What defines responsible AI? What’s your company’s “AI North Star?” Will AI eventually function more like a colleague or will it become (gasp!) an overlord?

In a recent webinar, “Creating a strategic AI roadmap with Microsoft Generative AI,” which you can view on-demand here, AI experts from Argano and Microsoft presented real-world examples and use cases to help businesses in all verticals define and design their own AI strategies.

High-level takeaways were that AI can and is being used to personalize customer experiences, automate workflows, and unlock creativity. No real surprises there in WHAT AI is being used for, but definitely some surprises in exactly HOW it’s being used.

BUT. (Yes, a big “but.”) There was guidance, even warnings, shared that should interest any company beginning its AI journey.

Namely, that AI without a unified strategy (a “North Star,” guiding its application throughout the enterprise), without a system for governance, and without training for users can create serious issues for the enterprise: data accuracy, data security, and worse.

So while the webinar team kept its focus on benefits, it also paid close attention to possible pitfalls, all while providing a foundational roadmap for the strategic and secure use of Microsoft generative AI.

AI as a path to greater revenue

At its core, AI helps the enterprise discover, unlock, and optimize new efficiencies. One way the team both detailed and demonstrated this was by providing examples where AI enabled staff to increase their focus on more strategic tasks rather than the “grunt work” (which could be managed by AI).

Of specific use in this arena is Microsoft Copilot Studio, which helps organizations automate endless workflows, functioning as a virtual assistant. (Note that Copilot Studio replaced Microsoft’s earlier, similar application, Power Virtual Agent, part of the Power Platform low-code development suite.)

AI-fueled customer engagement

A large part of the webinar centered on how generative AI is transforming the customer experience, both on the customer side (e.g., by helping engage customers with chatbots and self-service tools) and the employee side (e.g., by helping CSRs enjoy more complete views of every customer and case).

The team dug into how AI can generate new content to improve customer service, leveraging existing FAQs and knowledgebases to create new, more personalized responses to customer inquiries.

There is a critical caveat here as well: in creating new content, most companies will want to put guardrails around how and where generative AI can source material, thereby better ensuring content does not, for example, introduce a competitor or incorrect/inappropriate content, or compromise confidentiality. AI can not be allowed to “run free”; it must follow the mentioned North Star.

Generative AI security issues and ideas

When ChatGPT came online, there was no “how to” guide with it. Just pump in a question or a task and watch it go. That approach, however, will not work for most companies.

A major takeaway in the presentation was that effective use of AI requires training. One participant even suggested that AI should, in most cases, be isolated, “sandboxed,” to ensure proper handling of confidential data.

Training on proper management of generative AI solutions is a cornerstone to proper governance and critical to creating a rewarding experience for staff and customers alike. For any AI rollout, keeping people at the center of the equation remains critical to its strategic application, its effective rollout, and the overall security of your business.

Listen to the whole discussion here, or contact us to get started on your own AI roadmap.