foster an AI-driven culture, organizations must shift their focus from theoretical interest to practical application. Restructuring performance management frameworks to prioritize measurable outcomes tied to AI adoption is critical. One organization created standardized definitions for business processes and performance metrics, successfully encouraging leaders to prioritize AI-powered operational models over traditional processes.
3. Prioritize People and HR’s Role in Transformation
HR plays a pivotal role in aligning organizational culture and talent with AI objectives. By fostering AI fluency and enabling executive training, HR can help alleviate employee concerns while cultivating enthusiasm for new initiatives. An example comes from a financial institution that empowered business leaders through strategic HR partnerships, facilitating the adoption of a transformative product operating model.
4. Establish Governance Frameworks for Rapid Innovation
From the outset, it is essential to develop governance frameworks that balance compliance with innovation. A three-layered governance approach allows organizations to maintain oversight while facilitating agile AI delivery. By creating clear approval paths and escalating procedures, organizations can ensure compliance without stifling innovation.
5. Engage the Right Partners for AI Success
Collaboration with knowledgeable partners can greatly enhance an organization’s AI transformation journey. Organizations are more successful when they engage partners who offer industry expertise, technical skills, and guidance on cultural shifts. An example includes a global insurance company that leveraged an AI transformation partner to establish sustainable capabilities and develop governance models for enterprise-wide AI deployment.
6. Focus on Tracking Meaningful Outcomes
AI investments should not be measured solely by traditional cost predictions, as the rapidly evolving nature of AI can complicate this. Instead, organizations should anchor their metrics in measurable business outcomes. A marketing team that implemented generative AI for content creation successfully tracked ROI through reductions in localization errors and increased production speed, showcasing the tangible business value of their investment.
Conclusion
Becoming an AI-first organization calls for a synchronized transformation that encompasses seven critical dimensions: vision and strategy, business process redesign, culture and change management, infrastructure, skills development, governance, and industrialization. Technology alone will yield marginal results; however, when harmonized with organizational change, it can create substantial business value.
The AWS Customer Success Center of Excellence is committed to helping organizations navigate this journey, offering tailored strategies that embed AI holistically across operations and business processes. For further insights on becoming an AI-first organization, connect with your AWS account team or explore resources on the AWS Artificial Intelligence blog.
About the Authors
Bhargs Srivathsan, Sergio Klarreich, and Joseph Badalamenti lead AI initiatives at AWS, driving customer success and facilitating transformation that realizes measurable business outcomes. Their collective expertise spans diverse industries, ensuring organizations harness AI’s potential effectively and sustainably.