With business leaders under pressure to implement AI in their firm’s operations, many have deployed the tech hastily, only to scale back costly pilots when they inevitably fail. Too often their plans for the technology are grand. For many firms, slow and piecemeal adoption would be far more advantageous in the long term. Still, plenty of decision-makers are wondering where to start their implementation journey.
Organisations exploring the use of AI would do well to assess their organisational readiness for an AI-led transformation. An AI maturity assessment can help establish a realistic view of where AI can add value, what is currently feasible and how to structure and sequence the transformation. Using insights from these assessments, business leaders can evaluate their firm’s readiness to deploy the technology, outline a roadmap for doing so and establish governance and risk criteria.
Common gaps in AI readiness
Many major IT services and consultancy firms offer AI maturity frameworks, most of which follow a similar structures but may be tailored to particular platforms (such as Microsoft) or public-sector contexts (such as the OECD), with distinct approaches to assessing maturity. However, when it comes to AI implementation, every assessment will examine an organisation’s data quality, digital infrastructure, talent pipeline and AI governance.
The most common gaps in AI maturity involve data quality and fragmentation. Many companies have inconsistent schema across teams, unlabelled, mislabelled or duplicate data and limited quality assurance. Feeding such data into AI models invariably produces poor analyses and unreliable outputs.
Another common problem is the shortage of in-house talent to support AI deployment and ongoing operations. Many companies lean too heavily on vendors thanks to limited AI skills in their own teams. Insufficient training for non-technical staff and unclear ownership of deployment responsibilities can derail projects.
Once the assessment is completed, firms are often forced to scale back their plans for deployment. However, they’ll gain a structured roadmap for implementation to replace the scattershot approach that so often leads causes transformations to stall.
What’s more, maturity assessments will help leadership teams establish robust governance policies, ensuring that safety, data leakage, compliance and hallucination risks are addressed with appropriate safeguards. A growing share of organisations are failing in these areas, according to a recent pulse survey by EY, a consultancy. Many lack formal policies for AI use or sufficient monitoring protocols for AI failures. Some have little to no human oversight of AI edge cases.
Establishing metrics and tracking progress
After completing a maturity assessment, some businesses may choose to pivot from their original plan or abandon AI deployment altogether. Despite the prevailing narrative that every industry will be transformed by generative AI, implementing it right away may not be the best use of time or resources, particularly if doing so doesn’t align with wider business aims.
For organisations committed to quick adoption, however, keeping a board-level scorecard can help to track progress on data quality, internal skills and governance, as well as deployment milestones and productivity or financial impacts. Metrics on data quality, compute readiness and resolved security alerts, for instance, should be reviewed by the board and executive leadership teams monthly or quarterly. Once the AI initiative is scaled out, these metrics can help leaders compare actual versus expected value, track validated use cases and see how deployments align with strategic priorities.
While successful AI adoption is by no means straightforward, the first step for any leadership team is understanding their organisation’s readiness for AI transformation. By clarifying where data, skills and governance are genuinely ready and where they are not, firms can prioritise the deployments that make most sense and avoid costly overreach.
With business leaders under pressure to implement AI in their firm's operations, many have deployed the tech hastily, only to scale back costly pilots when they inevitably fail. Too often their plans for the technology are grand. For many firms, slow and piecemeal adoption would be far more advantageous in the long term. Still, plenty of decision-makers are wondering where to start their implementation journey.
Organisations exploring the use of AI would do well to assess their organisational readiness for an AI-led transformation. An AI maturity assessment can help establish a realistic view of where AI can add value, what is currently feasible and how to structure and sequence the transformation. Using insights from these assessments, business leaders can evaluate their firm's readiness to deploy the technology, outline a roadmap for doing so and establish governance and risk criteria.
