The artificial intelligence narrative has shifted. After two years of experimentation, proofs of concept, and carefully controlled pilots, organisations now face a far harder question: how to move AI from the lab into the fabric of the business.
The challenge has decisively shifted from experimentation to industrialisation. Success now hinges on treating AI as an infrastructure investment, not an innovation project. The core obstacles are structural—data quality, system integration, and governance gaps—not model capability.
Agentic AI compounds the urgency, demanding a “neutral control plane” for orchestration and auditability. The channel ecosystem is stepping in, with partners offering readiness assessments to bridge the deployment gap.
The 2026 CIO must architect for production-grade resilience, aligning technology with business process redesign and outcome-based economics.
One of the most common pitfalls in AI deployment is approaching it as a technology project rather than a business transformation.
Speaking to FutureCIO, Lynn Toh, senior director, adavnced solutions, Tech Data Singapore emphasises that organisations need to start by identifying the real business problem they are trying to solve, rather than selecting an AI tool first. “AI delivers the greatest value when it is designed around business workflows and desired outcomes, rather than existing processes that may already be inefficient.”
This distinction matters because applying AI to a broken process automates the brokenness. The TD SYNNEX Direction of Technology report (Report) echoes this, noting that market leaders are distinguished less by what they sell and more by how they operate, with AI-powered solutions becoming “the backbone of competitive advantage”.
The Report also identifies customer expectations and adoption—specifically, the difficulty of translating AI into customer-specific business value—as the top challenge partners face when implementing AI solutions.
Toh observes that customer conversations have become increasingly targeted. “Instead of asking what AI can do for them, organisations are now focusing on solving specific operational challenges, improving their productivity, and delivering measurable business outcomes.”
This requires an enterprise-wide AI vision that aligns business priorities, data, and governance, ensuring that AI initiatives can scale beyond isolated departmental use cases.
Toh identifies the root cause as a governance failure.
Lynn Toh
“An effective governance framework needs to bring together enterprise-wide AI strategy, data governance, security, compliance, and operational guardrails under a single operating model, rather than just managing AI initiatives independently.” Lynn Toh
The Lenovo-IDC global CIO Report, based on 3,120 IT and business decision-makers, found that while 60% of organisations believe they are in the middle-to-late stages of AI adoption, only 27% have established comprehensive AI governance frameworks.
The governance challenge becomes more acute as organisations move from experimentation to enterprise-wide adoption. Toh argues that governance must be applied consistently, with oversight of how AI is deployed, how data is managed, and how it is used across the business.
Crucially, it must also evolve beyond approving individual use cases to enable adoption at scale, which requires “centralised visibility into AI tools, data flows, and AI usage across the organisation to prevent fragmented deployments of what we call shadow AI.”
Before moving into production, Toh advises testing data and infrastructure readiness and validating the complete solution stack, including compute, storage, networking, security, and the AI platform itself.
Agentic AI and the shift in business risk
Perhaps the most significant shift on the horizon is the rise of agentic AI—systems capable of autonomous action. Toh frames this not as an architectural question but as a fundamental change in business risk.
“With agentic AI, software is now executing real-world tasks. Modifying databases, triggering operational workflows. The moment the software takes action autonomously, the control moves out of the IT department and straight into the enterprise’s risk management and C-suite accountability.” Lynn Toh
This distinction is critical. Traditional AI generates insights or content; agentic AI acts upon them. The Report notes that partners are already planning to launch AI infrastructure and cloud capabilities, with 53% of partners identifying AI as their top growth driver.
The Lenovo-IDC Report similarly finds that agentic AI is expected to replace generative AI as the CIO’s top technology priority in 2026, though only 21% of CIOs have deployed agentic AI in production, with 55% still exploring or piloting.
The security implications are significant. As Toh notes, “Recent incidents involving autonomous AI have made the security implications of agentic AI much more tangible.” Organisations need to rethink how identity, access, and governance extend to non-human actors.
“The question is no longer just who has access, but what has access, what it is authorised to do, and how organisations can maintain visibility over those actions.”
Measuring what matters
CIOs are under intense scrutiny to demonstrate return on investment. Toh argues that AI success should be measured by business outcomes rather than the number of pilots or proofs of concept completed. “Productivity gains, operational improvements, and measurable business value are becoming the metrics that matter most.”
She outlines three categories of measures: efficiency (cost savings and their auditability), growth (increased win rates, shorter deal cycles, higher attach rates), and business transformation (new services or products for revenue generation).
Toh adds that AI investment should be approached as a long-term business transformation rather than a one-off technology purchase, requiring investment across infrastructure, data, governance, and workforce capabilities.
This discipline matters because, as Toh notes, “We are seeing CIOs become more disciplined in how they evaluate AI investment, with greater emphasis on measurable outcomes and long-term business value rather than short-term experimentation.”
Workforce transformation and AI literacy
Technology adoption ultimately depends on people. Toh emphasises that AI literacy needs to become a cross-functional priority so that employees across the business understand how AI supports their roles and decision-making. The Report identifies the skills gap as a persistent challenge, with 68.8% of leaders concerned about hiring or retaining talent.
Toh points to research showing that nearly 75% of partners say AI is essential to their future.
“We see stronger adoption when employees are encouraged to experiment with practical, day-to-day AI use cases. Building confidence through real business applications, rather than theoretical training alone, will help organisations build a culture of continuous learning.” Lynn Toh
She offers a crucial distinction: “AI literacy teaches people to use a tool, but organisations, and especially CIOs, also need to imagine new ways of working together. Individual literacy can make us efficient, but collective imagination will make us competitive.”
A strategic imperative
The message from Toh is unambiguous: “Start now. There will never be a better time than now. Start by looking at the business use cases and the real business challenges the organisation is trying to solve. Then, plan around those use cases. There is no point in looking at different solutions and choosing the most popular.”
The Report reinforces this urgency, concluding that “the market is rewarding companies that combine strategic foresight with operational agility.” For CIOs, 2026 is not the year to wait and see—it is the year to decide where they will lead and move decisively.
Toh’s advice distils the challenge: choose the solution that helps solve your business challenges. It calls for focus, discipline, and alignment. In the race to move AI from pilot to production, those qualities will separate the leaders from the followers.
Click on the PodChats player to hear Toh share her perspective on architecting your AI strategy for production-grade resilience.
How can CIOs redesign business processes before deploying AI, ensuring we automate the right workflows rather than just digitising inefficiency?
What governance framework and infrastructure are needed to move AI pilots into production?
How can CIOs architect a “neutral control plane” to manage agentic AI, ensuring least-privilege access, audit trails, and oversight for autonomous decision-making?
With agentic AI raising the stakes, how should CIOs redesign identity and zero-trust architectures to secure at machine-speed, autonomous actions?
In the old days, CIOs were measured by the number of IT projects in the pipeline. In the era of AI, some suggest measuring CIOs by business outcomes and ROI instead. What are your thoughts here?
How should CIOs evolve pricing and investment models—perhaps towards consumption or outcome-based models—to make scaling AI economically sustainable?
What role can channel partners play in conducting AI readiness assessments and bridging the gap between pilot and production deployment?
What should be the CIO strategy for workforce transformation, building cross-functional buy-in and AI literacy to ensure adoption beyond the pilot team?
Synthesising everything we’ve covered, what is your advice for CIOs for 2027?
