The concept of Artificial Intelligence has been in the public domain for decades, usually in the form of a doomsday vision within a string of Hollywood sci-fi releases (insert Terminator reference here). However, as people increasingly recognise the less-scary realities of Artificial Intelligence, increasing numbers of businesses are starting to sit up and take note of its practical applications, particularly as the first movers start to reap its rewards.

But let’s take a step back. What is Artificial Intelligence, or ‘AI’? AI covers a wide range of software applications and algorithms which are not only capable of performing tasks normally requiring human intelligence but can perform them faster, more accurately and on a continuous basis (24/7) compared to its human counterparts.

Its applications are rapidly growing, but some current typical applications include:

  • machine learning and predictive analytics
  • speech recognition and natural language processing (NLP)
  • text language translation
  • image analysis
  • process decision making (Robotic Process Automation or RPA).

Reading this list, some possible applications to your business may already be starting to materialise. Irrespective of your industry, the chances are some of your competitors are already exploring those applications, if not implementing them.

For your organisation, the reality of implementing AI may currently sit on a far and distant plan. A wonderful dream of ‘what if…?’. It doesn’t have to be this way.

The reality is that the commoditisation of AI is within touching distance. So the question isn’t ‘what if…?’. It’s ‘are you ready?’.

The first step to utilising AI

If you’re looking to adopt AI into your organisation, take the first step and recognise ‘it’s not about AI, it’s about getting my data house in order’.

AI solutions are often associated with data analytics and ‘big data’ because at their core they process large datasets in order to learn patterns and rules, or ‘train’ predictive models.

Therefore, AI can only ever be as useful as the data you feed it. So, if your data isn’t accurate, relevant and timely, your AI implementation is never going to deliver any value – it will end up another failed IT project.

Now let’s consider how you can deliver data transformation within your organisation to pave the road to the benefits of AI that lay beyond.

Define your data vision and strategy

Your data can tell you everything you need to know about your business and where you should be focussing your efforts. It can highlight the products that are performing best/worst, identify new markets to move into, provide deeper insight into the buying behaviour of your target market and tell you how to better engage customers.

Therefore the first step of any data transformation should be to understand your current data vision and strategy to realise this, i.e. what you are trying to achieve through your use of data and analytics. This will inform some of the key decisions you need to make, such as:

  • What sources of data do we need?
  • What resources do we need to manage this data?
  • How heavily do we need to govern this data?
  • What analysis and insight is required to support our strategic decision making?

Shape your data operating model

The majority (85 per cent) of big data projects fail to meet expectations because people get hooked on the latest ‘shiny’ technology and jump in with both feet; they fail to properly consider the people, organisation, culture and execution in its entirety.

Reviewing your current data operating model, including your data assets and resulting analytics, your in-house talent that handles these inputs and outputs, and the wider firm culture and its alignment to your data vision can help you understand how your current setup is holding you back. From here, it’s about crafting these pillars of your business to underpin your data vision.

Land early successes then scale

Any complex data project is delivered in vain if you don’t make the change stick. This requires ongoing senior sponsorship and a workforce that recognises the value to be realised.

Therefore, delivering proof of concepts and evidencing early benefits can provide the impetus for more significant data transformations. For example, focusing on one area of the business or tackling one of your data operating model pillars including your assets, analytics, OD or culture first – then using these early successes to whet your organisation’s appetite for bigger wins down the road.

Are you ready now?

With the commoditisation of AI, entire industries will and already are being transformed. It’s a huge change when compared to the way we operate today. And while it may sound intimidating, it should be viewed as an enormous opportunity you can take advantage of. By recognising this now, you have the opportunity to set your data foundation and ensure you’re in a solid position to catch the AI wave when it hits.

If you have any specific questions about how to address the data challenges in your organisation, contact us and we’d be happy to offer some advice.


Simon specialises in large-scale digital and data change. He has delivered complex transformation programmes across multiple industries. In addition to being integral to the Data Transformation team, Simon is also Gate One’s AI lead.