AI-as-a-Service, trendy?

I’m observing with good interest the move into ”Data / AI as-a-Service”. I mean the combination of APIs and AI algorithms. Have you also noticed this trend?

In short. We have all been speaking about the rise of AI, APIs, Big Data, cloud technology, Internet of things (IoTs) and sensors technologies for years.

With AI-as-a-Service, I am talking about combining all those technologies together to access information from devices like electrical networks, production chains, or electric vehicles.

The objective is to provide added-value information on consumption, equipment, etc to business units and partners. But, clearly, we are going beyond traditional reporting. We are responding to real-time needs or issues:
– How to optimise electric vehicle charging or production cycles?
– How to optimise the energy consumption of photovoltaic panels or air conditioning for instance?

More and more organisations are deploying this strategy. I am continuously observing it. Especially in the manufacturing, energy, or automative industries. In Financial Services, the move is very similar: The combination of Open Banking, API and cloud technologies, combined with a banking license, fuels the rise of the Banking-as-a-Service offering.

More:

CRM systems are increasingly powered with Artificial Intelligence (AI)

One striking example I have seen is within the energy sector.

In this case, several data sets are exposed “as a Service” to internal units and partners to display energy and equipment dashboards and alerts: Alarms on equipment, time series on the energy consumption of buildings, and the like.

The effort started with setting up a data factory, where data engineers, data analysts and data scientists, regardless of the business unit, could test and implement their use cases. The data related to the consumption of water, electricity, and gas is aggregated and then integrated with the information related to the equipment and the topology of the buildings. A data transformation pipeline which is connected via APIs to the IoT automation platform, consolidates this real-time IoT data, standardises it, and refines it.

This sounds very technical. I agree. But what I like best is the business perspective of it: The connection with an ecosystem of partners and with marketplaces: Offer access to data sets, pipeline and algorithm libraries, off-the-shelf connectors that would allow partners to enrich their own offers. This is very interesting!

And while those business perspectives are important, the key question remain: How to monetise?

Interestingly we are starting to see some monetisation and data set sales testing.

Let’s follow the developments!

About the Author

Didier Dessens

CRM and Digital Experience Advisory

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