Taking AI "from acronym to action item"

In many of the articles and reports released about AI over the last couple months, the acronym should stand for actually ignorant. Suddenly everyone has an opinion about a technology they probably haven't used - this came to a head for me when Vistage prompted the article “How managers can leverage the productivity promise of generative AI” which is about as un-actionable and vague as they come, offering neither next steps for managers or clarity around the productive potential of the tools for small businesses.

As HCI has discussed before, AI adoption remains a lofty goal for most SMBs but is increasingly critical to manage rising costs and remain competitive in a shifting marketplace. Yet for many organizations, AI remains more of a buzzword than a practical reality. The complexity and novelty of the technology can make leaders hesitant to fully embrace it, fearing the potential risks and uncertainties associated with its implementation. However, to truly harness the power of AI and transform it from a flashy new toy into a valuable operational tool, companies must focus on getting the mindset, skillset, and toolset right.

The first step in this process is cultivating the right mindset. Leaders must approach AI adoption with a spirit of experimentation, acknowledging that there will be both successes and failures along the way. Over-rigidity of approach is an early killer of AI initiatives, and ‘playing’ with the tools is the most effective way to develop comfort with this new set of tools (just like you had to do with excel and email 20 years ago).

The right mindset also means creativity and clarity of the specific use cases and workflows that could benefit from being automated within the organization, focusing on areas where AI can save time, energy, money, and improve baseline quality. AI is never adopted at the organizational level and functions or individual workflows should be the initial point of assessing adoption opportunities. By identifying these opportunities and aligning them with the company's broader goals and priorities, leaders can create a compelling vision for AI's role in the organization's future.

Next, businesses must invest in developing the necessary skill set within their workforce. Upskilling and reskilling initiatives are crucial to ensure that employees at all levels have the knowledge and expertise required to effectively leverage AI technologies. So much of the conversation around these skills focuses on “technical ability”.

But the vast majority of companies don't need a machine learning engineer to train a new model from the ground up - most Vistage members barely have the data infrastructure needed for traditional analytics. Instead, critical thinking and the ability to craft intentional questions is both more useful and applicable for most businesses. By equipping staff with the tools to understand AI's capabilities and limitations, challenge its outputs, and refine its performance, organizations can create a workforce that is well-prepared to thrive in an AI-driven environment.

The toolset – selecting the actual vendors, models, or chatbots themselves – should be viewed as an end step of the adoption process rather than the beginning. I’ve had many CEOs ask me (and my son’s company which builds machine learning models for businesses) which AI tools they should adopt, only to shrug when asked what they plan on using them for.

Finally, It's essential to recognize that AI is only as good as the data it's trained on, making a strong data management foundation critical for any successful implementation. Before even investing in AI tools, companies must ensure that their data is properly structured for use. Once you’ve built the foundation of an experimental culture, basic critical thinking skills, and organized data management can you expect to see a proper roi from AI adoption.