Generative AI: The CIOs Apollo moment

The success of any gen AI initiative depends on its alignment with broader business objectives. Before diving into implementation, organizations must ensure that the use cases they pursue are linked to strategic priorities, such as revenue growth, operational efficiency, or customer satisfaction. This alignment prevents AI investments from becoming siloed projects disconnected from the core business mission. Among the essential KPIs for gen AI are productivity gains, cost savings and time reductions—metrics that provide tangible evidence to satisfy boardrooms.

Breaking down silos: A holistic approach to running Virtual Machines and Containers on the same platform at scale.

• Once you’re stuck in technical debt, catching up on investments not made is incredibly difficult.
• We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.
• It guides organizations through every phase of the process, from aligning gen AI investments with strategic goals to scaling successful pilots across the enterprise.
• Implementation must include a robust monitoring system to track progress against benchmarks.
• Learning and knowledge management applications go from feasible to operational differentiators when field operators have higher-functioning tablets, connect to 5G networks, and utilize AR/VR and other wearable devices.

At the same time, low-code/no-code platforms, citizen developers, and ubiquitous AI tools are decentralizing engineering and tech expertise. While embracing emerging technologies, it is also critical to double down on cybersecurity and compliance to maintain the trust of stakeholders. In addition to using emerging technologies to benefit customers, CIOs should ensure their employees have the opportunity to understand and benefit from innovations as well.

CIOs must reimage the digital workplace to move from AI hype to real-world productivity gains.

Over half (51%) agree that better access to AI tools is crucial for innovation and 76% say that businesses not empowering employees with AI will lag behind in the next one to two years. This phased approach allows for efficient risk identification, implementing a multi-layered defense, optimizing resources, and pursuing continuous improvement. Tools should also be used for model evaluation to support speed of evaluation, efficient risk detection, consistency, and comprehensive analysis. The complete OWASP generative AI Red Teaming guide provides a detailed checklist for each blueprint phase, which can be referenced.

Key considerations include the model’s architecture, data flows, and how the system interacts with the broader environment, external systems, data, and sociotechnical aspects such as users and behavior. OWASP, however, points out that AI and ML present unique challenges because models may behave unpredictably because they are non-deterministic and probabilistic. This democratization threatens control, but it’s also a multiplier of creativity, speed, and innovation. CIOs need to rethink operating models to balance democracy with governance. The goal is to enable innovation while maintaining alignment with organizational goals. The key is to build guardrails — ensuring security and scalability — while empowering employees to explore and create.

Our quintessential human ability to connect our brain power and work together towards a common goal is the only thing that makes us smarter than machines. It starts with an understanding that ChatGPT is different from the AI of the past few years, with more advanced natural language processing abilities and a more robust capacity to learn from prompts and fine-tuning. And it’s getting so good, so fast, that if businesses aren’t yet considering it a vital component for growth, they will need to very soon—or risk being left behind by their competition. • Use AI-driven document processing to automate form submissions, approvals and regulatory compliance. To learn more, read Reinventing Workplace Productivity, the first report in the new Lenovo Work Reborn Research Series 2025. As generative AI (genAI) becomes a strategic priority for organisations, Lenovo’s new research series – Work Reborn – reveals a critical disconnect.

He also participates in industry working groups such as the Cloud Security Alliance’s Incident Response Working Group and serves as the membership chair for Cloud Security Alliance D.C. Chris also co-hosts the Resilient Cyber podcast. OWASP recommends threat modeling as a key activity for generative AI Red Teaming and cites MITRE ATLAS as a great resource to reference. Threat modeling is done to systematically analyze the system’s attack surface and identify potential risks and attack vectors.

In the next five years, an estimated 40% of the skills needed in the workplace will change. CIOs should consider an open talent model — a hybrid strategy incorporating the enterprise version of gig work into the traditional employee base. This approach allows organizations to access skills they don’t have, pull in talent from across the globe, inject diverse perspectives, and accelerate innovation. It’s the first technology launched at scale with known flaws, from hallucinations and bias to ethical and security challenges. But like the space race, it holds the power to advance science and technology, redefine industries, transform a generation, and unlock human potential — if today’s leaders meet the challenge.

• Decision-makers should focus on projects with a clear path to ROI, strong strategic alignment, and measurable outcomes.
• Customers and partners trust us to secure their data and operations, and it is on us to ensure we are maturing our cyber defenses through leading technology, automation and best practices.
• This democratization threatens control, but it’s also a multiplier of creativity, speed, and innovation.
• Drip Capital, a fintech startup specializing in cross-border trade finance, provides a concrete example of how businesses can apply a structured approach to measuring the ROI of gen AI initiatives.

Short-term thinking — like focusing solely on three-year ROI — creates technical debt and kills innovation. Once you’re stuck in technical debt, catching up on investments not made is incredibly difficult. Rockets exploded in training, three astronauts were killed in a fire, and Neil Armstrong barely survived a near — fatal test flight.

Why measuring gen AI’s impact is so challenging

The role of the healthcare CIO is undergoing a rapid transformation — driven in large part by the rise of generative AI, which is reshaping not only technology strategies but enterprise leadership itself. Unlike traditional IT investments with predictable returns, gen AI’s impact often unfolds over months or years. This delayed realization of benefits can make it difficult to justify AI investments in the short term, even when the long-term potential is significant. The CIO is now a culture catalyst, architecting a digital infrastructure and building a learning organization that fuels continuous growth.

For field operations that perform a wider variety of work types in highly differentiated conditions, AI agentic experiences partner with field engineers to provide real-time information and guidance. Mission-critical workflows performed by more people, in real-time, and with higher data volumes are indicators of where AI can provide business value. A second opportunity is to consider the graywork and operations that lack digitized workflows and tools to connect field and back-office operations. Red teaming is a time-proven approach to testing and bolstering cybersecurity systems, but it has always needed to evolve alongside technology. The explosion of generative AI and large language models (LLMs) in recent years is only the latest innovation to come along and force the red-teaming world to adapt.