How to Build an AI Strategy that Works

By Michael Chalmers, MD EMEA at Contino

Six steps to boosting digital transformation through AI

In the age of artificial intelligence, the way we interact with brands and go about our work and daily lives has changed. No longer blithe buzzwords, AI tools and algorithms are solving real business problems, streamlining operations, boosting productivity, improving customer experience, and creating opportunities for advantage in a competitive marketplace.

However, many businesses struggle to unlock the full benefits that come with its adoption across the whole organisation. Making the most of AI requires a strategic focus, alignment with the specific operating model of the business, and a plan to implement it in a way that delivers real value.

Not all AI strategies are equal. To be successful, businesses need to set out how the technology will achieve objectives and identify the specific assets and case uses that will set them apart from competitors. The process of creating and delivering a successful AI strategy includes the following six essential elements that will help to bake in business success.

1. Start with your vision and objective

One slip-up companies often make when developing an AI strategy is a failure to match the vision to the execution. Almost inevitably, this results in disjointed and complicated AI programmes that can take years to consolidate. Choosing an AI solution based on defined business objectives established at the start of a project reduces the risk of delay and failure.

As with any project or initiative, it’s crucial to align your corporate strategy with measurable goals and objectives to guide your AI deployment. Once a strategy is set and proven, its much quicker and easier to roll it out across divisions and product teams, maximising its benefits.

2. Build a multi-disciplinary team 

AI is not an island. Multi-disciplinary teams are best placed to assess how the AI strategy can optimally serve their individual needs. Insights and inputs from web design, R&D and engineering will together ensure your plan hits objectives for key internal stakeholders.

It’s also important to recognise that with the best will and effort, the strategy might not be the perfect one first time around. Being prepared to iterate and flex the approach is a significant success factor. By fostering a culture of experimentation, your team will locate the right AI assets to form your unique competitive edge.

3. Be selective about the problems you fix first

Selecting ‘lighthouse’ projects based on their overall goals and importance, size, likely duration, and data quality allow you to demonstrate the tangible benefits in a relatively short space of time. Not all problems can be fixed by AI, of course. But by identifying and addressing issues quickly and effectively, you can create beacons of AI capability that inspire others across the organisation.

Lighthouse projects should aim to be delivered in under eight weeks, instead of eight months. They will provide an immediate and tangible benefit for the business and your customers to be replicated elsewhere. These small wins sow the seeds of transformation that swell from the ground up, empowering small teams to grow in competency, autonomy and relatedness.

4. Put the customer first, and measure accordingly

Customer-centricity is one of the most popular topics among today’s business leaders. Traditionally, businesses were much more product-centric than customer-centric. Somebody built products and then customers were found. Now, the customer is, and should be, at the heart of everything businesses do.

By taking a customer-centric approach, you will find that business drivers determine many technology decisions.  When creating your AI strategy, create customer centric KPIs that align with the overall corporate objectives and continually measure product execution backwards through the value chain.

5. Share skills and expertise at scale through an ‘AI community of practice’

The journey to business-wide AI adoption is 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 are more relevant than ever. Data science is about repeatable experimentation and measured results. Suppose your AI processes can’t be repeated, and production is being done manually. In that case, data science has been reduced to a data hobby.

6. Don’t fear failure: deploying AI is a continuous journey 

The formula for successful enterprise-wide AI adoption is nurture the idea, plan, prove, improve and then scale. Mistakes will be made, and lessons learned. This is a completely normal – and valuable – part of the process.

Lighthouse projects need to be proven to work, processes need to be streamlined and teams need to upskill. Businesses need a culture of learning and continuous improvement with people at the centre, through shorter cycles, to drive real transformation.

An experimental culture and continuous improvement, through shorter cycles, can drive real transformation. A successful AI strategy acts as a continually evolving roadmap across the different business functions (people, processes and technology) 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.