Overview: Many large organisations have begun their Artificial Intelligence (AI) journeys, but some have taken unusual directions, while others are adopting a wait-and-see approach. As a general rule, I believe most organisations should attempt to identify the most important use cases for AI and, using a basic scoring system, prioritise the easy, high-value use cases first.
A good option is to hire an AI-focused team to implement the AI program within your business. Hire people from the company, as they possess key domain knowledge, and pair them with IT experts, preferably those with strong AI skills.
Tip: All Digital Transformation Projects must cover People, Process and Technology in that order of importance.
Thought: The two biggest mistakes I have observed as of July 2025 in clients are a lack of clearly defined benefits of AI projects and poor-quality, insecure data.
1. AI Idea Generation: Collate a detailed list of possible ideas. I like to use SharePoint, it's a good idea to open ideas up to the business or make gamification of idea generation to get a good set of ideas. You'll get lots of overlap, distil it into unique ideas, and bring the team/stakeholders together.
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| Figure 1. AI idea filter funnel |
2. Business Impact: How does this help our customers or generate revenue, or does this improve our operations? Can this be quantified?
3. Technological Feasibility: Can technology meet the requirement, and at what cost? A high-level technical design is strongly recommended, as illustrated in Figure 2 below.
4. Implement: If the idea holds up to Business impact, and is possible, select the highest value to lowest effort ideas. It is a good idea to start with the simpler ideas to get the ball rolling.
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Figure 2. HLD example
Note: This post is looking at building AI to improve your business. AI also affects all internal staff, so train them. Allow them to use AI tools like ChatGPT, and train them not to expose internal proprietary information. Give your users the correct tools to be the most effective.
AI Digitalisation Transformation Checklist:- Data Governance Framework: Do you know what data you have, can you access it, and is it secure?
- Enterprise Data Model: Define the rules for quality and integration between the EDM areas (these may be functional or geographic), ensuring we share data and make it available.
- Master Data Management: Ensure the data and applications are working off the same sheet of music.
- AI Framework: What are the limitations on what you can do within business governance? How will you identify projects and prioritise them? How do you ensure alignment with the tools? AI is expensive; don't just start picking our individual solutions. I'd also call this 'measure twice, cut once.'
- Don't eat the whole Elephant: Pick small projects to start with, clearly defined goals that show the wins from AI.
- One Process: When the AI solution/project is built, will you still use the old way? It's expensive to create, document, and support multiple processes over the lifetime of solutions that have duplicate steps to accomplish the same task. Make your process simple and the preferred or only approach.
- People: Have they checked the process? Is it optimised and not merely converted from the old paper process to digital? Have you engaged key stakeholders and experts? We should have asked if this is the best process, but also verified it with non-technical stakeholders.
- Cost Optimisation and Value Realisation: AI-based transformations like Software development or engineering projects are about whole-life cost and value, ensure you are not just looking at building something.
- Measurements and KPI's: How do we measure value? Are you monitoring usage and costs? They tend to be the easier measurements. Security is also essential to measure, as well as what constitutes success. Many benefits are intangible, so list them out. Can a value be guesstimated for intangibles?
I'm sure there are plenty more items, but this would be a good check to run repeatedly. |
