The AI-Powered Enablement Team: From Adoption to Business Impact

AI is rapidly changing how go-to-market teams work. But while organisations are investing heavily in AI tools, platforms and automation, one question remains: Who is responsible for making sure people actually adopt them and turn that investment into business impact?

In the latest episode of The Enablement Pioneers, part of the Sales Transformation Lab podcast, Leah Dolan speaks with Anne Slough, a go-to-market enablement leader at Calibra with more than 30 years of experience across sales, revenue operations, enablement, customer success, consulting and research.

Their conversation explores why enablement needs a strategic seat at the AI table, how organisations can move beyond fragmented AI adoption, and what it takes to connect AI initiatives to measurable business outcomes.

Enablement Should Be Part of the AI Strategy — Not an Afterthought

Traditionally, enablement can be brought into the process once a strategy, technology or process has already been decided. Anne argues that this approach misses one of enablement's most valuable strengths: its direct connection to the people using the technology.

Enablement works closely with sellers, solution engineers, customer success teams and other go-to-market roles. That puts the function in a unique position to understand what is happening on the ground.

As Anne explains, enablement can provide the "voice of the field" early in the process.

This becomes particularly important with AI. A strategy designed without input from the people expected to use it can easily become disconnected from real workflows, creating friction rather than solving problems.

Anne recalls an earlier experience implementing a new telephony system for a call centre. The team believed they had designed an effective interface, but once employees began using it, they discovered that important information was positioned in places that didn't match how agents naturally worked.

The lesson was simple: technology should not be designed in a vacuum.

The same principle applies to AI.

AI Adoption Starts With the User

One of the biggest risks organisations face is creating AI strategies around technology rather than around the problems employees are trying to solve.

Anne describes organisations purchasing AI tools and distributing them across business units, with each team developing its own approach. While this can encourage experimentation, it can also create new silos around data, workflows, communication and AI usage.

For go-to-market teams, the challenge can become even more complicated because AI is already embedded into many of the tools they use.

Sales engagement platforms, content management systems, conversational intelligence platforms and revenue orchestration tools may all have their own AI capabilities.

The result can be AI confusion.

Employees may find themselves asking:

• Which AI tool should I use?
• Which tool is best for this workflow?
• Where should I find the information I need?
• Which AI agent should I use?
• How do all these tools connect?

Instead of expecting users to navigate an increasingly complicated technology landscape, Anne's team is experimenting with a different approach.

Creating One Place for Go-to-Market AI

At Calibra, Anne describes a "go-to-market harness" designed to bring multiple AI agents and data sources together in one place.

The idea is to give go-to-market teams a single interface where they can interact with AI using natural language, rather than constantly switching between different platforms.

For example, a salesperson could ask for information about a prospect and receive a research dossier containing relevant buyers, company information, business challenges and other useful context for the sales process.

Behind the scenes, the system connects to different tools and data sources.

For the user, however, the experience is much simpler.

They have a problem. They want an answer. They don't necessarily care which tool provides it.

This shift from "Which tool should I use?" to "What problem am I trying to solve?" could become increasingly important as organisations add more AI capabilities to their technology stacks.

Moving Enablement From Training to Business Outcomes

Another major theme from the conversation is the changing role of enablement.

Anne believes enablement has historically placed significant emphasis on instructional design, competencies, learning frameworks and training methodologies. While these remain important, she argues that enablement also needs to communicate in the language of business leaders.

That language is metrics.

Rather than asking simply whether employees completed a training programme, Anne's approach starts with a different question:

What business metric does the stakeholder need to improve?

Her team creates stakeholder agreements with the leaders they support and identifies a key metric for each relationship. Enablement activities are then designed around influencing that metric and reporting on progress.

This could include metrics such as:

• Quota attainment
• Deal velocity
• Win rates
• Sales cycle length
• Revenue
• Customer retention
• Churn

The shift is significant. Enablement is no longer simply asking, "Did people complete the training?"

Instead, the question becomes:

"Did our work help improve the business outcome we agreed to influence?"

Enablement and Revenue Operations Need to Work Together

Anne is also clear that enablement shouldn't operate independently from Revenue Operations.

Revenue Operations provides critical data, insights, planning and measurement. Enablement brings an understanding of the practical workflows and behaviours of the people using those systems.

Together, the two functions can identify where problems are occurring and determine where intervention is needed.

For example, enablement needs to understand questions such as:

• Where are deals getting stuck?
• Which pipeline stages have the biggest gaps?
• What is happening to deal velocity?
• Where are win rates changing?
• What behaviours could be improved?
• Which AI tools are actually being adopted?

This requires enablement professionals to become increasingly comfortable with data and analytics.

As AI becomes more embedded in go-to-market operations, understanding the numbers behind the business becomes just as important as understanding how people learn.

Solving Real Problems Is the Key to AI Adoption

Not every seller will immediately embrace AI.

Some employees may be excited to experiment with new technology, while others may prefer to continue working manually.

Anne's approach is not to simply tell reluctant users that they need to adopt AI.

Instead, she starts with their problems.

Where are they getting stuck?

What takes up too much of their time?

What repetitive task could be made easier?

For example, some sellers struggle with creating personalised outreach at scale. AI can help research prospects and generate messaging, while the salesperson remains responsible for reviewing and refining the output.

Once sellers see AI solving a real problem in their daily workflow, adoption can become much more natural.

This leads to an important principle:

Don't start with the AI tool. Start with the user's friction.

Then demonstrate how the technology can remove it.

The Human Still Needs to Be in the Loop

Despite the focus on AI, Anne strongly emphasises that technology should amplify human capabilities rather than replace the human relationship.

AI can make salespeople more efficient. It can help with research, preparation, personalisation and repetitive work.

But sales remains fundamentally a relationship-driven activity.

Trust and credibility still matter.

Human judgement still matters.

And AI-generated work still needs human oversight.

The goal isn't to remove people from the process. It's to give them better tools so they can spend more time on the parts of their role where human skills create the most value.

How Do You Measure AI Enablement?

For Anne, measuring AI enablement starts with adoption but doesn't end there.

Her team looks at several levels of performance.

First, they examine whether people are actually using the AI tools and go-to-market harness.

Then they look at operational and sales indicators such as:

• Deal velocity
• Win rates
• Average sales cycle
• Other stakeholder-specific leading indicators

Finally, they look at broader business metrics such as revenue and retention.

Anne is also careful to distinguish between correlation and causation.

Enablement is an influence function. It contributes to business performance, but it isn't the only factor determining whether an organisation hits its revenue targets.

This creates a more realistic approach to accountability: measure the influence of enablement while recognising that business outcomes are shared across multiple functions.

The Strongest Signal May Come From the People You Support

Numbers are important, but Anne also looks for another signal: direct feedback from the people enablement supports.

When sales leaders, sellers and solution engineers ask for more AI enablement and more support around the technology stack, that feedback demonstrates that the work is solving real problems.

Every sales leader at Calibra has a personalised enablement plan, and Anne says AI enablement has become part of those plans.

For her, that demand is a strong indication that the function is creating value.

Because ultimately, enablement can develop as many strategies and programmes as it wants. If the people using them don't find them useful, they won't create meaningful impact.

The Future of Enablement Is Becoming More Technical

Looking ahead, Anne expects the skill requirements for enablement professionals to continue evolving.

Future enablement teams will increasingly need expertise across:

• Data analytics
• AI
• Technology
• Business strategy
• Go-to-market operations
• Data requirements and quality
• Commercial metrics

The ability to understand how AI works, how data influences its outcomes and how technology fits into business workflows will become increasingly important.

This doesn't mean enablement loses its human focus.

Instead, the role becomes broader: understanding people, technology and business outcomes at the same time.

From AI Adoption to AI Impact

The conversation between Anne Slough and Leah Dolan highlights a broader shift taking place across enablement.

AI adoption isn't simply about rolling out new tools.

It's about understanding the people using them, identifying their friction, connecting technology to real workflows and measuring whether those changes are producing meaningful business outcomes.

Enablement can play a central role in that process by bringing the voice of the field into AI strategy, working closely with Revenue Operations and leadership, and helping teams turn AI from a collection of tools into something genuinely useful.

The future of AI-powered enablement may therefore be less about teaching people how to use more technology — and more about helping them use the right technology, in the right workflow, to solve the right problem.