7 Ways a Recession Could Affect AI and Machine Learning in 2023
The Conference Board’s chief economist, Dana Peterson, told CNBC this week that 98% of the CEOs polled are getting ready for a recession, up from 95% earlier this year. Bank of America strategists predicted that the United States would enter a recession within the next 10 to 12 weeks.
How would this influence both AI users and the vendors who provide AI tools and expertise? Here are seven significant ways that a recession could affect the sector:
Use cases that are well-defined will be essential
According to Artem Kroupenev, VP of strategy at machine performance provider Augury, while a recession may have a short-term negative impact on the AI workforce, there are some AI-driven use cases that will see faster growth and adoption.
“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 are easy to adapt by non-expert users,” he said. “At the same time, we’re seeing a retreat from solutions where the value is unclear or the use case has yet to be well-defined.”
A well-defined industrial use case is machine health, also known as predictive maintenance. The combination of AI and IoT drives rapid and dramatic operational improvements while requiring no significant changes in user behavior to be adopted at scale.
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Businesses that already employ AI will benefit
If COVID is any indication, there will be two scenarios for the use of AI in the enterprise during a recession. The ‘already haves’ will continue to reap the benefits of their previous AI investments, retaining or expanding the cost savings they currently enjoy while using the technology to enhance customer and employee experiences, gain a competitive edge, and thrive on their top line.
Those who ‘have not’ will, on the other hand, tread carefully. Even in the best of circumstances, betting big on unproven technology is a risky move. So, unless AI is already producing results, it will take a gutsy executive to double down on the investment now. Instead, executives will rely on tried-and-true cost-cutting strategies.
Surely, the use of AI should not be viewed solely as a cost-cutting measure. Indeed, given the skills and labor shortages that many industries are experiencing, automation and AI have never been more important. Many businesses will struggle to deliver even the most basic services unless AI technology augments their already overburdened workforces.
The introduction of new technology, such as generative AI, will speed up
A global recession hastens the introduction of new technologies. Through new generative AI services built on large data sets, companies are gaining new confidence and excitement about AI, specifically conversational AI. This will result in pilots and a willingness to try new solutions to better manage the new economic situation. As a result, businesses will discover a previously unknown open door.
These solutions will be tested in 2023, gaining preliminary traction, but they will be a driver of sector layoffs. It is reasonable to expect that by 2023, we will have new technologies in place, new investments in the workforce to support such technologies and a rapidly growing AI-based line of business for companies in the space. As we approach 2024 and achieve additional successes in this area, human resources will be shifted to educate and train employees to handle and manage these new services.
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Organizations may be compelled to rely on AI more than ever
Contrary to popular belief, a 2023 recession may force organizations to rely on technology more than ever before, resulting in a rapidly expanding AI landscape, according to David Raissipour, chief technology and product officer at cybersecurity service provider Mimecast.
“With the possibility of under-resourced teams already competing for talent – such as cybersecurity – companies will likely look to adopt AI solutions to augment business critical operations, including driving cyber protection,” he said. “Bad actors tend to capitalize on economic uncertainty, knowing that there are fewer resources and more human error during these times.”
Furthermore, a difficult budgetary environment in which top management must make decisions about the cyber solutions that fulfill the most critical needs. It will result in an organization that is more susceptible to cyberattacks.
He added that new AI technology should be expected to be developed in order to solve a variety of potential cyberattacks while mitigating the negative business risks posed by a recession. “There is an opportunity to develop AI technology that can delve deeper – whether it’s a single email or a chain of communications – to understand social graphs and metadata, enriching algorithms to better identify risks.”
Sacked ML talent will find its way into startups
Recent layoffs in machine learning are most likely the result of new hires rather than long-term employees who have been working with ML for years. Since ML and AI have become more prevalent in the last decade, many large technology companies have begun employing these types of workers because they can afford the financial cost and retain them from rivals—not necessarily because they are needed. Given the oversupply in larger corporations, it’s not surprising that so many ML workers are being laid off.
Moreover, as the period of ML talent hoarding comes to an end, it may herald a new era of innovation and incentives for startups. With so many people looking for work, we can expect many of them to leave big tech and work for small and medium-sized businesses or startups.
To fill the void left by fewer people on highly technical teams, companies will have to rely even more on automation to maintain productivity and ensure projects are completed. Companies that use ML technology should also put more systems in place to regulate and oversee performance, as well as make more data-driven decisions about how to manage ML or data science teams.
The expense of running AI will be a key focus
AI is currently outrageously expensive and consumes enormous amounts of energy. To prepare for a potential recession, organizations must seek to reduce costs while increasing the acceleration of AI performance by orders of magnitude.
Only then will AI truly become efficacious and enter our daily lives. One of the best places for companies and vendors to start is with AI performance optimization tools. Executives should examine their technology stack to determine which platforms provide the best ROI and how AI can support employee work. Organizing these tools will benefit businesses that are cutting costs. In a turbulent economy, it lowers overall infrastructure costs.
Businesses will invest in AI projects that will directly impact revenue
If a recession occurs in 2023, businesses will be more likely to invest in AI projects that have a direct and immediate influence on revenue generation rather than take the risk of investing in long-term fundamental research in order to maximize their bottom line. This will result in restructuring and layoffs, which we are already seeing.
Simultaneously, businesses will recognize the potential of AI and capitalize on its benefits. In recent years, AI technology has rapidly evolved and become more accessible and practical.
Professionals’ productivity can soar like never before, by incorporating AI into regions such as software design and development, document review management, medical diagnosis, and drug discovery.
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