Canadian Manufacturing

LXT releases The Path to AI Maturity 2023, an executive survey

by CM staff   

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Nearly half of organizations now rate themselves as AI mature, and believe this will help navigate economic downturns through improved business agility, resilience and time-to-market.

Findings from LXT’s recent survey of 315 senior executives who provided an assessment of their company’s current AI maturity.

TORONTO — LXT released its second annual executive survey, The Path to AI Maturity. The report found that organizations have evolved in their AI journeys, with 48 per cent of respondents rating themselves at the mature level, where AI is in production or already a part of business DNA. To achieve this, nearly half invest $76 million or more annually in AI, while only 1 per cent of respondents spent $1 million or less.

“As organizations continue to make progress in their AI deployments, the business value is becoming increasingly clear,” said Mohammad Omar, Co-founder and CEO, LXT. “Given the current economic climate, businesses see the benefit of successful AI deployments in improved business agility, business resilience and quicker time-to-market. We are pleased to see that over 80 per cent of enterprises have implemented a data strategy to drive the success of their AI initiatives.”

LXT, in partnership with research firm Reputation Leaders, commissioned a survey in late 2022 of 315 senior decision-makers with verified relevant AI experience at US firms with annual revenue of over $100 million and a company size of more than 500 employees.

At 48 per cent of organizations, AI is in production, or already a part of business DNA.


In the survey, executives were asked to place their companies on the Gartner AI Maturity Model scale, and nearly half of respondents report that their organizations have reached AI maturity (48 per cent vs. 40 per cent in 2021), from Operational (AI in production, creating value) to Transformational (AI is part of business DNA). On average, 46 per cent 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.

AI investment remains strong, with nearly half of organizations investing $76 million or more annually. With 49 per cent of all organizations invest $76 million or more annually in AI, while only 1 per cent spend $1 million or less. For companies with revenue over $500 million, 58 per cent invest more than $75 million. Training data and product development accounted for the largest share of AI budgets. And 87 per cent of organizations are willing to spend more for higher-quality AI training data.

AI 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.

NLP and speech/voice recognition are the most highly deployed AI applications. Natural language processing (NLP) and speech/voice recognition solutions are the most highly deployed AI applications, followed by predictive analytics and conversational AI (CAI). There were just two industries where NLP and speech/voice recognition solutions were not the top applications deployed – these were financial services (financial reporting) and manufacturing/supply chain (robotics). However, in an open ended question, organizations cited CAI applications as having the greatest ROI.

More than 90 per cent say they have made good or excellent progress in managing AI bias.

“The importance of responsible AI is top of mind in most organizations,” said Phil Hall, LXT Chief Growth Officer. “And diversity in data and the workforce behind it is essential to success in managing bias.”

The survey found that data diversity and risk management are extremely or very important to more than 90 per cent of respondents. Nine out of ten believe they have made excellent or good progress when it comes to managing bias in AI models. The most important way stated by respondents to ensure this is to manage diversity in annotation teams.


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