Companies are rushing to adopt artificial intelligence (AI) is becoming more widely available, but most have not implemented the metrics needed to measure their return on investment.
Many also lack a comprehensive AI strategy and are purchasing products primarily for their features, according to IBM. AI Readiness Barometer Study The report was released this week. Only 17% of the companies assessed in the report have a well-defined AI strategy, and the majority, 38%, are still in the midst of developing an AI strategy. Another 30% have an AI strategy that focuses on specific use cases, while 7% admitted to having an AI strategy that they ultimately discarded or were unable to implement effectively.
The report found that approximately 43% had adopted AI due to the increasing availability of AI-powered business applications. The study commissioned by IBM, conducted by the research firm Ecosystemsurveyed 372 technology and business leaders across five ASEAN markets: Singapore, Indonesia, Thailand, Malaysia and the Philippines.
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Furthermore, while 85% recognized the power of AI, only 22% measured its value and took note of the report. This means that the majority Lack of clear return on investment (ROI) metrics to determine whether your AI investments generate internal efficiencies or drive external revenue.
There are also gaps between how organizations rate their Preparing for AI and the reality of this state as assessed in the study, Ecosystm CEO Ullrich Loeffler said at a press conference in Singapore. He explained that the research firm collected data to assess organizations’ readiness and maturity to implement their AI roadmap based on four criteria, including culture and leadership, database, and governance framework. The scores were aggregated and used to place organizations into one of five stages of AI readiness, spanning “traditional,” “emerging,” “consolidation,” “transformative,” and “AI-first.”
While 39% of respondents consider their organizations to be in the transformation stage, Ecosystm’s assessment places only 4% in this category. Another 16% of companies said they were prioritizing AI, but Ecosystm found that only 1% qualified for this stage of AI readiness.
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AI-first organizations score highly in four key areas, including governance, where they have dedicated roles to oversee the function and have developed ethical AI solutions. These companies also have a data-centric strategy that offers seamless data access and an AI-powered workforce, including a centralized data team with strong AI and machine learning capabilities.
Explaining the dearth of companies moving forward with AI adoption, Loeffler noted that while it is easy to achieve proof of concept, it can be challenging for companies to achieve scale in their AI implementation.
He also stressed the need for organizations Monitor and evaluate the impact of its adoption to ensure that your AI applications are Delivering the benefits as planned.
According to the study, 63% of companies are using AI to power intelligent document processing, 60% are leveraging the technology for help desk and support applications, and 57% are using it for payment and billing automation. Another 56% are using AI for technology documentation, while 55% are using it for content creation and strategy, and 55% are using it for recruiting purposes.
About 25% of organizations said identifying use cases to pilot or run proofs of concept is their top AI priority. 22% said improving data quality, interoperability, and consistency is their top AI priority, while 21% cited the need to upskill and reskill employees to be data-ready.
Some 39% said their organization had limited AI expertise, with few specialists in certain areas, and 26% used AI within their existing applications or platforms and had no standalone AI capability.
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The study further highlighted the lack of a governance framework as a concern, with only 18% of organisations having a dedicated role for data and AI governance. 66% distribute this responsibility across departments or teams, and approximately 3% have no clear policies or defined responsibilities around AI governance.
Additionally, only 12% have the processes in place to track variations in AI model performance or model drift, which can impact outcomes over time, the report said.
“The tangible benefit for organizations lies in scaling AI to accelerate innovation and productivity,” said Catherine Lian, general manager for ASEAN, IBM. “Unfortunately, many technology and business leaders overestimate their organization’s ability to successfully implement AI. AI readiness requires strong leadership, a robust data strategy, the right talent to execute it, and a well-thought-out governance framework to ensure responsible and ethical use of AI.”
“Without these solid foundations, organizations risk implementing deployments that focus solely on the capabilities of the technology and fail to consider the long-term impacts on the business,” Lian said.
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Hans Dekkers, IBM’s general manager for Asia Pacific, also highlighted the need for AI alongside automation to help organizations keep pace with the speed of change.
ZDNET asked if there was an increased risk of incidents like the one CrowdStrike Disruption Should organizations increasingly rely on automation to keep up with patch management and other key work processes?
Dekkers said automation is crucial to freeing employees from repetitive, time-consuming tasks and boosting the pace of transactional processes.
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However, automation must be implemented correctly to avoid errors, he said.
Loeffler added that this should also be part of an organization’s governance framework, including ensuring that third-party AI applications comply with the company’s AI security policies.