Editorial
In the modern workplace, technology has infiltrated nearly every aspect of our jobs thanks to digital transformation. As artificial intelligence (AI) has become increasingly prominent in the last few years, I've witnessed firsthand the ways AI is reshaping how we approach work, problem-solve and support our teams. There is something about AI that stands out above all other technologies, though: the polarizing differences between early adopters and skeptics.
Like any new technology, there is a steep adoption curve. However, with the release of OpenAI’s ChatGPT to the public in November 2022, the world has seen a generative AI explosion. Nearly everyone has tested gen-AI for one use case or another, but how or when those use cases translate to the workplace is another story. In fact, in 2023 research, McKinsey shared that “Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22% say they are regularly using it in their own work.”
That 22% represents the early adopters, but that means there are 78% that are not using gen AI regularly at work. Coming from the IT trenches myself, I recognize how hard this divide is to manage in the ever-evolving digital workplace.
Getting Everyone on Board With AI
Our collective journey into the realm of AI has illuminated a spectrum of attitudes among professionals. On the one hand is Marc Andreessen (of “Software is Eating the World” fame) who makes a compelling case for “why AI will save the world.”Likewise, based on its research on generative AI’s business value and economic impact, McKinsey has suggested that, “Generative AI is poised to unleash the next wave of productivity.”
On the other hand are AI doomers and corporate nay-sayers, like authors Daron Acemoglu and Simon Johnson who say AI has the potential to “do great harm to jobs, privacy and cybersecurity.”
So how does a modern IT team balance this juxtaposition while delivering a cohesive digital employee experience across an enterprise? Before early-adopters start deploying AI tools across the corporate network, organizations should take these three steps.
1. Start With a Clear Vision
For any new tool or technology to be effective, it needs to tie back to the business strategy and clear use cases. AI cannot be owned by the IT team. Instead, AI initiatives must align with the strategic objectives of an organization at the highest levels to ensure relevance and impact. Having executive guidance on the company’s stance on AI can help reign early adopters from deploying shadow IT and can make skeptics feel a little more comfortable that the use of AI is well thought out and tied to strategy.
2. Invest in Data
AI is only as good as the data that feeds it. For an IT team to support the use of AI in the workforce, they first need to trust the data it’s trained on. With AI integration, the role of data emerges as the linchpin determining the success or failure of our efforts to augment, co-pilot, and deploy AI as the "easy button" of any business operations. The quality, breadth and ethical handling of data fundamentally shape the efficacy and trustworthiness of AI models. Data serves as the lifeblood of AI systems, powering algorithms and enabling informed decision-making. Yet, the misuse or misinterpretation of data can undermine the foundations of AI, leading to biased outcomes and erosion of user trust.
We must prioritize a robust data governance framework, ensuring transparency, accountability, and integrity across the data lifecycle to ensure AI is effectively supporting the business needs. By investing in quality data, you can increase the trust in AI of skeptics and ensure early-adopters are getting the best results possible.
3. Measure and Monitor Performance
Early adopters are going to want to test AI in many areas of their work. While skeptics are going to be reluctant to have any AI tools in the tech stack. IT teams need to have complete visibility of what is running on the network, both on and offline, to best support the full spectrum of AI adoption securely. As with rolling out any new technology or system, users will likely run into challenges and report issues. With a clear picture of the health and performance of the enterprise IT estate, however, IT teams can more easily troubleshoot disturbances and identify root causes. For example, for the skeptic who keeps reporting a slow machine after installing a new AI tool, full visibility will allow the IT help desk to see if it’s really the AI tool causing an issue or a network or device performance issue. For the early adopter who wants to install a co-pilot to run across applications, IT can make sure their current hardware is set up to handle the extra performance load?
I know that the journey toward AI integration is not without its challenges, of course. Skepticism and resistance to change linger, rooted in fears of job displacement and uncertainty about the unknown. As IT leaders, we must demystify AI, especially machine learning and generative AI, debunking misconceptions and illustrating its potential value as a force for augmentation rather than replacement.
In navigating the new era of a soon-to-be AI-first world, companies must embrace a culture of continuous learning and adaptation. AI early adopters should be encouraged and empowered to collaborate with their skeptical and middle-ground exploring colleagues when it comes to implementing AI to ensure that knowledge isn't confined to any particular group.
Just as proactive IT strategies anticipate and address issues before they escalate, our approach to fostering collaboration ensures that diverse perspectives and experiences are valued and integrated into our work. This inclusive environment not only encourages innovation but also ensures that our organizations stay agile and adaptable in the ever-evolving landscape of AI technology.
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