Artificial intelligence continues to dominate business conversations, but many organizations are still asking the same questions:
- Where should we start?
- What use cases deliver the greatest value?
- How do we encourage adoption?
- How do we create business impact instead of simply implementing new technology?
These topics were at the center of Katalyst Group’s recent Fireside Chat, AI-Based Transformation in the Finance Function: From Insight to Impact, where finance and transformation leaders from several organizations shared practical experiences, lessons learned, and real-world examples of AI adoption in action.
Panelists pictured: Fernanda Noronha, Shree Mukhopadhyaya, Brent Crawford, Chari Graham, and Kevin Kaye
The conversation moved beyond AI headlines and focused on what organizations are doing today to create value, drive adoption, and prepare their teams for the future.
While each organization’s approach differed, several common themes emerged.
1. Transformation Is More Than Automation
One of the most compelling ideas from the discussion was:
“Automation gives you time. Transformation creates value.”
Organizations often focus on AI’s ability to automate repetitive tasks, reduce manual effort, and improve efficiency.
Those benefits are real, but they represent only the beginning of the opportunity.
True transformation occurs when organizations use that newfound capacity to redesign processes, improve decision-making, generate insights, and create measurable business value.
The goal isn’t simply doing the same work faster. It’s doing better work.
2. Start with Strategy, Not Technology
One of the most common mistakes organizations make is beginning with a tool instead of a business objective.
Successful AI initiatives typically start by answering questions such as:
- What problem are we trying to solve?
- What outcome are we trying to achieve?
- How will we create value from the capacity AI creates?
Technology should enable the strategy, not define it.
Organizations that begin with clear business goals are often better positioned to evaluate solutions, measure success, and drive adoption.
3. Culture Can Accelerate or Limit Adoption
The discussion highlighted that AI adoption varies significantly from one organization to another.
Some organizations have cultures rooted in continuous improvement and experimentation. Others operate in more traditional environments where change occurs more gradually.
Regardless of culture, successful implementation requires:
- Executive sponsorship
- Clear communication
- Employee engagement
- Opportunities to learn and experiment
- A willingness to challenge existing processes
Technology alone does not drive transformation. People do.
4. AI Champions Make a Difference
Many successful organizations are identifying AI champions, super users, and change agents who help drive adoption across teams to:
- Test new capabilities
- Share best practices
- Support peers
- Surface new opportunities
- Help reduce resistance to change
Creating a network of advocates often proves more effective than relying solely on top-down mandates.
Employees are more likely to embrace change when they can learn from trusted colleagues who are using the technology in their daily work.
5. Start Small and Learn Quickly
Several leaders shared examples of successful AI projects that began as relatively small, low-risk initiatives.
Examples included:
- Contract redlining
- Lease agreement analysis
- Reconciliations and exception management
- Workflow automation
- AI-powered agents
Rather than attempting large-scale transformations immediately, these organizations focused on learning, building confidence, and demonstrating value.
Small wins often create momentum for broader adoption.
6. Data Readiness Remains Critical
One recurring theme was that many AI challenges are not technology challenges.
They’re data challenges.
Organizations frequently struggle with:
- Disconnected systems
- Inconsistent data sources
- Limited visibility across departments
- Incomplete information
AI tools become significantly more valuable when organizations have a strong foundation of accessible, reliable, and well-organized data.
Before pursuing increasingly sophisticated AI initiatives, many companies are focusing on improving data quality and accessibility.
7. AI Should Elevate People, Not Replace Them
Perhaps the most optimistic theme of the evening centered on the role of people.
AI’s greatest value may not be in replacing professionals but in enabling them to contribute at a higher level.
As routine tasks become more automated, finance professionals can spend more time on:
- Analysis
- Strategic decision-making
- Business partnership
- Problem solving
- Stakeholder engagement
Several panelists described AI as a “thought partner” that helps employees move from being primarily doers to becoming reviewers, advisors, and leaders.
The future belongs to organizations that use AI to enhance human capabilities, not simply reduce tasks.
Continuing the Conversation
One of the most valuable aspects of the evening was hearing how organizations in different industries and at different stages of AI adoption are approaching many of the same challenges. While tools and use cases may vary, the panelists consistently emphasized the importance of strategy, culture, leadership, and continuous learning.
The conversation reinforced an important reality: successful AI adoption is not primarily a technology initiative.
It is a leadership initiative.
Organizations that combine clear strategy, strong change management, quality data, and a culture of continuous improvement will be best positioned to create lasting value from AI.
The technology will continue to evolve rapidly. The organizations that benefit most will be those that remain focused on outcomes, embrace learning, and empower their people to adapt and grow alongside the tools.
Thank you to our host Lockton and panelists and attendees for contributing to such a thoughtful discussion. We look forward to continuing the conversation as organizations navigate the opportunities and challenges presented by AI.