Uniting Generations X, Y, Z, and Baby Boomers: The Overlooked Secret to AI Success

Modern organizations are very aware of the need to effectively use generative AI to improve business operations and product competitiveness. According to tests Forrester reports that 85% of firms are experimenting with Generation AI, and a KPMG US study found that 65% of executives imagine it is going to have a “large or very large impact on their organization over the next three to five years, far outweighing any other emerging technology.”

As with any recent technology, adopting and implementing gen AI will undoubtedly be a challenge. Many organizations are already struggling with tight budgets, overworked teams, and fewer resources; subsequently, firms need to be especially strategic when it comes to implementing gen AI.

One of the critical (but often ignored) elements of gen AI success is the people behind the technology in these projects and the dynamics that exist between them. To get the most value from the technology, organizations should build teams that mix the domain knowledge of native AI talent with the practical, hands-on experience of IT veterans. By nature, these teams often span different generations, different skill sets, and different levels of business understanding.

Ensuring that AI experts and business technologists work together effectively is paramount and will determine the success—or shortcomings—of a company’s gen AI initiatives. Below, we’ll look at how these roles impact technology and how they’ll best work together to drive positive business outcomes.

The Role of IT Veterans and AI Talent in Generation AI Success

On average, 31% of organization technology consists of legacy systems. The more experienced, successful, and complex a business is, the more likely it is that there is a large footprint of technology that was first introduced at least a decade ago.

Realizing the business promise of any recent technology—including AI Gen.—relies on an organization’s ability to capture maximum value from these existing investments. This requires a high degree of contextual business knowledge, the kind only IT veterans possess. Their experience managing legacy systems, combined with a deep understanding of the business, creates an optimal environment for embedding AI Gen. into products and workflows while sustaining the company’s forward momentum.

Data science graduates and AI talent also bring key skills, namely proficiency with AI tools and the data engineering skills needed to make those tools effective. They have a deep understanding of how to apply AI techniques—whether natural language processing (NLP), anomaly detection, predictive analytics, or other applications—to an organization’s data. Perhaps most significantly, they understand which data to feed into those tools and have the technical expertise to transform it into something that’s usable by those tools.

There are several challenges that organizations may face when incorporating recent AI talent into their existing corporate professionals. Below, we’ll look at these potential obstacles and how to mitigate them.

Creating a place for next-generation artificial intelligence

The primary challenge organizations may face in creating these recent teams is resource scarcity. IT teams are already overwhelmed with the task of keeping existing systems running at optimal performance—asking them to redesign their entire technology landscape to accommodate the AI ​​generation is a daunting task.

It could also be tempting to isolate gen AI teams due to lack of labor capability, but then organizations risk difficulty integrating the technology into their core application stacks in the future. Companies cannot expect significant gen AI advances by isolating PhDs in an office isolated from the business—it is crucial that these teams work in tandem.

Organizations might have to adjust their expectations in the face of those changes: It can be unwise to expect IT to maintain its existing priorities while learning to work with recent team members and educating them on the business side of the equation. Companies will likely need to make difficult decisions about cutting and consolidating previous investments to build internal capability for next-generation AI initiatives.

Explanation of the problem

When introducing any recent technology, it’s essential to be clear about the problem space. Teams need to be completely aligned on the problem they’re solving, the end result they need to achieve, and what levers are needed to unlock that end result. They also need to be aligned on what the obstacles are between those levers and what it is going to take to overcome them.

An effective way to get teams on the same page is to create a performance map that clearly connects the goal end result to supporting levers and obstacles to ensure resource alignment and clarity on performance expectations. In addition to addressing the above aspects, the performance map must also include how each aspect might be measured to hold the team accountable for business impact through measurable metrics.

By delving into the problem space somewhat than speculating on possible solutions, firms can avoid potential failures and over-rework after the fact. This is comparable to the wasted investment seen during the big data boom a decade or so ago: the belief was that firms could simply apply big data and analytics tools to their enterprise data and the data would reveal opportunities. Unfortunately, this turned out to be incorrect, but firms that took the time and care to deeply understand their problem space before adopting these recent technologies were able to unlock unprecedented value—and the same might be true for generational AI.

Improving understanding

Continuing education is becoming increasingly popular among IT professionals to gain data science skills and more effectively implement AI initiatives in their organizations. I’m one of them.

Today’s data science graduate programs are designed to concurrently meet the needs of recent college graduates, mid-career professionals, and senior executives. They also provide the additional benefit of higher understanding and collaboration between IT veterans and AI talent in the workplace.

As a recent graduate of UC Berkeley’s School of Information, most of my cohort was mid-career professionals, a handful were CEOs, and the rest were fresh out of undergrads. While not required to succeed in AI, these programs provide a great opportunity for experienced IT professionals to learn more about the technical data science concepts that may drive AI in their organizations.

Like each of its technological predecessors, gen AI presents each recent opportunities and challenges. Bridging the generational and knowledge gaps that exist between seasoned IT professionals and recent AI talent requires a deliberate strategy. By considering the advice above, firms can set themselves up for success and drive the next wave of gen AI innovations inside their organizations.

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