Reasons for this vary - but foremost among these is a failure to tackle business critical problems. Paradoxically, it is important to note that criticality is not a direct correlation with absolute dollar value. Rather, the AI enablement discussion should always aim to tackle problems that the business has proven to be critical.

At the same time, not all organisations are looking to frontline their AI teams on business critical problems. Instead, their core focus may be geared at branding or Proof of Concepts (PoC’s) to drive greater AI awareness within the company. Regardless of priorities, it is important to acknowledge the aims and set up the teams accordingly. An innovation center that is structured as a cost center cannot innovate.

Time

In addition to solving the right problems, commit to a time-frame in which results are expected. Crucially, ensure clarity on the time-frame within which the above impact needs to be delivered. In order to ensure that these timelines are tethered to reality - who with real-world experience in implementing AI for a related domain has vetted this timeline?

Complexity

Finally, it is also important to ascertain the technical feasibility and complexity required to build out for.

Taking an example use-case to build a colloquial speech analysis product – does sufficient labelled data exist to train a machine-learning model? What is the cost and time of annotating this? Will this be a constant effort? If these models are being deployed as internal APIs, what is the effort associated with implementing a monitoring framework? Has the impact of leveraging platform interactions to label data for further supervised learning problems been evaluated? What does Quality Assurance in this domain look like?

This is often a good point to start hiring the first (senior) data scientists, who should be able to formulate an accurate estimate for these.

Measuring and Enabling Success

Subsequent to crystallizing the value-proposition, it is time to formulate an appropriate evaluation paradigm and structure the CoE correctly.

When it comes to evaluation - Have metrics been identified that reflect a successful implementation? Linking back to the impact section, AI output metrics that are synergized with business KPIs are often an easy place to start. Avoid the trap of using machine learning metrics for individual models (like AuC, precision or recall) to evaluate success. Instead, validate what a particular AI product has enabled (e.g. KYC time-reduction, cost-per-acquisition)

When it comes to structuring the CoE, it is often the case that an internal champion is appointed to spearhead the unit. In such setups, it is of paramount importance to ensure a separation of strategy vs execution. The unit head need not be an AI expert. However, their mandate ought to be strategic in nature. The execution of the solution should be the responsibility of a technical lead, who absolutely needs to have a proven track record of building AI products.

Cultural Pivots are the Secret Ingredient

The few truly successful AI transformations I have seen have been a result of close collaboration between the business units and the AI teams to identify and solve critical pain points. This can often be a difficult transition to navigate, as individuals within the business may feel like they’re ceding control to a technology or process that they do not fully understand. Thus, a virtual embedding of data scientists within the business is strongly recommended, so that the process of building the AI solution is as transparent and democratized as possible.

To conclude, I’d like to draw the reader’s attention to Andrew Ng (ex-Head of AI at Baidu) who had this to say on the topic of enabling enterprise AI- “A shopping mall with a website isn’t an e-commerce company.” In the same vein, an organisation which has an AI team is not an AI-driven organisation.