Are You Ready for AI’s Realities?

From conferences stages to office corridors to political campaigns, there’s no denying it, AI is the obsession of the world right now.

Nvidia’s staggering $35bn quarterly revenue, obviously fuelled by the generative AI boom, is proof that businesses are throwing their weight behind the chips that will accelerate development and innovation of the next AI tools.

But there is a bigger question here, are companies prepared for what it takes to make AI work for them?

In Cisco’s 2024 AI Readiness Index a different picture is painted. Approx. 8,000 senior executives were surveyed across six critical categories: strategy, infrastructure, data, governance, talent, and culture. Out of those, only 13% emerged as "pacesetters," those that are fully prepared to leverage AI’s potential. That figure is 1% down from last year. Most businesses fall into the four different categories “chasers, (moderate preparedness)” “followers, (limited preparedness)” or “laggards (unprepared)”.

From my perspective, is this about whether you are already using AI? Or is it about how well you’re using it? Those questions will help you see where the gaps are in your usage or not.

The Illusion of AI Readiness

A number of folks like Eddie Short have commented that AI isn’t just another technology to bolt onto your existing operations, it’s a fundamental shift in the way that you think about your business and how it will fight for its position in the future.

But here’s the thing, even with many of us saying this, many organisations still approach it with the same mindset they’ve applied to every tech trend before it:

“Throw some money at it, hire a few specialists, and poof magic dust is sprinkled.”

Do you think that works?

Nope I don’t! We've seen what happened with Big Data and Data Science Mark Stouse sees the latter problems all the time.

Because, without a clear strategy tied to measurable business outcomes, AI investments risk becoming expensive experiments. Infrastructure without alignment to real business needs is just sunk cost. Governance without clarity creates bottlenecks. Talent without the right culture is wasted potential.

Tick Tock

Back to Cisco’s research, 85% of leaders believe they have less than 18 months to implement an effective AI strategy or face tangible negative consequences. This isn’t a scare tactic; it’s a reflection of the accelerating competitive landscape. Those who fail to act, or act poorly, will definitely be left behind.

But time isn’t the saviour that you think it is! Many leaders may understand what AI can do, but where they fall down, is when it comes to defining why and how. That’s where the game is won or lost, not in the technology itself, but in its execution.

Focus on Outcomes, Not Inputs

The key to AI readiness lies in reframing the conversation. Forget the jargon and the hype, and as I always say, start with the outcomes. Here are four areas that you could initially start to focus on to bring some reality to your situation:

1. What problems are we solving? AI for AI’s sake is a waste of resources. Gosh how many times have I said that before about some other forms of hype! Define the use cases that directly tie to value creation, whether it’s reducing operational inefficiencies, improving customer experiences, or unlocking new revenue streams.
2. What’s holding us back? Most organisations struggle with tangled systems, siloed data, and unclear decision-making frameworks. Addressing these foundational issues is far more critical than investing in the latest algorithm. I mean you can experiment and do some “innovation” but when it comes to putting your experiment into production, you will have a bigger hill to climb if you don’t at least attack some of the root causes.
3. Who’s accountable? AI isn’t just a tech problem; it’s a business imperative. That means clear accountability from the top, with leaders driving the cultural change needed to make AI stick. In most of the conferences or panel talks I’ve been on, most of the conversations start with tech imperatives and guess where they end up: at the cultural end of the barrel as that kills pretty much every kind of transformation you are attempting to achieve.
4. How do we measure success? If you can’t define success in business terms, your AI strategy is flawed. Every initiative must tie back to measurable outcomes, from cost savings to revenue growth. Measuring isn’t about data or technology; it’s about measuring whether you achieved the outcomes you were looking for. Many transformations get stuck on such measures as data quality metrics, sure you can look at those from a use case perspective, but please don’t make that front and centre. These are back-office metrics, put the business outcomes measures front and centre.?

Your Data & AI Readiness Checklist

You may want to review your AI readiness and have a long list of data and technical components to review, but that’s far from what you want to be looking at! If I’m going to start reviewing my data quality, what’s the point? I don’t know what I’m doing yet in terms of business outcomes or use cases, so anything related to data or technology, should be fairly low down the pecking order.

To help you get a clear picture of where you stand, here’s a checklist of questions tailored to ensure your data and AI strategy is built around the use cases that will drive value: (I've used the five areas in the Cisco research and added the questions I would ask)

Strategy:

• Have we identified specific business use cases that AI can address?
• Are these use cases aligned with our strategic objectives and measurable outcomes?
• Have we determined which type of AI (e.g., machine learning, neural networks, generative AI etc.) is best suited for each use case? Or do we really need AI?

Data:

• Do we have access to the data needed to support our chosen use cases?
• Is our data structured and clean enough to enable meaningful insights?
• How well does our data align with the requirements of the AI models (e.g., training data for machine learning)?

Infrastructure:

• Do we have the technical capabilities to support the identified use cases?
• Can we scale our infrastructure as the use cases evolve or expand?
• Are our systems flexible enough to integrate with AI tools designed for these specific needs?

Governance:

• Are there clear governance frameworks to ensure that AI use cases are ethical, compliant, and secure?
• How do we monitor and manage risks specific to the use cases we are deploying?
• Do we have a clear escalation process if AI outcomes don’t align with expectations?

Talent:

• Do we have the expertise to select and implement AI models that fit our use cases?
• Are we investing in the right mix of technical and business skills to ensure use case success?
• How are we fostering collaboration between data, technology, and business teams?

Culture:

• Is there a shared understanding across the organisation of how these use cases will drive value?
• Are teams empowered to experiment with AI solutions for their specific challenges?
• How are we addressing resistance to AI adoption tied to these use cases?

I hope the checklist will help in the definition and development of your AI use cases.

Remember, and this is fact. AI is a tool, not a magic wand. Its success lies in how well it is aligned to your business needs and embedded into your operations. The next 18 months for those 85% of leaders consulted in the Cisco survey are critical. Start with the use cases that matter most, build from there, and focus on execution.

The question is, will your organisation be one of the 13% that gets it right?

If you're struggling to translate AI ambition into actionable outcomes, let’s talk. I can help you align your data and AI strategy to the use cases that will deliver real value.

Don’t wait, because your competition certainly isn’t.