Your AI Success Depends On Mastering The AI Value Chain

A key challenge to ROI success with AI is that most organizations still struggle to convert AI investment into measurable business value. To win with AI, organizations must create value that is relevant to customer needs. This means that:

• Simply deploying more powerful AI models or infrastructure will not create value. Tech and business leaders need to understand where their organization sits in the AI value chain, where value is accumulating, and how they can strengthen their relative position.
• AI transformation is a continuous change initiative. The AI value chain is changing continuously as tech vendors enter each others’ areas of activity, thus blurring the boundaries of value creation.

AI Models Are Just One Input To Drive Value

Most AI models are undoubtedly generating very impressive results. However, AI models have their limits. To drive measurable business value, business and tech leaders need to:

• Treat AI transformation as a holistic change initiative. Even the best model cannot generate much value for the customer if data quality is poor, workflows remain disconnected, employees do not trust the technology, governance creates friction, costs are excessive, or underlying business processes remain unchanged.
• Understand the role of their organization in the AI value chain. True competitive advantage from AI depends on how effectively business leaders connect proprietary data, computing resources, industry models, applications, workflows, and human expertise to drive customer experiences and efficiencies.

Complexity Means That AI Returns Can Take Years To Materialize

AI initiatives are rarely standalone technology projects. AI leaders must first define the desired business outcomes and then determine how AI, processes, data, and human judgment can combine to support these business outcomes. This means that AI initiatives are:

• Part of complex transformation initiatives that go beyond tech rollouts. They often require improvements in data, operating models, employee capabilities, workflows, governance, and business models.
• Exposed to longer time horizons for roadmap development. Complexity means that ROI from AI use cases can take far longer to materialize than the typical payback period expected from simple conventional technology investments.

IT Service Providers Play A Key Role In Boosting AI Value Creation

The strategic question for AI leaders, therefore, is no longer just how to work with foundation models. The question is increasingly how to manage the emerging multi-model AI stack with legacy tech and other AI components. Tech and business leaders increasingly look to IT service providers for help because IT service providers:

• Bring critical competencies to tackle AI comprehensively. They bring competencies regarding sector trends, process redesign, systems integration, governance, cybersecurity, and change management capabilities.
• Understand how to navigate ecosystem orchestration. They help align models with workflows, regulatory requirements, and human decision-making. This way they help enterprises move from AI experimentation to secure, compliant, and repeatable operational AI deployments.
• Pinpoint customer value creation in end-to-end, AI-powered offerings. The strategic question is not simply Which AI technology should we buy? It is: Where in the AI value chain can we create, capture, and defend the greatest customer value?

PAC Can Offer Help With Navigating The AI Value Chain

PAC’s report Master The AI Value Chain To Maximize Customer Value helps tech and IT leaders as well as AI vendors explore the strategic questions of where value is created, captured, controlled, and accumulated in the AI value chain.