How AI Platforms Have Rewritten Enterprise Technology Buying Steps

Picture of Yasmien Mereb

Yasmien Mereb

Senior Manager, Research

How B2B marketing teams need to pivot

AI is now influencing how enterprise technology decisions are made from the very start. It’s changing:

  • Who gets involved
  • What buyers prioritize
  • How solutions are evaluated once they are in the mix

For B2B marketing teams, this means messaging, positioning and sales alignment in the usual playbook need to reflect how buyers are actually behaving.

Based on what we’re seeing across enterprise technology conversations with customers and partners within the past year, there are six clear interconnected shifts shaping AI buying today.

1. AI decisions start at the top, then spread across the business

In most organizations, AI adoption begins with a leadership push. The CEO wants AI embedded somewhere in the business. This creates urgency, but it rarely comes with a clear plan.

From there, activity spreads quickly. Business units start exploring where AI could fit. Product, marketing, and customer teams often lead early pilots because they can test ideas quickly and measure impact.

Quite a few customers we spoke to had sentiments like the following:

“Our CEO is pro-AI. We trialled ChatGPT right away for marketing inspiration and found it pretty helpful in suggesting ideas we hadn’t considered in the past.” – A B2B technology provider

This early phase is fragmented by nature. Different teams move at different speeds, and multiple use cases emerge at once.

Fragmentation is what leads to the next shift.

2. Central IT steps in to bring control

As soon as AI moves beyond early experimentation, IT leadership becomes more involved. CIOs and CTOs start to pull activity back into a more structured approach.

Their focus is consistent:

  • Aligning AI initiatives to a central strategy
  • Preventing duplicate or disconnected tools
  • Managing risk, compliance and governance
  • Avoiding “shadow AI” across the organization

At this point, buying decisions become more formal. Business units no longer act alone. They need alignment with central IT before anything scales.

We’ve heard a variation of the following sentiment repeatedly:

“It was a free-for-all for a while. We needed reasonable corporate AI implementation guidelines that kept us compliant while still allowing innovation.” – A managed service provider CIO

This is where many deals stall. And once IT’s involved, the criteria for evaluation changes.

3. The focus moves to structural considerations

When IT takes a stronger role, attention moves away from surface-level capabilities and into the structure behind them. Organizations want to move forward with AI, but they want to do it without losing control.

Buyers spend more time assessing controls. This shift determines how AI interacts with enterprise data and systems. It also determines how safe and scalable a solution is.

 The controls include:

  • Guardrails and governance
  • Observability and monitoring
  • Orchestration of AI agents and workflows

This concern links directly to how buyers think about risk.

4. Buyers are focused on protecting data, IP and flexibility

Going beyond risk, a clear theme emerges. Once conversations reach this point, organizations turn to thinking about data protection.

Data is a core asset that underpins competitive advantage, and buyers are careful about how it is used within AI systems.

This shapes several key concerns:

  • Maintaining ownership of intellectual property
  • Controlling how data is used within models
  • Avoiding dependency on a single vendor
  • Meeting regulatory requirements across regions

This is also why vendor lock-in is back in focus. Many organizations want flexibility in how they build their AI stack, especially given how quickly the space is evolving.

These concerns influence where and how companies start using AI at scale.

5. Most organizations start with small, low-risk use cases

With these risks in mind, enterprises tend to begin in areas where impact is measurable and exposure is limited. These are often the same areas where uncontrolled experimentation originally occurred, but with new constraints in place.  

Common starting points include:

  • Customer service
  • Sales
  • Marketing

These functions allow teams to test AI in a controlled way. Customers told us results can be tracked, and mistakes are less likely to cause wider issues.

“Round 2 of AI trials at the marketing department were conducted with risks in mind. We knew that positive results would be more likely to translate organization-wide.” – A B2B marketer at a Fortune 500 company

However, this approach creates a challenge.

Many AI projects stay at this level. They improve efficiency, but they do not deliver meaningful business change. Only a small share of deployments evolves into more complex use cases that drive new revenue or reshape how the business operates.

This gap between early success and long-term value is where many organizations get stuck.

Our analysis shows that the ones that move forward tend to take a different approach.

6. Real value comes from scaling into specific, vertical use cases

The strongest outcomes come from more focused applications of AI. These are usually tied to specific industries and more complex workflows.

Organizations see better results when solutions reflect:

  • Industry regulations
  • Domain-specific data
  • Established operational processes

This is where AI moves beyond efficiency and starts to influence competitive positioning.

Even broad platforms are adapting to this by building industry-specific capabilities or working with partners who bring domain expertise.

For buyers, this makes it easier to trust the solution and justify investment at a larger scale.

What this means for B2B marketing teams

Taken together, these shifts point to a more structured and cautious approach to AI buying.

  • Decisions start with urgency but quickly become governed.
  • Influence expands early, then refocuses around IT.
  • Evaluation moves deeper into architecture, beyond features.
  • Early use cases prove value, but long-term success requires more focus.

For marketing teams, this changes what campaigns look like.

You need to show:

  • How your solution fits into an existing technology environment
  • How it’s governed and controlled
  • How it protects customer data and intellectual property
  • How it avoids long-term constraints such as vendor lock-in
  • How it supports progression from pilot to competitive advantage
  • Where it delivers value within a specific industry

These are what buyers are trying to work out internally.

AI is now part of core business decision-making. The way organizations approach it reflects that. The more clearly your playbook can support those conversations, the more relevant your campaigns become, and the more likely you’ll generate the leads you’ll need.

 

*Quotes are representations of sentiments conveyed in conversations and cannot be attributed to individual enterprises.

The rules of paid search just changed. Let's make sure your strategy is ahead of it.

Connect with our Search team.