7 ways a recession could impact AI and ML in 2023
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What would a 2023 recession mean for the artificial intelligence (AI) and machine learning (ML) sector?
Dana Peterson, chief economist at the Conference Board, told CNBC this week that 98% of CEOs he surveyed are preparing for a recession, up from 95% earlier this year. And Bank of America strategists said last Friday that the US could slip into a recession in the next 10 to 12 weeks.
How would this affect both users of AI and the providers who provide AI tools and expertise? Here are seven key ways a recession could affect the sector:
1. Well-defined use cases will be key.
A recession could have a negative impact on 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.
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“In the industrial AI space, we’re seeing a narrower focus on solutions that demonstrate quick and concrete value within well-defined use cases and that are easy to adapt by non-expert users,” he said. “At the same time, we’re seeing pushback on solutions where the value isn’t clear, or the use case isn’t yet 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 drives rapid and dramatic operational improvements and requires no significant change in user behavior to adopt them on scale.”
2. Businesses already using AI will reap the benefits.
If COVID was any indicator, there will be two scenarios for enterprise use of AI during a recession, according to Wayne Butterfield, partner, ISG Automation, a unit of global technology research and advisory firm ISG. Those who already have, he explained, will continue to reap the benefits of their previous AI investments, maintain or expand the cost savings they already enjoy while using the technology to improve customer and employee experience, gain competitive advantage and grow their top line.
Those who have not, on the other hand, will continue to tread carefully. “Betting big on an unproven technology is a bold move at the best of times,” Butterfield said in an email. “So unless AI is already delivering results, it will take a brave manager to double down now. Instead, managers will turn to other proven methods of cost reduction.”
Of course, the use of AI should not be seen simply as a cost-cutting exercise. In fact, automation and AI have never been more important given the skills and labor shortages facing many industries. Many organizations will struggle to deliver even the basics without AI technology augmenting their already stretched 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 technology.
“Enterprises are gaining new confidence and excitement about AI, and specifically conversational AI, through new generative AI services built on big data sets,” he said. “This will lead to pilots and a willingness to experiment with new solutions as a way 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 be proven and gain preliminary traction during 2023, but he does not see this as a driver of layoffs in the sector. “I believe we will exit 2023 with new technologies in place, new investment in the workforce to support such technologies, and with a rapidly growing AI-based range of businesses for companies in the space,” he said. “As we get closer to 2024 and with further successes in this space, employee resources will be shifted to educate and train employees to handle and manage these new services.”
4. Organizations may be forced to rely on AI more than ever.
Contrary to popular belief, a 2023 recession could force organizations to rely on technology more than ever, leading the AI landscape to expand rapidly, predicts David Raissipour, chief technology and product officer at cybersecurity services company Mimecast.
“With the likelihood of under-resourced teams already fighting for talent – such as cybersecurity – businesses will likely look to implement AI solutions to complement business-critical operations, including driving cyber protection,” he said. “Bad actors tend to feed on economic uncertainty, with the understanding that there are minimal resources and increased human error during these times.” Additionally, a difficult budget environment where organizational leadership must make decisions about the cyber solutions that address the most critical needs results in an organization that is more vulnerable to cyber attacks.
It should be expected that new AI technologies will be developed in an effort to solve a variety of potential cyberattacks, he added, while mitigating the adverse business risks posed by a recession. “There is an opportunity to develop AI technology that can dig deeper – whether it’s a single email or a chain of communications – to understand social graphs and metadata, thereby enriching algorithms to better identify risks to identify.”
5. Laid-off ML talent will trickle down to 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 the more long-term staff who have been working with ML for years. “As ML and AI became a more common technology in the last decade, many large tech companies started hiring these types of workers because they could handle the financial costs and keep them away from competitors – not necessarily because they were needed,” he said. said . From this perspective, it is not surprising to see so many ML workers being laid off due to the surplus within larger companies.”
However, as the era of ML talent hoarding ends, it could usher in a new wave of innovation and opportunity for startups, he explained. “With so much talent looking for work right now, we’re likely to see many of these people flow out of big tech and into small and medium-sized businesses or startups,” he said.
However, to fill the void of fewer people in deep technical teams, companies will need to lean even further toward automation to keep productivity high and ensure projects are completed. “We also expect to see companies using ML technology put more systems in place to monitor and control performance and make more data-driven decisions about how to manage ML or data science teams,” he said.
6. The cost of managing AI will become a major focus.
Currently, AI is incredibly expensive and consumes large amounts of energy. According to dr. Vishal Sikka, CEO and founder of systems software company Vianai, says organizations must seek to lower costs and accelerate AI performance by “many orders of magnitude” to prepare for a potential recession.
Only then, he explained, can AI become truly effective and enter our everyday lives. “One of the best places for enterprises and vendors to start is to look at the tools for optimizing AI performance,” he said. “Leaders need to examine their technology stack and determine which platforms offer the best ROI and how they support employees’ work with AI’s help.”
Organizing these tools, he added, would benefit companies that are making budget cuts. “It reduces overall infrastructure costs in a turbulent economy,” he said.
7. Companies will invest in AI projects with direct revenue impact.
If a 2023 recession were to occur, in order to maximize their bottom line, companies would be more likely to invest in AI projects that have an immediate and direct impact on revenue generation – rather than risk investing in long-term fundamental research to invest. , said Plamen Minev, technical director, AI and cloud at IT services company Quantum. “This will lead to restructuring and downsizing, which we are already seeing,” he said.
At the same time, enterprises will realize the potential of AI and take advantage of its benefits, he added, noting that AI technology has developed rapidly in recent years and become more accessible and practical.
By implementing AI in areas such as software design and development, document review management, medical diagnosis and drug discovery, “professional staff can soar their productivity like never before.”
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