Scale AI Responsibly in 2026: 5 Practical Steps
▼ Summary
– CIOs are shifting from AI pilots to enterprise-wide deployment, focusing on scaling AI safely and with measurable business impact.
– A key strategy is putting AI at the core of business operations to drive growth, competitive advantage, and transformation.
– Organizations must scale their IT infrastructure, with a hybrid approach, to provide a secure and efficient foundation for AI.
– Interest in agentic AI is rising for automating complex tasks, but it introduces new challenges in control, security, and management.
– Most organizations lack robust AI governance, making the establishment of clear rules for responsible and transparent use a critical priority.
The coming year marks a pivotal shift for businesses, moving beyond initial AI experiments to full-scale, enterprise-wide deployment. This transition demands a strategic focus on scaling artificial intelligence in a manner that is safe, responsible, and delivers clear, measurable business value. According to a recent playbook developed by Lenovo in collaboration with analyst firm IDC, success hinges on building stronger foundations in data, skills, infrastructure, and governance.
The research, which surveyed 800 executives, indicates that AI is now a defining force in how companies operate and compete. The race is no longer about who adopts AI first, but who can scale it most effectively. IDC’s Ewa Zborowska outlined five critical steps for CIOs to guide this scaling process successfully.
First, leaders must embed AI at the very core of their business strategy. It should be viewed not merely as another tool, but as a fundamental enabler for transformation, growth, and building a competitive edge. CIOs are encouraged to partner closely with other business units to translate strategic priorities into concrete AI use cases, complete with clear owners, key performance indicators, and timelines. This requires looking beyond the technology itself to understand the tangible value it brings to operations and customer experiences.
The second step involves moving from proof-of-concept to identifying clear proof of value. Organizations are increasingly past the testing phase and are now focused on leveraging AI to directly impact business outcomes. Key priorities include boosting revenue and profit, enhancing customer satisfaction, and improving employee productivity. Implementations are now geared towards operational efficiency and discovering new avenues for business generation, reflecting a maturation in how AI’s potential is harnessed.
Third, scaling AI is impossible without a robust and scalable IT infrastructure. While challenges like training and upskilling exist, the most significant hurdle identified is ensuring the underlying technology foundation can support AI workloads effectively. The research notes that a large majority of organizations plan to use on-premises or edge deployments within a hybrid environment. This necessitates integrating AI securely into the existing IT estate and managing it intelligently from both a technological and financial perspective.
Fourth, with interest in agentic AI, systems that can autonomously execute complex tasks, rising rapidly, new management concerns emerge. Early applications are seen in areas like security operations and customer service. However, deploying these autonomous agents introduces challenges around data quality, workflow redesign, and maintaining control. A crucial role for CIOs will be to help the business identify where agentic AI is genuinely beneficial and where traditional approaches remain sufficient, all while implementing strong controls to manage security and prevent uncontrolled proliferation.
Finally, governing AI responsibly is a non-negotiable pillar for sustainable scaling. The research reveals a significant gap, with only 30% of CIOs having established comprehensive AI governance policies that address security, privacy, and data protection. Building trust is essential, both internally and with customers and partners. This confidence is achieved by ensuring governance evolves in lockstep with adoption, establishing clear rules and policies, and continuously upskilling people to work with AI ethically and effectively.
(Source: ZDNET)
