How to solve AI outsourcing challenges? Part 1

The problems we discussed in the two previous articles, can be solved in three ways:

  1. You change your organizational structure to become A.I. ready
  2. You choose a more expensive A.I. outsourcing partner which does not only delivers outstanding solutions in A.I. but also covers everything else from process to project management and has deep knowledge in your industry.
  3. You hire intermediary consultancies who connect both worlds.

1 – Changing your organizational structure to become A.I. ready

Many people think that being A.I. ready just includes technical changes. We saw over the last decade that many big and mid-size companies invested a lot of money in upgrading their IT and database architecture. But that is just one part of the story. Companies also have to have the right processes in place and staff to envision, plan, execute and maintain A.I. projects.

Many of us have been in leadership innovation workshops about topics no one at the table really understood. Although the food and the setting might have been inspiring, the outcome and the realization of the ideas however was slim. Why? Because getting A.I. ready needs to involve the whole company and especially an educated top management. Hiring three data scientists and locking them in an office does not really yield the highest outcome.

A.I. needs to be first and foremost understood, fostered and actively lived by the top management. If the top management does not understand the need and necessity of change and does not understand the basics of artificial intelligence and data science, how can they make the ultimate decisions for it? So a broad leadership education is key to getting your company A.I. ready. Moreover, A.I. needs to be continuously represented at the table of the top-management, e.g. as a Chief Data Officer (CDO) or Chief AI Officer (CAIO). Many times, it can be seen that the topic of Data or A.I. is not found in the organizational chart. There might be a CTO or CIO, but data or even A.I.?

Furthermore the organization as a whole needs to change how they are conducting their business. Many, many companies nowadays still live with post-its, fax, in-house phone-calls and piles of papers on their desk. Most of the times, we see that changing to creating continuous data points during the work leads to a lot of resistance within the companies. If we want to know who is doing what when and why, employees have to be much more transparent in their way of working. Time tracking, use of ticketing systems, correct documentation, the actual use of specialized software like CRMs or JIRA. And furthermore the top leadership has to join in too and demand this change in behavior.

Moreover, the company needs to hire a diverse group of A.I. personnel to actually envision, plan, run and implement the projects from a business and technical standpoint and each project might demand different data scientists. Because not every data scientist is able to conduct every A.I. project in the same quality. Someone who is specialized in autonomous driving might not be the best fit for creating and analysing texts. The question I always ask my clients is “Congratulation, you hired your first data scientists. Please point to the person in your organizational chart who will be able to lead them in a professional way?”

As you see, changing your company to become A.I. ready is a long-term project which involves changes in culture, processes and staff.

In the next section, we will describe on the one hand at one-stop A.I. outsourcing partners and on the other hand at the mix of intermediary consultancies with specialized  A.I. outsourcing partners.

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