Phase 3

Ongoing transformation leadership.

We can provide leadership within your company to ensure transformation happens

Our Philosophy

Before you launch any AI initiatives, it is critical to have a proper strategic plan that is customized for your company's needs.

Watch the video to learn more from our founder, Steve Kilts.

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Without leadership, nothing changes. We can act as your "Fractional Chief AI Officer" to educate, iterate, and ultimately help make the change happen.

Fractional Chief AI Officer
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Executive level AI leadership to champion AI initiatives. We'll continue to further align strategy, guide adoption, educate, identify areas for further improvement, and ultimately drive transformation across the organization.

Stakeholder Alignment

Team Training

Internal Championing

Quarterly reviews

Iterative Development & Optimization

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If the systems are not being used or showing expected ROI, it is important to understand why and to iterate on the development. This is where the rubber meets the road, without adoption, nothing changes.
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Iterative development

Monitoring & Support

Ongoing tool optimization

Frequently Asked Questions

What does ongoing transformation leadership look like in practice?

It's a process of alignment, decision-making, education, and iteration. We help to ensure individual contributors are getting the most out of the tools, and assist leadership teams turn quarterly insights and feedback into clear next steps. This ensures that the realized ROI keeps evolving alongside capability.

How does the Fractional Chief AI Officer integrate with our existing team?

Much like other fractional officers, we partner with executives, department leads, and internal champions to connect strategy with day-to-day execution and ensure accountability. When operating in this capacity, we ensure the leadership team is aligned with the boots on the ground and help to drive the strategy forward.

How do you measure whether transformation is sustaining?

We track adoption, performance, and ROI against the original roadmap. Each cycle refines metrics and expands successful use cases rather than adding disconnected pilots.

What happens if priorities or leadership change?

Our model is built for adaptation. Quarterly interviews and reviews keep the program aligned with evolving goals, even through turnover or shifting market conditions.

Can the role evolve or taper over time?

Yes. As internal ownership strengthens, the engagement often transitions from active leadership to periodic advisting, keeping accountability without dependency.

How do you decide what to improve after the technical rollout?

We analyze usage data, team feedback, and ROI patterns to pinpoint friction points and high-impact opportunities for optimization. This information can be fed back to our development teams to further improve the tools and increase adoption.

What does a typical iteration cycle look like?

Each cycle runs on an average eight week cycle (depening on the project complexity: prototype → test → deploy -> measure → improve -> scale. The focus is on compounding wins, not isolated projects.

How do you balance experimentation with stability?

We validate improvements in controlled environments before rollout, so innovation accelerates without disrupting live operations.

Who’s involved in the optimization process?

After evaluating usage, ROI, and other key metrics, we are able to determine if the tools are being utilized properly and if the company is not receiving expected benefit. If the team has been educated properly, we will talk to the end users and bring the information back to our team to improve the products.

How does this phase stay relevant as AI evolves?

By continually reassessing tools, models, and workflows to align with business goals. The process itself evolves as capabilities advance.

Still have questions? Get in touch with our team and we'll be happy to answer your questions and provide advice.