AI & ML

The AI upskilling guide for executives

Becoming an AI-enabled company is more about change management than the technology itself. Here’s what CEOs, boards, and leaders must consider to earn employee trust and drive meaningful productivity gains with AI.

Technology often advances faster than organizations change. The US government started using typewriters two decades after they were invented. Fax machines didn’t become ubiquitous in American offices until the 1980s, more than 15 years after Xerox released the Long Distance Xerography. And while Visicalc was providing a game-changing alternative to paper ledgers and mechanical adding machines all the way back in 1979, spreadsheet software didn’t make its way onto job descriptions until the mid-1990s, following the advent of Excel.

Another example? AI. Today, 90% of executives think AI can drive revenue growth, and 77% believe they need to adopt AI quickly to keep up with competitors, but there’s still a gap between interest and actual implementation, with only 1% of executives saying that AI is fully integrated into company workflows and driving substantial business outcomes.

90% of executives think AI can drive revenue growth

Introducing AI into your organization’s operations—and eventually your product—requires skillful implementation and skillful change management. Or, said another way: being successful with AI requires making a deep investment in not just your technology but also your people and their skill development.

Your team can be one of the most effective drivers of AI adoption within your company. When employees (especially individual contributors) are committed, equal partners in the process, they can point leaders to the best AI tools and use cases, testing and giving rapid feedback to support iteration, mentoring their peers, and raising red flags when issues arise. However, if employees don’t see tangible benefits to integrating AI into their daily workflows, receive sufficient training that will help them succeed with new tools, or trust that leaders are making informed decisions, addressing very real risks, and protecting employee and customer interests, AI initiatives fall flat. More candidly, when fear is encoded in the leadership’s messaging— “use AI or become obsolete”—curiosity and ambition can be extinguished.

Because employees’ abilities and perspectives on AI can change, it’s actually the quality of leadership’s involvement in the change process that usually makes or breaks AI adoption. Many executives already understand the stakes of getting it right. Sixty-four percent of CEOs believe that succeeding with AI will depend more on people’s adoption of the technology than the technology itself, and there’s data to support that perspective. Companies with high AI adoption are much more likely to report that their most advanced AI initiatives are meeting or exceeding ROI expectations, compared to organizations with lower usage.

So what can leaders do to create an organizational culture that embraces AI, where everyone on the team is using new tools responsibly to drive meaningful innovation and profitable growth?

We asked Bessemer Operating Advisor and AI and human capital expert Susan Youngblood just that. Susan Youngblood led global HR and talent teams at IBM and BNY Mellon before heading people for the operations team at Refinitiv (now part of the London Stock Exchange Group), where she drove AI and automation adoption across a 9,000-person organization. She now advises companies of all sizes on navigating the evolving AI landscape and preparing for the future of work.

In this AI adoption guide, we share essential guidance for executives who are introducing AI into their company’s day-to-day operations, including Susan’s advice on how to get (and stay) up to speed on AI innovation, create feedback loops and build trust with employees, and identify and close skill gaps to increase your team’s competence and confidence with AI.

Key executive takeaways on how to AI upskill your company:

Start with leadership education. Executives must personally experiment with AI tools and stay current on capabilities to credibly lead and model adoption across the organization.
Tie AI to clear business objectives. Define specific, measurable goals aligned to strategy—avoid adopting AI for its own sake or deploying tools without clear use cases.
Invest in targeted, role-specific training. One-size-fits-all upskilling programs fail; build tailored learning paths tied directly to job functions, workflows, and new AI-integrated processes.
Create employee trust and feedback loops. Transparent communication, open forums for employee input, and visible leadership learning foster a high-trust culture essential for AI adoption.
Make AI adoption a CEO- and board-level priority. AI transformation is cultural and cross-functional—CEOs must own the vision, secure resources, and ensure full leadership alignment.

The state of AI adoption in the corporate sector

How employees use AI at work varies by industry, role, and demographic—but one thing is clear: AI adoption is rising across the corporate sector. More than 75% of organizations and 88% of technology companies were using AI in at least one business function during the first half of 2024, up from 55% in 2023, according to one McKinsey survey. While the majority of companies are deploying AI in employee systems to support day-to-day operational tasks, 35% of leaders surveyed by PwC said they’d already implemented generative AI in both employee and customer systems.

Companies using AI for product and business model innovation are still a minority, but that should still be the north star for leaders. For example, AI experimentation in the healthcare industry is at an all-time high, but most of it is still within the proof-of-concept phase.

When it comes to internal use cases of AI, the rates of employees who have access to AI integrations and tools are generally lower than the rate of companies that have implemented them. In a Deloitte survey, 40% of employees working at companies that had at least one AI use case reported having access to AI tools and 60% of those with access used AI on a daily basis, suggesting that companies are mostly integrating AI into secondary workflows and workflows specific to certain functions or roles (vs. daily workflows and cross-functional workflows).

But just because an employee hasn’t been given access to AI tools doesn’t necessarily mean they aren’t using AI. An often-cited report from early 2024 found that 78% of knowledge workers who are using AI at work were using tools that weren’t provided by their employers. This might be why some leaders are underestimating their employees’ familiarity with AI. In the same McKinsey survey, 12% of employees reported using AI to complete 30% or more of their daily tasks, whereas executives estimated only 4% of employees were using AI to that extent.

Whether it’s employees or executives who are spearheading AI depends on the organization— and in most cases, individual employees and executives will vary widely in their level of expertise, experience, and enthusiasm about AI. While having benchmarks can be helpful, it’s up to each leadership team to make an honest assessment of how AI is being used within their organization today, and, with input from employees, determine opportunities for AI to enable people to become more effective in their roles and advance the company’s goals in the future.

Roles and responsibilities of leaders in encouraging AI use

Introducing AI into your organization’s operations is a massive undertaking that can’t be led by the CTO or VP of Data alone. Even if you decide to limit your initial AI use cases to one function such as customer service or marketing, AI adoption needs to be a board-level priority, and every leader in your organization will still have an essential role to play in designing, executing, and managing your company’s AI strategy, and safeguarding against risks.

The board

“AI has great potential but also carries substantial risk. Board-level oversight is crucial to ensure that the company’s investments in AI align with the strategy, support leaders in appropriately weighing trade-offs and addressing risks, and help put proper governance in place to ensure AI is implemented responsibly and continuously monitored after implementation,” explains Susan.

Only 58% of executives completed a preliminary assessment of AI risks in their organization in 2024, suggesting board members and leaders alike need to put more emphasis on safe and ethical AI use vs. “AI at all costs.” In fact, many companies, especially SMBs and startups, do not have an AI use policy, which creates substantial risk.

CEOs

Because AI is technically complex, some CEOs may want to hand over the AI reins to the CTO (or another technical executive) and move on. But AI adoption is about more than just implementing technology. It involves defining a vision and leading a broad cultural shift within your organization. And when it comes to the company’s culture, the CEO is irreplaceable.

“CEOs lead the AI transformation by setting a clear roadmap and objectives and fostering a company culture that embraces AI,” says Susan. “This last part is crucial. Communicating with employees throughout the AI adoption process—including talking honestly about mistakes made and new lessons learned—helps create a culture of trust and openness that’s essential when making any change to the way people work, and particularly when introducing AI.”

We’re already seeing CEOs of companies sharing memos to their teams about becoming AI-first organizations, signaling how top-down messages play a role in communicating a new direction for a company. While the messaging sets the tone, what matters most is the follow-through: making sure that employees have the bandwidth, learning and development resources, and space for experimentation.

The executive team

“Executives are responsible for ensuring any investments in AI make sense for the business, customers, and team,” says Susan. Each leader has unique context and expertise, so the entire leadership team should work together to set the goals of AI adoption, create guidelines for responsible AI use within the company, identify and vet use cases and solutions, and create opportunities for learning, feedback, and experimentation.”

All executives have a responsibility to communicate openly about AI—something that Susan emphasized throughout our conversation with her. “Employees' trust in management is at an all-time low. Leaders must be able to clearly explain to employees why AI adoption is a priority, show how these new tools will enable them rather than replace them, and then credibly follow through on what they say.”

Technical executives

Technical executives partner with the CEO and board on AI governance plans, and oversee the implementation of AI solutions. All executives can identify potential use cases for AI, and even suggest AI-enabled products and services, but data, engineering, and security leaders are the ones with the expertise to vet those use cases and solutions to make sure they are feasible, assess and address cybersecurity risks, etc.

Non-technical executives

“Non-technical leaders should understand AI’s potential benefits, limitations, and ethical considerations, particularly within their specific function. That way, they can identify areas where AI could improve outcomes or solve problems within their organization, advocate effectively for AI initiatives, address concerns within their teams, and create a tight feedback loop between technical teams and end-users to see if experimental use cases are helpful and working as intended,” says Susan.

One of the best ways to become familiar with the capabilities of AI is through experimentation. Trying various AI tools out is a great way for leaders to learn and it sets an example for the rest of the team. “Employees who don’t consider themselves technical might assume they aren’t cut out for using AI, let alone contributing to the company's AI initiatives. But, like many technologies, you don’t actually need to understand the underpinnings of the technology in order to use it. Non-technical leaders who take the plunge and start using AI themselves can bust the myth,” says Susan.

Human resource leaders

Chief human resource officers (CHROs) are often given the responsibility for upskilling and reskilling the organization (one of the later phases in the AI adoption process that we’ll discuss in the next section). In addition, people operations and recruiting are both areas where AI use cases can introduce a lot of risk, so CHROs and other HR leaders need to advocate to protect employees and candidates from the potential bias introduced by AI, and make sure that use cases comply with regulations and internal policies. These are big responsibilities for any one leader to take on, so CHROs will need the full backing of the CEO and the rest of the executive team—otherwise, they’ll be set up for failure.

Key phases of the AI adoption process

“The rapid rise of AI has pushed leaders into new, uncharted territory. Practically every day, new AI capabilities are being announced and staying informed can be daunting. Many leaders don’t feel they have the relevant knowledge or experience to speak about AI with any authority, let alone make informed decisions on behalf of their organization,” says Susan. Here are the four key phases of the AI change management process.

Phase 1: Educate yourself as a leader

Not every leader has to be an expert. However, any executive or senior manager who’s going to be involved in developing your company’s AI initiatives should have at least a baseline understanding of the technology’s current and future capabilities, business applications, risks, and benefits.

There’s no silver bullet. Educating yourself about AI requires an ongoing commitment. While it’s tempting to push it to the bottom of your to-do list, especially when you have competing, time-sensitive priorities as many executives do, remember: it’s possible that some of the challenges your department is facing right now are issues that can be addressed by AI—if not today, then tomorrow. So there’s a huge benefit to staying up-to-date about what AI can do and how those capabilities are progressing.

Phase 2: Define clear objectives for AI

A lot of leaders feel pressure to “be on the cutting edge” when it comes to new technologies, but increasing AI adoption for its own sake is counterproductive and expensive. “When leaders first start talking about AI, the initial thought is sometimes to put AI everywhere it has an application, but that's generally not the best approach,” explains Susan. “AI is expensive and the ROI of each investment should be clear.”

Integrating AI into workflows where it isn’t reliable, doesn’t add value, or is unlikely to be used can also end up degrading the quality of work output, compromising your customer experience, diminishing employees’ trust in leadership, and possibly creating problems from a regulatory, privacy, or security standpoint.

To really pay off, your organization’s investments in AI need to align closely with your company’s overall strategy, and achieve specific business goals. Those goals should be relevant, narrow, timebound, and measurable—especially for early AI efforts which will require a good amount of experimentation and iteration.

Think about what your organization needs to accomplish over the next year, and then work backward to see where you think AI can have the most impact across the organization. If you’re releasing a new product in the next year, you might set a goal to reduce engineering bugs by X% over the next quarter or improve customer time-to-resolution by Y%.

It’s important to set realistic targets and properly invest to achieve them. Some companies are overshooting; 47% of employees using AI reported having no idea how to achieve the productivity gains their employers expect from AI, suggesting that leaders may not be correctly estimating the potential value of selected AI tools, choosing the right tools, or properly training their teams, all of which significantly impact the ROI of AI investments and can increase frustration and cause employees to lose trust in leadership.

Once you clearly define your goals and set your targets, it will be easier to assess:

What are the ideal initial use cases for AI?
Which employees need to be trained? On what?
Is this experiment on track for success?
How should we adapt our experiment if not?

We’ll provide more guidance on how to select and test use cases for AI in our upcoming guide on the more technical aspects of AI adoption.

Phase 3: Engage employees for feedback and buy-in

Tenured employees understand how work gets done—usually better than senior leadership does. They can tell you which tools, systems, and processes function as intended and which don’t; where there are redundancies and gaps; which teams have healthy cross-functional collaboration and where communication breaks down; and who’s the unofficial expert who can explain that new process or fix that thing that’s always breaking.

People in your organization with this kind of deep institutional knowledge likely already have ideas for tasks and workflows that AI could support or automate. In fact, two-thirds of managers report getting questions from their team about how to use AI at least once a week and recommend tools to their teams to solve problems, suggesting a groundswell of employees are using free or consumer plans of AI tools even in the absence of top-down AI investments.

However, just because there are people within your organization who have valuable insights on the organization’s operations—-or even experience experimenting with AI—doesn’t mean they’ll embrace the company’s formal AI initiatives or contribute their ideas unprompted.

At this phase, leaders need to make an active effort to engage employees in the process—and here’s how:

1. Be transparent.

Employees in “high-trust” companies are more than twice as likely to feel comfortable using AI tools than staff in companies with low trust scores—and transparency is always an essential ingredient to building trust.

“We are all learning about AI at the same time, and that’s an anomaly that can help or hurt adoption, depending on what approach leaders take,” says Susan.

Leaders who share their own experiences with AI—including successes and challenges— set the stage for an open dialogue where employees feel comfortable sharing in kind. But when leaders act as if they have all the answers, it has the opposite effect, squashing curiosity and the desire to experiment.

“One of the most effective ways I’ve seen a CEO gain the trust of employees was talking honestly about their own learning process and experimentation—how they were using AI (or trying to), the failures they’d had, and where they saw opportunities. It humanized the CEO and made it clear that no one had all the answers on AI, and that helped open up two-way channels of communication with employees and encouraged them to be active partners in the AI adoption process,” Susan reflected.

2. Communicate about AI initiatives early.

You wouldn’t expect a reorganization to go smoothly without effective internal communications. Introducing AI into your company’s operations is a similarly seismic cultural shift that requires frequent, transparent communication between leaders and employees during the planning, experimentation, and implementation phases.

The data suggests that many leaders are still having discussions and making decisions about AI behind closed doors. Only 42% of enterprises who launched AI initiatives in early 2024 had regular internal communications about the value created from AI, and just 19% established a compelling change story about the need for AI adoption.

Simply bringing in AI tools and mandating that employees use them will backfire. “A law firm bought and implemented several AI tools assuming they already had buy-in to do so from the wider organization,” recounts Susan. “Because they hadn’t actually taken the time to help employees understand why the firm was doing this and how it would benefit the team, including how they would be trained, the investment totally fell through.”

3. Make the case for investing in AI.

“People need to understand how AI adoption can advance the company’s goals, what’s in it for them individually, and how leadership is weighing benefits against costs and risks. The goals you set will come in handy when it comes to explaining to the team what you think AI can help the organization accomplish and how you came to those conclusions,” says Susan.

In an announcement to his team about building an AI-first company, Box CEO Aaron Levie explained that the primary purpose of using AI within the organization was to eliminate day-to-day “drudgery” to speed everyone up and redirect energy from operational tasks towards efforts that really benefit Box’s customers.

“We know that the most important strategic thing we do is deliver for our customers and any minute that doesn’t go into that is lost time,” Aaron wrote. “We want to use AI to ensure more time, on average, is spent on the things that really matter, so we'll use AI to help onboard Boxers faster, get everyone access to experts on any topic, to make decisions more quickly, iterate on new ideas more quickly, augment our code writing (safely), better serve customers, and more.”

Aaron was explicit about safe and responsible AI use being a priority at Box. “Data security and privacy is of utmost importance when adopting AI. For any use of AI with any proprietary data, we must use sanctioned tools only. We [also] expect a high degree of oversight of what AI is producing in your workflows, as you are still accountable for any output that comes out of AI.”

4. Create opportunities for questions, feedback, and ideas.

Having employees participate in developing AI initiatives helps boost adoption once AI tools become available. But more importantly, it radically improves outcomes with AI. Employees can help identify tactical problems that AI can solve, see risks and trade-offs leadership might not have considered, and set up and test experimental AI use cases.

That’s why it’s so important to give broad access to AI tools and integrations, even when they’re new. In Susan’s experience, some of the early adopters who give valuable feedback and help other employees learn new tools aren’t always people who are seen as being the most tech-savvy or the ones who are asked to participate in testing initially. Spotlighting the contributions of these employees may encourage others who do not consider themselves to be “technical” to share their input.

Box CEO Aaron Levie similarly emphasized the value of widespread employee participation in the memo to his team. “We can't always imagine what the best use-cases are for AI, and we're constantly wowed by Boxers that are coming up with their own use cases. We will keep sharing best practices and ideas across the company, like at Friday lunches, in our OKRs, and [elsewhere].”

In addition to communicating about AI initiatives early and often, leaders should create regular, open forums where employees can share their experiences, make suggestions, and raise concerns (as the Box team does during their Friday lunches). “Make sure that employees have an opportunity to give honest feedback on how things are going. When there’s negative feedback, it’s important to act quickly to address it. This builds trust in leadership,” says Susan.

Even employees who aren’t as proactive about using AI can play a role in identifying promising use cases, if leaders are creative. Susan shares one way she solicits ideas: “When I’m managing a new team, I ask each person in our initial 1:1 meeting to tell me one thing that drives them crazy about their job, so I can help reduce or eliminate the amount of time they spend doing it. In the vast majority of cases, people talk about mindless work or work that doesn’t seem to make a difference. And fortunately, AI is eliminating the need for us to do these types of repetitive, low-skill tasks so we can focus on the work that requires uniquely human skills—strategic thinking and using creativity to solve tough problems—and is far more valuable to the company.”

Phase 4: Identify and close employee skill gaps

“One of the biggest challenges executives face right now is ensuring that their workforce has the necessary skills to work alongside generative AI,” says Susan. “The rapid adoption of AI means that many employees may not have the technical proficiency needed to use AI tools effectively, creating an urgent need for well-designed upskilling and reskilling programs. Many companies are still in the early stages of this process and how they’re approaching it is quite varied.”

While executives see AI literacy as a critical capability their workforce will need in the coming year, only 22% strongly agree that their organization has integrated AI knowledge, skills, and abilities into professional development plans for employees. And nearly half of employees want more formal training and believe it is the best way to boost AI adoption, but surprisingly, 20% have received minimal or even no support so far.

Interest in AI and familiarity with AI go hand-in-hand, so training is an opportunity to increase employee receptiveness to AI—or lose their trust. Leaders might feel tempted to try to close AI skill gaps as quickly as possible, but in Susan’s experience, rushing to upskill your team with a one-size-fits-all approach won’t work.

To be effective, training must be tailored to teach employees how to use AI within the context of their specific role. If the training is too general, or not relevant to them, employees may become frustrated and even anxious, thinking they’ll be expected to use the new AI tools with inadequate education. This can sour them to the broader AI initiative.

“If I’m a recruiter, I don’t necessarily need to sit through hours of training on how LLM’s work. I just want to understand how I can leverage the specific tools that will make me more effective in my role, and learn how to get the most out of them.”

Traits of effective AI upskilling programs

1. Prioritized by the CEO

AI upskilling has to be a top-line priority for the organization and given the resources to match.“If you want to upskill an organization of 30,000, 3,000 or even just 300 people, it’s going to require the support of the CEO and entire leadership team, and a considerable investment of time and capital,” says Susan. “The CEO and other leaders must signal that AI upskilling is a business imperative, not optional enrichment.”

“The responsibility for upskilling is typically handed off to the Chief Human Resources Officer. While it’s fine for the CHRO to take point, initiatives of this size and importance require the thoughtful input of all leaders, at the early stages of program development and as AI capabilities continue to advance. To create learning paths targeted to different employees and how they’ll interact with AI, the organization will also need to make an investment in AI tools and platforms that enable microlearning, experimentation, and immediate application—in contrast to a one-time learning program.”

CEOs will need to ensure that the rest of the leadership team is playing their part in the upskilling process. “Executives and managers are responsible for bringing their teams along. Without their support, efforts to train and motivate people will lose momentum,” explains Susan.

“There was one large technology company I know of that wasn’t making any progress on upskilling, but once the CEO realized it was a problem and made it an explicit priority, everyone started paying attention. The organization went from being a laggard to ahead of their peers in this area very quickly—just because the CEO made it clear he wanted to see results.”

Box’s CEO also made upskilling every employee an explicit goal of the company’s AI initiative, writing, “We want every Boxer to become proficient in AI, and we will [provide] more education on what AI can do for every role, [and] also encourage everyone to explore its potential through the infinite set of resources online.”

2. Based on credible data

After insufficient resources, inaccurate data is one of the biggest barriers to building an effective upskilling program. Skills survey data is time-consuming and gets stale quickly, so most companies—even the few that do collect this data through surveys—don’t have accurate, up-to-date information on what skills their employees do and don’t have.

“Until very recently, we didn’t have tools that really allowed you to dynamically assess skill sets within your organization,” says Susan. “And when you start with inadequate or inaccurate data, it’s very hard to identify which employees require training and what types of content would be the most relevant to them. It’s important to have current data so that trainings are targeted and you have a clear view of where to invest resources.”

Fortunately, people are building solutions to address this pain point. “AI tools are becoming available to help you infer what skills your employees likely have by pulling data from job descriptions, resumes, meeting notes, performance reviews, Slack messages, and other sources. It’s cheaper, more efficient, and more effective than running a survey, which will become outdated shortly after it’s conducted.”

3. Targeted to use cases and employee needs

Data on employees’ skills and experience is one essential ingredient for understanding what employees within different functions, roles, and levels need to learn to become more competent with AI. The other is identifying any additional skills required to use individual AI tools or integrations, go through new workflows, and monitor and improve AI’s output in cases where tasks are being fully automated.

“Training that includes hands-on experiences with new tools gives opportunities for employees to ask questions and see the tools’ effectiveness first-hand. If your company entirely redesigns a workflow by automating certain tasks with AI, it’s important to let teams practice the new workflow in a test environment. Employees should also be taught any new capabilities that are required, such as data verification, exception handling, or writing prompts. Real behavior change happens when employees apply new skills directly to their daily tasks. When AI learning is woven into the work itself, it becomes relevant and immediately valuable. This approach accelerates capability building, reinforces new ways of working, and ensures investments in AI upskilling translates into measurable business outcomes,” explains Susan.

We’ve seen the value of this approach here at Bessemer Venture Partners. Bessemer isn’t just investing in AI leaders like Perplexity and Anthropic—we’re also putting these tools to work across every team, from finance to marketing. At a recent offsite, an entire day was devoted to exploring real-world AI integrations, featuring team-led demos and representatives from Perplexity highlighting powerful enterprise features. This hands-on experience showed everyone in the organization just how versatile and transformative Intelligent Search can be. But it was also just the beginning, sparking curiosity, further learning opportunities, and collaboration across different functions.

But training doesn’t just have to happen in a formal setting. One of the best ways for employees to develop professionally is through the example and mentorship of their peers. “Make sure to create a structured way for early AI adopters to showcase what they're learning or experimenting with, whether that’s through a lunch and learn, during a standing meeting, or on a Slack channel. And make sure it’s rewarded.”

Red and green flags of internal AI initiatives

In addition to the performance metrics you selected in the defining objectives phase, Susan shares early signals that can help you quickly assess whether your AI initiatives are headed in the right direction, or whether changes need to be made to earn the support of your team.

Red flags	Green flags
🔻 Leaders are asking the team for feedback on AI experiments and tools but employees are mostly silent.	✅ People are sharing their experiences and perspectives on AI (good and bad) with peers and leaders, sometimes unsolicited.
🔻 Training is being offered to the team but few people are enrolling or people start the training but quickly drop out.	✅ Trainings have high attendance or completion rates, or there are low rates, but employees are communicating what’s wrong.
🔻 Most people are completing AI trainings, but they aren’t using AI tools or otherwise integrating AI into their daily work (i.e. because they find the trainings aren’t applicable or are too theoretical).	✅ When people have concerns about AI or have feedback on training effectiveness, they raise them—even when it’s about big picture risks or fears that integrating AI into their daily work will lead to job elimination.
🔻Use of AI is a top-down mandate and decisions are being made by executives without input from the team. Meanwhile, employees are discussing issues in private.	✅ Interest and ideas related to AI come from the top-down and bottoms-up, and the biggest drivers of AI adoption are peer-to-peer, with early adopters sharing their success stories.

AI transformation is an organizational change

It bears repeating: the transition to becoming an AI-enabled company is as much about change and people management as it is about technology implementation. Don’t expect things to happen overnight. AI transformation is a marathon, not a sprint, and long-term success depends on having capable leadership, transparent communication, targeted upskilling, and a culture where experimentation and feedback are encouraged. When executives prioritize people—investing in their skills, trust, and engagement—AI becomes not just a tool for efficiency, but a driver of true innovation and sustainable growth.