The Business AI sector is very different from the consumer one. In the last five years, AI has finally been proliferating the enterprise world, which is very different to work with than your classic B2C scenario. B2C is much more reactive — you build something and you throw it at a million users and see what happens. And if it doesn’t work, you can pull it back.

With B2B, you cannot do it because you have a limited number of customers and each one of them is paying you. That’s why AI vendors need to do the research in advance and make something enterprise clients want. Instead of having a large number of users out there that all contribute a little bit of data to you, business AI vendors may have very few users that contribute a lot of very valuable, very private data. 

The Transfer Of AI Opportunity From B2C To B2B

For AI companies, wherever there is an area of human activity that generates activity data, there is a ripe opportunity be it B2B or B2B, such as collecting data for shipping containers, data from a wind farm, recording emails from a salesperson, recording location metrics of drivers, etc. 

As long as people can collect reliable comprehensive non-manually entered high volume activity data from wind farms, from shipping containers and from many salespeople in one place, we can start seeing the big picture and then use it to analyse the macro trends and predict what the best next action for operators such as the salesperson and the Uber driver can be. That’s where it brings in network effects and significant acceleration of growth. 

The better the predictions are, more and more players will be lured in to become part of the network, contribute even more data into this shared graph which becomes kind of this virtuous cycle. 

In the past, we have seen that when data model changes, systems of record may also die out. For example, there were systems of record like CRMs built on not relational but on hierarchical databases in the 70s. Today, they don’t exist anymore. As the data model for the AI shifts, the way we consume the software is changing.  According to experts, the next generation of data model is likely to be a graph-based  that allows you to train AI in the best way possible way. 

Lesson From B2C: The Bundling Of Business AI Services 

There are cycles of bundling and unbundling playing out here already in the latest wave of machine learning applications. The use cases are getting increasingly precise and tailored to the end-user. As a consequence, we have systems which are now richer and deliver more promises to the user and then help them do their job and more and more ways. 

We see this happening in B2C where the bundling of services is happening as companies figure out the right workflow. B2C companies are designing their products to be simpler. The UX is designed such that there’s a level of intent that you can observe from the user. 

Take Uber as an example of bundling of services. Started as a cab aggregator, the platform has extended to other businesses like food delivery, freight and other things from the same app. Once the company finds an optimal user interface that allows for suggesting best next action, then it just makes sense to bundle in more and more functionality and take over more and more of the attention span of the users. 

Industries are switching into the business model of collecting activity data, understanding its scale and turning into best next action in the years ahead. The more sensors, edge computing devices salespeople and Uber drivers you have in the network, the more data you collect the more the patterns of behaviour you see. When you put them all into one centralised graph, the smarter this graph becomes for everybody the better the predictions it can produce about the best next actions. 

Similar to B2C, the next wave of business AI software makes workers lives easier through increasing orchestration of the knowledge worker labour force. Instead of you pulling data and looking for it in a bunch of excel reports, salesforce reports or websites, it’s actually being pushed in prepackaged personalised actionable insights. Here, everything workers need to know to complete an action is right there, pushed through one single channel through which workers are most likely to engage. 

What The Future May Look Like For Business AI

10 years from now, Fortune 500 companies may look very different than now because some enterprises have not been collecting data and training their ML models early enough. But, on the other hand, it can be tricky for AI to succeed in the enterprise as you have to make the organisation comfortable with your security posture your privacy poster. It’s much harder because you have to make those companies very comfortable with the fact that their data will be anonymised, aggregated and will not be shared with competitors, will not be leaked if you get hacked. 

The flip-side here is the value you create with AI will be significantly amplified by the historical proprietary knowledge of the company. So, we need to know what’s in the CRM in order to not give enterprise users random suggestions. Therefore, having access to this proprietary information that a company has under the lock by the security and IT is where you have to go and be very transparent and open. This requires working with the IT security teams to only get access to data but also work with them to explain to them the value that the end-users are going to get.

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