Are you ready for AI? The first questions IT leaders should be asking before they deploy.

Assessing whether your business is ready for AI isn’t just about technology. Here’s how you can start asking the right questions to make AI a commercial success with cultural impact. 

Are you ready to implement bespoke Gen AI into your business?  

It’s a loaded question, isn’t it? Defining what it means to be ‘ready’ for AI isn’t as simple as looking at your technology stack and deciding whether it’s ready to integrate (although that is a big part of it). AI readiness is multifaceted, and complex. There’s the technological aspect, yes, but that’s just the beginning.

What about the cultural considerations? The security concerns? Are you ready to think about it commercially, and demonstrate results to the board? Is that board ready to embrace it, and buy into the whole, transformative process: not just the surface idea?  

Gen AI is exciting. It’s world changing. It has the power to redefine how we work. Yet it’s also new, and not without its challenges. Although AI implementation may be a business initiative, driven by myriad different departments all looking to benefit from this exciting new technology, it is IT leaders who are facing the most pressure to take the concept of AI and translate it into something practical, usable and commercially viable.  

Unlike a lot of technologies that have come before it, the difficulty with AI isn’t persuading the board and the business that they need to invest in it: it’s more about managing their expectations and making sure they get it right.  

Despite the fact that 69% of companies started investing in AI over a year ago, 47% of c-suites still think their company’s AI development is too slow. Meanwhile, 43% of data leaders say data issues are creating a roadblock, while 45% are concerned about the boundaries of responsible AI use. Then, there’s the wonderfully uplifting statistic that 70-80% of AI projects fail.  

So, no pressure IT leaders.  

Yet none of those things take away from what we said before. AI is exciting. It is world changing. And you are going to be pivotal to driving its success for your business.  

We want to make that process as smooth as possible. We want you to feel the excitement of Gen AI and be able to implement it with confidence, drawing on your existing expertise and commercial vision to secure success from your very first iteration of the technology.  

In this series, we are going to be taking you on a deep-dive into AI, tailored to your position as an IT leader. You don’t need a PhD in AI to make it work for your business,  but to have confidence in your delivery and lead from a commercial standpoint, you will benefit from having a deeper understanding of how it works, why it is so revolutionary, and what exactly it is capable of. 

To start with, let’s look at some of the most important questions you should be asking yourself as you embark on your Gen AI journey. 

Am I already behind the curve of AI implementation? 

If you’re asking yourself whether you’re behind the curve with AI development, you are probably not. For the majority of businesses, AI is still at the consideration or early deployment stage. Some have introduced small-scale, focussed use cases, or are using publicly available models to enhance existing ways of working. Few have launched fully transformative, specialised AI software across the entire business, even those that have the models and software available to do so.  

This is about to change. We’re on the precipice of full, wide-scale adoption of bespoke AI across a whole host of use-cases, but right now, it is still a nascent technology. Everyone understands that it has vast potential but practically implementing it – and mitigating the risk – is still a key challenge.  

In highly regulated industries like finance and insurance, for example, there is still a reticence to apply AI to external or customer-facing applications, even purely conceptually. On the other hand, sectors like retail are perhaps more likely to introduce AI as a customer-facing tool such as a chatbot, rather than taking the leap to more critical internal operations that rely heavily on data. 

One of the most important aspects of Gen AI implementation, however, is vision. Just thinking about how you can use it and coming up with viable use cases is an excellent first step. Our approach is always business first, technology second: you set the course and we will work together to find the technological solution to bring it to life.  

Is our data ready for AI implementation? 

Generative AI systems are extremely sensitive to poor-quality data, especially LLMs. If you have poor quality data, or fragmented, inconsistent datasets and silos across the business, it will hamper the results you get from your initiative.  

This is nothing new: you’ve faced the same issues before with any data-focussed innovation. As always, the great thing is that there are now fast, efficient ways to address data quality and governance using – you guessed it – AI tools. Plus, from a cultural perspective, AI gives an excellent incentive to promote data governance across the business.  

AI may also require some changes to how you label, link and annotate your data and metadata. Some legacy systems may struggle to support this, and you might need to upgrade to newer, more flexible repositories with better integration across applications. 

In short, if your data isn’t ready now, it certainly isn’t an insurmountable challenge. 

Can we trust AI?  

There are two prongs to this question: one technical, and one human.  

As much as AI is generating excitement, it is also creating scepticism. Fears range from the ethical (is AI here to take my job, steal my information, etc) to the practical (will AI be accurate, is it secure, can I trust what I’m relying on it to do). Then, there’s the issue of confidence and change fatigue (do I need to learn yet another technology, am I going to get it right, what if I use it wrong).  

There is a lot of misunderstanding around AI protocols and parameters that will need to be addressed if you want to garner trust for AI in your business. Most people’s interaction with AI tools will be publicly available models like ChatGPT or Midjourney, where security is an issue, parameters are broad, and the information you share genuinely isn’t secure (be very careful if your business is using the public versions of these Gen AI tools and make sure you are communicating the risk widely).  

Yet when you implement Gen AI as a business tool, using an application like Copilot or Azure AI for instance, you set your own guardrails. These rules mean that input can be validated to ensure proper usage, creating a closed environment that can be iteratively improved using your data, without putting it at risk.  

What is a good first step to implementing AI in my organisation?  

A good way to bring all key stakeholders on board, share ideas and start thinking commercially and strategically about AI is through an initial workshop. We have been conducting AI workshops with our clients that bring decision makers and influencers together to educate them on the core concepts of AI, how to apply them to a business, and how to create commercially viable use-cases from the start. Even if you are already well into your AI exploration, a workshop is a good way to give your strategy focus and get buy-in across the board.

Interested to find out more, simply enquire here commercial AI workshop.