AI May Help Power Tough Times, But Start AI Before the Downturn


Those choosing to start their AI journeys during a downturn face the dilemma of investing in expensive technology when revenues may be in short supply.

There’s been no end to the chatter about a potential recession occurring sometime through 2023. It’s not clear which direction the economy will go, but one thing is certain: artificial intelligence (AI) is tooling many businesses now have at their disposal to help them navigate any rough weather they encounter,

That’s the word from a recent survey of 315 executives by LXT, which finds nearly half of organizations, 48%, now rate themselves as “AI mature,” up from 40% in the previous year’s survey. Respondents believe this will help “navigate economic downturns through improved business agility, resilience and time-to-market.”

If things do go sour for the economy, those companies that have well-functioning AI in place will have a built-in advantage, says Wayne Butterfield, partner with ISG Automation, a unit of global technology research and advisory firm ISG, quoted in VentureBeat.

“The ‘already haves’ will continue to reap the benefits of their previous AI investments, maintaining or expanding the cost savings they already enjoy while using the technology to improve customer and employee experience, gain competitive advantage and grow their top line,” he says. Those choosing to start their AI journeys during a downturn face the dilemma of investing in expensive technology when corporate revenues may be in short supply.

The survey’s authors define “AI maturity” as artificial intelligence in production or already a part of business DNA. To achieve this, the survey also shows, nearly half invest $76 million or more annually in artificial intelligence, while only 1% or respondents spent $1 million or less. Training data and product development accounted for the largest share of artificial intelligence budgets. And 87% of organizations are willing to spend more for higher-quality AI training data.

See also: Will Synthetic Data Drive the Future of AI/ML Training?

NLP and Speech recognition are the most commonly deployed AI applications — 57% of survey respondents have deployed natural language processing (NLP) and speech/voice recognition systems. After NLP and Speech solutions, a wide range of solutions have been deployed including predictive analytics, conversational artificial intelligence and computer vision..

Types of AI or machine learning applications deployed:

  • NLP and speech/voice recognition (57%)
  • Predictive analytics (48%)
  • Conversational AI (e.g. chatbots, virtual assistants, in-car systems) (46%)
  • Computer vision (44%)
  • Security/fraud applications (44%)
  • Biometrics/facial recognition (41%)
  • Augmented reality/virtual reality (41%)
  • Financial reporting (40%)

Still, artificial intelligence initiatives often fall short. On average, 46% of all AI projects still fail to reach their goals, although success improves with greater maturity. The top challenges in getting to AI maturity are a balance of technology (integration, quality data) and human (talent, training) factors.

Artificial intelligence strategies are primarily driven by the need for business agility, anticipating customer needs  and technological innovation – interestingly, cost savings is not a dominant driver. To support these strategies, the most mature organizations are relying more on supervised machine learning methods.


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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