Octavio Marquez serves as the president and CEO of Diebold Nixdorf (NYSE: DBD), a leading innovator in reshaping banking and retail experiences.
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AI is not only integral to my professional role; it also plays a significant part in my everyday activities. I regularly utilize AI applications such as Copilot and Grok for exploring business concepts, enhancing operational efficiencies, and even planning my morning runs while traveling in unfamiliar cities. These experiences highlight AI’s capability to address problems swiftly and intelligently, which is essential for banks and retailers aiming to meet customer demands. At Diebold Nixdorf, we’re harnessing AI to facilitate a transformation in retail and banking, from innovations in self-checkout to proactive ATM maintenance strategies. The message is unmistakable: AI is not static—it requires timely action.
The Divergence of Retail and Banking
The retail sector operates at a rapid pace, where narrow margins and high customer expectations necessitate speed. Companies like Amazon leverage AI to manage inventory instantly, while numerous banks still depend on outdated systems that delay data processing for days. This competitive environment has turned retail into a testing ground for AI advancements such as fresh produce recognition in self-checkout systems, automatic age verification for purchases, and minimizing losses via real-time error detection during transactions. These implementations are not mere trials; they are transformative solutions enhancing customer experiences and offering tangible efficiency to retailers.
Conversely, the banking sector tends to proceed more cautiously. The priorities of risk management, regulatory compliance, and customer trust take precedence. However, excessive caution can lead to stagnation. Banks that took significant time to embrace cloud technology cannot afford similar delays with AI. Fintechs and challenger banks are currently integrating AI into their core operations. If established institutions fail to respond swiftly, they risk losing considerable market share and their status as trusted advisors to AI-empowered rivals in the near future.
Transitioning from Follower to Leader
Research from Deloitte indicates that the performance gap between “AI innovators” and “AI laggards” is becoming increasingly evident. Innovators report enhanced returns on investment alongside quicker operational improvements. In contrast, laggards are grappling with readiness issues, with only 7% feeling equipped in terms of talent, 16% regarding risk and governance, and just 20% on the technology infrastructure front. The takeaway is straightforward: To evolve from laggard to innovator, acting now—strategically and responsibly—is imperative; waiting for AI to “mature” is not an option.
Our approach at Diebold Nixdorf reflects this mindset. Instead of hastily integrating AI into every facet of our operations, we prioritized addressing a critical question: What challenges are we aiming to resolve? We established an AI steering committee composed of leaders across legal, IT, and various business units. Their role isn’t to hinder progress but to outline parameters, cultivate alignment, and ensure that our AI initiatives foster both innovation and trust.
For instance, in the past year, our AI-driven predictive maintenance has successfully reduced ATM downtime by over 2.3 million hours for our international banking clients, ensuring round-the-clock access to cash. Within our organization, we’ve implemented AI tools to assist our employees in enhancing writing quality, accelerating coding capabilities, and automating routine tasks. These achievements contribute to a culture that perceives AI as a facilitator of progress rather than a disruption, yielding measurable returns on investment and minimizing resistance to change, all while encouraging innovation and adding value for our customers.
Establishing a Strong Base
Quality data is crucial for AI’s success. Disjointed data silos or inconsistent data governance can undermine even the most sophisticated algorithms. Many organizations mistakenly attribute challenges to “technical issues” when the real culprit is often a data problem. While auditing, cleansing, and consolidating data may lack glamour, it is crucial.
Strong governance enables agility rather than impeding it. Clearly defined roles, credible data sources, and established guidelines facilitate faster operations with assurance. Although laying these foundations necessitates investment, the resulting enhancements in innovation speed and customer trust are invaluable.
If the task seems daunting, start small. Focus your initial AI projects on clear objectives that can demonstrate tangible results within three to six months. Reserve more complex concepts for subsequent phases. Implement a straightforward scoring system to identify your top two or three ideas, then assemble small teams with diverse expertise, including product leads, data specialists, legal/privacy experts, and business stakeholders.
Set explicit goals for each team, such as minimizing downtime or expediting manual processes, and designate a timeline for demonstrating progress. Begin by incorporating AI into existing workflows instead of displacing them. Use AI as a supporting mechanism, complementing human oversight, experimenting with various versions, and scaling successful implementations. Finally, treat your data as a product: Assign ownership, monitor quality, and establish alerts. Incremental enhancements in data quality will yield significant dividends as your organization evolves.
Managing Challenges and Risks
Accelerating AI adoption does not equate to neglecting risks; it necessitates prudent management. Watch for potential bias, inadequate data quality, untrustworthy vendors, and resistance to change. Minimize bias by ensuring fairness through balanced data and ensuring that models are interpretable, especially when making high-stakes decisions.
Establish governance from the outset: Involve legal and compliance teams in early pilot projects and apply a risk-assessment framework to determine the level of oversight required. At Diebold Nixdorf, we facilitated AI adoption by selecting internal advocates, providing hands-on training, and developing quick-response reference materials for addressing challenges. Automating model evaluations and implementing alerts for human review when necessary is also advisable.
By adhering to these principles, you can navigate swift advancements in AI responsibly.
The Era of Implementation
We have progressed past the hype surrounding AI. Gartner’s “Trough of Disillusionment” signifies the phase where organizations acknowledge that AI is not an immediate solution and requires consistent, disciplined execution. Successful adopters will regard AI as an essential business practice rather than merely a novel trend.
In retail, AI is redefining shopping experiences. In banking, it personalizes interactions, forecasts cash requirements, and reduces operational expenditures. At Diebold Nixdorf, our AI solutions empower banks to oversee cash networks and self-service channels with unrivaled efficiency.
The critical question is not if AI will transform banking and retail—it already is. The true challenge lies in determining who will take the lead. Organizations that prioritize swift action, establish clear guidelines, and focus on data can position themselves to deliver faster services, personalized customer interactions, and cost efficiencies. Do not delay waiting for AI to become “risk-free.” Evaluate your data, align your teams, and take decisive action today to maintain your competitive edge.
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