As CIO at Workiva, Kim Huffman leads the company’s internal technology, security, and data initiatives. With over two decades of experience in technology leadership, Huffman has seen the CIO role evolve from order taker to strategic business partner.
Workiva, a publicly traded SaaS company based in Ames, Iowa, delivers cloud solutions for regulatory, financial, and ESG reporting. In 2024, the company reported $739 million in total revenue.
We spoke with Huffman about her three-pillar framework for evaluating startup partnerships, her strategy for integrating cutting-edge technologies into existing systems, and her structured four-part approach to responsible AI adoption. Her insights reveal how technology leaders can balance innovation with operational stability.
This conversation is part of our “Early Adopters” series that highlights business leaders who are driving digital and AI transformation at major companies. For initiatives to succeed, there must be strong partnerships between Innovation teams and business executives. Through these interviews, we share perspectives from functional leaders who are putting emerging technology into practice.
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Technology Partnerships & Emerging Tech
When evaluating and implementing new technologies, how do you balance quick wins versus long-term transformational change?
The CIO role has evolved dramatically over the past five years — transitioning from order taker to strategic business partner. Success comes from balancing quick wins alongside initiatives that drive long-term growth and transformation. This requires strategic prioritization and a deep understanding of business processes, which can only be achieved through close collaboration with business partners. When evaluating technologies, I focus on how vendors solve specific business outcomes rather than simply implementing tools.
What characterizes your most successful startup-enterprise technology partnerships?
When evaluating startup partnerships, I look for three critical elements. First is measurable value — not just promised capabilities, but demonstrated outcomes we can validate.
Second is scalability. The most successful partnerships involve solutions with inherent flexibility that extend beyond single use cases. For AI solutions specifically, we prioritize technologies that can be deployed across multiple functions and teams, maximizing enterprise-wide impact.
Third, it’s about the relationship and trust. Does the startup want to be a trusted partner? There are often issues or challenges in deployment with any new vendor. It’s important to establish trust that the startup will be a partner to us as we encounter those challenges.
You’ve written in the past that “collaboration loses its effectiveness when each team is playing with different equipment.” How do you balance experimenting with cutting-edge startup solutions while maintaining a cohesive technology strategy?
I’m passionate about the technology space because it’s always changing. Curiosity and constant learning are essential as a technology leader. You must embrace innovation and disruption while ensuring reliable support for daily business operations.
Curiosity and constant learning are essential as a technology leader. You must embrace innovation and disruption while ensuring reliable support for daily business operations.
To stay informed, I participate in advisory boards, go to conferences, and tap into the VC community, where they’re funding hundreds of seed-stage companies. These activities help me understand where the market is going. I make it a priority to keep one foot in what’s new and rising in the startup ecosystem, and also how to thread that into our existing tech portfolio.
Of course, I approach this exploration with security and responsible usage as guiding principles. But frankly, any technology leader who isn’t actively monitoring emerging and disruptive technologies is doing their organization a disservice.
Strategic CIO Role
How do you measure the business impact of your technology initiatives?
The technology team’s OKRs must directly align with organizational objectives. To build strong business partnerships and drive meaningful outcomes, we create joint OKRs across business and technology functions. I maintain shared OKRs and outcome metrics with all my business partners.
We ensure every technology investment clearly connects to tangible business value and strategic priorities. For instance, if our goal is to increase partner channel conversion rates, that becomes a shared objective between myself and our VP of Partners and Channels.
Shared goals and OKRs across the business and the technology organizations are critical to the company’s long-term growth and success. After all, there’s a technology component to most business changes today.
How do you approach cross-functional collaboration when implementing new technologies?
Partnership between technology leaders and business partners is built on trust, communication, collaboration, and open-mindedness. We’ve definitively moved beyond the outdated model where IT and business operate in separate silos, tossing requirements “over the fence.” To drive meaningful tech-enabled business transformation, you must develop strong relationships. Collaboration isn’t just preferred — it’s non-negotiable.
Delivering quick wins helps establish credibility and demonstrates what effective execution looks like. Additionally, when problems arise, transparency and open communication are essential, regardless of whether the issue originated on the business or technology side.
I dedicate significant time to nurturing these business partnerships, maintaining weekly meetings with all key business stakeholders across the organization. It’s vital for technology leaders to recognize they have a seat at the table.
You’ve written previously that “there’s substantial value at stake” with automation, yet less than 20% of companies have scaled it across their business. What obstacles have you encountered when implementing enterprise-wide automation initiatives?
The biggest obstacle many CIOs encounter when implementing end-to-end enterprise automation is data quality. When scaling automation initiatives, the data layer — both its quality and governance — becomes critical.
In many cases, automation efforts falter or are deemed unsuccessful because of poor data quality or inconsistencies in the data structure. This becomes even more evident with AI implementations, where the adage holds true: a strong AI strategy must be built upon a strong data foundation.
As we continue adopting AI technologies, these data challenges will only become more pronounced. Organizations need to recognize that data quality is absolutely essential to overcoming the hurdles of enterprise-wide automation and enabling smooth end-to-end data flows. Without addressing these foundational data issues, companies will continue to struggle with scaling automation successfully across their operations.
AI and Future Technologies
Where have you seen GenAI deliver the most immediate impact in your organization? In what areas has it been less beneficial than expected?
The most immediate benefit we’ve seen is in employee productivity. By embedding GenAI capabilities into platforms our teams already use or introducing targeted standalone tools, we’ve accelerated previously time-consuming, low-value tasks. This productivity boost has enhanced our employees’ day-to-day work across the organization.
While these initial gains came quickly, I believe the next phase will focus on delivering deeper value. We need to continue iterating as we expand beyond the productivity layer and integrate more deeply into specific functions.
As for disappointments, implementation is challenging. When testing new technology, we enter with a value hypothesis, ROI projections, and a business case. When we don’t see the expected value realized, we need to identify the root cause: Is it the technology itself? The implementation approach? Or something else entirely?
GenAI adoption represents a new kind of change management challenge. Sometimes employees have concerns about job security, creating a ‘fear of obsolescence’ that drives lower adoption rates, especially in today’s market. When value falls short, we must determine whether it’s the technology, our deployment strategy, internal communications, change management approach, or underlying employee concerns.
GenAI adoption represents a new kind of change management challenge… When value falls short, we must determine whether it’s the technology, our deployment strategy, internal communications, change management approach, or underlying employee concerns.
At Workiva, we’ve been very intentional about positioning AI as a tool to enhance our employees’ capabilities and improve their day-to-day work, not replace them.
How are you implementing a thoughtful approach to AI adoption while balancing potential risks around data privacy, copyright, and regulatory concerns?
As technology leaders today, we must balance innovation and AI opportunities with risk management and compliance. We’ve developed an AI program built around four key pillars.
First is literacy and awareness. We ensure everyone understands how AI works, the associated risks, the implications of sending data to models, why sensitive data must remain within our organization, and how to use AI responsibly.
Second is use case identification and ROI evaluation. We’ve established a formal assessment program where cross-functional teams — including business stakeholders and technical specialists — evaluate potential applications. They examine the technology, security requirements, and data usage considerations.
Third is deployment, and fourth is scaling. This involves driving adoption while maintaining safe and responsible usage throughout the organization.
The key is creating a structured framework to manage AI implementation internally. At each stage, we emphasize awareness, literacy, risk assessment, and compliance. We aim to move fast, but we also want to be thoughtful, pragmatic, and safe.
Looking beyond GenAI, what emerging technologies do you believe will be most transformative for compliance and reporting over the next 3-5 years?
We’ll see a natural evolution from current GenAI capabilities toward agentic AI — a transition that’s already beginning to emerge. This maturity progression will lead to active assistants that work alongside employees daily, augmenting their capabilities.
The key questions become: How is this technology developed? What implications does it have for the workforce? And critically for compliance and reporting functions, how do we ensure it remains secure and safe? I anticipate we’ll move from productivity enhancement to intelligent assistance and eventually to standalone agents. This trajectory will transform many industries.
Sales and go-to-market functions will likely experience significant disruption from these technologies. Legal could be another major area of impact. At Workiva, we’re building intelligent assistance directly into our product offerings. While we’ll never replace a CFO with an AI agent, we can dramatically accelerate the work of SEC reporting managers or compliance professionals — reducing their routine workload by 10-15 hours weekly and enabling them to focus on strategic decision-making and higher-value activities.
Paulina Karpis leads Early-Stage Platform for B Capital, a global multistage venture firm investing in B2B startups.
Photo by Walter Frehner on Unsplash
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