AI is no villain: six steps to build your AI strategy

Artificial intelligence (AI) is often lauded as an enemy of the peace: taking jobs, listening in on conversations, and making biased decisions on our behalf. But this is no Dalek threatening to take over the world for some ambiguous evil purpose. It is our own actions as humans that control whether the AI revolution is a success for all, or spirals out of control. Embracing science fiction just for a moment by welcoming AI as a real member of the team (think K9 and R2-D2) will give businesses a competitive edge in today’s crowded marketplace. But first, we’ll need an AI strategy, to train it up with the resources, time and investment it deserves to thrive.

Here are some top tips that will help you create the right AI strategy for your business, aligning the technology with your business needs to deliver a successful competitive advantage:

1. Have clear objectives before you start your AI journey

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. Who is on your dream 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 such as 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 your team will locate the right AI assets to form your unique competitive edge.

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3. Identify the real problem; don’t assume it is AI

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. Put your customers’ needs first

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.

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5. Foster AI and upskill teams

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 journey doesn’t end there; there is room for improvement

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.

The ways in which AI can be used are constantly evolving. It can be applied across multiple departments and functions, from people management to processes and technology. There is always a home for AI. With the right team and business objectives in mind, the competitive edge that the right AI strategy can give your business is next to none. By consciously nurturing AI, and giving it the right food for success, there is a wealth of good to come from these technological teammates.

Written by Michael Chalmers, managing director EMEA at Contino

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