AI STRATEGY
A
s CIO of Workiva, Kim Huffman is helping shape how enterprise technology enables smarter, datadriven decision-making at scale. Heading up the company’ s business technology organisation – encompassing business systems, architecture, data and analytics, PMO and operations – she brings more than two decades of experience building high-performing technology teams that drive transformation.
With 85 % of the Fortune 1000 using Workiva’ s AI-powered platform to centralise data for global sustainability reporting, Kim’ s role combines AI, governance and wider business strategy.
Here, she discusses technology leadership, enterprise AI adoption and empowering teams to deliver lasting customer value.
Q. AS REGULATORY FRAMEWORKS TAKE EFFECT, HOW SHOULD ORGANISATIONS FUTURE-PROOF THEIR AI COMPLIANCE STRATEGIES?
» As regulatory requirements evolve, the need to communicate and implement changes across the organisation increases. This is especially challenging for global companies navigating differing departmental priorities and regional regulations.
Audit and risk teams are now expected to provide real-time, data-driven insights to boards, executives and investors. Yet most compliance strategies are still built on tools and operating models designed for a very different era.
Now is the time for businesses to adopt new reporting technologies and procedures that support compliance teams in meeting global legislation and disclosure obligations. Businesses must move away from manual, disjointed tools such as spreadsheets and email in favour of unified, cloud-based platforms that can keep pace with AI.
Unlike traditional processes, AI can continuously analyse risk, control, audit and compliance data across the organisation. A key benefit we are seeing is that time saved from manual tasks can then be reinvested into deep data analysis and root-cause identification, empowering teams to make informed decisions.
Q. WHAT ARE THE BIGGEST CHALLENGES COMPANIES FACE WHEN ALIGNING AI DEVELOPMENT WITH REGULATORY STANDARDS?
» Audit and risk managers face an increasingly dynamic risk landscape, with challenges ranging from data security and cybersecurity to geopolitics. Aligning AI development with regulatory standards poses complex internal and external hurdles, primarily driven by technical debt, complex threat landscape and resource scarcity. One of the most significant challenges to AI integration stems from antiquated processes and a history of underinvestment in the application and data infrastructure needed to support transformation.
88 August 2026