Tech firms are cutting a skill that’s vital for AI

To capitalize on the power of AI, tech firms need the right talent. But the skills required are complex and changing.

Cross-functional management can help AI initiatives break through to achieve enterprise adoption and success. “At many companies, you see a very siloed approach to GenAI adoption,” Schulz said. “Whoever is overseeing deployment is responsible to ensure its success.”

Funding can provide another barrier, Schulz said. “Often, an enterprise’s AI funding gets housed in the office of the CTO or CIO — you get a very siloed decision, and it is viewed as a software purchase rather than a transformational tool for your workforce.”

“We have some fundamental challenges that inhibited innovation before AI, and I don’t think they’re that different now,” Ranzau said. “There’s an incredible amount of hype, and a fundamental belief that GenAI is completely revolutionizing how we’re working. But we’re expecting people to just intuitively know how to work differently without reinforcing those behaviors.”

“There needs to be a cultural transformation at many organizations, to address those fundamental problems that existed before AI,” Ranzau said. “Mature organizations that are actually effective at driving initiatives have cross-functional teams that are nimble and iterate quickly. They’re not trying to revolutionize everything, they’re tackling one thing, bringing it to completion and changing a way of working.”

Cultural change

Even in a tech company that has a culture of disruptive innovation, employees often respond best to change that is controlled.

“There’s a way to incrementally ease change into an organization’s culture,” Ranzau said. “From a product perspective, tech companies find more success when they ease in AI capabilities with minor conveniences rather than a big bang.” That controlled approach requires someone who sets the vision, schedule, communications and reinforcement — possibly with training, especially for those who are willing to help champion the new capabilities.

“In the tech industry, there is a training element where you show people how they can best use an AI capability, and that gets them thinking through other ideas,” Schulz said. “Maybe it's not a structured use-it-in-this-manner use case, but rather it’s giving them ideas to engage with it and make it accessible.”

When firms foster effective cultural change, they can improve AI understanding, usage and product development across the enterprise.

Once tech companies establish the human skills that pave the path to growth, they can use AI capabilities to help fuel the engine.

Schulz said that AI-generated code can empower agility. “If someone is thinking about adding a feature, like a product manager who's getting real-time feedback from their customers, they could generate proposed code for engineers based on the feedback they’ve received and what needs to be added to the product. It’s another lift they can do before even getting to the engineers, so they can conceptualize it and actually get code behind it, then the engineers can run with it. The time to development much shorter and actually allows for more responsive feedback.”

To extend that agility, Ranzau said, “Tech firms could be using AI for quality assurance. That can accelerate dev cycles, making it quicker to find problems in the code.” Firms can use AI to drive their regression testing, reducing the time and effort for analysis. AI could also help firms compare the output from multiple developers, to identify if one drives greater processing efficiencies or applies other best practices. These processes can be streamlined in a way that was not possible with traditional automation, but AI can also fuel new external capabilities.