That ’ s why it ’ s important to develop a strong infrastructure strategy for AI deployments from the start . Here ’ s what to consider .

Roadblocks to success

Frequently , companies leading significant AI research and development do so without important initial input from IT . As a result , teams unfortunately produce shadow AI – AI infrastructure created under IT ’ s radar , which is challenging to successfully operationalise and ultimately unproductive . Companies can avoid shadow AI by leading with an infrastructure strategy specifically optimised for AI .

The survey highlighted incalculable costs as the top issue ( 21 %). From the need for new investment in people and equipment , to unforeseen costs along the winding road between AI design and deployment , to rapid innovation and shifting of technology needs , AI investment is potentially massive and difficult to predict . Moreover , internal disconnect between IT and AI development can result in low ROI if the company fails to deploy the technology .

The lack of in-house expert staff also poses a significant challenge . Companies typically need to bring on specialised developers ,

which can be costly and requires time for new staff to learn the business to meet AI design with organisational goals .

Inadequate IT equipment also blocks companies from envisioning how AI fits into their operation . According to the survey , many worry their current infrastructure is not optimised to support AI and fear data centres have reached full capacity .

Roadblocks at the strategy phase are largely similar across industries but specific infrastructure decisions can vary based on industry . Legal or compliance requirements , such as GDPR , as well as the type of data and work processes involved , all factor into AI infrastructure decisions .

The study found that 39 % of companies across industries use major public clouds – most often these were manufacturers looking for flexibility and high-speed . Meanwhile , 29 % of respondents prefer in-house solutions with support from consultants – most often financial , energy and healthcare companies that wish to keep their personally identifiable information ( PII ) data under tight security and greater control .

Elements of successful AI infrastructure

With so many companies starting from ground zero , it ’ s imperative to nail-down a clear strategy from the start , since rearchitecting later can cost a lot of time , money and resources . There are

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