Deciding Between AI-Enhanced and AI-First Business Processes

The business world is buzzing with AI experimentation. However, a structured approach for moving from experimentation to production, although necessary, is often lacking, which hinders AI’s transformative potential. This article introduces a decision-support framework, implemented as a decision tree, to help executives mitigate the risks associated with decisions relating to moving AI experiments to production, focusing on AI-enhanced vs AI-first business processes.

Introduction: The Need for a Structured AI Strategy

This is a pivotal year for AI in the enterprise. The pace of technology advances remains blistering. Cross-industry surveys (here and here) show that enterprises are experimenting with AI at an unprecedented level. The outcome of these experiments will determine whether AI finally becomes the transformative force envisioned since the 1950s, or remains a much-promising technology.

AI experimentation in the enterprise is often driven by technology groups that are testing the technology’s feasibility to address a stated problem. It lacks the decision-making rigor required for enterprise transformation. Based on our years of experience applying AI to address enterprise challenges, we recommend that business units exploring AI’s potential in their operations undertake five actions:

1. Set the innovation horizon. Ensure this period is sufficient for experimentation, solution development, deployment, and evaluation of outcomes.
2. Align the AI strategy. Ensure the business unit’s AI strategy is consistent with the innovation horizon and the overall corporate AI strategy.
3. Avoid common pitfalls. Resist the urge to start too many experiments or to experiment for too long before deciding how to proceed.
4. Select and categorize processes. Choose the business processes for experimentation and determine whether to enhance them with AI or redesign them as AI-first.
5. Lead a collaborative effort. Form a close partnership with HR and IT organizations, but lead the overall AI effort. The collaboration of these three groups enables AI adoption in ways that no single function can accomplish alone.

We translated our experience that AI requires experimentation before scaling into a framework to help decision-makers in a structured manner, and de-risk these actions. In this article, we focus on the last two of the actions (process selection and collaborative organizational execution).

A Decision-Support Framework for AI Transformation

We implemented our framework as a decision tree. This implementation:

• Reflects real-world complexity. AI adoption is not a single decision but a sequence of interdependent ones. Each node in the tree captures a critical juncture informed by cross-functional inputs.
• Provides structure. The tree is logical and repeatable, while remaining flexible enough to serve use cases from finance to manufacturing.
• Promotes organizational alignment. With clearly defined decision points, it fosters a shared language among business, IT, and HR leaders.
• Scales and is actionable. Each node is supported by detailed content that can be customized to specific domains or regulatory constraints.

From Experimentation to Productization

One of the decisions in the tree involves identifying the business process experiments set. This set should include processes where the use of AI may (remember that we’re starting with hypotheses) significantly improve productivity, increase operational efficiency, or enhance customer experience. Selected processes must offer the opportunity to measure AI’s contribution, rely on data that is available or acquirable, and be situated in operational environments that allow for experimentation without unacceptable risk.

The experimentation’s results enable the business organization to determine which of the tested processes should transition to the productization set. These are the business processes where AI proved its value, the technical and organizational conditions for scaling exist, and there is a clear path to integration or redesign. Before embarking on productization, each process in this set must then be classified as one that can be AI-enhanced (infused with AI but maintaining its existing structure) or become AI-first (redesigned from the ground up with AI at the core).

AI-Enhanced vs. AI-First: A Practical Example

To make this distinction more concrete, consider the core finance operation of paying a vendor. This is a well-understood, standardized workflow (which we have further simplified for this article) that provides a useful lens through which to evaluate AI-enhanced versus AI-first transformation.

Consider the simplest form of the typical vendor payment process:

• Receive invoice
• Verify details
• Match the invoice to the purchase order
• Route for approvals
• Schedule the payment
• Archive the documents

In the AI-enhanced version, the existing process remains largely intact but is improved with AI at key steps:

• Intelligent document processing automatically extracts and validates invoice data.
• AI models detect anomalies between invoices and purchase orders.
• Approvals are routed based on learned behavior and decision history.
• Payment timing is optimized to manage cash flow and capture early discounts.

In contrast, the AI-first version reimagines the process, making AI the process’s engine:

• AI systems proactively monitor issued purchase orders and work logs to validate what payments are due.
• Payments are triggered autonomously, based on system-level validation.
• AI continuously manages vendor relationships through performance tracking, payment optimization, and exception handling.

The Decision Criteria: A Strategic Choice

Deciding whether a process should be AI-enhanced or AI-first is not simply a technical call. It is a strategic decision shaped by business objectives, IT constraints, and workforce capabilities. The table below, whose contents are included in a node of our framework’s decision tree, outlines the logic applied to this decision.

The Power of Cross-Functional Collaboration

The table presented in the previous section illustrates why a closely aligned partnership among the business, IT, and HR functions is essential.

• The business unit assesses strategic significance and the current process’s role in competitive positioning.
• IT evaluates data availability, systems integration, and real-time capabilities to support redesign.
• HR determines whether the talent exists or can be developed to support the envisioned model.

Many of the criteria for determining which processes to include in the experiments set, which to advance into productization, and which to enhance or redesign, cannot be evaluated by the business unit alone. For example, IT is best positioned to assess whether the data infrastructure, system architecture, and latency constraints can support AI-driven execution. HR must assess workforce readiness, change capacity, and whether reskilling or hiring is needed.

Without such collaboration, organizations risk deploying AI systems that are technically infeasible, culturally unsupported, or operationally unsustainable. When this triad—business, IT, and HR—is established early, organizations accelerate alignment, adoption, and long-term success.

Conclusion: A Path to Actionable AI

As AI becomes deeply embedded in enterprise transformation agendas, the decisions surrounding where and how to apply it will define which organizations lead and which lag. Our framework, through its decision tree implementation, provides a structured, cross-functional approach that enables business units to navigate this journey independently or with trusted advisors. By combining experimentation with strategic productization and distinguishing between AI-enhanced and AI-first opportunities, organizations can move decisively from isolated pilots to scaled enterprise impact. The path may be complex, but with the right framework and partnerships in place, it is now actionable.

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