Hint: It all starts with the problem statement.
Imagine this: You’re the head of a growing company and invested heavily in a promising AI project. The goal is simple: streamline your operations, cut costs, and unlock new insights from your data. But months into the project, things start to unravel. The results are inconsistent, your team is frustrated, and stakeholders are questioning if the investment was worth it. You feel the pressure mounting, knowing that the window for proving AI’s value is closing fast.
What went wrong?
It turns out that you and your team jumped into building a solution after some quick brainstorming about possible LLM’s and AI stacks, but you missed a crucial step - the problem statement - truly understanding the problem. All too often, projects start off ambitious and flashy, with a focus on "How can we use this new technology?" rather than focusing on the essential. Without a clear grasp of the actual problem and its root causes, even the most advanced technology won’t produce meaningful results. This is where many AI initiatives stumble. That’s why, at NimbleNova, we developed our Problem Statement Canvas to help organisations get it right from the start.
What is a problem statement?
A problem statement concisely describes an issue that needs addressing. It’s user-centric, identifying key stakeholders, outlining their pain points, examining root causes, and highlighting the potential gains from resolving the issue. A strong problem statement serves as a compass for the entire project, ensuring that the solution aligns with organisational objectives and delivers real value. This user-centric focus is essential from a Design Thinking approach, guiding the team to develop solutions that truly meet users' needs and create meaningful impact.
Turning chaos into clarity: Here’s why we built the canvas
Before you blame the tech, take a closer look at the foundation—most AI initiatives fail due to a muddled problem statement. Here are some common pitfalls:
- Tech-first thinking: Jumping straight to technology discussion without understanding the user’s needs.
- Siloed teams: Different departments work without a unified objective (solving your problem statement), which causes them to miss the bigger picture. For instance, Legal may worry that there is no AI policy in place, IT might hesitate to take solutions into production, and the business team could feel the urgency to move now to gain a competitive edge. Each has valid concerns, but the core problem remains unsolved.
- Lack of transparency: Stakeholders don’t fully trust the AI’s output, which hinders adoption.
- Overlooking data quality: Incomplete data retrieval leads to unreliable results.
- Unfocused goals: Trying to solve too many problems at once, diluting the impact.
These challenges scream for a better approach—one that cuts through the confusion and keeps you on track. That’s where NimbleNova’s Problem Statement Canvas comes in. A game-changer that transforms vague ideas into clear, actionable direction. By guiding you through a structured, step-by-step, co-created process, the canvas focuses on your ‘why’ before you ever touch the ‘how.’
The 8 steps of the Problem Statement Canvas
- Context: Why are we discussing this issue?
- Stakeholders: Who are most affected by the problem?
- Quantified pains: What specific pain points do they face?
- Root causes: Why is this happening? What processes or behaviours contribute?
- Organisational objectives: What goals are related to this issue?
- How critical: What risks exist if this problem isn’t solved?
- Limitations: What constraints need to be considered?
- Quantified gains: What benefits will come from resolving the issue?
Follow these steps, and you'll craft a problem statement that’s not just thorough but razor-sharp—laying down a rock-solid foundation for your AI project to thrive.
Worried that answering these questions with your team and stakeholders might spark a lot of debate? Fantastic, bring it on! You want those discussions about the value you’re aiming to unlock now, not when you’re already halfway through coding.
How does it work in practice? A real-world example
To illustrate the power of the Problem Statement Canvas, let’s dive into a real-world example with one of our clients.
Client background: Our client is a company that creates customised medical equipment for children—think wheelchairs, prosthetics, and mobility aids, all tailored to each child's unique needs. It’s a complex process involving dealers, healthcare providers, and customer service teams to ensure the correct specifications reach the - already fully booked - production line.
The challenge: This complexity comes with a significant challenge: errors. Human mistakes in the order entry process often lead to incorrect specifications—like mismatched sizes, wrong materials, or missing features. The impact was costly: frustrated customers, a backlog of rework and custom-made products which can go straight in the bin.
Their initial approach—more controls, more frustration: Facing this issue, the company went for the most obvious solution. They decided to tighten up the process and to build an AI driven knowledge base for the dealers. New mandatory fields in their CRM, extra checklists on order forms for dealers, and an additional review step before orders went to production. On paper, it seemed foolproof: more checks would mean fewer mistakes, right?
The reality—more steps, same problem: But in practice, this approach backfired. The new control layers only made the order process slower and more cumbersome. Customer service and sales teams spent more time filling out forms and double-checking details than actually engaging with dealers. Errors persisted, sometimes because the process was so tedious that steps were rushed or skipped or simply because the human mind can only handle so many details simultaneously. Frustration mounted, and the core problem—cognitive overload in managing complex custom orders—remained unresolved.
Applying the Problem Statement Canvas—finding the real issue
This is where Nimble Nova’s Problem Statement Canvas made a difference. By guiding the team through a deeper exploration of the issue, the canvas helped them uncover what their initial approach missed:
Context: There is significant pressure on the production lines to increase capacity. While custom medical equipment requires precise specifications that can’t afford mistakes.
Stakeholders: Dealers, customer service teams, parents, children, and healthcare providers all suffer when errors occur.
Quantified pains: Frequent specification errors lead to delays, production rework, and unhappy customers.
Root causes: The real issue wasn’t that employees needed more checklists; it was that dealers were overwhelmed by the complexity of customization without any intelligent support tools to guide them through the intake and order process.
Organisational objectives:
- Increase Operational Efficiency: Achieve a 20% reduction in production errors
- Enhance Customer Satisfaction: Improve customer satisfaction scores by 15%
- Boost Profit Margins: Increase overall profitability by 10%
- Strengthen Market Position: aiming for 5% market share growth in the next year.
How critical: Not solving this would lead to potential damage to the company's reputation and significant financial costs. In the long term, dealers may shift to other manufacturers, impacting revenue. In the short term, it could relieve production lines and reduce associated costs.
Limitations: Personalised needs couldn’t be simplified, and there wasn’t enough staff capacity to endlessly double-check every order.
Quantified gains: By reducing errors and automating tedious processes, the company could improve accuracy, customer satisfaction, and production efficiency.
Problem Statement
In summary, the organisation and the dealers are facing frequent errors in the customization process for personalised medical equipment due to the high cognitive load on staff and a lack of advanced support tools to handle complex specifications resulting in production faults.
This is negatively impacting strategic goals for operational efficiency, customer satisfaction, and profitability. This issue is critical because continued errors lead to production delays, dissatisfied customers, and increased rework costs, affecting the company’s reputation and profitability.
It requires addressing resource limitations to ensure a reduction in errors and additionally resulting in an improvement in customer satisfaction and contributing to an increase in profit margins.
The game-changer: AI-assisted support
Armed with a clear problem statement, the company pivoted to a smarter, more strategic solution: AI support for the dealers and their sales teams.
Here’s what changed:
- Automated suggestions: AI provided suggestions for materials and configurations based on similar past orders, helping dealers confidently navigate the complexities of custom orders.
- AI-Assisted order validation: AI automatically reviewed orders for inconsistencies and flagged potential errors before they reached production, saving time and catching mistakes that humans might miss.
- Reduced cognitive load: By lowering the cognitive load on employees, the AI support allowed them to focus on meeting customer needs rather than battling with endless forms.
The results: less stress, more success
With the Problem Statement Canvas guiding them to the real issue, the company’s new approach delivered focus and therefore tangible results. Errors dropped, production moved faster, and customer satisfaction soared. Instead of a rigid, frustrating process, the team now had a dynamic, AI-supported workflow that matched the complexity of their products.
Key takeaway
This example illustrates why focusing solely on surface-level fixes, such as increasing access to information and implementing controls and checks, often fails to address the root problem. Using NimbleNova’s Problem Statement Canvas, this company didn’t just apply a band-aid; they found the core issue and addressed it head-on, leading to sustainable success.
Ready to successfully start your AI initiative? We are!
At NimbleNova, we’re committed to helping organisations realise their AI ambitions. Starting with a well-crafted problem statement ensures that your AI projects are set up for success.
Ready to define your challenges with clarity and purpose? We understand that aligning everyone on the plan can be challenging and achieving buy-in is essential. Our Design Thinking Consultants are equipped with engaging workshop materials designed for collaborative deep-dive sessions, helping you to refine your problem statement together. Let’s get in touch and start solving the right problems together!
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