Building AI solutions that actually deliver
AI is the new shiny tool that every organisation feels compelled to bolt onto their strategy. Boards are asking, “What are you doing about AI?”. Founders are promising investors that they’ll “be AI-driven”. However, AI is not the solution, it’s a tool. The challenge and opportunity is to identify small, high friction workflows where AI can eliminate pain and drive measurable outcomes. Nail those and you’ll unlock compounding value over time.
Building AI solutions that actually deliver
AI is the new shiny tool that every organisation feels compelled to bolt onto their strategy. Boards are asking, “What are you doing about AI?”. Founders are promising investors that they’ll “be AI-driven”. However, AI is not the solution, it’s a tool. The challenge and opportunity is to identify small, high friction workflows where AI can eliminate pain and drive measurable outcomes.
Product teams often fall into the trap of launching into large AI initiatives that attempt to transform either the entire product or business in one go. The problem is that these projects are costly, they are slow to implement and often fail because of their complexity.
The better path is to start small and focus on workflows with the biggest pain points and the highest return on investment. Nail those and you’ll unlock compounding value over time.
Why small beats big in AI product development
AI success is tightly linked to the context in which it’s applied. That makes broad AI-everywhere programs risky. Therefore, small workflows of the kinds of tasks that are repetitive, manual, and clearly defined are fertile ground. They provide three advantages:
- Clear pain points: Everyone feels the friction, whether it’s someone in Sales stuck in CRM admin or a Product Manager drowning in customer feedback.
- Measurable ROI: It’s easier to demonstrate impact when a team saves hours per week or they accelerate sales or product development velocity.
- Faster iteration: Small use cases allow you to experiment, fail fast and make improvements without putting the business at risk.
This start small, prove value and expand later approach is how successful AI solutions scale.
Case study: AI in the sales cycle
Consider the sales journey, from the first touchpoint through to closing a deal. It’s riddled with time-consuming, manual tasks. Here are just a few high ROI opportunities for AI:
- Lead generation: AI can analyse incoming leads and automatically enrich them and then rank them by fit and intent. Sales people waste less time chasing the wrong opportunities.
- Opportunity nurturing: AI can provide guidance and coaching throughout the sales process for sales people. AI can suggest and highlight the next best actions at each sales stage from researching potential clients and preparing for meetings to generating proposals which helps opportunities move faster and are more likely to close.
- Pipeline health: AI can flag stalled deals or at risk accounts by analysing patterns in CRM activity, customer sentiment and the sales stages of the pipeline.
None of these require a wholesale reimagining of sales. They are targeted improvements to specific friction points and collectively they can dramatically increase productivity and revenue.
Case Study: AI in product development
Product development is another area full of manual workflows. From customer research to release management, AI can assist where it matters the most:
- Customer discovery and requirements gathering: AI can process customer interviews, survey data and support tickets to identify common themes. This gives product teams a faster path to insights of problems to solve for.
- Customer feedback loops: Rather than manually tagging and categorising feedback, AI can automatically group comments by product area, customer segment, sentiment and priority. This helps teams prioritise their roadmaps based on real customer data.
- Software engineering: Code completion tools are already saving engineers hours per week. AI can also suggest test cases or identify potential bugs early.
- Release management: Teams can make more informed data-driven decisions about release strategies and scheduling based on historical data and performance.
These are small interventions and none of them replace the core work of product teams but AI frees teams to focus on the creative and strategic tasks that actually move the business forward.
The product mindset: AI as a tool, not the hero
The hero is the customer and the pain point that they’re trying to solve. AI is simply another tool in the toolbox to help them get there faster, cheaper or better.
Start with the question “Where is the biggest friction in the customer journey?” and not “How do we use AI in our product?”.
Once you’ve mapped the pain points, ask whether AI is the right tool to address them. Sometimes it is or sometimes a process redesign or a simple automation will do the job better.
Scaling the small wins into an AI flywheel
The key to starting small is that success compounds. A team that saves 5 hours a week on admin can reinvest that time into higher value work.
Over time, these small wins create momentum.
They also generate data and learning that inform the next wave of AI initiatives. Before long, you’ve built a flywheel with workflows that fuel adoption, adoption that fuels ROI, and ROI which justifies scaling AI into other workflows.
So, if you’re building AI into your product strategy, start small. Find the friction. Deliver value fast. Then expand. That’s how you turn AI from a shiny tool into a real growth engine.
About the Author – Matt Brown is Chief Technology & Product Officer at TransformSales.ai