Episode summary: Imagine you work in a large organization with tens of thousands of employees across multiple countries, a business that’s been around for over a hundred years, and all of a sudden you have people in one department who are interested in applying chatbots, colleagues in another department who wish to implement sentiment analysis and still another department that wants to begin using AI for fraud and risk analysis. How do you manage to put all these pieces together?
That is exactly the situation that Muriel Serrurier Schepper found herself in. Muriel is the Business Consultant Advanced Data Analytics & Artificial Intelligence at Rabobank in Naarden, Netherlands. In this episode, Muriel and I discuss the Artificial Intelligence Center of Excellence at Rabobank, where she manages projects and has connected ad virtual and physical team across the company which is comprised of over 60,000 employees spread across the world.
The role of this interdisciplinary team is to make sense of artificial intelligence applications, find the right vendors for these applications and make the most intelligent decisions about projects and allocation of resources for the company as a whole. This allows the company to make, broader, stronger decisions across multiple departments rather than having each department use AI independently. In this way, AI becomes a driving force for the company rather than just a novelty.
We were put in touch with Muriel through a kind connection from KNect365, a publishing and events firm based in London.
(Readers with a strong interest in the banking space may be interested in reading our full “AI in Banking” article from earlier this year. The article includes assessments of the the AI applications and initiatives of America’s top 7 banks – ranked by revenue.)
Guest: Muriel Serrurier Schepper
Expertise: Setting up an Artificial Intelligence Center of Excellence (AI Cell).Generating an AI project pipeline: a.o. RFI’s artificial chat & Automated Speech recognition. Managing AI projects: Artificial chatbots, IBM Watson Explorer projects, Pepper Robot projects. AI research projects: Enterprise Crowd Sourcing (TU Delft and IBM Center of Advanced Studies) and Techruption projects. Inspiring internal and external people about Applied Artificial Intelligence (AI) at external conferences and internal workshops and events.
Brief recognition: Muriel Serrurier Schepper is currently the Business Consultant Advanced Data Analytics & Artificial Intelligence at Rabobank Digital Bank in Naarden, North Holland Province, Netherlands.
Bringing AI applications into a large business is an orchestrated effort across departments, requiring an alignment around a company’s needs – and an assessment and allocation of it’s data science / AI talent resources
It may be important for large, well-established businesses to develop some form of “AI center for excellence” in order to implement AI usefully. This entails bringing together a virtual team of experts for each department. For example, in banking this may mean experts from customer contact, fraud and risk management, and data analysis. This prevents the needs for and the implementation of AI solutions from becoming fragmented throughout the company.
The value factor here lies within establishing a team which can work together to asses the need for AI across the company network and then make informed decisions about which solutions can help multiple departments. This makes the most of company knowledge and experience as well as available resources.
Insight to Action: How can other big enterprises implement this idea? Below are some of the points and principles that Muriel highlighted as most important in her own experience:
- By creating a multi-disciplinary team: When creating a center of excellence, one of the initial and most important steps is to put together a team of experts from each department so you can discuss the needs for and implementation of AI across the whole company.
- By facilitating communication: Encouraging intra-departmental communication allows employees to keep abreast of which AI solutions are need where, which one are being piloted, and how they are working out. Finding areas where projects can overlap creates an aggregate benefit for the company overall. This also enables a company to run simultaneous pilots with different vendors to explore the true potential of AI solutions.
- By seeking out problematic areas within departments: Interdisciplinary discussion of departmental needs highlights the areas where different forms of AI can strengthen the company as a whole. While deploying these AI solutions across the enterprise can be a challenge, it prevents the fragmentation of intelligence and resources. This also allows companies to combine budgets and create larger pilot studies.
- By maintaining realistic expectations: Implementing AI throughout an enterprise is not going to eliminate the need for a human workforce. Although many AI solutions augment the jobs of human employees (such as collecting and collating data) humans will still be needed in many areas to contextualize this information.
(Readers who are just beginning to apply AI within their business may want to read our popular beginner’s guide titled: “How to Apply AI to Business Problems”.)
Interview Highlights on Rabobank’s Lessons Learned in Applying AI:
The following is a condensed version of the full audio interview, which is available in the above links on Emerj’s SoundCloud and iTunes stations.
(3.50): What got Rabobank to a point where you knew that the company needed a specific locus of control for these decisions?
Muriel Serrurier Schepper: What we saw at the end of 2015 is that many domains within the bank which operate in silos were looking into different AI solutions and were talking with a lot of vendors…We realized if we carried on this way, we would end up with four different vendors and we would let the vendors learn a lot about applying AI in business, while we ourselves would be very ignorant.
We also saw that a lot of people who were dealing with these vendors actually didn’t know what they were talking about at all. So we felt it would be good to create a center of excellence from where we could coordinate and bring in knowledge to select and implement AI solutions within the company an also being able to bridge between the different silos to help them work together and find one solution together.
(5.38): Who is actually there in this “AI center of excellence?” What kinds of staff need to be a part of this kind of effort?
Muriel Serrurier Schepper: It’s actually a virtual team and we work with people from the innovation department where they were already in place concentrating on AI for a while. We also work with our fraud and economic crime department where a lot of machine learning projects are going on…the cell itself is also located within our data science department.
(8.11): What are some of the different initiatives that are now rolling their way out across Rabobank that were not in existence two or four years ago?
Muriel Serrurier Schepper: A good example is a chatbot. We had a large customer contact center and IT helpdesk, which offer chat services all still done by human agents. We started to kick off last year, a project of reviewing the market…selected two vendors and we are now undergoing to production pilots with the chatbot.
(17.06): What are some of the challenges you’ve run into and lessons learned in taking a larger, more established company and aiming to bring AI into it?
Muriel Serrurier Schepper: A lot of AI companies come in…and tell us there’s no humans needed any more. Then we start a project and oops, we still do need to do a lot of work ourselves…That continues throughout all levels within the company.
(22.38): What have been the demands for AI talent within your company and how have you dealt with that?
Muriel Serrurier Schepper: So far, with the solutions that we’ve chosen is that they already incorporated some AI models in their solutions, so we had relatively small involvement from our data science department…The advantage I see from that is that you can move faster because these companies have already created something and that is their expertise.