The question weighing on the minds of most founders and CEOs we talk to today isn’t whether AI will reshape your business—it’s how you’ll lead through it. While the technology is powerful, it’s leadership that turns potential into progress.

So, what does it mean to lead an AI-first company? It’s not about turning your organization into a research lab or replacing employees with bots. It’s about creating a culture where AI is embedded into how you work, think, develop, and compete.

It’s about leading less like a technologist and more like an architect.

Recognizing how quickly the pace of AI innovation and leadership is evolving, here’s what we’re seeing work today (mid-2025) to design the environment where AI can thrive inside your organization.

Shift the Mindset from Siloed Tools to Strategic Decision Making

Too many companies still treat AI like an isolated project—something the engineering team explores on the side. But real transformation starts when AI becomes the default lens for decision-making.

That means asking:

• “How might AI help us hit this goal?”
• “Could AI automate 50% of this process?”
• “How can we solve this problem differently—with AI in the loop?”

This isn’t about replacing people. It’s about amplifying them—giving your teams capabilities they didn’t have before, whether that’s generating code, summarizing meetings, or personalizing outreach at scale.

At one of our portfolio companies, an AI-powered customer support suite with AI chat bots, automated content suggestions, conversation review, QA and ticket routing, is driving +70% faster first reply times and higher customer satisfaction.

Build the Operating System for AI Leadership

You don’t need a five-year roadmap. You need an executive operating system—a repeatable set of habits and structures that allow your organization to learn, experiment, and grow with AI.

Here’s what that looks like:

1. Model the Behavior Yourself

Start using AI in your own workflows. Use enterprise ChatGPT to draft a board memo or prep interview questions. Analyze data with AI. And talk openly about what works for you and what doesn’t.

When your team sees you engaging directly, it sends a clear message: this isn’t just a tech initiative—it’s a leadership priority.

2. Set Quarterly AI Goals

Push your teams to go beyond curiosity. What’s one real AI use case they’ll test this quarter? Add it to your OKRs. Review progress regularly. Make outcomes measurable. Clear targets help move AI from concept to action.

At Mainsail, we challenged our portfolio of software companies to meet a set of Engineering, Product and Productivity Goals for AI Adoption by the end of 2025, reassuring that they have technical and strategic support from Mainsail AI Labs, our Operations Team and their Boards of Directors to achieve them.

3. Empower Local Experimentation

The power of AI isn’t centralized—it’s distributed. Let teams in HR, finance, operations, and customer success identify their own use cases. Give department heads clear permission to spend time exploring and implementing what fits their workflows.

Another Mainsail portfolio company hosted a one-week innovation sprint where engineers, designers, and product came together to learn, experiment, and push boundaries with AI tools while trying to solve real problems for their customers and teams. This effort led to a significant uptick in internal adoption, three new products shipped, and a mindset shift for the AI-reluctant to AI excitement.

Before trialing and implementing new AI tools, however, be sure to review the Terms & Conditions for each tool, engage legal counsel to assess IP and other risks, and set guardrails around what information they can input.

4. Create Lightweight Structures

You don’t need heavy process. Just enough scaffolding to share what’s working. That could be:

• A small cross-functional AI council to surface risks and coordinate efforts.
• A Slack channel for AI wins and prompt hacks.
• A shared doc for tracking experiments and learnings.

Boostlingo took the AI council idea further and worked with Mainsail to create an AI Advisory Board to guide the responsible and impactful integration of AI development into its product offerings.

5. Celebrate Wins Early and Often

Whether it’s a prototype that saves 10 minutes or a chatbot that cuts CX response time, make it visible. We are sharing wins and case studies of all sizes into our all-hands meetings at Mainsail as well as at our cross-portfolio summits and online syncs. These early successes build confidence and momentum—and they lower the barrier for others to join in.

6. Budget Time and Resources

You can’t build an AI-first culture off the side of someone’s desk. Allocate real budget and more importantly, time—for learning, piloting, and scaling promising experiments. That sends a message: this matters.

Handle the Human Element Thoughtfully

As AI adoption grows, so does uncertainty.

Some of your team will feel overwhelmed or worried about their role in an AI-enabled world. Address that directly. Reframe AI as an Ironman suit, not a pink slip. Make clear it’s about augmentation, not automation. And that it’s OK to fail.

Support this with:

• Targeted training (e.g. one-hour workshops on prompt writing)
• Peer-led sharing (highlight early adopters as internal mentors)
• Low-stakes exploration (host mini hackathons or internal demo hours)

The culture you create around AI is just as important as the tools you choose.

Final Takeaway: Start Small, Lead Loudly

You don’t need to be the deepest AI expert in the room. But you do need to create the conditions for discovery.

Start using the tools. Empower your teams. Set the tone. Celebrate learning. And build just enough structure to keep momentum moving.

So, ask yourself: What’s one AI experiment you can sponsor—or start—this week? It doesn’t have to be big. But it does need to start. Because when your team sees you lean in, they will too. And that’s when real transformation begins.

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