AI Only Increases the Need for Control

Last week I discussed the potential for AI adoption to increase an enterprise’s risk of lock-in to incumbent UCaaS providers. We’ve also looked at the challenges posed by the issues around compliance related to AI implementation. These compliance issues represent another thing for enterprises to be concerned about when selecting UCaaS providers, and may even make hybrid cloud-premises architectures a long-term strategy, rather than just an interim step on the way to a fully cloud-based end state.

A couple of recent articles amplify these challenges and help explain why a purpose-designed hybrid architecture may be the best strategy for the AI era in enterprise communications. On No Jitter, Terri Coles writes about the dilemma of how an enterprise should treat the AI-generated work product that results from communications sessions – the summaries, task assignments, and records that UC platforms’ AI assistants generate and distribute.

Many enterprises lack the tools or processes to attribute ownership to material generated by AI meeting summaries, which means follow-up can fall through the cracks and that no one may ultimately be responsible for making sure the transcripts and other summaries even accurately represent what was said in the meeting, Coles writes. This can be inefficient at best and legally perilous at worst.

The largest concern is that as these un-reviewed, unapproved documents proliferate at the scale of a large enterprise, the uncertainty becomes almost institutionalized. Coles quotes Allen Martinez, chief AI architect at Noble Digital Agency: “The biggest risk is not some dramatic [AI] failure. It is invisible, compounded errors and a total loss of legal defensibility.”.

So some of the problem is organizational: there clearly must be policies in place dictating responsibilities for ensuring accuracy and follow-up for AI-generated documentation. But the problem gets worse if these assets are locked away in a service provider’s databases rather than owned by the enterprise, according to James Alan Miller, writing at Search Unified Communications. Enterprises, he writes, “need tighter control over where communications data lives, how it is governed, who can access it and what happens when that data becomes an input for AI systems.”

Miller notes that it’s not just AI-generated data, but also “meeting recordings, voice data, chat transcripts, shared files, metadata…and workflow signals. That data can cross regions, vendors, clouds and AI systems. It can also fall under different privacy, retention, sovereignty and compliance rules depending on where it is created, stored or processed.”

The safest, most compliant course of action may be to ensure that these assets and records are stored in the enterprise’s own databases, Miller writes.

AI creates a new level of responsibility for IT professionals who specialize in communications systems. Previous generations of compliance challenges were more straightforward: You might be required to retain a new type of communications, like instant messages; or you might need to have policies and storage space to deal with video meeting recordings.

But as Coles’s article suggests, AI inserts itself into the work processes themselves, and introduces new types of issues you didn’t have to worry about before. An IM might contain inaccurate information, but that content could clearly be attributed to the person who wrote it. If there’s a mistake in an AI transcript, whose fault is that – the vendor who made the software? The enterprise personnel who didn’t proofread the file after the fact?

Enterprises were slow to adopt UCaaS when these services initially rolled out. Cynics claimed IT people were worried about losing their jobs, but what they were really worried about losing was control over not just compliance, but critical issues like cost and security. AI isn’t doing anything to make it easier to complete the transition to an all-cloud communications environment.

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