AI strategies are fast emerging from organisations, many in response to the hype created by generative AI. Data Leaders brought together Chief Data Officers to share best practices on how to prepare for a successful deployment.

Data Leaders members can access the full summary from this discussion via the Data Leaders Hub.

1. Align AI Capabilities with Business Goals

While company executives are seeking answers to how generative AI can quickly help them realise customer excellence, cost savings and new revenue opportunities, CDOs strongly advise pushing boards to define a specific vision statement that clearly articulates the business objectives they want to achieve. This North Star metric can galvanise the organisation and accelerate the rollout of the strategy.

2. Create a Definition for Artificial Intelligence that is Understood Company-wide

Perhaps the biggest hurdle that CDOs face is the lack of understanding around artificial intelligence. While many believe their level of understanding to be sufficient, confusion and misperceptions of what AI is and what it can do are widespread. Creating and communicating a clear definition of what artificial intelligence is and the role it is intended to play in achieving company objectives is a key step in setting correct expectations.  CDOs urge their peers to position AI as a tool that enhances and augments current working practices rather than replacing them.

3. Collaborate with Business Partners to Source AI Use Cases

An early priority for CDOs is the surfacing of AI use cases within their business units. This gives CDOs the opportunity to test and refine a strategy, as well as begin engagement and education activities. One approach has been to launch a company-wide survey, gathering use case ideas and then selecting a sample of business leaders with whom to partner. Focusing on solvable business problems that are technically feasible, the next steps will be to co-create prototypes while guiding the partner through governance, success metrics and maintenance. The mid-to-long-term goal will be to empower business units with sufficient support to set up and run their own AI Centres of Excellence.

4. Take a Balanced Approach to Governance

Governance is vital, especially in regulated industries, but overly restrictive policies can hinder innovation. One CDO observed that a normal reaction is to “panic impose” restrictions. Instead, they advise that nurturing early adopters and learning from their experiences is more beneficial than shutting them down completely. All CDOs agreed that understanding what can be done should be the primary focus.

5. Use a Sandbox

A common strategy among CDOs is to build an internal sandbox for business and local domains. This both advances the testing of use cases and helps to foster more engagement across the company. A key piece of advice was to offer users a blueprint or template from which to begin; this saves time on setting up the infrastructure for each use case. Finally, they advised handing off successfully adopted innovations so they are fully supported by the individual business team (e.g. a chatbot for a product).

6. Focus on Engagement KPIs Now, ROI Later

While senior leaders are hoping fast returns from AI, CDOs agree that engagement and adoption are the most critical performance indicators in the early phases of deploying a robust AI strategy. Therefore, they advocate securing centralised budget to fund the co-creation of the initial AI use cases and engagement plan to establish AI Centres of Excellence (CoEs) without the pressure of simultaneously providing ROI. Once established the CoEs will be fully funded, managed and KPIs owned by the business domains, supported by the central AI or Data Office.

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