Generative AI is moving so fast since the blockbuster release of ChatGPT in November 2022 that even one of its creators — OpenAI — admits it’s hard to keep up. That’s the premise of a new report the company released today, Staying Ahead in the Age of AI: A Leadership Guide, which distills lessons from OpenAI's work with large, recognizable enterprises including pharma giant Moderna, cosmetics signature Estée Lauder, Notion, and multinational banking/financial services firm BBVA.
The report offers five guiding principles — align, activate, amplify, accelerate, and govern — but here are our top 10 takeaways for enterprise technical decision makers across industries:
1. Tie AI Strategy to Clear Business Value
The report stresses that alignment is not optional. Leaders need to articulate why AI is central to survival — whether that’s holding pace with competitors, responding to rising customer expectations, or fueling growth. The Moderna example is telling: its CEO mandated that employees use ChatGPT 20 times per day, sending a clear signal that AI was core to operations.
In today’s climate, where early adopters are already reporting 1.5x revenue growth over peers, clarity of purpose isn’t a luxury — it’s a survival mechanism.
Enterprise leaders should think beyond hype cycles. With tech spending under pressure in 2025, boards and investors are demanding measurable returns. A vague “we’re using AI” won’t cut it. Instead, organizations must establish explicit adoption metrics tied to KPIs — whether that’s faster deal velocity, lower customer service costs, or accelerated R&D cycles.
2. Role-Model AI Use from the Top
The report underscores something simple but powerful: people mimic what their leaders do. When senior executives publicly share how they use AI — analyzing customer trends, speeding contract reviews, drafting presentations — it creates cultural permission for teams to experiment. At OpenAI, CFO Sarah Friar regularly discusses her use of ChatGPT, which has helped normalize adoption across her department.
This isn’t just optics. With skepticism about AI accuracy still widespread, leaders showing hands-on use can cut through hesitation. For CIOs and CTOs in particular, demonstrating AI in daily workflows sends a stronger message than any memo. It proves AI is not just “for the techies” but integral to every role, from finance to HR.
3. Invest in Role-Specific Training
Almost half of employees say they don’t feel trained to use AI, according to the report. Training isn’t a “nice to have” — it’s the number one determinant of adoption.
The San Antonio Spurs professional basketball franchise increased its organization's AI fluency from 14% to 85% of employees by embedding training into daily work, not treating it as an extracurricular.
For companies grappling with skill shortages, this is a big deal. Embedding AI fluency into the flow of work can unlock productivity without expensive headcount additions. As competition intensifies for generative AI–literate talent, organizations that upskill their own people will be positioned to scale more quickly and cheaply than those trying to hire externally.
4. Build Internal AI Champions
OpenAI recommends developing a network of “AI champions” — employees who mentor peers and share use cases. This grassroots layer can accelerate adoption in ways top-down directives cannot. Champions help normalize usage, answer questions, and spot new opportunities, especially in lean teams juggling multiple responsibilities.
It’s not just about cheerleading. AI champions serve as a distributed R&D function, surfacing workflow improvements that leadership may overlook. In sectors like finance, retail, and healthcare, where frontline staff spot inefficiencies daily, these networks can become engines for continuous innovation.
5. Create Space for Safe Experimentation
The report makes it clear: without deliberate space for tinkering, AI use stays theoretical. Companies that set aside dedicated time for experimentation — monthly hackathons, AI “Fridays,” or no-code prototyping sessions — see practical innovation emerge. Notion’s hackathon famously birthed Notion AI, which is now a core product feature.
In a market where tools evolve monthly, experimentation ensures enterprises don’t just consume vendor roadmaps but shape their own. For decision makers, this means budgeting time as much as dollars. Allocating a few hours per month for structured trial-and-error can yield products, services, and processes that pay off exponentially.
6. Turn Scattered Wins into Shared Playbooks
AI wins often get trapped in silos. One team builds a useful prompt library, another optimizes customer support workflows, but no one else benefits. The report urges companies to create centralized hubs — in Notion, SharePoint, or Confluence — where employees can access training, guides, and success stories on enterprise AI pilots and deployments.
This is particularly critical in 2025 as enterprises wrestle with tool sprawl. With dozens of AI pilots underway at once, knowledge hubs prevent redundant projects and accelerate scaling. Leaders who build these repositories can turn experimentation into institutional learning, preserving value long after early adopters move on.
7. Streamline Decision-Making for AI Projects
AI innovation moves at internet speed, but enterprise approvals often crawl. The report emphasizes the need for lightweight intake and prioritization processes so teams can submit AI project ideas, get feedback quickly, and understand how priorities are set. Estée Lauder’s centralized GPT Lab, which gathered more than 1,000 employee ideas and scaled the best, is one model.
For leaders, the lesson is clear: if it takes months to approve AI pilots, competitors will beat you to market. In 2025’s AI arms race, agility isn’t just cultural — it’s operational. CIOs and COOs must rethink governance processes to eliminate bottlenecks while still managing risk.
8. Form Cross-Functional AI Councils
To avoid duplication and turf wars, OpenAI recommends creating small, empowered AI councils. These groups, sponsored by senior executives, can unblock projects, set priorities, and keep alignment with compliance and risk requirements. Spanish bank BBVA’s central AI network is cited as a successful example.
Cross-functional oversight is especially relevant now as AI governance regulations tighten globally. With the EU AI Act set to enforce strict compliance requirements and U.S. agencies eyeing AI risk frameworks, enterprises will need structures that align innovation with accountability. Councils provide a scalable way to bridge ambition and oversight.
9. Reward High-Impact AI Usage
Incentives matter. The report highlights companies like Promega, which tracked AI usage across teams and invested further in high-usage groups, effectively rewarding innovation. For decision makers, this means recognizing contributions in promotions, performance reviews, and resource allocation.
At a time when budgets are tightening, this approach can also help surface the highest ROI projects. By channeling resources to proven wins, organizations can avoid over-spending on scattershot pilots and double down where value is already evident.
10. Balance Speed with Governance
Finally, OpenAI warns that moving fast doesn’t mean ignoring risk. Instead, enterprises need lightweight, evolving safeguards. A “responsible AI playbook” that defines what’s safe to try and what requires escalation can help teams act quickly without constant compliance reviews. Quarterly audits and plain-language guidance are recommended.
With regulators globally moving to impose standards on transparency, bias, and data use, the governance conversation is no longer optional. The winners will be companies that bake in flexible guardrails early, balancing speed with safety. Leaders must ensure compliance frameworks evolve as fast as the tools themselves.
Bottom Line
OpenAI’s new report frames AI not as a bolt-on tool but as a wholesale shift in how organizations work. For leaders, the message is urgent: align your teams, activate training, amplify successes, accelerate decisions, and govern responsibly.
Those who master these moves won’t just stay afloat in the AI tide — they’ll ride the wave to competitive advantage.
Generative AI is moving so fast since the blockbuster release of ChatGPT in November 2022 that even one of its creators — OpenAI — admits it’s hard to keep up. That’s the premise of a new report the company released today, Staying Ahead in the Age of AI: A Leadership Guide, which distills lessons from OpenAI’s work with large, recognizable enterprises including pharma giant Moderna, cosmetics signature Estée Lauder, Notion, and multinational banking/financial services firm BBVA.
The report offers five guiding principles — align, activate, amplify, accelerate, and govern — but here are our top 10 takeaways for enterprise technical decision makers across industries:
1. Tie AI Strategy to Clear Business Value
The report stresses that alignment is not optional. Leaders need to articulate why AI is central to survival — whether that’s holding pace with competitors, responding to rising customer expectations, or fueling growth. The Moderna example is telling: its CEO mandated that employees use ChatGPT 20 times per day, sending a clear signal that AI was core to operations.
In today’s climate, where early adopters are already reporting 1.5x revenue growth over peers, clarity of purpose isn’t a luxury — it’s a survival mechanism.
Enterprise leaders should think beyond hype cycles. With tech spending under pressure in 2025, boards and investors are demanding measurable returns. A vague “we’re using AI” won’t cut it. Instead, organizations must establish explicit adoption metrics tied to KPIs — whether that’s faster deal velocity, lower customer service costs, or accelerated R&D cycles.
2. Role-Model AI Use from the Top
The report underscores something simple but powerful: people mimic what their leaders do. When senior executives publicly share how they use AI — analyzing customer trends, speeding contract reviews, drafting presentations — it creates cultural permission for teams to experiment. At OpenAI, CFO Sarah Friar regularly discusses her use of ChatGPT, which has helped normalize adoption across her department.
This isn’t just optics. With skepticism about AI accuracy still widespread, leaders showing hands-on use can cut through hesitation. For CIOs and CTOs in particular, demonstrating AI in daily workflows sends a stronger message than any memo. It proves AI is not just “for the techies” but integral to every role, from finance to HR.
3. Invest in Role-Specific Training
Almost half of employees say they don’t feel trained to use AI, according to the report. Training isn’t a “nice to have” — it’s the number one determinant of adoption.
The San Antonio Spurs professional basketball franchise increased its organization’s AI fluency from 14% to 85% of employees by embedding training into daily work, not treating it as an extracurricular.
For companies grappling with skill shortages, this is a big deal. Embedding AI fluency into the flow of work can unlock productivity without expensive headcount additions. As competition intensifies for generative AI–literate talent, organizations that upskill their own people will be positioned to scale more quickly and cheaply than those trying to hire externally.
4. Build Internal AI Champions
OpenAI recommends developing a network of “AI champions” — employees who mentor peers and share use cases. This grassroots layer can accelerate adoption in ways top-down directives cannot. Champions help normalize usage, answer questions, and spot new opportunities, especially in lean teams juggling multiple responsibilities.
It’s not just about cheerleading. AI champions serve as a distributed R&D function, surfacing workflow improvements that leadership may overlook. In sectors like finance, retail, and healthcare, where frontline staff spot inefficiencies daily, these networks can become engines for continuous innovation.
5. Create Space for Safe Experimentation
The report makes it clear: without deliberate space for tinkering, AI use stays theoretical. Companies that set aside dedicated time for experimentation — monthly hackathons, AI “Fridays,” or no-code prototyping sessions — see practical innovation emerge. Notion’s hackathon famously birthed Notion AI, which is now a core product feature.
In a market where tools evolve monthly, experimentation ensures enterprises don’t just consume vendor roadmaps but shape their own. For decision makers, this means budgeting time as much as dollars. Allocating a few hours per month for structured trial-and-error can yield products, services, and processes that pay off exponentially.
6. Turn Scattered Wins into Shared Playbooks
AI wins often get trapped in silos. One team builds a useful prompt library, another optimizes customer support workflows, but no one else benefits. The report urges companies to create centralized hubs — in Notion, SharePoint, or Confluence — where employees can access training, guides, and success stories on enterprise AI pilots and deployments.
This is particularly critical in 2025 as enterprises wrestle with tool sprawl. With dozens of AI pilots underway at once, knowledge hubs prevent redundant projects and accelerate scaling. Leaders who build these repositories can turn experimentation into institutional learning, preserving value long after early adopters move on.
7. Streamline Decision-Making for AI Projects
AI innovation moves at internet speed, but enterprise approvals often crawl. The report emphasizes the need for lightweight intake and prioritization processes so teams can submit AI project ideas, get feedback quickly, and understand how priorities are set. Estée Lauder’s centralized GPT Lab, which gathered more than 1,000 employee ideas and scaled the best, is one model.
For leaders, the lesson is clear: if it takes months to approve AI pilots, competitors will beat you to market. In 2025’s AI arms race, agility isn’t just cultural — it’s operational. CIOs and COOs must rethink governance processes to eliminate bottlenecks while still managing risk.
8. Form Cross-Functional AI Councils
To avoid duplication and turf wars, OpenAI recommends creating small, empowered AI councils. These groups, sponsored by senior executives, can unblock projects, set priorities, and keep alignment with compliance and risk requirements. Spanish bank BBVA’s central AI network is cited as a successful example.
Cross-functional oversight is especially relevant now as AI governance regulations tighten globally. With the EU AI Act set to enforce strict compliance requirements and U.S. agencies eyeing AI risk frameworks, enterprises will need structures that align innovation with accountability. Councils provide a scalable way to bridge ambition and oversight.
9. Reward High-Impact AI Usage
Incentives matter. The report highlights companies like Promega, which tracked AI usage across teams and invested further in high-usage groups, effectively rewarding innovation. For decision makers, this means recognizing contributions in promotions, performance reviews, and resource allocation.
At a time when budgets are tightening, this approach can also help surface the highest ROI projects. By channeling resources to proven wins, organizations can avoid over-spending on scattershot pilots and double down where value is already evident.
10. Balance Speed with Governance
Finally, OpenAI warns that moving fast doesn’t mean ignoring risk. Instead, enterprises need lightweight, evolving safeguards. A “responsible AI playbook” that defines what’s safe to try and what requires escalation can help teams act quickly without constant compliance reviews. Quarterly audits and plain-language guidance are recommended.
With regulators globally moving to impose standards on transparency, bias, and data use, the governance conversation is no longer optional. The winners will be companies that bake in flexible guardrails early, balancing speed with safety. Leaders must ensure compliance frameworks evolve as fast as the tools themselves.
Bottom Line
OpenAI’s new report frames AI not as a bolt-on tool but as a wholesale shift in how organizations work. For leaders, the message is urgent: align your teams, activate training, amplify successes, accelerate decisions, and govern responsibly.
Those who master these moves won’t just stay afloat in the AI tide — they’ll ride the wave to competitive advantage.
