In the survey, Deloitte explored the transformations happening inside firms applying AI and machine learning to drive value. During the pandemic, digitization efforts prompted many companies to adopt AI-powered solutions to back-office and customer-facing challenges. A PricewaterhouseCoopers whitepaper found that 52% percent of companies have accelerated their AI adoption plans, with global spending on AI systems set to jump from $85.3 billion in 2021 to over $204 billion in 2025, according to IDC.
However, only 40% of respondents to the Deloitte survey agreed that their employer has an enterprise-wide AI strategy in place. While 66% view AI as critical to their success, only 38% believe that their use of AI differentiates them from competitors and only about one-third say that they’ve adopted “leading operational practices” for AI.
“The risks associated with AI remain top of mind for executives,” Deloitte executive director of the AI institute Beena Ammanath said in a statement. “We found that high-achieving organizations report being more prepared to manage risks associated with AI and confident that they can deploy AI initiatives in a trustworthy way.”
Embracing AI is a marathon, not a sprint
To this end, “AI-fueled” businesses leverage data to deploy and scale AI across core processes in a human-centric way, according to Deloitte. Using data-driven decision-making, they enhance workforce and customer experiences to achieve an advantage, continuously innovating.
Organizations with an enterprise-wide strategy and leaders who communicate a bold vision are nearly twice as likely to achieve high-level outcomes, Deloitte reports. Furthermore, businesses that document and enforce MLOps processes are twice as likely to achieve their goals “to a high degree,” four times more likely to be prepared for AI risks, and three times more confident in their ability to deploy AI products “in a trustworthy way.”
MLOps, a compound of “machine learning” and “information technology operations,” is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. MLOps essentially aims to capture and expand on previous operational practices while extending these practices to manage the unique challenges of machine learning.
“Becoming an AI-fueled organization is to understand that the transformation process is never complete, but rather a journey of continuous learning and improvement,” Deloitte AI principal Nitin Mittal said.
Companies successfully adopting AI also haven’t ignored cultural and change management, the Deloitte report found. Those investing heavily in change management are 60% more likely to report that their AI initiatives exceed expectations and 40% more likely to achieve their desired goals. As for organizations that have undergone significant changes to workflows or added new roles, they’re almost 1.5 times more likely to achieve outcomes to a high degree, while 83% of the highest-achieving organizations create a diverse ecosystem of partnerships to execute their AI strategy, according to Deloitte.
But only 37% of decision-maker respondents reported a major investment in change management, incentives, or training activities, highlighting roadblocks companies will need to overcome. “By embracing AI strategically and challenging orthodoxies, organizations can define a roadmap for adoption, quality delivery, and scale to create or unlock value faster than ever before,” Deloitte AI principal Irfan Saif said.
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