What does the future hold for artificial intelligence (AI) and machine learning (ML)? Is 2022 going to be the year where machines will prevail? While AI used to be something software vendors could brand their solutions as (even when they weren’t), times have changed. With a market—in a helicopter view—divided into two camps, vendors who use a predefined AI framework and vendors who create their own, we must understand what it means for users.
What are the key barriers to AI adoption?
Financing
The first barrier to AI adoption is financing. Solutions using real artificial intelligence (AI) are often tagged as “advanced,” and they come with a price tag. The goals of implementing new tech are usually about reducing cost and increasing efficiency. We saw the same with the invention of looms, the production belt, and the increased use of automation in IT.
Now it’s merely the next chapter, but the calculation remains the same: can we reduce the operating cost of human labour with the new technology? Where’s the break-even point?
Take IT security as an example. How many hours does it take to investigate suspicious behaviour? How many analysts are digging through log files, and how much does it cost overall? It’s a task you can easily outsource to a machine, which will show results within seconds—where humans would need hours. While this doesn’t make the analyst’s role redundant, it certainly allows them to be reassigned to the tasks requiring creativity.
However, as previously mentioned, with increased AI adoption, affordable solutions are more readily available. It’s no longer only the global players who can afford the latest technology.
In the next 12 months, we will see an increased adoption rate, from large enterprises to SMBs. The outlook is now shifting from “we don’t need this stuff”—to “this could be interesting. Let’s try it.” This stance will only become more common as the year progresses.
Harnessing Solutions
This brings us to the second obstacle to AI adoption: complexity. All AI-based solutions require customisation to succeed, regardless of whether it’s off the shelf or bespoke. This usually requires development resources. It doesn’t matter if they’re provided by the vendor as a package deal or taken from in-house talent, as the latter would increase the cost again.
Here too, we see an advanced use of technology, or a mix of different ones, to be precise. Some of these solutions come with what’s called low/no-code interfaces, and everyone who can create meaningful charts can deal with such a system. The consequence of an AI solution is a lack of trust. An artificial intelligence solution, like humans, will make its own decisions.
And like humans, it’s a process based on experience, knowledge, and training. But who provides this training? The bottom line is the more this learning process happens in-house, the higher the trust level. But it doesn’t end here. Once implemented, an artificial intelligence-based solution should be doing the hard work and improving efficiency while in use.
Hello, Computer!
Most of us deal with business software everyday. It could be customer relationship management (CRM), resource management, warehousing, or lead handling in sales. All of these come with reports, and though they may have varying levels of quality, they usually do the job. But occasionally, we require something specific, and this is where the problems start. Some of these solutions are dinosaurs and don’t make it easy to create custom reports.
It’s not unheard of for a DBA to get dragged out of their usual tasks to create a custom query and retrieve some data points the CEO requires for a meeting. Wouldn’t it be much easier if there was a box for free-form text? We could simply write, “give me the sales numbers for APAC, segmented by country,” and it would result in a nice chart in 20 secs.
This isn’t science fiction—in fact, many of us have used a chatbot already, which is what it is. We now need to improve the quality of the responses instead of, “Sorry, I didn’t understand the command.” We will get there eventually. AI- and ML-based solutions will inevitably become more accessible in 2022 as their affordability and ease-of-use increase.
In a hybrid world still finding its way out of a pandemic, human interaction continues to be limited. With this in mind, 2022 might be the right time to take advantage of these solutions.
Sascha Giese is the Head Geek™ at SolarWinds.
