As excitement around AI continues to build among businesses of all sizes, many leading voices are urging caution for those who might scale too quickly.
At its recent Think EMEA event in London, IBM revealed research which claimed efforts to stay aware of potential risks and challenges posed by AI deployment and scaling are falling somewhat short of expectations.
So what can companies do in order to balance the hype and excitement around what AI can offer, with the need for sensible and thorough governance.
AI as the enabler
“What we are seeing is that if you don’t provide an answer to security, to accountability, to responsibility, it is going to be very hard to scale AI in the corporate environment – particularly when we’re talking about highly regulated industries – so for us, it’s more of an enabler, than something that is going to slow down the adoption,” Ana Paula Assis, Senior Vice President and Chair for IBM EMEA and APAC told us in response to that very question during a media Q&A at the event.
“The question here is really, how do you balance the speed of innovation with the implementation of governance?”
IBM recently released research on the current state of AI and governance, finding that 80% of the companies surveyed claimed that an AI incident had been escalated to board level. However only 40% of those had then been able to use the incident to secure new investment in governance, or cement a new policy.
“I think the reason for that is because it’s very hard to balance that innovation with the application of tools, processes, methodologies, to ensure that you are deploying AI at scale in a safe way,” Assis continued.
IBM is reportedly well placed to discuss such growth and progress with AI, with the company’s earlier keynote at Think EMEA claiming it had seen savings of around $5.5 billion in terms of improved productivity and efficiency utilizing AI to improve core processes – money that it was able to redirect to areas including R&D and innovation.
“But we are seeing this as an enabler, and as a key component of the architecture for agentic AI going forward, because these companies have tremendous responsibility – they need to ensure that they have trust from their clients, and they will only be able to do that if they have AI that they can observe and that they can govern.”
“AI is no longer just supporting the business,” Assis said. “It has become the business model. The organizations pulling ahead are not just adding AI, but rethinking how systems, decisions and processes work together with AI.”
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