AI Reframe: Moving Beyond Tools to Capability Maturity

Pop culture is chock-full of “tools vs. capability” metaphors. For example, light sabers pack plenty of punch in Star Wars movies, but they’re no match for the Force.  Tools are powerful in their own right, but their value is optimized when they’re wielded as part of an overarching capability.

A similar dynamic holds for artificial intelligence (AI) and other advanced automation solutions, including e-invoicing.

A West Monroe report argues that companies on the leading edge of the AI curve manage it as a capability rather than a technology: “Most companies still treat AI as a technology – something employees interact with to prompt, generate, or assist at the edges of their work …Leading organizations take a different approach. They build AI into the core of how work gets done – where it shapes decisions, coordinates actions, and drives measurable results across the business.”

Doing so is not easy, of course, and the report highlights three obstacles organizations frequently encounter on AI-enablement journeys:  

1. Productivity trap: While new AI tools can expedite communications, research and other tasks, deeper components of business process, such as reconciliations, approvals, and exception handling, are often neglected, creating bottlenecks.
2. Limited scalability: Many pilots excel at specific parts of a process but lack the broader context needed to scale effectively across the organization.
3. Fragmentation from discrete pilots: In many companies, standalone pilots have proliferated, resulting in “more integration work, more governance overhead, and a growing stack of point solutions that still fail to fix the underlying issues,” according to West Monroe.

Like other parts of the organization, tax departments face growing pressure to adopt AI as concerns over “AI sticker shock” ripple though C-suites and finance groups in response to price hikes and new pricing models (based on usage) from AI providers.

West Monroe encourages teams adopting AI to determine whether those solutions meet conditions that boost returns on investments and help build a sustainable AI capability. Three requirements are particularly relevant to indirect tax teams:

1. Connecting the right data to AI tools: Ensuring that AI solutions have access to complete and accurate data, as opposed to isolated records, provides the context required to understand entire processes
2. Embedding AI into workflows: This lays the groundwork for AI outputs to trigger actions rather than recommendations that may or may not drive tangible improvements.  Utilizing AI agents effectively will likely be critical.
3. Making AI decisions transparent and traceable: This enables tax groups to trust AI outputs, which is a prerequisite to acting on those outputs.

That last requirement is crucial if tax groups are to leverage the full force of a mature AI capability. “When organizations understand how AI reaches conclusions and can audit its outputs, the technology becomes usable in high stakes environments,” notes Vertex Vice President of Technology Strategy Chris Zangrilli. “That shift is what allows AI to move from promise into practice.”