Staying ahead in the age of AI

The pace of AI progress is unlike anything we’ve seen in technology. Early adopters are already growing revenue 1.5× faster than peers, yet many companies feel the pace is too fast to plan for effectively. One of the questions we hear most is how to keep up, enable employees to adopt AI, and build an AI-first organization.

Drawing on the experience OpenAI has had with leaders at companies like Estée Lauder, Notion, the San Antonio Spurs, and BBVA, this playbook shares five practical steps - Align, Activate, Amplify, Accelerate, and Govern - to help your organization move quickly and confidently as AI continues to advance.

5 Pillars of AI Adoption

• Align: How to align your company, employees and leadership on your AI strategy.
• Activate: How to enable and motivate teams to use AI.
• Amplify: How to amplify wins and use cases across your teams.
• Accelerate: How to speed up decisions to keep up with AI.
• Govern: How to speed up decisions to keep up with AI.

By the end of this guide you should have clear next steps on how to keep ahead of AI progress.

1. Align: Getting Everyone on the Same Page

Employees adopt change faster when they clearly see how new AI initiatives enhance their skills, enable more meaningful work, and contribute to their company’s competitive advantage. Leaders play a critical role in driving this alignment by explicitly communicating the purpose behind AI initiatives, demonstrating their commitment, and actively supporting employees throughout the transition.

• Be specific on why AI adoption is key to your company's future, whether it's keeping pace with competitors, responding to evolving customer expectations, or sustaining growth. When employees hear a thoughtful “why,” it creates trust and clarity, helping them see how these changes align with their own work and goals.
• Define a measurable goal that connects AI adoption to everyday work. This could be new use cases, frequency of AI tool usage, or setting benchmarks for team experimentation, and incorporate these goals into company planning and KPIs. Communicate this goal through allhands or company updates to build momentum and signal that AI is part of how work gets done.

Example:
The CEO of Moderna set a clear expectation that employees should be using ChatGPT 20 times a day, reinforcing AI adoption as a core part of how work gets done across the company.

Example:
Ask senior executives to regularly share how they use AI in their roles. Hearing directly from leadership about how AI helps them stay ahead of market trends or quickly analyze customer insights normalizes and encourages AI use and experimentation.

Example:
Our very own CFO, Sarah Friar, regularly shares how she uses ChatGPT and actively encourages her team to experiment, making them one of the most advanced AI adopters at OpenAI.

• Line-of-business leaders are best placed to connect AI initiatives to the realities of each team’s work. Encourage them to hold sessions that highlight relevant use cases, invite feedback, and answer questions. This helps employees connect AI to their everyday work and understand its value.

Tip: Try out the GPT “ChatGPT Use Cases for Work” to identify ways that different teams can use generative AI in their roles.

• Do employees understand why AI is critical to our strategy? Run periodic pulse surveys to gauge employee clarity on AI strategy.
• Are we transparently communicating our progress? Maintain and openly review a dashboard that clearly tracks progress toward your company-wide AI adoption goal.
• Are functional leaders actively helping employees understand how AI supports their department’s goals? Review how often functional leaders hold AI-focused team sessions and what feedback or questions emerge.

2. Activate: Equipping Your People to Use AI

Nearly half of employees say they lack the training and support needed to confidently adopt generative AI. Yet, they rank training as the single most important factor for successful adoption. Companies that move fast invest in supporting their employees' learning. This means making space for experimentation, equipping teams with department specific training, and normalizing the need for learning.

• Ask your Learning & Development team to create clear, role-specific training that moves employees from basic AI awareness to hands-on use, prioritizing skills that directly support real workflows rather than abstract concepts. For example, the San Antonio Spurs boosted AI fluency from 14% to 85% by embedding training into the flow of daily work instead of treating it as a separate initiative.
• Identify and train passionate employees to serve as internal AI mentors. These champions help colleagues become confident AI users through workshops, informal coaching, and spreading enthusiasm.

Tip: Assign one owner to join OpenAI’s Champion Network (available to API and ChatGPT Enterprise customers) to access resources and ideas for launching and activating your internal network.

• Give employees regular time to explore AI tools. Try dedicating the first Friday of each month for teams to workshop how AI could improve their work. Pair this with no-code hackathons where cross-functional teams can prototype real solutions, and fast approvals to ensure promising ideas move forward.

• Directly link AI engagement to performance evaluations and career growth. Use OKRs or similar mechanisms to set clear, role-specific goals, like identifying workflows to enhance with AI or piloting new use cases. Highlight meaningful AI contributions during promotion and recognition conversations, so employees see experimentation as central to their professional success.

• Are employees actively using AI tools and leveraging learning opportunities? Track Daily and Weekly active users, or GPT shares along with enrollment and completion rates for training programs. Provide support to teams with lower adoption.

• Are we explicitly recognizing AI adoption in performance and career development? Ensure career ladders and performance standards include AI-related language. Track and report on how often AI impact influences promotions, reviews, or recognition.

• Are hackathons and protected experimentation time leading to tangible outcomes? Track how many ideas from these sessions move beyond prototyping into production or live pilot phases.

3. Amplify: Sharing Successes Across the Organization

The fastest way to scale AI impact is to stop solving the same problems in silos. Amplifying progress means turning scattered wins into shared knowledge, documenting successful prompts, workflows, and use cases so other teams can reuse, improve, and build on them.

• Build a single, easy-to-access hub (e.g., in Confluence, Notion, or SharePoint) where employees can find everything related to AI: training resources, hackathon dates, policies, guides, and best practices. A centralized hub reduces confusion, saves time, and prevents teams from reinventing the wheel.
• Regularly highlight impactful AI projects, practical lessons, and easily replicable wins through monthly newsletters, internal webinars, or short segments in all-hands meetings. Showcase a balance of big breakthroughs and smaller, everyday successes. Share the steps teams took so others can easily apply these insights to their own workflows.

Tip: Set up an “AI Newsletter” project in ChatGPT to quickly turn raw notes, updates, or success stories into a polished, consistent newsletter format each month.

• Establish dedicated communities (such as Slack or Teams groups, or an internal AI Center of Excellence) to promote peer-to-peer learning, realtime collaboration, and rapid sharing of insights. Lean on your AI champions to regularly encourage discussion, share useful resources, and keep the conversation active and engaging.

• Directly link AI engagement to performance evaluations and career growth. Use OKRs or similar mechanisms to set clear, role-specific goals, like identifying workflows to enhance with AI or piloting new use cases. Highlight meaningful AI contributions during promotion and recognition conversations, so employees see experimentation as central to their professional success.

Tip: Encourage employees and leaders to publicly share their AI successes on social networks, creating positive feedback loops and recognition for teams demonstrating progress.

• Is there a clear, consistent rhythm for sharing what’s working with AI? Aim to share at least three new AI wins, use cases, or resources per month across the company, through newsletters, internal posts, or team meetings.
• Do employees know exactly where to find trusted, up-to-date AI resources? Confirm a single, well-maintained hub with a named owner and visibly update activity (at least twice monthly).
• Are people contributing to and building on each other’s AI work? Track participation in AI forums or channels (number of active contributors, threads, and shared resources).

4. Accelerate: Removing Friction from Idea to Production

In order to scale quickly, teams need flexible infrastructure, clear decision-making authority, and lightweight approvals. Accelerating means removing friction and ensuring good ideas move quickly from pilot to production.

• Make sure teams can quickly access the data and AI tools they need to test and build. If it still takes weeks, or months to get approval for basic tooling or to pull clean data, your infrastructure is holding you back. Many companies are increasingly empowering employees to recommend the AI tools they find most valuable, accelerating adoption and productivity.
• Create a simple, transparent way for teams to submit AI project ideas, get quick feedback, and understand how priorities are set. This reduces confusion, avoids duplicated efforts, and ensures energy goes to the most promising use cases.

Example:
Tip: Use ChatGPT to draft your intake form, project brief template, and prioritization rubric. Then use ChatGPT's reasoning capabilities to test potential projects against the rubric for an initial assessment.

• Create a small, executive-sponsored group with authority to unblock projects surfaced through your intake process, resolve cross-functional issues quickly, and fast-track approvals for high-potential initiatives. The council’s role is to remove friction while keeping efforts aligned with broader company goals and ensuring that risk, compliance, and governance considerations are addressed early.

Example:
BBVA formed a central AI network to review ideas, prioritize high-value use cases, and ensure smooth collaboration across departments. This approach has helped them move projects from proof-of-concept to production faster while keeping teams aligned on business impact.

• When specific teams create efficiencies or cost savings with AI, give them the resources or time to reinvest in further innovation. Recognizing and rewarding these wins not only accelerates progress but also signals that high-impact teams earn the freedom to keep pushing boundaries.

• Are teams able to move quickly from idea to pilot to production? Track time-to-production for AI projects. Review stuck or stalled efforts monthly to identify systemic blockers.

• Do teams have fast, reliable access to the tools and data they need? Review request and approval times for AI tools, platforms, and datasets.

• Are high-impact AI efforts being prioritized and resourced? Review which projects get funded or staffed relative to their business impact.

5. Govern: Building Guardrails for Rapid Innovation

Moving fast doesn’t mean ignoring risks. It means having clear, practical guidelines so teams can move quickly within established safeguards. Good governance should support rapid action, not create new roadblocks.

• Document practical, easy-to-follow guidelines that help teams use AI responsibly, and make decisions consistent with those guidelines instead of needing manual compliance reviews each time. Focus on what’s “safe to try” and what require