What is an AI strategy and how do you build one?

Modern data science is no longer about researching breakthrough AI, but focusing on solving business problems with existing tools and proven algorithms. From manufacturing to marketing, AI is increasing productivity and streamlining business operations.

In an increasingly crowded marketplace, business leaders are looking to AI-driven solutions for an edge over their competition. However, many businesses adopting AI lack the key ingredients of strategy, operating model and execution framework necessary for achieving business-wide AI adoption. 

An AI strategy outlines how the technology will achieve set business objectives and, more importantly, identify AI assets that grant the business a competitive advantage and are difficult for competitors to replicate.

Now that we know what an AI strategy is, here are some tips to create the right one for your business align your organisation and effectively execute.

1. Align your corporate strategy with your objectives

During AI transformation projects, companies often make the mistake of separating the vision from the execution, resulting in disjointed and complicated AI programs that can take years to consolidate. This can be easily avoided by choosing AI solutions based on concrete business objectives that have been established at the project’s outset.  

It’s important to align your corporate strategy with measurable goals and objectives to guide your AI deployment. Once complete, the strategy can be easily escalated down into divisional- or even product-level strategies. 

2. Establish a multi-disciplinary AI team 

Form a multidisciplinary team to assess how the AI strategy can best serve their individual needs. Having members from different departments in your AI team, for example, web design, R&D and engineering, will ensure your strategy will meet objectives for key internal stakeholders. 

You may not deploy the right strategy in the first instance, so iteration is crucial. By fostering a culture of experimentation you team will locate the right AI assets to form your unique competitive edge.    

3. Choose the right problems

This might seem like common sense, but the problems you’re looking to overcome have a large impact on your success. Some problems are not AI problems at all, and for the ones that are, the business should advocate the delivery through small lighthouse projects that act as a beacon for their capabilities. 

In identifying ‘lighthouse’ projects, your business will need to assess the overall goal and importance of the project, its size, likely duration and data quality. 

Lighthouse projects tend to be able to be delivered in under eight weeks, instead of eight months, and will provide an immediate and tangible benefit for the business and your customers. These small wins are then multiplied to sow the seeds of transformation that swell from the ground up, empowering small teams that grow in competency and display increased autonomy and relatedness.

4. Execute backwards from the value chain

Customer-centricity has become one of the most popular topics among today’s business leaders. Traditionally, a lot more businesses were product-centric than customer-centric. Products were built and then customers were found. 

When creating your AI strategy, create customer-centric KPIs that align with the overall corporate objectives. It is important to constantly measure product execution back to these customer-centric KPIs. By taking a customer-centric approach, you will find a lot of the technology decisions are now decided by business drivers.

5. Scaling out your AI community of practice

The journey to business-wide AI adoption will be iterative and continuous. Upon successful completion of a product, the team should evolve into what’s known as an ‘AI community of practice’, which will foster AI innovation and upskill future AI teams. 

In the world of rapid AI product iterations, best practices and automation still apply and are in fact more relevant than ever. Data science is about repeatable experimentation and measured results. If your AI  processes are non-repeatable and everybody is changing production by hand, then it is no longer data science but data hobby. 

6. The AI strategy is a continuous journey 

As with any successful project, the formula for enterprise-wide AI adoption is: nurture the idea, plan, prove, improve and then scale. 

Lighthouse projects will need to be proven to work. Teams will need to be upskilled. Processes will need to be streamlined. There will be mistakes made and lessons learnt. And all of this is okay. Businesses need to focus on a culture of learning and continuous improvement with people at the centre, through shorter cycles, to drive true transformation.

By focusing on experimental culture and continuous improvement, through shorter cycles, to drive true AI transformation, business can drive true AI transformation. An AI strategy acts as a constantly evolving roadmap across the different business functions (people, processes and technology etc.) to ensure your chosen solutions are working towards your business objectives. In short, let your business goals guide your AI transformation, not the other way around.