Why the sovereign cloud matters in enterprise communications

Decentralized data storage was much easier to manage before data was launched into the clouds. Now, geographic and regulatory concerns abound.
Deploying technology is really all about deploying change that is intended to improve processes and/or productivity. However, as has been presented zillions of times, improvements in technology happen much more quickly than modifications to any regulatory structure designed to place guardrails around that technology. This may never have been truer than it is today with rapid, ubiquitous AI deployment across virtually every aspect of our existence(s). But the bottom line is this: Within an organizational structure, regardless of the industry, a key component of its evolution is often about retaining compliance with existing legal and regulatory strictures. It’s all about compliance, even if the regulations no longer fit the system they were designed to manage.

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Workplace AI Use Demands Careful Monitoring

While AI tools offer valuable shortcuts, workplace misuse is difficult to track and litigation from unreliable AI outputs is just beginning to emerge.
I’m in the second semester of teaching an undergraduate class in ethics, with a focus on use of AI tools, and I am both hopeful and skeptical that AI tools can be used beneficially in most contexts. However, and this is a big enough HOWEVER to warrant capital letters, these tools are only as good as the people using them. AI tools are precisely that — tools. Nothing more.

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Watch for Legal Risks in Everyday AI Use

When AI outputs results are either misused or misapplied — most notably without human oversight — the risks and consequences can be outsized.
In the current federal regulatory environment, where regulatory protections and structures are often deemed as burdensome and impediments to business, it’s not difficult to understand why federal AI regulation has gone virtually nowhere. In fairness, it also must be noted that getting federal legislation drafted and passed is a monumental task in and of itself, and in the current environment, it’s even more difficult. However, the need for additional guardrails on how AI was used has become increasingly obvious, and where Congress has failed—or been unable—to act, the states have stepped up. In some cases, they’ve followed the roadmap laid by international bodies like the executive body of the European Union, the European Commission, and in others, some states have followed the lead(s) of others. This has led to a crazy quilt of differing regulations and approaches to regulation.

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Be Aware of the Risk of AI Bias

Asking, “How can this possibly go wrong?” should be an essential exercise required by managers deploying AI tools.
How and why might you be concerned about identifying and managing bias in an AI-driven workspace? Sounds pretty dry, but the fact is that it’s a fascinating, thought-provoking, and an essential exercise absolutely required by managers deploying AI tools. There are a number of key vulnerabilities that those who have adopted AI tools in the CX space—or plan to--should be well-aware.

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AI Tools Demonstrate Hidden Risks of Imperfect Outputs

As AI use grows, so does the need for disclaimers, vetting AI-generated results, and avoiding expensive errors.
When it comes to deployment of AI, some deployers are listening to their lawyers. Many documents that are created with AI now contain disclaimers like this one which I found at the bottom of a document reviewed in legal community platform Justia: “Some case metadata and case summaries were written with the help of AI, which can produce inaccuracies. You should read the full case before relying on it for legal research purposes.” At least, this entity has the courage to announce and warn about its use of AI. At least this entity has the courage to announce and warn about its use of AI. Many documents and entities are not, including a radio station with which I’m familiar, which uses AI to produce weather reports, signed off by “staff meteorologists” who are not real. The AI-generated forecasts are close to accurate, which I view as positive but not determinative, but the named staff meteorologists who provide the forecasts are identified as real people, which they are not.

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