AI is already unlocking significant financial and productivity gains for companies in the technology and telecommunications sectors. Those that are furthest along in their AI deployment are reporting productivity improvements of 15% to 25%, with some approaching 30% uplift in EBITDA, by fundamentally reimagining how work gets done—and these benefits will likely continue to grow.

AI transformations are resetting expectations for CIOs and IT organizations. Business stakeholders are demanding more, while AI-native vendor solutions require deeper integration across a broad range of data sources and network components. Many CIOs recognize they lack the capital, infrastructure, and data foundations needed to scale AI. As a result, they face a decision: how to prioritize support across the business to unlock value fastest.

At the same time, the forces reshaping the technology and telecommunications sectors—tariffs, deglobalization, inflation, scarce talent, and geopolitical volatility—add pressure on IT budgets and operating models. Bain & Company research finds that most CIOs expect to see a positive bump in their organization’s performance due to AI, while about half of CIOs also expect negative impacts from inflation and geopolitical tensions.

Technology leaders face a tough reality. AI is now critical for progress but also creating a much higher bar for CIOs and IT organizations. It requires serious investment in modern platforms, data foundations, and governance. Most IT budgets can’t absorb that level of change. But there are real costs to waiting. Competitors are investing in AI to speed up R&D, boost productivity, and transform their business models. The gap is widening for those who hesitate or can’t afford to keep up.

What’s more, leaders know it’s not enough to solve the issues inhibiting today’s AI solution development; they’re looking three to five years out. While no one can predict the exact demands of those future systems, one thing is clear: Creating a modular, flexible design is the only practical way to stay ready for whatever comes next.

The CIO’s mandate: Reinvent IT as a critical partner in AI transformation

CIOs play the linchpin role in the long-term success of AI transformation. Best-in-class IT organizations can accelerate progress by prioritizing investment and resources and by changing the ways that work gets done. IT organizations that can’t make this transition hinder progress and limit the speed of transformation. At companies that have turned IT into an accelerator, CIOs focus on five key priorities.

Rethink AI product ownership, data, and platform governance. Sustaining AI workflows requires dedicated project management to continuously adapt to a fast-evolving vendor landscape. Legacy IT and data approval processes are too slow and rigid to keep pace. CIOs need to modernize their governance and compliance, streamlining decisions while balancing risk with speed.

Accelerate execution to match the pace of AI. Business stakeholders expect progress at the pace of AI innovation, which is now reaching a weekly cadence. IT must respond, shifting to rapid, agile deployment models that reduce reliance on long roadmaps and RFP cycles.

Double down on customer centricity. To keep pace with the business, IT teams must build and deploy with the customer at the center, translating user needs into solutions. Progress is now measured in hours and days, not weeks, with tight feedback loops guiding continuous refinement.

Free up investment capacity for high-impact AI. Reduce technical debt, consolidate internal software, and rationalize spending on vendors to lower ongoing costs. Redirect those savings toward the IT investments needed to capture AI’s productivity benefits while maintaining disciplined cost control.

Invest in foundational systems and data to enable AI. Deploying AI without fixing underlying data and technology foundation issues leaves gaps in architecture and data quality that can quickly become a bottleneck. Without a modern, scalable technology stack, AI initiatives can stall and fail to deliver on promises of speed and value.

Building the foundation for full potential AI

Of course, focusing on these areas is essential, but in the long term it won’t be enough to transform the enterprise into an AI organization. The tech platforms that today’s systems sit on were not designed to support AI and its collaborative agents. Companies will need to redesign their technology architectures to enable the AI and agent future that’s now upon us. (For more, read “Why Agentic AI Demands a New Architecture.”)

Three principles in particular stand out as being critical for the success of this type of transformation.

• Modernize the core platform. Organizations will need tech foundations that make core business capabilities easy for agents to discover and use in real time. Legacy, batch-based systems need to evolve into modular, API-enabled platforms that can respond to real-time events. Adopting modular, industry-standard frameworks can accelerate this shift. In the near term, these modern systems will need to work alongside existing infrastructure, adding some architectural complexity.
• Scale data access. Scalable access to structured and unstructured data is essential. Most organizations still lack the required ingestion pipelines for unstructured sources such as documents, emails, voice recordings, images, videos, and call transcripts. These data sources hold the information that agents need, particularly in manual or exception-driven processes where necessary knowledge often resides outside core systems of record or even outside the organization. One European bank addressed this by building infrastructure to unify structured and unstructured data into a holistic customer view, enabling more automated, personalized engagement at scale.
• Ensure interoperability of agentic services. As agents roll out across the tech stack, consistent interoperability standards, such as the Model Context Protocol (MCP), and frictionless integrations will be critical for breaking down silos and capturing the full value of agentic AI. Most organizations will need to support a mix of frameworks, including custom agents built on engineering tools, prebuilt agents embedded in vendor platforms, and dynamically generated agents in data platforms. Increasingly, these frameworks are becoming more agentic. For example, a software development life cycle (SDLC) agent could coordinate specialized agents (design, analyst, engineer, quality assurance) that collaborate to deliver a complete solution, from concept to deployment.

Some decisions make themselves

In technology and telecommunications companies, AI isn’t an overlay on yesterday’s stack. It’s a stress test of your architecture, data, and operating model. The companies pulling ahead didn’t just find a better pilot. They fund modern IT at higher levels, then translate their spending into reusable platforms, cleaner data, and faster deployment.

Leaders in these industries consistently devote a higher share of revenue to IT than peers in other verticals, because they understand that modern IT is the engine of product velocity and operating leverage. Companies that continue to treat IT budgets as if technology were pure overhead end up paying for it in delays, rework, and missed growth.

The decision for CEOs and their technology leaders isn’t whether they can afford to modernize. It’s whether they can afford to fall further behind while competitors industrialize AI. The mandate is to reallocate, simplify, and invest. If you don’t, the organization will remain trapped in a legacy stack that can’t support what’s coming.