Your Enablement Team Has an AI Problem. It Just Doesn't Know It Yet.

I have had dozens of conversations with enablement leaders over the past few months. Smart, experienced people who care deeply about developing their teams and staying ahead of the curve. And yet, when I ask them what AI tools they are using, the answer is almost always the same: "We use Copilot" or "I've had a play with ChatGPT."

That's it. One tool. Maybe two if they're feeling adventurous.

I want to be clear: this is not a criticism. These are busy professionals doing demanding work, and the AI landscape moves at a pace that makes it genuinely hard to keep up. But it is an observation that should concern us. Because if your entire AI strategy amounts to one tool that someone in IT rolled out, or one chatbot you tried because it was trending on LinkedIn, you are not in a position to help your organisation navigate what is coming next.

And what is coming next is significant.

The One-Tool Trap

There is a pattern repeating across enablement functions. A tool gets introduced, usually Copilot because it comes bundled with the Microsoft ecosystem, or ChatGPT because it was the first generative AI product to break into the mainstream. People use it for a few tasks: drafting emails, summarising documents, maybe generating some content for a training module. And then they stop exploring.

The problem is not the tool itself. Copilot is useful. ChatGPT is powerful. But relying on a single AI tool in 2026 is a bit like relying on a single search engine in 2005 and assuming you had the internet figured out. You are seeing a fraction of what is possible, and you are building your understanding of a transformational technology through a very narrow lens.

For enablement teams specifically, this creates three risks:

First, you are limiting your own capability. Different AI tools are built for different purposes. A tool that is excellent at generating text might be poor at analysing data, building visual content, or creating interactive learning experiences. If you only know one tool, you only see one set of possibilities.

Second, you are falling behind the people you are supposed to enable. Sales teams, product teams, and marketing functions are already experimenting with multiple AI tools. If enablement is the last function to get literate, you lose credibility and relevance fast.

Third, you cannot advise on what you do not understand. Enablement's role is increasingly about helping organisations adopt new tools and ways of working. If you have not personally explored the breadth of what is available, how can you credibly guide others through it?

What Should Enablement Teams Be Thinking About?

This is not about becoming an AI engineer or spending your weekends testing every new product that launches on Product Hunt. It is about developing a working literacy across the categories of AI tools that are most relevant to your function.

Conversational AI: Beyond the Default

If you have only used ChatGPT or Copilot, you owe it to yourself to try at least two or three alternatives. Claude (from Anthropic), Gemini (from Google), and Perplexity all approach conversational AI differently. They have different strengths in reasoning, research, content generation, and how they handle nuance. Try giving the same prompt to three different tools and compare the outputs. You will be surprised at how different the results are, and that difference matters when you are building learning content, synthesising research, or advising stakeholders.

AI for Content Creation and Design

Enablement teams produce a lot of content: decks, one-pagers, playbooks, videos, job aids. The tools available for AI-assisted content creation go well beyond asking a chatbot to write a paragraph. Think about tools for presentation design, image generation, video creation, and voice synthesis. Products like Gamma, Beautiful.ai, Synthesia, HeyGen, and Canva's AI suite are changing what a small team can produce without a design function behind them.

The question for enablement leaders is not "should we use these?" but "what would our content strategy look like if production time dropped by 60%?" That is a fundamentally different conversation to have with your leadership team.

AI-Powered Practice and Roleplay

This is the area with the most direct relevance to enablement's core mission. AI roleplay tools allow sales reps, managers, and customer-facing teams to practise conversations at scale, without needing a facilitator in the room. The technology has matured rapidly, and there are now several platforms worth understanding: Hyperbound, Second Nature, Yoodli, and others are all operating in this space. Some organisations are even building custom solutions using AI studio platforms.

The point is not to evaluate them all right now. The point is to know they exist, understand the different approaches they take, and start forming a view on where AI-powered practice fits in your enablement architecture.

AI for Research, Analysis, and Insight

Enablement teams are often expected to synthesise competitive intelligence, market trends, and internal performance data. Tools like Perplexity, Elicit, and NotebookLM are purpose-built for research and analysis workflows. They do not just generate text; they help you find, verify, and structure information in ways that a general-purpose chatbot was not designed for.

If your team spends meaningful time pulling together research, you should know what these tools do. Not because they replace judgement, but because they dramatically compress the time between question and insight.

AI Agents and Workflow Automation

This is the frontier that most enablement teams have not even started thinking about. AI agents are tools that do not just respond to prompts but take actions: scheduling, sending follow-ups, updating systems, triggering workflows. The line between "AI tool" and "AI colleague" is blurring quickly.

For enablement, this opens up questions about onboarding automation, coaching nudges, content delivery, and learner experience. It is early days, but the teams that start understanding agentic AI now will be the ones that shape how their organisations adopt it, rather than reacting after the fact.

The Real Question: Who Is Shaping AI Literacy in Your Organisation?

If enablement is not leading the conversation about AI adoption and capability building, someone else will. And that someone might be a vendor with a product to sell, or an IT function that understands the technology but not the human change management required to make it stick.

Enablement has a genuine opportunity here, arguably an obligation, to be the function that helps organisations move from "we bought an AI tool" to "our people know how to work with AI effectively." But you cannot do that if your own AI experience is limited to one or two products that you happened to encounter first.

The shift required is not dramatic. It starts with curiosity: set aside time each week to try something new. Test a tool you have not used before. Run a comparison. Share what you learn with your team. Build a point of view.

Because the organisations that get AI adoption right will not be the ones with the best tools. They will be the ones with the best enablement behind them.

Where to Start (Depending on Where You Are)

Here are four practical suggestions, each a step up in complexity from the last. Think of them as a progression. If you are getting started with AI beyond your default tool, begin with number one. If you are already comparing tools and feeling confident, skip ahead. The goal is to meet yourself where you are and take the next meaningful step forward.

1. Run a Side-by-Side Prompt Test

Difficulty: Low

This is the simplest way to break out of the one-tool trap. Take a task you already do regularly and run the same prompt through three different AI tools. Do not tweak the prompt between tools; give them identical input and compare the outputs side by side.

Step 1: Choose a real task from your current workload. Good examples: writing a facilitator guide introduction, summarising a competitor's quarterly earnings call, drafting coaching questions for a new product launch, or creating a brief for a stakeholder meeting.

Step 2: Write one clear prompt. Be specific about what you want: the format, the audience, the tone, and the length. The more specific your prompt, the more useful the comparison will be.

Step 3: Run that exact prompt in three different tools. Use your current default (likely Copilot or ChatGPT) plus two alternatives.

Step 4: Compare the outputs. Look at structure, tone, depth, accuracy, and how well each tool followed your instructions. Note where one tool clearly outperformed the others, and where the differences were more about style than substance.

Step 5: Save your comparison notes. Over time, this builds into a personal reference for which tools suit which tasks.

Tools to try: ChatGPT (OpenAI) for conversational content and brainstorming. Claude (Anthropic) for long-form writing, nuanced reasoning, and working with documents. Gemini (Google) for research tasks and Google Workspace integration. Perplexity for research queries with cited sources and competitive intelligence.

This is not about picking a winner. It is about training your own eye to recognise what good AI output looks like across different contexts, and understanding that your default tool is not automatically the best tool for every job.

2. Audit Your Content Workflow for AI Opportunities

Difficulty: Low to Medium

Most enablement teams have a rough sense that AI could save them time, but have not mapped exactly where the leverage sits. This exercise changes that.

Step 1: Pick one of your team's core deliverables. A new hire onboarding module, a sales playbook update, a competitive battlecard, or a quarterly business review deck all work well.

Step 2: Map every step from start to finish. Write them all down: the briefing, the research, the first draft, the design and formatting, the review cycle, the revisions, the final distribution. Be honest about how long each step actually takes.

Step 3: For each step, ask two questions. First: "Could an AI tool do this faster or better than we currently do it?" Second: "What type of AI tool would be needed?" (content generation, design, research, summarisation, reformatting)

Step 4: Identify your top three opportunities. Look for the steps where the time investment is highest and the AI capability is most mature. These are your quick wins.

Step 5: Test one opportunity immediately. Pick the most promising step from your audit and try running it through an AI tool this week.

Tools to try at each stage: Perplexity, NotebookLM (Google), or Claude with web search for research and briefing. Claude, ChatGPT, or Gemini for first draft content. Gamma (presentations), Canva AI, or Beautiful.ai for design and formatting. Claude or ChatGPT for review and editing, tone and consistency checks. ChatGPT or Claude for repurposing across formats, converting a document into email copy, talking points, or social posts.

Most teams find the biggest time savings are not in the obvious places like first-draft writing, but in less glamorous steps like research synthesis, reformatting content across channels, or generating variations for different audiences.

3. Build One Deliverable Using Multiple AI Tools

Difficulty: Medium

This is where you move from exploring AI to actually working with it. Pick a real deliverable already on your to-do list and commit to producing it using AI tools at every stage of the process.

Step 1: Choose a deliverable with multiple production stages. A competitive battlecard, a new product launch kit, or a learning module all work well because they involve research, writing, design, and distribution.

Step 2: Use an AI research tool to build your brief. Feed in source material (competitor websites, earnings transcripts, product documentation) and use the tool to synthesise key themes, identify gaps, and generate an initial structure.

Step 3: Use a conversational AI tool to draft the content. Give it the research output from step two as context, along with your brand voice guidelines and audience profile. Iterate on the output until the substance is right.

Step 4: Use a design or presentation tool to create the final format. If it is a deck, use Gamma or Beautiful.ai. If it is a one-pager or visual asset, use Canva AI. If it involves video, explore Synthesia or HeyGen.

Step 5: Use AI to generate distribution variants. Take your finished deliverable and ask a conversational AI tool to create an email summary, a set of Slack talking points, a LinkedIn post, and a 60-second briefing script. One deliverable, multiple formats, in minutes.

Step 6: Document what worked and what did not. Note which tools added genuine value, where you still needed to do heavy manual editing, and how the total production time compared to your usual process.

This exercise is what moves you from "I've tried AI" to "I understand how AI fits into my work." It also gives you a credible story to tell when your stakeholders ask what AI does for their teams, because you will have done it yourself, end to end, with a real piece of work to show for it.

4. Design an AI-Augmented Workflow and Pilot It

Difficulty: High

This is where it gets serious. Instead of using AI to speed up an existing process, you are redesigning a workflow with AI built in from the ground up. Not as an add-on or a shortcut, but as a fundamental part of how the workflow operates.

Step 1: Choose one enablement workflow to redesign. Good candidates: new starter onboarding (first 30 days), product launch readiness, ongoing coaching and reinforcement, or competitive intelligence updates.

Step 2: Map the current workflow as it exists today. Include every touchpoint, every piece of content, every human interaction, and every handoff. Be brutally honest about what works and what does not.

Step 3: Redesign the workflow with AI at each stage. For each step, ask: "What would this look like if AI were handling the preparation, personalisation, or follow-up?" For example: AI-generated pre-read summaries before each onboarding session. AI roleplay practice between live training days. AI-drafted coaching prompts sent to managers based on their team's assessment results.

Step 4: Select the tools for each AI-enabled step. Map specific tools to specific tasks in your redesigned workflow. Be realistic about what is available today and what would require custom configuration.

Step 5: Pilot with one team or cohort. Keep the scope contained. Run the AI-augmented workflow alongside your existing process if possible, so you compare outcomes.

Step 6: Measure and iterate. Track completion rates, time to competency, participant feedback, and production effort. Use what you learn to refine the workflow before scaling.

Tools to consider for common workflow redesigns: Hyperbound, Second Nature, or Yoodli for sales conversation practice at scale. NotebookLM for creating tailored study guides from source material. Claude or ChatGPT to generate manager coaching prompts based on performance data. Conversational AI tools to adapt generic content for specific roles, regions, or experience levels. Zapier AI, Make, or Microsoft Power Automate for connecting AI outputs to your existing systems.

This is the kind of initiative that positions enablement as a strategic function, not a content factory waiting to be disrupted. It is also the initiative that will get the attention of your leadership team, because you are not talking about AI. You are demonstrating what it looks like when enablement leads the way.

A Final Thought on Keeping Up

AI is not slowing down to wait for any of us. The tools available today will look different in six months. The capabilities we think of as cutting-edge right now will be table stakes by the end of the year. The most important habit you build is not mastery of any single tool, but a consistent commitment to staying curious, testing new things, and sharing what you learn with your team. Block time for it. Protect it. Treat it as professional development, not a side project. The enablement leaders who will be most valuable to their organisations in 12 months are the ones building that muscle now.

Need Help Getting There?

These four steps will give you momentum, but they are starting points, not a destination. The deeper questions, like how to build an AI capability framework for your enablement function, how to evaluate which tools deserve real investment, and how to bring your stakeholders on the journey, require a more tailored conversation.

That is exactly the kind of work we do at Catalyst Enablement Group. Not selling you a tool, but helping you build the strategy, literacy, and confidence to make AI work for your enablement function.

If you are reading this and thinking "we need to move on this," get in touch. Let's talk about where you are and where you want to be.