What are we even doing here?

My father has a saying "How do I know what I think if I don't hear myself say it?", he brings it out when we start ribbing him on his tendency to talk a little more than is required. Of course in jest its funny in the moment, but there is some greater wisdom there. Especially when you layer it on to a team dynamic. When we consult with our clients or partners on AI solutions we see the same thing over and over. A team of good intentioned stakeholders, some passionately present, some shackled to the effort because they are the only subject matter expert in the organization, and some not even aligned or aware. As a team they need to know what they think, but they cannot get there without hearing themselves say it.

Over time I have refined a simple whiteboarding exercise to facilitate the dialing in of the team, to identify clearly the exact problem that AI is going to solve for us. There are five frames to work through collectively.

Your team needs to be a good mix of contributors, executive sponsors, department leaders, technologists, front line operators. The goal is to provide a cross section of opinions and biases that help to inform the bigger picture and refine the power behind the effort.

White Board # 1 - Industry Analysis

This whiteboard framework applies to all industries, its to provide a quick collective analysis of how your team views your industry challenges, the trends and the KPI that matter. For example, in the logistics industry, companies often face issues like high operational costs and inefficiencies in route planning, which can lead to increased fuel consumption and longer delivery times. By understanding trends such as the rising adoption of IoT and real-time tracking, you can tailor your AI solution to address these pain points effectively. This comprehensive analysis ensures that your solution is both innovative and highly relevant to your industry's needs.

White Board #2 - Expertise Assessment

In any industry the next step is to assess your expertise. Each team has its own unique combinations abilities. To refine this you must define your core competencies, gather input from SMEs on all levels of the business touching the solution and clearly declare your knowledge gaps. Often the last one is where teams drop the ball. Intellectual honesty relative to your limitations is truly an amazing skill to have, develop it among your team and the humanity of what you are doing will bubble up to the top a bit better. But I digress... Continuing the example in the logistics sector, working with logistics managers and fleet operators can provide invaluable insights into supply chain management and route optimization. By harnessing this expertise, you can pinpoint areas where AI can have the most significant impact. This collaborative approach ensures that your AI solution is not only technically sound but also practically viable, filling in any knowledge gaps with advanced analytics and AI implementation experience.

White Board #3 - Solution Goals and Objectives

Setting clear goals and objectives is crucial in any industry when developing an AI solution. This involves defining business objectives, setting short-term and long-term goals, and establishing success metrics. In the logistics industry, for example, you might aim to reduce operational costs and improve delivery efficiency. Short-term goals could include implementing real-time tracking systems, while long-term goals might focus on predictive maintenance. By aligning every step with the company’s overarching business objectives and measuring success through established metrics, you ensure that your AI solution delivers tangible and measurable outcomes.

White Board # 4 - Feature Identification

This board here is really why we are doing this entire exercise. The ideation up until this frame leads your team through a purview of the reality of your position. Identifying the right features for your AI solution is essential, regardless of the industry but without the effort to bring everyone to the same page, and broaden the entire perspective of the team real value is always left on the table. Now you can brainstorm potential features, prioritize them based on impact and feasibility, and categorize them into core, secondary, and tertiary groups. For a logistics company, core features might include real-time tracking and dynamic routing, secondary features could be predictive maintenance, and tertiary features might involve demand forecasting. This purposeful approach allows you to focus on the most critical features first, ensuring that your AI solution delivers immediate value while laying the groundwork for future enhancements. Its the immediate realization of value from an AI solution that we are chasing. If we do not implement our solutions iteratively and quickly, advancements in the main models will engulf our solutions rendering the efforts obsolete.

White Board #5 - AI Capabilities Mapping

Now this frame will require technologist or consultant input, someone with expertise in AI technologies to help with mapping AI capabilities to the identified features. This a critical step in any industry. This involves matching features with specific AI technologies, identifying required data sources, and assessing technological feasibility. For instance, in the logistics industry, real-time tracking might require IoT integration and data analytics, while dynamic routing could need machine learning and optimization algorithms. By aligning these capabilities with your data sources and evaluating technological feasibility, you create a clear roadmap for implementation. This quick mapping process ensures that your AI solution is not only innovative but also grounded in practical, achievable steps.

This exercise should be achievable with any group of people trying to solve a problem. These five simple frames will prove invaluable to your team and refining your efforts in AI. Each one of these topics could easily be turned into deep dive sessions for your team. Start off at high level, go through all five in one session, the double down on grinding into the deep details on each in future sessions. This will get the ball rolling and propel your team with new momentum in deploying AI solutions either internally or as an AI play in your industry.

White board the way,

Benjamin Justice