How AI adoption has yet to reveal its real potential

Top artificial intelligence (AIScientists warn that radiologists may be forced out of their jobs by deep learning. to This is what healthcare professionals recommend. AI The doctor-patient relationship will be redefined to Tech executives promise that fully autonomous cars will soon be possible. AI has In recent years, there have been many failures in predictions.

Despite all the amazing advances made in AIIt has yet to Play its In many industries, this role is transformational. But, when you compare it to other industries, to It is not surprising that other technological milestones, such as the steam engine and electricity, were also achieved. AI adoption Slow.

Ajay Agrawal (Professor at Toronto University) and Joshua Gans (Authors of the New Book), are Avi Goldfarb and Avi Gans Prediction and PowerBelieve that we have reached a point in which the power of AI It is obvious, but its widespread adoption has yet to come. And to You can better manage the challenges that are in your way of harnessing the power of AIIt is important to understand both the uses and the systems it operates in.

Systems and point solutions

In Prediction and PowerThe authors simplify current AI Technology is software that predicts outcomes. For example, whether a customer will buy a product they recommend or whether a financial transaction will turn out to be a success. to Fraudulent. It is clear that machine learning (ML), models can now make amazing predictions when given the right training data. 

It is however, a matter of when. to Organizations must face different challenges when integrating predictive machines into their products and applications. 

“AI’s technical advances were and continue to be very impressive. So it is natural to expect that their applications may grow at the same pace,” VentureBeat spoke with Gans, Agrawal, and Goldfarb. “They haven’t, and our research set out to find out why. We ended up thinking beyond the AI point solution that people were focusing on, thinking about the practicalities of realizing value from AI in current systems. It was clear that there was a problem. To really use AI you have to be open to a wider set of actions but, for many organizations, they are not prepared for that.”

Point solutions are low-hanging fruits. AI. These are applications that allow organizations to already perform prediction. One of the examples that the authors  mentioned is VerafinA Canadian company called. AI to Predict fraud. Based in St. John’s, Newfoundland, Verafin became Canada’s first AI unicorn, acquired by Nasdaq 2020: $2.75 Billion Neither Verafin nor St. John’s was on the radar of analysts who were making predictions about commercial AI In Canada in the past years. 

Verafin’s success can be attributed to its implementation of an important strategy AI Point solution. Predicting fraud has Financial institutions have always had a significant role in the work they do. Replacing their existing systems with newer ones is an important part of that. AIA predictive engine that delivers better results requires minimal organizational changes. 

Other domains AI adoption It is not just necessary to make technological changes, but also to redesign the entire system, including the product, organization structure, goals and alignment of incentives. Companies find it more difficult to do this. to adopt AI to its Full potential.

“Our focus on the possibilities of prediction machines had blinded us to the probability of actual commercial deployments,” The authors write in Power and Prediction. “While we had been focused on the economic properties of AI itself — lowering the cost of prediction — we underestimated the economics of building the new systems in which AIs must be embedded.”

The “Between Times” This is AI adoption

Agrawal, Gans, and Goldfarb summarize the current state of Agrawal, Gans, and Goldfarb. AI As the “Between Times” This is AI, which means we are between the demonstration of the technology’s capability and the realization of its Widespread promise is reflected adoption.

There’s precedence for this. The main benefit of electricity to manufacturers in the 1890s was fuel savings. This was because people viewed systems from the perspective the steam engine. But electricity wasn’t just a cheaper steam engine. Its main purpose was to separate energy from the steam engine. its source. Source. to Next, have a steam-engine installed to Your factory. This was the way most factories were built, and it took until 1920s to change this. potential to not fully realized. In that era, factories were built with the understanding that the power generator could easily be located far away so that electricity could be transported. to Any point within the facility equipped with a power outlet or cable.

AI scientist Andrew Ng has described AI As the “new electricity.” And Google CEO Sundar Pichai has That was the conclusion AI Is “more profound than electricity.” They might be correct. But in the Between Times, what we’re mostly seeing is the adoption Point solutions include ML-powered fraud prediction (video transcription, image classification, etc.

“We are at that stage where, if AI is going to be transformative, we will start to see the seeds of that transformation soon. It will likely first come from startup ventures utilizing AI to launch completely new business models,” Agrawal, Gans, and Goldfarb were quoted. 

Point solutions are currently won by incumbents. However, history has shown that established organizations are slower. to Make the necessary system changes to adapt to new technological advances.

“Startups have an advantage in that they do not have to change the old. They can start from a blank slate,” The authors stated. “But, at the same time, history is telling us that current business leaders should be even more vigilant in understanding AI’s transformative potential.”

The insurance industry, for example, has a long history. has Much to Profit from AI. Insurance companies with large assets already use it AI Point solutions to You may be asked to calculate premiums or process claims. The task of processing claims and calculating premiums is not the only one that needs to be done. real Chance of AI Business models that focus on maximizing premiums or reducing claims are in danger. Innovative insurtech companies have the potential to create new systems and workflows. AI to Predict and mitigate risk rather than transferring it from another party to another. 

“The disadvantage for startups is that it is rarely the case that current incumbent firms offer no value for the new system. Thus, at some point, that will become a challenge for them. In the past, this has led to a round of mergers and acquisitions,” The authors stated.

The future of AI adoption

While the tug-This is-war between incumbents and startups continues, what’s for sure is that the full potential of AI has yet to It will manifest itself. The future of AI will probably be new applications and new systems that are fundamentally different from what we’ve seen today.

“We believe that there are many more opportunities still to be had by adopting AI as point solutions or applications that are not too disruptive for enterprises,” Agrawal, Gans, and Goldfarb were quoted. “The real transformation can only come when the technical advances in AI are so pronounced that it is worthwhile to consider building new systems around them. We are hopeful that time will come but there is plenty of value to be had on the ‘smaller’ side of the technology before that point.”