AI governance and a clear roadmap are missing in enterprise adoption
Businesses are rushing to adopt artificial intelligence (AI) tools are becoming more and more popular, but most have yet to implement the metrics needed to measure return on investment.
Many companies also lack a comprehensive AI strategy and are buying products primarily for their utility, according to IBM. Research on AI Readiness Index published this week. Only 17% of the companies assessed in the report have a clearly defined AI strategy, while the majority, 38%, are still in the process of developing an AI strategy. Another 30% have an AI strategy focused on specific use cases, while 7% admitted to having an AI strategy that they ultimately scrapped or were unable to effectively implement.
The report found that about 43% have adopted AI due to the increased availability of AI-enabled business applications. The study was commissioned by IBM and conducted by research firm Ecosystemsurveyed 372 technology and business professionals across five ASEAN markets: Singapore, Indonesia, Thailand, Malaysia and the Philippines.
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Furthermore, while 85% acknowledge the power of AI, only 22% measure its value and report on it. This means that most lack of clear ROI (return on investment) metrics to determine whether their AI investments are delivering internal efficiencies or generating external revenue.
There is also a gap between how organizations evaluate Ready for AI and the reality of this situation 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 the readiness and maturity of organizations in implementing their AI roadmaps across four criteria. These criteria include culture and leadership, data foundation, and governance framework. The scores were aggregated and used to place organizations into one of five stages of AI readiness, including “traditional,” “emerging,” “consolidated,” “transformational,” and “AI-first.”
While 39% of respondents said their organizations were in the transformation phase, Ecosystm’s assessment placed only 4% in this category. Another 16% of companies said they were AI-first, but Ecosystm found that only 1% qualified for this AI readiness stage.
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AI-first organizations are highly regarded across four key areas, including governance, where they have dedicated roles overseeing the function and have developed ethical AI solutions. These businesses also have a data-centric strategy that provides seamless access to data and an AI-powered workforce, including a centralized data team with strong AI and machine learning capabilities.
In explaining the dearth of companies making progress in adopting AI, Loeffler noted that while it is easy to achieve proof of concept, businesses can struggle to scale their AI deployments.
He went on to emphasize the need for organizations to monitor and evaluate the impact of their implementation to ensure their AI applications are benefit as desired
According to the study, 63% of companies use AI to support intelligent document processing, 60% leverage the technology for support and assistance applications, and 57% use it to automate payments and invoicing. Another 56% tap AI for technology documentation, while 55% use AI for content strategy and creation, and 55% leverage AI for recruiting purposes.
About 25% of organizations cited identifying use cases for piloting or running proof-of-concept experiments as their top AI priority. 22% considered improving data quality, interoperability, and consistency as their AI priority, while 21% cited the need to upskill and reskill employees to be data-ready.
Some 39% said their organization has limited AI expertise, with a few experts in certain areas, and 26% use AI within their existing applications or platforms and have no standalone AI capabilities.
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The study also highlighted a lack of governance framework as a concern, with only 18% of organizations having a dedicated AI and data governance role. 66% allocate this responsibility to departments or teams, and around 3% have no clear policies or defined responsibilities around AI governance.
Additionally, only 12% reportedly have a process for monitoring AI model performance variations or model drift, which can impact results over time.
“The tangible benefits for organizations lie in scaling AI to accelerate innovation and productivity,” said Catherine Lian, general manager, IBM ASEAN. “Unfortunately, many technology and business leaders overestimate their organization’s ability to successfully deploy AI. AI readiness requires strong leadership, a robust data strategy, the right talent to execute, and a well-thought-out governance framework to ensure responsible and ethical use of AI.”
“Without these solid foundations, organizations risk deploying only the capabilities of the technology without considering the long-term impacts to the business,” says Lian.
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Hans Dekkers, General Manager of IBM Asia Pacific, also noted the need for AI along with automation to help organizations keep up with the pace of change.
ZDNET asked if there was an increased risk of such incidents. CrowdStrike Incident — Should organizations increasingly rely on automation to keep up with patch management and other critical workflows?
Dekkers said automation plays a key role in freeing employees from time-consuming and repetitive tasks, while also speeding up transactional processes.
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However, he said automation needs to be implemented properly to avoid errors.
Loeffler added that this should also be part of an organization’s governance framework, including ensuring that third-party AI applications meet the company’s AI safety policies.
