How collaboration between traditional companies and tech start-ups creates a winning team

Reiner Kurzhals


In my first RISING GIANTS (I) article I wrote about how tech start-ups can help traditional enterprises, in particular SMEs, to leverage the massive hidden data potential through AI & Machine Learning. Now, I will explain how this works operationally.

In a nutshell:

  • Start very small, very fast, at low cost and with top-level support.
  • Involve only a few people but choose people who love to work with data.
  • Pick the low-hanging fruit: Identify one use-case and solve it within 4 weeks.
  • Implement this use-case in your daily work to make working life much easier and more effective.
  • Spread the word about your success and develop new fans of your work - and then move on to the next use-case.

1. Start very small, very fast, at low cost and with top-level support

Make sure you have support at the top C-level and convince them to start out with a pilot project as a proof of concept (PoC). The advantage of a PoC is that we can start quickly, with a low, well-defined budget and a clear time-frame to finish (usually within 4 weeks). This way you can prove that the data-driven approach is superior in terms of results and return on investment than traditional approaches. Even in the rare event of a negative PoC, the lean approach allows you to save time and money – it prevents you from going in the wrong direction early on. Thus, the overall risk of investment in terms of money and time always remains very low.

2. Involve only a few people, but choose people who love to work with data

When beginning a data science project with a proof of concept, it is always recommended to involve just a handful of people. All of them should believe in the PoC. In a best-case scenario, they should identify themselves early on as true “fans” and should always have an open mindset towards new technology and new ways of doing things. Basically, they are advocates of data and true believers of everything goes digital! Do not conceal the PoC from others, but refrain from spreading the word too early and before you can demonstrate initial results.

3. Pick the low-hanging fruit: Identify one use-case and solve it within 4 weeks

As part of the proof of concept, the core team should identify one use case that does not involve the entire organization and where most (ideally all) of the data is already available. For simplicity’s sake and to guarantee fast results, i.e. implementation within only 4 weeks’ time, it might be necessary to conduct the initial PoC on just a sample of the corresponding data. Finding a suitable use-case is one of the biggest challenges and not an easy task. However, experienced data science experts usually can accomplish this quickly. Finally, once the PoC is completed, results should be spread with all people involved operationally. Seeing the benefits of eliminating routine work tasks will automatically infect them with your enthusiasm.

4. Implement this use-case in your daily work and make working life much easier and more effective

The goal of the proof of concept is to demonstrate that the technology used, principally machine learning models, works on the selected (sample) data. As members of the PoC team see that much routine work is eliminated and effectiveness is dramatically increased, the PoC needs to be shifted into a dynamic mode, based on (nearly) real-time data. Simply put: It is time to make the PoC part of your day-to-day business. Be careful not to overwhelm your team by immediately implementing the next front-end system. Instead, incorporate the results provided by the intelligent machine learning solution by making the existing systems smarter. Providing your employees with better support in an environment they are used to, will allow you to gain their support much faster.

5. Spread the word about your success and develop new fans of your work - and then move on to the next use-case

Assuming the PoC is successful, spread the word by pointing to concrete results. For example, “We outperformed the internal sales forecast by 25%, where each percentage point gained is worth $100,000 a month. The money saved is due to efficiency increases along the entire supply chain. We made our principal improvement by reducing stock costs.” Emphasize the fact that increasing efficiency, usually by replacing routine tasks through machine learning, increases on-the-job satisfaction drastically – employees can now devote their time to much more fulfilling tasks. Seeing such an improvement to their daily work, it is guaranteed that employees will become fans quickly.

A successful first PoC is only the first step along your company’s transformation journey into a fully data-driven company. It is now time to improve the daily work of all your employees. Thus, eventually all your employees will become true data advocates.

In my next article (Rising Giants III), I’ll write about specific industries where AI and Machine Learning use-cases create significant returns on investment.

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