Check out the AI Strategy Playbook, by MIT Technology Review Insights in association with Boomi.

The more pertinent considerations for an AI strategy are outlined below.

Data Liquidity – The accessing of curated and tailored data from various sources to extract relevant information and apply it effectively to a business objective

Data Quality – Typically the first port of call for any AI deployment is to assess and cleanse your data, and develop a plan around your legacy IT infrastructure. This is significantly more challenging for large companies with deeper data repositories than smaller more nimble firms. Remember, garbage-in results in garbage-out when you’re either building your own Large Language Models (LLMs), or leveraging commercially available LLMs.

Governance, Safety and Security – This is the critical operational oversight to determine what to do – in terms of the AI Strategy, how to manage the strategy and peel of projects that align to the objectives, and then the review of these projects in relation to sustainment to company policy that inevitably evolves over time.

Communities of Practice – Typically revered by technical employees, and a key to human capital retention, communities of practice align the employees that will develop and possible use the tools on your roadmap. It’s critical to allow for interactions, learning opportunities and peering to bring about ideas, generate enthusiasm for the work, and to create informal checks and balances so as to let your teams thrive as they navigate the evolving AI landscape.