25 Proven Tactics to Accelerate AI Adoption at Your Company

What does it actually take to drive employee AI adoption at your company?

This is the question we get asked more than any other. And after building AI revenue infrastructure for mid-market businesses, we've learned something crucial:

The biggest barrier to AI adoption isn't technology, it's organisational change.

In nearly every project we take on, the AI works. What fails is adoption. Employees struggle with vague mandates from leadership, procurement bottlenecks that push them to shadow IT, and zero guidance on which high-impact use cases to tackle first.

The tools are ready. The organisations aren't.

But when companies crack the adoption code, the results speak for themselves. We've seen clients reclaim 10+ hours per week per rep in sales operations. Industry leaders like Duolingo went from 100 courses in 12 years to 150 courses in just 12 months with AI's help. Intercom is reporting durable 20% year-over-year productivity improvements from AI-assisted development.

From our implementation work and studying these AI-forward companies, we've identified five steps that consistently drive real adoption:

1. Explain the how

2. Track and reward adoption

3. Cut the red tape

4. Turn enthusiasts into teachers

5. Prioritise the high-impact tasks

Here's exactly how to make each one work.

1. Explain the How

"We're going AI-first" means nothing if employees don't know what that actually looks like in their day-to-day work.

This is the first failure mode we see: leadership announces an AI initiative, everyone nods, and nothing changes. The companies that succeed, including our most successful client implementations, provide specific tactics employees can adopt immediately.

What Actually Works

Define specific behaviours, not aspirations. When we onboard clients to Symphony, we don't say "use AI more." We define exactly which workflows should use AI, which tools to use for each, and what good output looks like. Tobi Lütke at Shopify did the same in his famous memo, he didn't just say AI is a "baseline expectation," he shared concrete tactics like making AI prototyping part of their GSD process.

Create a forcing function. Sometimes you need to declare a moment. Wade Foster at Zapier called an all-hands-on-deck "code red" after ChatGPT launched, shared a playbook, and gave employees a week off to put it into practice. We've seen similar results when clients dedicate even a single day to hands-on AI workflow building.

Embed with teams to find the opportunities. Darragh Curran, Intercom's CTO, set a goal to "2x productivity with AI" and spent a week every month embedded with individual teams to identify the specific 2x opportunities. This is essentially what we do in our discovery phase, you can't mandate adoption from a conference room.

Lead by example in real time. The most effective adoption driver we've seen: leaders sharing their AI workflows live. When someone brings a problem to Hilary Gridley at Whoop, she says, "Want me to show you how I solve this with AI?" Seeing a colleague save time with AI is the strongest incentive for others to try it themselves.

The Pattern

Vague mandates create vague results. Specific workflows, demonstrated live, create adoption.

2. Track and Reward Adoption

You can't improve what you don't measure. We treat AI adoption like any other business initiative, track inputs (who's using AI) and outputs (what business value it's creating), then reward the people leading the charge.

Tactics We Recommend

Make AI adoption part of performance reviews. This signals that leadership is serious. Shopify asks employees to rate colleagues on a 1-to-5 scale for how well they "reflexively use AI tools for improving and amplifying work outputs." When it's in the review, it gets attention.

Publish usage by team. Transparency creates accountability. At Ramp, leadership shares the number of AI power users (5+ actions per week) by team. Nobody wants to be the team at the bottom of the dashboard.

Track function-specific impact. Generic "AI usage" metrics are less useful than function-specific outcomes. Zapier tracks impact by department—in sales, AI auto-packages lead engagement data for reps, saving 10 hours per week per rep. That's a number leadership cares about.

Use proxy metrics when direct measurement is hard. Intercom tracks merged pull requests as a proxy for developer productivity. Find the metric that already exists and correlates with the outcome you want.

Make it a daily habit with challenges. Whoop ran a 30-day challenge with bite-sized 2-minute tasks and rewarded employees who kept the longest streak. Gamification works—especially for building new habits.

The Principle

People change behaviour with the right incentives. Connect AI adoption to outcomes that matter—team performance, business results, career advancement, and adoption accelerates.

3. Cut the Red Tape

Here's a pattern we see constantly: companies have long approval processes for AI tools, so employees just use AI from their personal accounts. Shadow AI is already in your organisation. The question is whether you control it.

Cut the red tape if you want AI usage inside your security and compliance framework.

What Works

Create an AI learning budget. Duolingo gave every employee $300 to try AI tools, courses, and subscriptions. This incentivises experimentation within approved boundaries. We recommend something similar for clients, even a modest budget signals permission to explore.

Assign someone to own fast-tracking approvals. Zapier assigned a lead PM to work with procurement, legal, and engineering specifically to eliminate AI tool bottlenecks. If nobody owns the process, the process doesn't improve.

Give employees dedicated time. "No time" is the most common reason employees cite for not trying AI tools. Intercom's CTO pushed managers to give employees dedicated time to skill up. Block the calendar or it won't happen.

Provide multiple approved options. Shopify provides access to Claude, Perplexity, Gemini, Cursor, Copilot, and Claude Code. Different tools work better for different workflows. Let employees find what works for them within the approved set.

Let employees nominate tools. Whoop lets employees nominate tools they're excited to trial. The people doing the work often know which tools would help most, tap into that enthusiasm.

Our Take

Don't let procurement become the bottleneck that pushes AI usage underground. The companies winning at AI adoption have streamlined approval for experimentation while maintaining appropriate security controls. This is a solved problem if you prioritise it.

4. Turn Enthusiasts Into Teachers

Every organisation has AI enthusiasts, people who've already figured out powerful workflows and are eager to share. The companies that scale adoption fastest turn these enthusiasts into force multipliers.

This is one of the highest-leverage moves we recommend to clients.

How to Activate Your Internal Experts

Create AI champions in each department. Identify the people already passionate about AI and give them a formal role to train colleagues. Peer-to-peer learning often works better than top-down mandates, there's less resistance and more practical relevance.

Build internal prompt libraries. Shopify encourages employees to contribute to a growing library of AI prompts and agents. When someone figures out an effective workflow, it becomes institutional knowledge instead of trapped in one person's head.

Host regular demo sessions. Short, practical demonstrations of real workflows drive adoption faster than training decks. Show people saving time on tasks they actually do, not theoretical capabilities.

Create channels for sharing wins. Slack channels where people share AI successes create social proof and surface use cases others hadn't considered. Make the wins visible.

Pair skeptics with enthusiasts. Sometimes the best way to convert an AI skeptic is to sit them next to someone who's genuinely excited and productive with the tools. Let the results speak.

The Reality

In most organisations, 10-15% of employees are AI power users while the rest haven't changed their workflows at all. The fastest path to broad adoption is turning that 10-15% into teachers. Everything else is slower.

5. Prioritise the High-Impact Tasks

Not all AI use cases are created equal. We've seen companies waste months on edge cases and experiments while their core revenue processes remain entirely manual.

The companies seeing real ROI focus on high-impact, high-frequency tasks first.

Where to Start

Identify the repetitive knowledge work. Look for tasks that happen frequently, require synthesis of information, and currently consume significant time. Email drafting, meeting summaries, research compilation, and first-draft creation are common high-value targets.

Map the workflow before automating. Before deploying AI, understand the current workflow end-to-end. The best AI implementations don't just speed up existing steps, they eliminate unnecessary ones entirely. We always start client engagements with workflow mapping for this reason.

Focus on revenue-impacting processes. Sales research, lead qualification, proposal generation, and customer communication typically yield the fastest measurable ROI. Start where the money is.

Start with willing teams. Don't force AI adoption on resistant teams first. Start with the enthusiasts, prove the value, then let success stories pull others in. Resistance melts when results are undeniable.

Measure in hours saved, not features shipped. The metric that matters most for early adoption is time reclaimed. Once employees see they're getting hours back each week, they'll find new use cases on their own.

Our Approach

When we implement AI infrastructure for clients, we always start by mapping their highest-frequency, highest-impact workflows, specifically in revenue operations. There's no point automating edge cases when core revenue processes are still manual.

The goal isn't to use AI everywhere. It's to use AI where it creates the most leverage.

Implementation Checklist

If you're serious about driving AI adoption, here's the sequence we recommend based on what we've seen work:

Week 1-2: Foundation

• Define what "AI-first" means in specific, actionable terms for each department

• Identify your internal AI enthusiasts and champions

• Audit current AI tool usage (including shadow IT)

• Streamline the procurement process for approved tools

Week 3-4: Activation

• Give employees dedicated time to experiment

• Create shared channels for AI wins and learnings

• Host the first demo session with real workflows

• Establish tracking for AI usage metrics

Month 2+: Scaling

• Add AI adoption to performance review criteria

• Publish team-by-team usage dashboards

• Run a 30-day adoption challenge

• Expand from pilot teams to broader organisation

Ongoing: Optimisation

• Regularly audit for high-impact workflows not yet using AI

• Update prompt libraries and agent configurations

• Share ROI metrics to maintain executive support

• Keep procurement process fast for new tools

The Bottom Line

The technology for AI transformation is ready. The question is whether your organisation can adapt fast enough to capture the value.

The companies pulling ahead aren't necessarily using better AI, they're better at driving adoption. They've solved the organisational change problem, not just the technology problem.

Recent data shows only 8% of workers use AI daily. That gap between AI-forward companies and everyone else is widening. The tactics above are how you close it.

We've implemented these patterns across dozens of client engagements. They work, but only if you actually execute them.

One Second AI helps mid-market businesses implement AI revenue infrastructure that actually gets adopted. Our Symphony transformation replaces manual sales and marketing operations with autonomous AI agents, and includes the organisational change work that makes adoption stick.