AI tools are rapidly maturing, and some cutting-edge companies are getting 80-90% adoption of AI across their employee base(!) It’s making the sense of urgency for the rest of us palpable. Could AI make a company 2x, 3x, or someday 10x more efficient and effective? If we DON’T pursue it, will our competitors eat our lunch?

I’ve spoken with dozens of C-Suite leaders about how they’re building toward an AI-forward culture - and the challenges they’re overcoming. Their tactics vary, but patterns are emerging. While any one company may not do ALL of the items below, this checklist can act as a menu or guide:

1. Set the Vision and Intention

2. Fund It Like You Mean It

3. Pick the Right Use Cases (aka: Don’t Boil the Ocean)

4. Make It Business-Driven, Not Just IT-Driven

5. Create an AI Governance Board

6. Build a Culture of Experimentation

7. Go Beyond Training—Build Muscle

8. Offer Free-Choice Education Budgets

9. Run Cross-Functional Hackathons

10. Host an AI Leaders Retreat

11. Integrate AI into Hiring, Performance & Talent Processes

12. Add AI-Dedicated Roles Where Needed

13. Consider a Center of Excellence

Let’s dig in…

1. Set the Vision and Intention

With so many competing priorities in a business, only the things that are visioned, goaled, repeated, and measured get done. In the last month, we’ve seen the CEO of Shopify and the CEO of Duolingo both release manifestos to their teams, setting their vision and intention in substantial ways (Read Shopify’s here, and read Duolingo’s here.) While a CEO manifesto will prioritize and drive the entire company, any leader can set a vision and intention for their own team as well. Might we vision and repeat: "AI is going to help us do the best work of our lives. It’s going to help us double or triple our productivity and company growth." Leaders have to believe it, message it, and fund it to bring the vision to life.

2. Fund It Like You Mean It

Nothing is worse than the corporate “priority” that’s “so important we’ve dedicated an intern and 5k to making it successful” (cue the Dilbert Cartoon). Leading companies are experimenting with new tools, bringing in AI experts for workshops, training, and consulting. Many are hiring, or anointing a technical team member to be a point person for a department or the company to lead the charge. Chief AI Officer titles are emerging. A business transformation is not cheap. Despite recommendations that “everyone should be experimenting with AI” to drive bigger change, many companies are investing in focused, dedicated expertise to accelerate everyone’s progress. Nights and weekend volunteer time by ambitious employees may or may not be enough to beat your competitors.

3. Pick the Right Use Cases (aka: Don’t Boil the Ocean)

While AI can be used (and is being used) in almost every department and every software tool at this point, it’s helpful to pick the highest-impact use cases and focus on a few big wins instead of trying to boil the ocean (Please excuse the “Duh").

A lot of CMOs, for instance, are hiring AI experts to do working sessions with individual teams and leaders to map out the most repeatable processes for automation - SEO, SDR Accelleration and Data Enrichment are all areas seeing some of the biggest impacts in marketing (See my recent blog 2025 State of AI in B2B Marketing Scorecard). While we would like “every employee to be using AI,” going deep in some really impactful ways might be more valuable than “spreading the peanut butter too thin” on every person in every team on every project.

In marketing, I’m seeing higher-impact projects coming out of demand generation teams and operations teams who already sit at the intersection of business strategy and technical expertise. They are driving the biggest wins in:

• Repeatable workflows

• Data-heavy processes

• High-ROI automation

4. Make It Business-Driven, Not Just IT-Driven

I’ve started to see some companies’ IT departments owning AI and AI tools and trying to get adoption from different departments. Unsurprisingly, they are getting slow traction. Business units need to own their use cases and care deeply about the outcomes. IT can support, secure, and guide—but business leaders must drive the change for it to actually happen.

5. Create an AI Governance Board

While smaller companies might play “fast and loose,” moving quickly without more oversight, established companies need to make sure that their customer data (and their own corporate security) is being protected. It’s critical to assemble a governance board that includes IT, legal, and business teams to consider risks and set protective and informative policies, especially as tools and use cases continue to evolve. Early governance requirements can guide tool and architecture selection - on-premises and “air-gapped” solutions are back in style as companies look for greater security in their AI implementations. Not all vendors are equal in these offerings.

6. Build a Culture of Experimentation

Many blogs could be written on this topic - but suffice to say, AI-Forward companies are experimenting with lots of tools, a lot:

• They are using different LLMs for different aspects of their AI queries and automations (and are finding that they provide significantly different results).

• They are putting AI automation tools head-to-head and evaluating both effectiveness and costs.

• They are using the native AI capabilities in their current software tools, but also building new, custom, external AI processes and data inputs that plug into existing systems (Read my blog from last week about this “company insights button, that the VP of Growth at SupportLogic put into their Salesforce instance).

The recommendation from many CXOs is to sign shorter contracts, start testing and optimizing, and know that tools are rapidly evolving. The test and optimize phase of AI might last many years!

7. Go Beyond Training—Build Muscle

Basic training is table stakes. High-performing orgs run:

• Team-specific deep dives (e.g., AI for RFP writing or data QA)

• Hands-on working sessions

• Ongoing coaching, peer demos, and lunch-and-learns

• Monthly outside speakers, and more

Tools are only as good as the team members using them.

Since the technology and tools are continuing to evolve at a very fast clip, training is not going to be one-and-done, but ongoing, like going to the gym.

8. Offer Free-Choice Education Budgets

Over the years, I've worked for several companies that had a dedicated training budget for each employee that was “free choice” (As much as $1500/ year default, more upon application). I loved this allocation and used it up each year attending conferences, buying books, and taking courses to improve my skills. Ambitious employees might be excited to have an educational allocation to select specific ways they would like to grow their AI skills.

If employees join industry groups, AI clubs, or attend conferences ask them to presentat learnings at the next team meeting.

Encouraging employees to take initiative should flow through the layers of management - I used to have my directs clarify professional growth goals in their annual and quarterly goals - and we would discuss what types of training or conferences would help them on that journey. I asked them to do the same with their directs.

9. Run Cross-Functional Hackathons

Hackathons are generally associated with technical teams and companies, but can be invaluable for driving AI innovation, and more importantly, the business-user-technical user relationships that help drive innovation and transformation in everyday work. Atlassian had a very strong tradition of Hackathons - we would do one every quarter, year after year. For a period of two days, people across the company would join cross-functional teams to pursue an idea of how to make our product, company, processes, or customer experience better. A marketer would get to know an engineer, or a customer support rep, or an accountant, as we all worked together around birthing a shared idea, implementation, or demo. The hack-athon itself created some fantastic innovations that later became part of our products or processes — but the relationships formed also helped people reach out to each other when they needed other advice — in an AI world, this could help create AI-driven processes and automations - but also create bonds to optimize, troubleshoot or expand those deliveries over time.

10. Host an AI Leaders Retreat

One AI-Forward company I know is hosting a multi-day retreat for the people with the most AI hunger and accomplishment in their organization to vision, learn, hack, and prioritize. The CXO who shared this retreat told me, “Some people are mildly interested in AI, and others at our company are obsessed like teenagers with video games.” The investment in this retreat will spark relationships, ideas, and AI implementations for the company, but also might create a company value visible to all who hear about the retreat: “The AI innovators here are going to be supported and rewarded - jump on board and join us next time.”

11. Integrate AI into Hiring, Performance & Talent Processes

If AI is going to be transformative, it needs to show up everywhere:

• Corporate, team, and individual OKRs

• Performance reviews (i.e. What did you accomplish using AI this year?)

• Hiring priority and processes

• All-hands “AI Wins” show-and-tells

• AI Innovator Awards every quarter (If you are doing this, LMK!)

The more AI wins and star employees are recognized and reward, the more AI will become part of your company culture and operating system.

12. Add AI-Dedicated Roles Where Needed

While “everyone should be using AI,” many AI-forward companies are dedicating more technical team members to support and guide deeper AI impact in their department - and that’s starting to move from volunteer “weekend warriors” to a dedicated headcount.

One company has recently hired an “A3 Director - AI, Automation and Analytics.” Another company has replaced two SDRs with one senior data scientist dedicated to creating automations, AI processes, and data augmentation, and targeting to help all the SDRs be smarter and more effective.

Everyone can and should be thinking about how their work can be massively accelerated using AI, but giving your team the talent to bring that to life might include dedicated support versus only personal improvement.

13. Consider a Center of Excellence & Knowledge Repository

With teams creating amazing artifacts via AI, the next frontier will be in how to share those artifacts across teams to get consistency of insight and reuse. For instance, several AI-forward companies have complex processes for generating company overviews and insights documents drawn from dozens of public sources, including earnings calls. I wrote about one such process last week, but I’ve seen half a dozen versions coming from sales, marketing, product, and strategy teams. I haven’t talked to a company doing this yet, but Maria Pergolino, Lead EIR at Scale VC and former CMO of Active Campaign predicts that companies will create a Center of Excellence and Knowledge Repository where complex artifacts can be shared consistently across departments so that a) each employee doesn’t need to create their own and b) there’s consistency of the insights and materials. One could imagine that many different documents could be created with different prompts, and that some hallucinations could be missed, while other teams might have ‘cleaned’ their versions. Especially for multi-step AI (using the customer overview as a prompt for other materials), having clean, effective, consistent reference materials might be critical.

Perhaps Centers of Excellence in the future will keep track of what hallucinations to look out for, will have AI tests of their own (GrowthX, a leading AI services firm) has an AI bot to validate every fact in a document (created by AI or not). Companies might centralize best practices and core knowledge materials for mass reuse across the company.

What have I missed? What else are you seeing?

Here’s a checklist, cause, why not?

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.