The rise of AI-powered decision-making has transformed the way businesses operate, reshaping everything from customer interactions to entire industries. But as algorithms take center stage, a critical question emerges: how do we ensure humans remain at the heart of it all? On the CLARITY.SHOW podcast, Andreas Welch – renowned AI leader and author of The AI Leadership Handbook – joined Karyna Mihalevich, a Chief Product Officer and SAP Intelligent Enterprise Ambassador, to explore how leaders can steer their organizations through this era of disruptive innovation.
Fast Doer vs. Smart Decision Maker
To kick off the conversation, Andreas introduced a practical framework for understanding AI’s role in automation and autonomy. He described how businesses can map their AI journey using a two-by-two matrix, shedding light on the transition from rule-based systems to data-driven autonomy.
Historically, software followed “if-then” rules, creating predictable but rigid outcomes. Today, machine learning and AI agents can navigate uncertainty, making decisions based on data patterns rather than predefined rules.
“AI agents are shifting from simply automating repetitive tasks to handling complex goals, like creating marketing briefs or answering customer queries,” Andreas noted. This distinction between automation – doing tasks faster – and autonomy – making decisions independently – forms the foundation of understanding AI’s potential.
The discussion naturally flowed to practical considerations for adopting AI solutions.
Off-the-Shelf vs. Open Source
One of the key questions businesses face is whether to adopt off-the-shelf AI solutions or develop models using open-source frameworks. Andreas highlighted the trade-offs:
Off-the-Shelf Models: These offer convenience and speed, with providers like OpenAI handling the infrastructure and scalability challenges.
Open Source Models: These provide transparency and control but require significant investment in infrastructure and expertise.
“Open source seems free at first glance, but hosting and maintaining these models incurs costs,” Andreas emphasized. “For quick pilots, off-the-shelf solutions are great. However, large-scale implementations may benefit from the flexibility of open-source models.”
Karyna added that business goals should also guide decisions. For instance, on-premise models may be necessary for industries with stringent security requirements, while open-source solutions may suffice for limited use cases with specific data needs.
Embracing Failure and Learning
The conversation then shifted to a more human aspect of AI leadership – how organizations must adapt their culture to thrive in this transformative era.
AI projects often involve uncertainty, making failures inevitable. Andreas stressed the need for a cultural shift where failure is viewed as a stepping stone to innovation rather than a deterrent. “AI projects are more like research endeavors,” he explained. “They require iterative processes and a mindset that values learning from setbacks.”
The expert elaborated on how organizations can reframe failure as a catalyst for innovation. He recommended setting clear success criteria at the start of any project to ensure teams understand what progress looks like. “It’s not just about hitting targets but about having clear exit strategies when things don’t work out. This helps teams pivot without feeling like they’ve wasted effort,” he noted.
Karyna shared a compelling example from her own experience, describing how she fostered an environment where her team felt safe to take risks. “We encouraged everyone to document what didn’t work and why, turning missteps into a playbook for future projects. This shifted the narrative from failure being something to hide to something to celebrate as part of growth.”
The speakers also touched on the role of leadership in creating this shift. Andreas pointed out that leaders must model this behavior themselves. “When leaders openly discuss their own failures and the lessons they’ve learned, it sends a powerful message to their teams that innovation and resilience go hand in hand.”
Keeping Humans at the Center of AI
As AI reshapes workflows, concerns about job displacement and role evolution loom large. Both Karyna and Andreas agreed that AI should be seen as a tool for enhancing human capabilities, not replacing them.
“AI is here to make us more efficient, taking over manual, repetitive tasks so we can focus on what’s uniquely human – communication, decision-making, and problem-solving,” Karyna said.
Andreas encouraged professionals to familiarize themselves with AI tools. “The more you experiment with these tools, the more productive and future-proof your career will be,” he added.
The Future of Prompting: A Fading Skill?
A provocative statement from Andreas’ book sparked debate: “In the future, there won’t be prompting.” He clarified his statement, explaining that as AI systems become more sophisticated and seamlessly integrated into applications, the need for explicit prompts will diminish.
“We’re already seeing this with tools like Microsoft Copilot and Canva,” he noted. “They handle context and personalization in the backend, making interaction intuitive and reducing the need for explicit prompts. You’re not thinking about how to phrase a command – you’re just doing your work, and the AI supports you.”
Karyna added to the discussion by highlighting how this evolution mirrors the trajectory of user interfaces in general. “Think about the early days of computing when we had to memorize specific commands. Now, we tap, swipe, or even just talk to our devices in natural language. The same thing is happening with prompting, it’s becoming invisible as AI anticipates our needs.”
Despite this shift, Andreas stressed the importance of learning prompting skills today. He described prompting as a gateway to understanding AI’s capabilities and limitations. “When you craft a prompt, you’re essentially training yourself to think like the AI – what it needs to deliver the result you want. That insight is invaluable, especially for leaders trying to implement AI effectively in their organizations.”
The Bottom Line
As we look toward the future of AI, Andreas Welch emphasized that its integration into daily workflows will only deepen. Rather than being a standalone tool, AI will become an integral part of business operations, enhancing productivity and decision-making.
Central to effective leadership in this transformation is a human-centric approach. Both Karyna and Andreas agreed that AI augments human capabilities rather than replacing them.
By automating repetitive tasks, AI allows teams to focus on innovation, strategy, and creativity. Leaders who embrace this mindset, aligning AI strategies with business goals and fostering a culture of adaptability, will be best positioned for long-term success. It’s not too late to embrace AI, Andreas pointed out. This is only the beginning, and the potential for growth and innovation continues to expand.