Most AI initiatives stall before they start. Not because of lack of ambition, but because they begin in the wrong place.
At Slingshot, we’ve seen the difference between companies that chase AI and those that apply it with purpose. The leaders getting traction are not treating AI as a feature, but are using it to solve real problems, accelerate product thinking, and empower the right people to move quickly and learn fast.
If you’re looking to make AI part of your strategy, here are four principles that move teams from exploration to execution.
Summary
The most successful AI efforts don’t start with models; they start with problems. By focusing on real needs, empowering small teams, and using AI to speed up product thinking, companies can move faster, learn more, and build smarter.
Start with a Problem, Not a Model
There’s a growing misconception in the market that AI itself is the product; it’s not. AI is a capability, not a solution. If your team starts with the technology instead of the problem, you’re likely to end up with an expensive prototype that never ships.
“There’s this misconception that once you have an LLM or something with machine learning, that’s it. It’s done,” said Sarah Bhatia, Product Strategy at Slingshot. “What makes a really successful AI product is the surrounding infrastructure and the experience around that.”
This misconception is everywhere. Leaders often feel pressure to “add AI” to an existing product or launch an AI project just to keep up. But that approach is backward. AI is not the starting point; it should solve a clear and specific problem.
Take Slingshot’s CivicPulse hackathon we did with the Center for Neighborhoods. They needed to navigate complex zoning data and local legislation. By feeding messy legislative records into a large language model, the team used AI to summarize and organize that data by neighborhood and category. The result wasn’t ‘an AI product.’ It was a usable tool that helped people stay informed about their communities.
That is the point: AI was not the destination. It was the engine behind the solution. If your team cannot define the job AI is supposed to do, you are not ready to build.
Empower a Small, Curious Team
You don’t need a company-wide initiative to get started. You need a focused, motivated group that has the space to explore and the trust to fail.
“Start by empowering a small and curious group and giving them that room to experiment,” said Sarah Bhatia. “Let them lead that conversation for your business.”
Slingshot teams have seen strong momentum by pulling together lightweight design sprints, internal hackathons, and guided brainstorming sessions with cross-functional teams. These short bursts of focused time, often just a few days, can lead to working concepts that are immediately testable.
This smaller team can also surface internal champions. These are the people who understand both the business and the tech and who are excited to push forward. They are often not the ones with “AI” in their title. They are product managers, designers, engineers, and marketers who identify opportunities and capitalize on them.
At Slingshot, we’ve added AI engagement to our employee review process. Team members are now expected to explain how they’ve experimented with AI and how it’s improving their workflows. That kind of cultural reinforcement matters.
Chris Howard, Slingshot’s CIO, made the point clearly: “If you have no time carved out for your team to learn and explore AI, then you probably are not advancing.”
Use AI to Accelerate Product Thinking
AI does not only belong in the product; it belongs in the way you build products.
Think about your current product development lifecycle: User research, idea validation, competitive analysis, prototyping, and testing. All those steps take time and coordination. AI can compress those cycles and improve the quality of output.
“There’s a lot of value in the speed we’re able to iterate,” said Sarah Bhatia. “Not just from a cost perspective, but in how it lets us test and improve faster.” That speed gives teams the ability to explore more ideas in less time, increasing the chances of uncovering stronger, more effective solutions.
Let’s break down some examples of how AI changes product thinking, not just production:
- Legacy data becomes an asset. “These legacy products are sitting on a gold mine of data,” said Chris Howard. “You could feed years of usage data into AI and start to explore possibilities.”
- Rapid iteration saves budget. Instead of waiting weeks for research findings, teams can use AI to simulate customer personas, analyze open-text feedback, or storyboard ideas in hours.
- Faster feedback loops. During Slingshot’s internal hackathon, teams combined design and development roles into a single sprint. The feedback loop was immediate. The result was a validated proof of concept in under 24 hours.
- AI tools inside your stack. From Postman to Figma, modern product tools increasingly include embedded AI features. The right ones can help your team move from concept to test with less friction and greater insight.
AI Implementation is not just about speed. It’s about increasing the volume of good ideas, the velocity of testing, and the likelihood of shipping something people want.
Win Small Before You Scale
You don’t need to bet the company on your first AI initiative.
“If you’re a CEO trying to figure out AI, maybe you don’t start with integrating it into your product,” said Chris Howard. “Maybe you start with how you do marketing or back-office activities.”
Smaller, internal use cases are easier to manage and lower risk. They create momentum without creating noise. They also give your team a chance to learn before taking on customer-facing products.
Doug Compton, Principal Developer at Slingshot, added: “These tools are the worst they’re going to be. They’re only getting better.”
The biggest threat isn’t failure, but inertia. And the second biggest threat is wasting time on a vague initiative that never delivers results.
“Not having success metrics is a big issue,” said Sarah Bhatia. “There still needs to be KPIs, just like any other product.”
Whether you’re improving response times, reducing manual work, or enhancing a user experience, define and build around how you will measure success.
Bonus: Remove the Fear Factor
One of the most common roadblocks to real AI adoption is not technical problems, but emotional ones.
“People are afraid of it,” said Doug Compton. “You have to get your employees used to AI without being fearful of it. Help them see how it can make their jobs easier.”
That mindset starts with leadership. CEOs should be vocal and visible in their approach to adopting AI within their organizations. Make it clear this is not a headcount reduction strategy, but a growth strategy.
AI does not replace people. It enables your team to work smarter, test faster, and explore ideas that used to take months. It helps existing teams move into more strategic roles while automating repeatable tasks.
Companies that embraced earlier waves of change (whether cloud, mobile, or agile) did not shrink; they expanded.
AI is no different: the winners will not be the largest companies, but those that move with purpose.
Final Thought: Strategy Before Speed
Yes, AI can help your company run more efficiently. But speed without strategy is just wasted energy.
If your organization is considering AI adoption, here are the questions every CEO should be asking:
- What problem are we trying to solve?
- Who is accountable for driving it forward?
- What existing data or workflows can we use?
- How will we measure success?
- What is the smallest possible win we can ship fast?
Get those answers right, and the rest will follow.
The companies succeeding in AI are not the loudest; they are the clearest. They start with focused problems, equip small empowered teams, measure results, and expand from there.
Want to learn more about AI?
Written by: Savannah Cherry
Savannah is our one-woman marketing department. She posts, writes, and creates all things Slingshot. While she may not be making software for you, she does have a minor in Computer Information Systems. We’d call her the opposite of a procrastinator: she can’t rest until all her work is done. She loves playing her switch and meal-prepping.
Edited by: Chris Howard
Chris has been in the technology space for over 20 years, including being Slingshot’s CIO since 2017. He specializes in lean UX design, technology leadership, and new tech with a focus on AI. He’s currently involved in several AI-focused projects within Slingshot.
Expert: Sarah Bhatia
Sarah Bhatia excels at bringing people together with over a decade of experience in product and collaboration. She focuses on cross-functional teamwork, strategic development, and solving big problems. A self-described daredevil, she isn’t afraid to ask questions and take calculated risks.
Expert: Doug Compton
Doug, born and raised in Louisville, discovered his passion for technology at age 11 by writing computer games. This hobby evolved into a career. His diverse interests include snorkeling, science, WWII history, and real estate, and he uses his free time to develop new mobile and web applications.
