Digital Strategy for AI
Key Takeaways:
Feeling behind on AI is normal — focus on readiness, not hype: assess goals, data and systems before investing.
Use the 5‑layer AI readiness framework (data governance, integration, enablement, use cases, competitive advantage) to prioritise practical pilots.
Start with a short readiness workshop, run 1–3 high‑impact pilots, measure ROI, then scale. Book a readiness check to get started.
A 5‑Layer Framework to Make Your Business AI‑Ready
Many companies feel left behind by noisy AI headlines. That’s normal. This post outlines a practical 5‑layer framework to assess AI readiness and prioritise investments so you can get started on your digital strategy for AI.
Why it's OK if You Haven't Started Your AI Journey
The world is abuzz with companies purporting to be well underway with their AI journeys. Executives are reporting positive impact from the use of agentic AI (autonomous agents) whilst others are saying it hasn’t lived up to the hype. In fact, Gartner predicts that 80% of enterprises will have used Generative AI by 2026, up from less than 5% in 2023.
This representation – and perhaps over-representation – of leaders talking about AI already in play, makes the vast majority of companies not yet underway on their AI journey feel inadequate.
Well, we’re here to say that’s completely normal and nothing to be worried about. At least not today.
Being honest with ourselves, it would be unreasonable if every business, large or small, profitable or not, decided to jump onto the AI bandwagon expecting to see profits suddenly change. We know that for any digital innovation to be successful there needs to be some ballast in the bank which allows it to be designed, tested and bedded in. This is the case with any investment, but especially for AI as it requires a decent investment of finances, effort and time.
From a financial perspective, most companies could probably secure funding required for an AI solution to whichever problem it is you’re trying to solve – especially if you’ve done a good ROI calculation. The biggest challenge comes in the effort and time investment. This is specifically because you need a good data and AI strategy around your AI initiatives.
Imagine deciding to deploy an AI algorithm to do your shopping without giving it any context of how you live your life, or what’s already in the pantry, or that you have dietary requirements, or which country you live in, or that you have a weekly budget… the list goes on, and the result would be chaos.
The point here is about getting your strategy right from the outset. That is, defining what your business is trying to achieve and how digital enablers, like AI, can help achieve those, then stating the approach to delivering that. Once this strategy is understood you can move to consider how digital initiatives should support delivery of the business, which data should be made available to support decisions, how those data are governed, which systems are important and what plans for change exists in the business.
There will be plenty of progressive companies that have been thinking about this for the past 5+ years, and there will be even more that haven’t even started on this journey. So, where to start?
Consider our 5-layer framework for arriving at an AI-ready state.
1. Foundational Layer: Data Governance & Management
This is the bedrock. Without clean, accessible, and relevant data, AI initiatives will struggle. It’s about mapping all your data sources, setting clear governance policies, tracking where your data comes from and how it’s used, and making sure your data is accurate and up to date. This layer needs to be robust, simple to use, and clear in purpose, ultimately building trust around your data.
2. Integration Layer: System & Interoperability
To enable automation and AI, your systems need to talk to each other. This means setting up data flows and using appropriate middleware to connect everything together. The goal here is flexibility and scalability, so your digital ecosystem can grow with your ambitions.
3. Enablement Layer: AI & Automation Infrastructure
Here’s where AI becomes a force multiplier. With the right technologies in place, like centralised master data management, copilots for search and reporting, and unified platforms for deploying AI models, you’re setting yourself up for reliable, effective, and trustworthy AI operations.
4. Use Case Layer: Business-Driven AI Applications
Focus on high-impact, repeatable use cases that align with your business goals. This layer is all about delivering real, tangible value, so your use cases have to be really closely aligned to your business objectives.
5. Competitive Advantage Layer – How AI Creates Differentiation
This is how you differentiate yourself. By delivering faster, smarter, and more tailored outputs than your competitors (while keeping human oversight for critical decisions) you create a true competitive advantage. AI enablement isn’t just a nice-to-have; it’s a requirement to stay relevant.
How Long Does AI Readiness Take? Typical Timelines and Costs
Putting timeframes and costs on any of these depends entirely on your business, ambitions and resources. However, getting a temperature check on readiness is as simple as workshopping the business benefits for AI enablement. That is precisely where Skyo Digital can help.
Next Steps: Getting and AI Readiness Check
We offer digital maturity assessments and AI readiness checks, and many more services, shown on our services page. Get in touch for a conversation about how you can get started.
