7 ways a recession could affect AI and ML in 2023

What would a 2023 recession mean for the artificial intelligence (AI) and machine learning (ML) industry?

Dana Peterson, chief economist at the Conference Board, told CNBC this week that 98% of CEOs surveyed are preparing for a recession, compared to 95% earlier this year. And the strategists at Bank of America said last Friday, the US could enter a recession in the next 10 to 12 weeks.

How would that affect both the users of AI and the vendors who provide AI tools and expertise? Here are seven major ways a recession can affect the industry:

1. Well-defined use cases will be key.

A recession could negatively impact the AI ​​workforce in the short term, but there are some AI-driven use cases that will see faster growth and adoption, according to Artem Kroupenev, VP of strategy at machine performance provider Augury.

“In the industrial AI space, we are seeing a narrower focus on solutions that quickly and concretely demonstrate value within well-defined use cases and are easily adaptable by non-experienced users,” he said. “At the same time, we are seeing a fallback to solutions whose value is not clear, or the use case has yet to be well defined.”

Machine health, or predictive maintenance, is a good example of a well-defined industrial use case, he explained: “The combination of AI and IoT delivers rapid and dramatic operational improvements and does not require a significant change in user behavior to move to the on scale.”

2. Companies that already use AI will reap the benefits.

If COVID were an indicator, there will be two scenarios for AI business use during a recession, according to Wayne Butterfield, partner of ISG Automation, a division of global technology research and consulting firm ISG. The “already have,” he explained, will continue to reap the benefits of their previous AI investments, maintaining or extending the cost savings they already enjoy, while using the technology to improve customer and employee experience, gain competitive advantage and grow their sales.

Those who ‘have not’, on the other hand, will remain cautious. “At the best of times, it’s a bold move to bet big on an unproven technology,” Butterfield said in an email. “So, unless AI is already delivering results, it will take a brave executive to double down now. Instead, executives will turn to other proven methods to cut costs.”

Of course, the use of AI should not be viewed as a mere cost game. Indeed, automation and AI have never been more important given the skills and labor shortages many industries face. Many organizations will struggle to deliver even the basics without AI technology augmenting their already overstretched workforce.

3. Adoption of new technology such as generative AI will accelerate.

According to Noam Fine, head of AI at telecommunications company Vonage, a global recession is an accelerator for the adoption of new technologies.

“Companies are gaining new confidence and enthusiasm about AI, especially conversational AI, through new generative AI services built on large data sets,” he said. “This will lead to pilots and a willingness to experiment with new solutions to better manage the new economic situation.” The result, he added, is that companies in the space will find “a new open door that wasn’t there before.”

He pointed out that these solutions will prove themselves over the course of 2023 and gain traction for now, but he does not see this as a driver of layoffs in the sector. “I think we will exit 2023 with new technologies, new workforce investments to support such technologies, and with a rapidly growing AI-based industry for space companies,” he said. “As we move closer to 2024 and with further successes in this area, staff resources will be shifted to educate and train employees on how to handle and manage these new services.”

4. Organizations could be forced to lean on AI more than ever.

Contrary to popular belief, a 2023 recession could force organizations to lean more on technology than ever before, causing the AI ​​landscape to rapidly expand, predicts David Raissipour, chief technology and product officer at cybersecurity services company Mimecast.

“With the likelihood of under-resourced teams competing for talent — such as cybersecurity — companies will likely want to deploy AI solutions to improve mission-critical operations, including boosting cybersecurity,” he said. “Bad actors tend to feed on economic uncertainty, with the understanding that there are minimal resources and more human error in these times.” In addition, a difficult budgetary environment that requires organizational leadership to make decisions about the cyber solutions that meet the most critical needs results in an organization more vulnerable to cyberattacks.

New AI technology is expected to be developed to solve a range of potential cyberattacks, he added, while mitigating the damaging business risks of a recession. “There is an opportunity to develop AI technology that can dig deeper — whether that be a single email or a communication chain — to understand social graphs and metadata, enriching algorithms to better identify risk.”

5. Fired ML talent trickles into startups.

Moses Guttmann, CEO and co-founder of MLops platform ClearML, says recent layoffs in machine learning are likely the most recent hires, as opposed to more long-term employees who have been working with ML for years. “As ML and AI became a more common technology over the past decade, many large tech companies started hiring these kinds of workers because they could bear the financial costs and keep them away from competitors – not necessarily because they were needed,” he said. . From this perspective, it is not surprising that so many ML workers are laid off, given the oversupply within larger companies.”

However, as the era of ML talent hoarding draws to a close, it could usher in a new wave of innovation and opportunity for startups, he explained. “With so much talent looking for work, we’re likely to see a lot of these people trickle out of big tech into small and medium-sized companies or startups,” he said.

However, to fill the void of fewer people in highly technical teams, companies will need to lean even further on automation to maintain productivity and ensure projects are completed. “We also expect companies using ML technology to adopt more systems to monitor and direct performance and make more data-driven decisions about managing ML or data science teams,” he said.

6. The cost of running AI will become a big focus.

Currently, AI is incredibly expensive and consumes massive amounts of energy. According to dr. Vishal Sikka, CEO and founder of systems software company Vianai, to prepare for a possible recession, organizations should look to reduce costs and improve the acceleration of AI performance by “many orders of magnitude”.

Only then, he explained, can AI become truly effective and enter our daily lives. “One of the best places for enterprises and vendors to start is looking at the tools for optimizing AI performance,” he said. “Leaders need to examine their tech stack and determine which platforms offer the best ROI and how they support employee work with the help of AI.”

Organizing these tools, he added, benefits companies that cut back. “It lowers overall infrastructure costs in a turbulent economy,” he said.

7. Companies will invest in AI projects with a direct impact on revenue.

If a recession hits in 2023, to maximize profits, companies will be more inclined to invest in AI projects that have an immediate and direct impact on revenue generation, rather than risk investing in long-term fundamental research, said Plamen Minev, technical director of AI and cloud at IT services company Quantum. “This will lead to restructuring and staff reductions, which we are already witnessing,” he said.

At the same time, companies will realize the potential of AI and reap its benefits, he added, noting that AI technology has evolved rapidly in recent years, becoming more accessible and practical.

By implementing AI in areas such as software design and development, document review management, medical diagnosis and drug discovery, “professional employees can skyrocket their productivity like never before.”