AI isn't magic, and it’s time to stop pretending

Artificial intelligence has been heralded as the game-changer of the decade, yet for many organisations, its potential remains locked behind buzzwords and hype.

At a recent panel discussion hosted by Workday at their Workday Rising EMEA event, senior leaders from Mondelez, Siemens, Aliaxis, and HiredScore pulled back the curtain on their AI strategies, offering candid insights into what it really takes to drive value in the workplace.

Their message was clear: the road to AI maturity is neither quick nor easy, but the rewards are transformative. From streamlining operations to reimagining process management, these leaders highlighted the tangible business impacts of thoughtful AI adoption.

“AI is a tool—not a magic wand. The key is understanding the challenges you want to solve and selecting the right tools to meet those needs,” said Volker Schrank, VP of Global Employee Experience & HR Technology at Mondelez. His comments echoed a broader theme: AI’s value lies not in flashy features but in its ability to enhance efficiency, decision-making, and competitiveness across the enterprise.

As finance leaders grapple with their own AI ambitions, the panel’s insights provide a roadmap—one that balances pragmatism with vision, while making the case for acting now. Delay, as the speakers warned, carries its own steep cost.

From Hype to Reality

One of the dominant themes of the panel was moving past the allure of AI’s possibilities and focusing on its practical, business-driven applications. “We’re not doing AI for AI’s sake,” stated Pierre Ramery, Vice President of IT Global Business Solutions at Aliaxis. He emphasised the importance of targeting specific pain points within the organisation where AI can create measurable value. “This is the sweet spot where we can generate value, otherwise, it’s just a gimmick.”

For Mondelez, the focus has been on leveraging AI to simplify HR processes and empower employees. Schrank described how the organisation uses AI to automate routine tasks, freeing up HR professionals to focus on strategic priorities. “How can we get HR work as much simplified and automated as possible so that our colleagues can focus on what really matters?” he asked. The organisation’s approach reflects a broader mindset: AI is a tool that works best when aligned with clearly defined business goals.

The panellists agreed that AI’s success isn’t about deploying it across every facet of the business but targeting areas where its impact will be most profound. Tanya Schroeder, Global Head of People & Operations Digitalisation at Siemens, shared examples of how AI has enhanced both recruitment and employee experience. “We started using AI in recruitment to improve matching CVs with job descriptions,” she explained. This shift not only sped up hiring processes but also ensured a fairer evaluation of candidates—a win for both the company and its workforce.

Schroeder also cautioned against viewing AI as a quick fix. “Don’t just throw AI at a problem and think it helps,” she said. “You really need to think about how AI fits into the process and complements the people involved.” This holistic approach, combining technology with a thoughtful integration into workflows, was a recurring theme throughout the discussion.

The Cost of Waiting

The panellists also warned about the risks of hesitation in adopting AI. “The cost of waiting is higher than many companies understand,” noted Schrank, highlighting two critical prerequisites for AI success: compliance readiness and data quality. “You first have to get your data in order before you can start with AI,” he said. Without clean and reliable data, organizations risk not only failing to realize AI’s potential but also falling behind competitors who are already making strides in this area.

This sense of urgency resonated across the discussion. Athena Karp, General Manager at HiredScore and a Workday leader, pointed out the broader consequences of delaying AI integration. “If your competitors are supercharged—30, 40, 50% more efficient—and you’re not, you’ll struggle to attract top talent and remain competitive,” she explained.

Karp emphasised that today’s workforce expects modern, AI-powered tools that enhance their productivity and skills. Organisations that fail to offer these tools risk not only operational inefficiencies but also a brain drain as employees move to companies further along the AI curve.

Ramery echoed these sentiments, noting that companies often wait without a clear reason. “Not sure what we’re waiting for,” he said candidly. His advice? Organisations must act, even if it means starting small. Small, targeted initiatives can help businesses build momentum and develop the internal expertise necessary to scale AI more broadly in the future.

Building an AI-Ready Culture

The discussion also addressed the cultural shifts required for successful AI adoption. Schroeder emphasised the importance of fostering a “start, fail, and learn” mentality. “If it doesn’t work, analyse why you failed, start differently, and learn from it,” she said. This iterative approach encourages experimentation and innovation while keeping organizational goals in focus.

AI literacy emerged as a key enabler. Ramery described how Aliaxis has rolled out AI education programs across all levels of the organization, from webinars to training curricula tailored to both beginners and experts. “A lot of people think AI is a black box that can do everything,” he said. “We have to educate people, from the board to the frontline.” This investment in AI understanding not only demystifies the technology but also ensures employees are prepared to work effectively alongside it.

Schrank added that technical training is only part of the equation. His team at Mondelez has paired AI education with design thinking workshops to ensure solutions are grounded in user needs. “It’s about focusing on the challenges and concerns of the end user,” he explained. This human-centered approach helps build trust in AI tools while ensuring they are genuinely useful and impactful.

Balancing Buy vs. Build

The panellists tackled a common dilemma faced by many organizations: whether to build AI capabilities in-house or to rely on vendor solutions. Schroeder offered Siemens’ perspective, emphasising the importance of leveraging existing tools. “There are so many things out there already,” she said. “You don’t need to create your own if you don’t have the people or resources. Start with what’s available and then fill in the gaps with more advanced, company-specific solutions.”

This pragmatic approach resonated with Ramery, who acknowledged the temptation to build bespoke solutions but warned of the rapid pace of technological advancements. “At some point, you think you have a competitive advantage by doing it yourself, but soon after, you find out it’s already on the shelf,” he said. His advice? Strike a balance between leveraging off-the-shelf solutions and experimenting internally to better understand AI’s potential.

Karp added that organisations must evaluate AI investments through the lens of long-term outcomes. For heavily regulated industries, like HR and finance, she argued that vendors with established compliance frameworks often provide a safer, faster route to implementation. “If your core business isn’t building AI tools, you won’t get the same resources or talent as companies that specialize in them,” she noted.

The panel agreed that the key lies in continuous evaluation—understanding when to pivot from internal experiments to scalable, vendor-supported solutions that align with business objectives.

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