Are you building AI into your disclosure processes? This is a question you should be asking yourself in this new year. As noted in this Workiva report entitled “2026 Predictions for Finance, Risk, and Sustainability,” a company noted it cut its internal audit processes by more than 50% using AI capabilities built into Workiva’s platform. It will be interesting to see what develops this year.
Here’s an excerpt from the Workiva report: “This is the high-stakes, dual-edged nature of AI: the enormous potential of AI-driven outcomes versus the peril of costly mistakes stemming from a lack of focus and fragmented data. General-purpose tools rely entirely on the quality of the data they are fed. When that data is unstructured, poorly governed, or sitting in silos, acting on it only offers a more efficient path to waste and inaccuracy.
Despite a rush of $30 billion to $40 billion in enterprise spending, a report by MIT suggests, 95% of enterprise generative AI pilots are failing, delivering an unsustainable return on investment. In contrast, Gartner estimates specialized tools are four times more cost-efficient than large language models and predicts that more than 60% of enterprise AI models will be domain-specific by 2028. We are already seeing this shift benefit Workiva customers. A global FORTUNE 100® financial services company recently cut internal audit timelines by more than half while using embedded, role-specific AI capabilities built directly into the Workiva platform.
In 2026, the temptation to chase every generative AI trend will give way to a practical approach grounded in defined outcomes, governed data, and the application of AI in and alongside core business processes. Governing innovation isn’t about stopping progress—its about balancing opportunity and risk. The competitive advantage won t go to the company that spends the most on generic solutions but to the one that executes the most rigorous and targeted deployment of fit-for-purpose AI capabilities across the enterprise.”
