The Impact of AI on B2B IT Teams

For years, B2B IT teams have had a reputation for moving toward the digital future at… well, let’s call it a “carefully considered pace.” And who can blame them? When you’re managing sprawling infrastructures, complex tech stacks, and the never-ending list of “critical priorities,” adopting new innovations can feel less like turning a corner and more like steering a cargo ship with a canoe paddle.

However, AI seems to be changing that stereotype. Nowadays nearly 30% of B2B decision-makers begin their research on AI platforms, and 78% of B2B organizations have implemented AI into at least one business functionality. Instead of inching toward transformation, many B2B organizations are suddenly finding themselves accelerating towards the digital future, sometimes by choice, sometimes by necessity.

With that in mind, let’s take a look at  why AI is giving B2B IT teams a long-overdue boost into the future, how it’s changing their day-to-day reality, and what forward-looking teams can do to stay ahead.

The Impact of AI on B2B IT Teams

AI’s growing influence across the B2B landscape is creating new pressures, new responsibilities, and new opportunities for IT leaders:

1. AI Helps IT Shift From Manual Automation to Intelligent, Adaptive Systems

Traditional workflows depend on inflexible logic: predefined triggers, fixed conditions, and carefully engineered sequences. 

AI changes that. Instead of building a thousand branches in a flowchart, IT can enable systems that continuously interpret context, predict needs, and determine the next best action automatically. 

When AI handles the repetitive, predictable aspects of workflows, IT gains the bandwidth to focus on innovation and higher-value initiatives; IT teams can shift from being reactive firefighters to proactive innovators. Instead of drowning in maintenance work, they can invest their time where it matters most: modernizing infrastructure, strengthening security posture, improving digital experiences, and exploring emerging technologies that could unlock new value for the business.

Organizations that operationalize AI in IT often report higher productivity, faster project delivery, and, perhaps most importantly, a renewed sense of purpose among their teams. When IT professionals are empowered to focus on solving business challenges rather than clearing backlogs, they’re able to contribute more strategically, partner more closely with the business, and drive initiatives that make a real impact.

2. AI Requires IT to Architect Infrastructure That Supports Real-Time Decision-Making

Whether it’s dynamic pricing, product search, personalization, or inventory forecasting, AI only performs well when it has access to reliable data in real time. That places enormous responsibility on IT teams to modernize the underlying architecture that hosts and syndicates product data. Legacy batch processes, overnight sync jobs, and sluggish APIs simply cannot support the expectations of agentic AI. Instead, IT must build environments where data flows continuously and updates propagate instantly across ERP, PIM, OMS, DAM, CDP, and other systems that fuel AI-driven decisions.

The challenge is both technical and organizational. IT needs to align data governance, system ownership, and update processes to guarantee every team contributes to a consistent and reliable flow of information.

The shift toward real-time decisioning also expands IT’s role in performance optimization. High-volume requests from AI agents create new strains on infrastructure. IT must evaluate caching strategies, compute scaling, cloud utilization, and network throughput to ensure AI operations don’t introduce friction or system instability, which leads us nicely to our next point.

3. AI Demands Seamless Interoperability Across the Entire Tech Stack

For an AI agent to retrieve pricing updates, access product content, initiate fulfillment steps, or trigger customer workflows, every underlying system must be fully interoperable. This places IT at the center of ensuring that the organization’s architecture is stitched together and deeply integrated. APIs must function consistently, authentication must work smoothly, and system dependencies must be managed intelligently. When AI calls for data or triggers an action, there can be no bottlenecks.

Interoperability becomes even more important as AI evolves beyond simple query-response patterns into autonomous orchestration. An AI agent might need to retrieve product specs from PIM, confirm stock availability via OMS, calculate delivery timelines through ERP, and adjust pricing dynamically based on a CDP data signal, all within milliseconds. IT ensures these systems can talk to each other without breaking or contradicting one another.

This need for interoperability also drives IT’s vendor strategy. Not every platform is built for AI enablement, and not every API performs equally under stress. IT must make decisions about which platforms integrate well enough to support AI at scale, which require middleware, and which need to be replaced. AI highlights integration problems that were previously invisible, and IT becomes responsible for solving them.

4. AI Relies on IT to Build Continuous Feedback Loops for Ongoing Learning

AI gets better when it can continuously learn, and IT plays a crucial role in enabling feedback loops that can continuously train AI models. IT teams are the ones responsible for creating the mechanisms that help AI understand what works, what doesn’t, and how to adjust its behavior. The value of AI compounds over time, and IT is responsible for ensuring those compounding effects actually occur.

This responsibility also extends to monitoring for bias and unintended behavior. Because AI transforms over time, IT must design guardrails that keep its learning aligned with business goals and compliance requirements.

5. AI Forces IT to Manage Tech Sprawl and Overlapping AI Capabilities

As vendors race to embed AI into their platforms, IT teams face a growing risk of redundant investments. One system offers AI search. Another offers AI content enrichment. Another offers AI recommendations. Without a strategic view, organizations quickly pay multiple times for similar capabilities. IT becomes responsible for evaluating where AI adds real value and where it overlaps.

Managing tech sprawl is also critical to maintaining performance and long-term scalability. Every new AI feature introduces additional compute demands and integration requirements. IT must prevent platforms from accumulating disconnected AI functions that inflate operating costs without improving outcomes. In this new era, tech consolidation is about both efficiency and survival.

6. AI Expands IT’s Responsibility for Data Quality and Data Readiness

In many B2B organizations, product data is spread across ERP, PIM, spreadsheets, vendor portals, legacy tools, and shared drives. AI magnifies the issues buried in these systems. Missing attributes, inaccurate dimensions, outdated certifications, or mismatched hierarchies directly undermine AI’s recommendations and predictions.

IT must define how information flows, who owns it, how frequently it updates, which systems are the sources of truth, and what validation rules prevent errors from contaminating downstream AI processes. AI thrives on consistency, and IT becomes the gatekeeper that enforces it. Good governance transforms data from a liability into an asset.

With AI relying so heavily on structured, consistent, and enriched product information, platforms like Akeneo PIM become foundational to successful AI adoption. Akeneo centralizes product data, enforces data governance rules, fills content gaps, and ensures every system receives complete, high-quality information. By giving IT a single source of truth, Akeneo PIM removes one of the biggest barriers to effective AI and empowers teams to deliver the accuracy and speed today’s AI-driven experiences demand.

7. AI Requires IT to Balance Innovation With Stability and Performance

AI accelerates the pace of innovation, but it also increases operational complexity. IT teams must support new models, new integrations, new data flows, and new compute requirements without destabilizing the systems the business relies on daily. Innovate too slowly, and the organization falls behind. Innovate too fast, and the infrastructure buckles!

Balancing both demands a strong architectural strategy and continuous monitoring. AI may be the catalyst for innovation, but IT ensures the organization remains functional and secure as capabilities expand. 

Where IT Goes From Here

As AI automates routine tasks, optimizes workflows, and enables real-time decision-making, IT teams are stepping into a new era where their work is more strategic, more collaborative, and more influential than ever before.

This shift comes with new responsibilities: architecting real-time data flows, ensuring interoperability across an increasingly complex tech stack, safeguarding data quality, and maintaining the stability and performance businesses depend on. But it also unlocks new opportunities for IT to drive innovation, accelerate digital transformation, and deliver smarter, more connected experiences for every team across the organization.

And at the center of all this progress is data; the clean, consistent, enriched information AI needs to function. When the data is right, AI can finally do what it promises, and IT teams can lead the business confidently into the future.

The B2B organizations that will thrive in this new era are preparing their data, modernizing their architecture, and empowering IT to build the digital backbone of tomorrow. With solid data foundations and a balance of innovation and stability, IT turns AI’s potential into meaningful business outcomes. The future of B2B commerce is intelligent, and IT is the team that will make that intelligence possible.