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Organizations havent been able to capitalize on the exponential growth in unstructured data in recent years despite the availability of sophisticated machine learning tools, according to the latest research from Info-Tech Research Group.

When it comes to the strategic use of machine learning, a quarter of respondents in Info-Techs latest tech trends report claim they wont be mature enough for at least another four years. Thirty-one per cent expect at least another year before they can hit the ground running. Just under 15 per cent claim theyre mature enough today to use machine learning to actually augment business. Out of the more than 200 global survey respondents, most of whom work in IT as a manager or director, 59 of them were from Canada.

It was a bit surprising to hear that technology which has been available for many years is still failing to be turned into a transformational force within organizations, according to Brian Jackson, Info-Techs research director for CIO, strategy, and digital transformation.

The technology is very available now, Jackson said in an interview. And we are seeing some organizations use it to build chatbots and other tools to automate customer service.

But Jackson says its a bit alarming to see such a lack of innovation around the use of machine learning outside of the startup scene.

Its like when people in the year 2000 thinking oh, the internet. I dont think thats going to be a big deal, he explained.

It reflectsa lack of maturity plaguing most IT departments. Only six per cent of survey respondents felt their IT departments maturity level had the capacity to drive change across the business. Even with more than 70 per cent of survey respondents noting AI and machine learning will be very important over the next five years, only 14 per cent felt their IT was ready to expand the business. Most organizations feel that IT is optimizing the business, while 34 per cent view IT as a support mechanism.

The streetwear collection business is tough. Getting your own collection off the ground, combined with having to source out designers, pattern cutters and merchandisers youre probably looking down the barrel of a six to eight-month process. Toronto startup Urbancoolab is an AI-powered fashion design platform designed to reduce the headaches associated with that process.

Info-Tech cited the startup in its research as a prime example of AI and machine learning running at the top of the value stream. The research firm went as far as to say the startup is reinventing a business category. Its tough to argue with the results.

Since 2020, Urbancoolab has worked with 30 celebrity artists to launch commercial designs. The research paper highlights how the startup can take a new design to market on its e-commerce site within 24 hours. Urbancoolab can find patterns in unstructured data in ways that humans cant, providing new designs rapidly. It can also be used to help confirm which designs will find the most market success. This lightning-fast turnaround is a big deal, but larger businesses playing in the same arena are simply not as nimble.

Many large companies lag behind disruptive first movers because they adhere to legacy processes and technology stacks, Info-Tech noted. That organizational structure was created long before AIs emergence, so applying AI in a meaningful way is difficult. Theres also a scarcity of true AI talent available on the market.

The untapped potential of AI and machine learning is obvious, but so are some of the uncertainties. Machine learning algorithms are only as good as the data used to train them. If the algorithms running underneath your datasets hood are limited or flawed, thats bad news for the company.

Most companies are in no position to hire a skilled AI scientist, making talent really hard to come by. Combine that with the ongoing privacy concerns related to machine learning algorithms touching customer or employee data, and businesses are faced with several uncertainties when asking IT to go beyond supporting the business.

Jackon says channel partners have an obvious opening to address these gaps.

Modern channel providers should look at themselves as the central service that your customers can rely upon to transform their business, he explained. We need companies that are able to look outside of themselves and look at opportunities to inject innovative new ideas by working with other companies in the same industry.

Info-Tech hosted a webinar recently going over some of the data from its trends report. An on-demand link can be found here.

Jim Love, Chief Content Officer, IT World Canada

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Research says organizations still struggle to cash-in on machine learning - IT World Canada