From productivity to process reimagination: tracking AI’s trajectory

If you’ve glanced at a recent financial report or tuned in to an earnings call this year, you’re well aware that companies are eagerly implementing artificial intelligence (AI) tools. While the adoption of AI and generative AI (GenAI) is also mentioned to signify that organizations are embracing innovation, the returns on these investments have been a mixed bag so far – a combination of value and hype.

The challenge resides in developing a more measured and disciplined approach to its adoption and usage, one that demonstrates incremental value. While the excitement surrounding artificial intelligence is abundant – and warranted – the technology’s long-term success will result from the careful, systematic implementation of AI solutions along with clear evidence of each solution’s value to customers and other stakeholders. AI “winners” will balance that excitement with a thoughtful strategy: these organizations will adapt to and manage its complexities and benefits without succumbing to hype.

As I’ve mentioned, Vertex is committed to a thoughtful approach to AI adoption and development. One of the most astute ways to integrate AI into an organization is by implementing use cases, evaluating the results, making adjustments and then developing better use cases. Measurement is a crucial part of that approach. To date, most companies have been measuring AI’s value based on productivity improvements. Productivity gains can be identified by quantitative metrics (e.g., time saved per task) or qualitative improvements (e.g. via user surveys). Forrester helpfully defines several types of productivity boosts that AI can deliver. These include:

• Labor time savings;
• Increases intrinsic motivation due to user experience; and
• The reworking, or reshaping, of workflows.

That taxonomy helps drive home the fact that productivity gains can be much broader than simply spending less time on a specific job. The final characteristic also points to where AI’s evolution is headed – toward process re-engineering.

A McKinsey report suggests that GenAI agents may soon help organizations reimagine traditional workflows by acting as “skilled virtual coworkers, working with humans in a seamless and natural manner.” For example, the traditional loan underwriting process could be reengineered by deploying a collection of individual AI agents that each handle a specialized task-based role (e.g., handling communications between borrowers and banks, completing documents, examining debt from cashflow statements, etc.): “A human user would initiate the process by using natural language to provide a high-level work plan of tasks with specific rules, standards, and conditions,” according to McKinsey. “Then this team of [AI] agents would break down the work into executable subtasks.”

Many processes throughout organizations, and their tax groups, are hampered by bottlenecks. What happens when AI and related technologies remove those bottlenecks? The answer is that it opens the door for business leaders and managers to reimagine those processes. Within tax groups, this type of rethinking will give a major boost to tax transformation initiatives.