Will AI be the next big thing?
As many a marketing or salesperson will attest, trying to flog the same old, same old becomes troublesome after a while. Something is needed to excite the market – and luckily, technology is an area where there is always something new and exciting in the works. Now, the biggest thing is artificial intelligence (AI).
However, alongside this are plenty of other areas – many of them allied – such as machine and deep learning (ML/DL). Advanced analytics is also a relatively untapped market, along with cross-organization collaboration and workflow management. Finally, hybrid working, forced on organizations by the COVID-19 epidemic, is now bedding in and requires some innovative services to ensure it works effectively.
The problem with the big one (AI) is that it is a bandwagon now. Almost every market seller is putting forward its existing products as the latest in AI – even though they are not. As with next big things (NBTs) throughout the ages, AI comes with an unhealthy dose of AI-washing. Far better to stick to things that your organization does offer – and what differentiates you in the market.
For example, start with the simple stuff:
Sure, this has been around for some time in one guise or another. Many workers have always worked away from the main office – field workers, remote workers, etc. However, the business has paid scant attention to them.
Now is your chance to sell something that really helps. Collaboration services that bridge the gap between the office and home environment (videoconferencing, workflow, cloud-based file storage, and collaboration) may sound like something other than an NBT. Still, a real, working solution is more important to an organization than going after the promise of something AI-driven.
No organization is an island. However, all too often, systems that stop at the organization’s perimeter have been implemented. That is not how organizations work – they need systems that streamline their suppliers and customers. While many will have automated ordering and delivery systems in place, others will not. Again, from product identification through contract negotiation to delivery and support, collaboration and secure workflow solutions are needed.
The more that these systems can be cloud-based, the better. Many organizations wish to avoid placing their data in the hands of a customer or supplier so that a third-party cloud-based MSP can be seen as a trusted third party. Again, not seen by many on the sell-side as an NBT, but certainly something exciting for the buy-side in many circumstances.
How about pseudo-AI?
This is what many are selling as full AI currently – and buyers are not happy with the outcome. Machine and deep learning, while an underpinning of actual AI, is more a case of creating data sets, recognizing patterns and events within those sets, and then kicking off an event based on what has been found.
An excellent use of ML/DL is in low-touch servicing. As an MSP, you may monitor a customer’s devices or network activity from afar. ML/DL can help to identify issues way before humans could. This is even more the case where an MSP monitors many customers’ environments and can aggregate the data anonymously.
Through ML/DL, the possible/probable failure of a device can be identified before it fails. Spare parts or engineers can be sent out to fix the issue before it also becomes impactful to the client. Similarly, on a security basis, ML/DL can help identify zero-day issues and can either patch a customer’s systems, block certain activities, or create a workaround so that the security risk is ameliorated.
The above needs advanced analytics
A massive database of data that is not analyzed is just a waste of IT resources. This is where analytics comes in: the use of solutions that can identify what is happening or what is likely to happen. Dashboards can be created for customers that show them what was happening to their environment – and what steps you, as the MSP, took to prevent or ameliorate any issues. This saves the customer from any impact.
Advanced analytics can also help to optimize maintenance windows. Many customers will work on a fixed cycle – for example, certain items or sub-items will be replaced after a certain period, whether needed or not. Suitable analysis of a large data set across more than one customer may show that such replacement cycles can be extended quite drastically, saving money, and helping customers in their sustainability endeavors.
This then brings us to true AI. First, what does the customer or prospect believe true AI is? Second, what are they hoping to get out of it? In this case, the desired outcome is far more important than the method to get there– which brings us back to what is the next NBT? Many desired outcomes can be delivered without the use of any NBTs, or by bringing together standard technologies and a bit of pazazz to create something that is more novel.
Overall, the key is to avoid trying and bamboozle the customer/prospect. It is to demonstrate that you can solve their immediate issues in a way that is provable and cost-effective. Once AI is a proven concept, AI bots that calmly listen to customer issues and provide deep, meaningful advice can be sold. For now, let’s just have AI create cute pictures of cats and have meaningless conversations with people on the internet.
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