Why AI is not just AI: the executive’s definitive guide to adoption

It can be hard to accelerate AI adoption when all you have in front of you is buzzwords and hype.

AI has captured the imagination of business leaders across the spectrum - and FOMO (fear of missing out) is driving many to craft AI strategies to move adoption forward.

But in the rush to create a one or two-year AI strategy and roadmap, what's often either overlooked or not well understood is that AI is not just AI.

Instead, there are a number of preparatory steps and actions that need to occur to get to the outcomes that AI promises to deliver. These fall into domains such as data, security, infrastructure and cloud. There is also internal capability building required, as well as appropriate policy guardrails and controls to ensure AI is used in a permissible manner.

AI is also an increasingly broad church, covering a number of technologies - machine learning, chatbots, copilots, generative AI, agentic AI, as well as underlying small and large language models. 

Additionally, AI technology is still firmly in its growth phase, and so the pace of change is dizzying, with new models and services launching daily, and wholesale new directions being unveiled at a pace that would challenge most organisations to keep up.

This creates uncertainty, both in terms of what's in the market today and what's going to come next. And it can be hard to accelerate AI adoption when all you have in front of you is buzzwords and hype.

Navigating all of this to identify the right AI and model for the right use case and applying it at the right time to get results and value, is a challenge that organisations will continue to face today and into the future. 

Finding a way - or a partner - that allows you to safely, securely and cost-effectively try and apply AI technology to production workloads is what will get organisations' and executives' AI ambitions over the line.

Setting the table

An analogy I often use when explaining the foundational elements that are needed to drive AI forward cohesively in any organisation is to think of it as a dinner party.

The table, tablecloth and plates represent the infrastructure that both houses the organisation's data and hosts the AI models. The data itself is the food on the plates. Each plate represents a data 'silo', but it must now be presented on the table in a cohesive and logical way. Then, once the table is set properly, and food is served on the appropriate plates, you bring in the waitstaff, who understand which plate goes where. 

The arrangement of the plates on the table ensures that the right dish is given to the right person, or the right data is fed to the right AI model. Just as a dinner host would not want to serve shellfish to a person with a seafood allergy, an organisation does not want to serve data to an AI model that would cause harm to any person or party.

When to use a restaurant

There is a lot that goes into hosting the perfect dinner party, but at the end of the day, what really matters is the outcome. 

For this reason, given the amount of effort and skill required, bringing in assistance - say, having the dinner party at a restaurant - can make sense as a cost-effective way to get to the outcome, while having experts handle the key elements of delivery.

This mirrors how many organisations are thinking about AI adoption. The outcome of harnessing AI is insights and better decision-making for your business. The time required to achieve that outcome is less when an expert partner is engaged to guide the organisation and supply all of the necessary knowledge and technology pieces. 

A Managed Services Provider (MSP) can be the right partner to stand up and maintain a cohesive view of infrastructure, the data layers and the AI elements needed to catapult the organisation into a position where it is able to consistently make better decisions. In addition, a key thing that an MSP can do is to innovate and invent the future so that organisations and executives do not have to do it themselves. This means creating cloud infrastructure, data analysis and AI services that can be easily consumed and cohesively connected to create a logical and seamless AI strategy, and enable an organisation's AI journey to take shape and progress.

For an organisation, this means having data and AI models close to one another - "in situ" - and closer to users, limiting the need to move data around to take advantage of AI services. It also means having access to AI services that organisations can use in a pay-as-they-go, pay-as-they-grow manner, optimising their investment. Or alternatively, powerful infrastructure that the organisation can use to host its own AI models or third-party sourced models, without the cost of buying that infrastructure themselves. Finally, an MSP can give organisations the ability to combine data sources to feed these models by connecting different applications and their respective data stores or repositories.

Importantly, all of this can be done cohesively, in a way that supports the organisation's AI ambitions and gets them to their desired outcome sooner. In this way, organisations can dine on their data sooner, gaining a competitive advantage in the process.