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Quick Guide: Introducing AI To Your Company

31 July 2018 , by Dries Cronje

AI is mature enough to add value to most industries. However, introducing it to your company can be quite daunting because there are no off-the-shelf-solutions or silver bullets. I have been through the same scenario a few times and have taken notes. Here's a three-stage process that will get you started.


Things to keep in mind while working through the guideline:

  • “Only sith lords deal in absolutes” - I’m not putting a time limit on steps, nor am I telling you what technology to use - explore, take risks - you will know what technology is a fit, and you will know when you are ready to move on to the next phase.

  • “Explore/Exploit” - in the beginning, try many different technologies and ideas (explore), and as you go along consolidate (exploit), BUT never stop exploring completely.

Stage 1: Solve a simple problem

This step is about building confidence within your company and showing the value of AI to them.

You don’t want to spend too much time in this first step - we are optimising for short iterations so you quickly have something to show for your efforts. Think: Short Build - Measure - Learn - Repeat - Cycles. Else it might look like you’re just throwing spaghetti on the wall or you could be wasting months on something that doesn’t end up working out.

Tip 1: That’s why you should choose a simple problem and favour those that have labeled data in abundance.

Tip 2: Based on this, choose a clear metric to measure success. AI is supposed to improve something and you need a benchmark to compare its performance against.

Tip 3: Build a team. It’s hard to do everything by yourself. Two to four people whose skills complement each other is ideal to build powerful systems - you stay lean and keep the management overhead low. Some useful skills are:

  • Data engineering,
  • Analytics,
  • Statistics,
  • Deep learning,
  • Machine learning,
  • Domain knowledge and
  • Presenting the findings and results in an engaging manner.

Tip 4: Make learning part of the process: Every cycle should inform how you go about your next one, so really look at your metrics, how the business responds to your report and what has worked or not worked.

Tip 5: Tell the story. This is super important because if you can’t effectively communicate the value to the rest of the business, none of what you’ve achieved or learned will matter. That’s why you should:

Once you’ve done this and gathered feedback, repeat!

Stage 2: Natural Progression (Automate, scale)

After having created excitement for your AI-efforts, this step is about collaboration, automation and scaling. You’re expanding to other teams, growing your own and reduce manual efforts.

At this point, people and departments in the company have started coming to you with build-requests that are larger and more complex than your first projects. You won’t be able to maintain the manual labor you and your team have put in in step 1 and you’ll have to scale. You’ll also know you’ve moved into step 2 once you start asking yourself the following questions:

  • Where would you do AI without interfering with existing production systems?
  • How do I scale this?
  • What tools do I use?
  • What are the roles going to be when the team grows?

Tip 1: Now it’s time to think cloud. Avoid platforms trying to lock you in - the best platforms follow industry-proven best practise and let you plug-and-play.

Tip 2: Should you go this route, go for a consulting partner who is willing to get involved - avoid sales pitches from suits.

Tip 3: Create data processing pipelines. Manually data processing takes huge amounts of time and has to happen ongoingly. Automating these processes is the easiest way to create value quickest.

It’s easy to forget in this step, but remember to stay lean: Build - Measure - Learn in short cycles.

Stage 3: AI as a platform

This step is all about exponentially taking advantage of AI across the entire business.

You and your team now have a number of successful projects with different departments under your belt. You have created enough excitement in the company to move into the next phase and turn your AI efforts into an API that people can easily make use of:

Golden rule: If it’s useful to one department, it’s probably useful to other departments.

Growth from solving individual problems is linear - growth from an always-available and easy-to-integrate platform is exponential. Think Google and how AI is used across services and products.

Tip 1: Take everything that was useful to a handful of people or departments individually and make it available at scale across boundaries to the rest of the organization.

Tip 2: In order to be effective, your AI-API has to be part of a bigger data strategy (or, alternatively: If your company doesn’t yet have an explicated data strategy, this might be a good forcing function to implement one) .The following issues will have to be addressed, to name a few:

  • Privacy concerns,
  • Security - who do you give access to what and
  • Cost.

Building an AI-API is obviously not a cheap undertaking. But while the cost, both time-wise and financially is big, the rewards are huge:

  • It makes your entire business run more effectively and efficiently - AI doesn’t just have to have outward-facing benefits. It all starts by freeing capacity and optimising processes.
  • You can expect exponential growth - If you make even incremental value accessible to several different departments, your API’s value grows exponentially. Google would never have been able to build quite so many products in such a short span of time had they not made their AI available across their different platforms.
  • You’re possibly opening up new opportunities you didn’t even know possible.


Dries is the AI lead at vehicle recovery company Tracker. Designing AI and cloud strategies, supported by tools and processes, is at the core of his daily endeavour towards AI excellence. When not building AI systems, Dries speaks at events, co-organises the Google Developer Group Cloud MeetUp and blogs.


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