IT teams aren’t as ready to deploy AI as they think, but they can be

If perhaps you are unfamiliar with the Gartner Hype Cycle, artificial intelligence has been firmly planted in the “Peak of Inflated Expectations” phase for the past 24 months. In particular, the advent of ChatGPT back in 2022 cemented certain basic concepts and concerns connected to AI into place for employees at all levels across every type of organization. As an endless slew of headlines and subject lines alike focused on AI, the door was summarily kicked open for AI vendors to lean into inflated expectations. Promises of innovation and impact were more important than results and that caught the eye of business leaders across the globe.

Now, ​​82% of senior decision-makers acknowledge significant pressure to adopt AI quickly, but only 23% have completed the development of their data foundation. Purchasing a shiny new AI tool is the easiest part of the process, while deployment is proving to be a major challenge. So, let’s look at some of the key ways IT teams can help their organization be ready for effective AI deployment in the year ahead.

Identifying needs and assessing value

One of the first things IT leaders need to do is to put effort into cutting through the noise. The best way to do so is by adopting a Business-Outcome-Driven Enterprise Architecture (BODEA) created through a step program, which will require the time and collaboration of full IT teams and other tech leaders within the organization. These individuals must work together to establish the business value of all technologies currently being utilized and assess how they connect to the company’s broader direction and strategies. Once the state of the current tech stack is fully assessed, gaps will be more readily identified. By knowing where the techstack is falling short, IT teams will be able to analyze the fit and potential value of new tools, prepared to push back in the case that decision-makers aim to onboard a poor fit.

Within this process, IT teams should create detailed objectives for seeking new technology, including parameters that each must meet, and identify which tools it will need to integrate with. That also means focused testing for products. Far too many companies are performing uncontrolled tests that won’t provide valuable feedback or map back to organizational goals. The more work done ahead of adopting modern AI solutions, the more capable IT professionals will be of distinguishing hype from verifiable value.

Data migration and management

Estimations state it is likely that more than 90% of large enterprises are using some sort of cloud technology, but the number of those that have migrated their data to the cloud, or at least a hybrid arrangement, is significantly lower. For AI to be most impactful, it needs access to an enterprise’s full suite of data—which requires a lot of power. As such, it is vital for IT teams to put in the time to create a seamless data infrastructure that can properly support the desired AI technology.

Having this infrastructure established ensures that the enterprise can support data-driven decision-making and deliver real-time responses to change. Whether it’s product innovation, customer experience, team productivity or even an ESG initiative, there needs to be an organizational process for how that data is collected, stored, shared and processed. Otherwise, AI will not be nearly as powerful as it is capable of. Beyond AI adoption, an established data infrastructure is essential for addressing governance and balancing a hybrid cloud can enhance protection.

Human skill and communication

Just as there needs to be business and data infrastructure in place for proper AI adoption, so too must there be quality human infrastructure. Enterprises should seek to create a dedicated committee that receives the resources and training necessary to take a deep dive into any new AI technology of interest. These committees also need to carve out time for testing AI tools before making a purchase as a final quality check.

Then, once a tool is selected, organizational champions should be chosen. These individuals will be responsible for knowing the ins and outs of the new product and act as the liaison between the business and the vendor. By doing so, there will be an internal voice that can support problem-solving and training for those using the tool. Speaking of, before a new solution is announced or implemented, a robust plan must be developed and put into place for the rollout, addressing what training each user may need, what exact use cases it applies to, the timing of expected implementation for each team or member, and so on. While a business needs to be prepared before adopting AI, its people need to be even more prepared before using it.

The possibilities are vast, but achieving meaningful outcomes requires more than just implementing AI tools. It demands a careful combination of vision, strategy, technology and human skill. To capitalize on AI’s full potential, executives and other decision-makers need to go beyond the buzzwords and create a well-defined, actionable, organization-wide plan. Now is the time to step back, assess current capabilities and lay the groundwork for an AI-driven transformation that goes beyond the hype and turns vision into tangible results.

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