In today’s landscape, AI isn’t a futuristic luxury, it’s a competitive necessity. 2025 has seen a surge in generative AI adoption: one survey finds 91% of middle-market firms now use AI in operations. Investors and executives are demanding real productivity gains, not just flashy pilots. For scrappy startups and growth-stage companies, the promise is clear: use AI to multiply output, not headcount.

AI as a Productivity Multiplier

Consider the bold example of Shopify. In April 2025, CEO Tobi Lütke mandated that teams must “prove they cannot use AI to get a job done” before hiring more people. Remarkably, Lütke notes some employees using AI “get 100 times more work done”. This isn’t a fairy tale, it illustrates how powerful AI tools can massively boost productivity for even small teams. Models are bigger and more intuitive than their predecessors, “more natural” in conversation and capable of solving complex writing and coding tasks with greater understanding. For startups, that means more work (like drafting proposals, generating marketing copy, or building code snippets) done in seconds instead of hours.

Case Study: Shopify’s AI-Driven Culture

Shopify’s internal memo offers a real-world playbook. By embedding AI into daily workflows, Shopify has essentially treated AI as a new team member. All employees are expected to use AI “as a critic, tutor and programmer” to improve efficiency. The result? Routine tasks get done extremely quickly, freeing people to focus on higher-value strategy and creativity. This reflects a key Chiri principle: AI woven into culture, not just stacked tools. Startup founders can learn from this: instill an “AI-first” mindset where employees view tools like generative AI as everyday collaborators.

Real-World Startup Strategies

For growing companies, the strategy is twofold. First, identify high-value tasks that AI can accelerate (like data analysis, report writing, or customer support triage) and automate them. Second, put humans in the loop to guide and validate AI outputs. For example, a 2025 survey of middle-market firms shows AI is widely used for text generation and workflow automation. A lean marketing team might use AI to draft personalized email campaigns in minutes, with a human marketer refining the output, achieving a 10X increase in content production without hiring extra copywriters. Similarly, a sales startup could deploy an AI agent to prioritize leads or produce sales insights, letting the small team focus on closing deals.

Bridging Tech and Talent

Crucially, this vision is human-centric. 10X output doesn’t come from firing people; it comes from empowering them with AI. Shopfiy’s approach shows employees who use AI effectively become dramatically more productive. Chiri advises startups to upskill staff early: train teams on prompting and AI tools so they become fluent AI users. Invest in simple governance and review processes to catch errors (as even advanced models can hallucinate.) The goal is an AI-augmented workforce, not an AI-alone workforce.

Embracing an AI Mindset

To realize these gains, leadership must champion the change. Define an AI strategy: set clear goals (e.g., “cut report prep time by 80%”) and track metrics. Align culture: encourage experimentation and share success stories. And prioritize value: focus on AI use cases that multiply impact. Remember, the aim is not to 10X headcount, but 10X impact with your team.

The era of “more people equals more growth” is fading. Startup and scaleup leaders have a historic chance to outperform bigger rivals by integrating AI thoughtfully. By making AI part of the company culture and strategy, small teams can achieve disproportionate results. As competitiveness heats up in 2025, the question for founders and CEOs is no longer if but how fast they will leverage AI to multiply their output.

“It’s not just a new way of working…it’s a new way of thinking.” This line from our own journey at Chiri has become a rallying cry for leaders navigating the AI revolution. Everywhere you turn, there’s talk of AI tools and workflows – but the biggest shift is happening between our ears, in the mindset of leadership. Consider this: nearly half of organizations developed an AI strategy in the past year (up from just 17% previously). Crafting a strategy is one thing; thinking differently as a leader to implement it is another. Let’s explore what forward-looking, AI-era leadership really means.

From Skeptic to Strategist
Many executives started as AI skeptics – unsure if it was hype or fearing its implications. That’s normal. But today’s effective leaders have transitioned from caution to curiosity. Rather than asking “Will AI disrupt our business?”, they’re asking “How can we disrupt with AI?”. This proactive stance is key. For example, a Chief Revenue Officer might traditionally focus on quarterly sales tactics; the AI-savvy CRO, however, is also thinking about how predictive algorithms can refine the sales funnel or how generative AI might enable hyper-personalized outreach. The difference lies in treating AI not as an external threat or toy, but as a core component of strategy – as fundamental as your market positioning or product roadmap.

Vision: Paint the AI-Enabled Future
Leaders need to articulate a clear vision of what AI empowerment looks like for their company. It’s not enough to say “we’ll use AI to be more efficient.” How will it change the game for your customers or employees? Perhaps your vision is that mundane tasks will be automated so your people can spend time on creative problem-solving – leading to faster innovation. Or maybe it’s about using AI to deliver a radically better customer experience (think response times in seconds, 24/7). Moderna’s CEO, for instance, merged the IT and HR departments under one leader because he envisioned an AI-first enterprise where tech and people are deeply intertwined. That bold reimagining of organizational structure came from a leader painting the future: one where AI is the connective tissue across all functions. As a leader, sharing such a vision rallies your team by showing them why embracing AI matters and what success will look like.

Cultivate an AI-First Team Mindset
Once the vision is set, great leaders focus on cultivating the mindset at all levels. This means encouraging experimentation and eliminating the fear of failure related to AI. Leaders at AI-forward companies often implement policies like “AI Days” (time for employees to play with AI tools for work problems) or celebrate small wins where someone used AI to improve a process. As a leader, your own tone matters immensely – if you speak about AI as an opportunity and openly share instances where you used it (or learned from a mistake using it), it normalizes that behavior. One CEO we know starts meetings by asking, “What did we learn from AI this week?” simply to reinforce that continuous learning is the norm. Contrast that with leaders who silently outsource AI decisions to IT and keep their teams in the dark – the latter breeds hesitation and passive attitudes. Forward-looking leaders make AI a team sport.

Empathy and Trust in the Age of AI
“AI thinking” isn’t just about tech; it’s also about people. High-performing leaders show empathy towards employees during this transition. They address the elephant in the room – fears about AI replacing jobs or making roles irrelevant. By openly discussing these concerns and emphasizing that AI is there to augment, not replace (backed by action, like reskilling programs), leaders build trust. For example, when implementing an AI tool in finance, a CFO might say: “This AI will handle the number-crunching, but your expertise is needed more than ever to interpret results and strategize. We’re investing in training you on this tool so it can elevate your role.” That kind of communication turns anxiety into motivation. A new way of thinking for leadership involves viewing talent not as headcount, but as humans with creativity that technology can enhance. Leaders who get this see less pushback and more enthusiasm for AI initiatives.

Decisiveness in a Fast-Moving Landscape
AI’s breakneck pace means ambiguity. New ethical questions, regulatory considerations, and an endless array of solution choices – it can paralyze decision-making. But strong leaders adopt a test-and-learn mentality rather than waiting for perfect clarity. They set guardrails (e.g., data privacy standards, human review checkpoints) and then empower their teams to try things. If something fails, it’s treated as a learning experience, not a fiasco. In this sense, an AI-era leader needs to be decisive yet adaptable – willing to commit to a direction on imperfect information, monitor closely, and course-correct as needed. Think of how quickly many companies had to form a stance on employees using tools like ChatGPT at work. Some banned it outright; others, more thoughtfully, created interim guidelines (“don’t paste sensitive data, here’s an approved internal alternative…”) and iterated as they learned. The latter approach reflects leadership that can make timely decisions in the gray zone, guided by principles but not frozen by uncertainty.

Lifelong Learning at the Top
Finally, the new leadership mindset is one of continuous learning. AI is not a one-and-done topic you delegate and forget. The most respected CEOs and executives we encounter are personally digging into AI trends, asking questions in meetings like “How would a generative model handle this task?” or even taking short courses on AI fundamentals. This hands-on curiosity sets the tone that everyone in the company should be learning. It also prevents the dreaded scenario of a leadership team out of touch with the tech driving their business. You don’t need to be coding models (just like you needn’t configure servers to lead a SaaS company), but you do need to grasp capabilities and limitations. In practice, this might mean having your CIO/CTO run quarterly “AI briefs” for the leadership team, or inviting guest experts to exec offsites. Leaders who keep learning maintain relevance and can better separate meaningful trends from fads when steering the ship.

Embracing AI isn’t just about budgeting for new software or launching initiatives – it’s about transforming your mindset as a leader and, by extension, the mindset of your entire organization. AI-first leadership is forward-looking, strategic, empathetic, decisive, and always learning. It challenges you to reimagine your business and your people’s potential with AI as an enabler. The exciting part? Leaders who adopt this new way of thinking are finding not only improved business outcomes but also more engaged, future-ready teams. In the end, AI will change the way we work – but it’s enlightened leadership that will determine whether that change is chaotic or transformative. As a leader, the choice is yours: cling to old patterns or blaze a trail with a new mindset. We suspect you wouldn’t be reading this if you weren’t inclined toward the latter. 😉

In the tech world, much has been made of the mythical “10x engineer” – that one developer who supposedly produces ten times the output. But today, AI-first thinking is redefining productivity across entire teams. With AI in the toolbox, every employee can dramatically amplify their impact. In fact, recent reports show 95% of independent workers say AI has made them more productive. It’s not just about superstar coders anymore, it’s about enabling 10X Everyone.

The Myth of the 10X Engineer
For years, startups hunted unicorn talent: the genius developer or data scientist who could seemingly do the work of ten. This “10x engineer” narrative led to a talent arms race, with founders believing a few rockstars were the key to outsized results. While great people matter, leaning solely on individual heroics is limiting. It can create bottlenecks and burnout, and it doesn’t scale culture-wide excellence. Plus, in smaller companies, you need every team member operating at peak potential – not just a lone ninja coder.

AI Levels the Playing Field
Enter the era of accessible AI. Today’s generative AI tools, from code assistants to content generators, are like power loaders for your staff, enabling ordinary employees to accomplish extraordinary work. A marketer with an AI copywriter can draft campaigns in a fraction of the time. A customer success rep armed with an AI assistant can resolve inquiries with expert-level precision. Even junior developers can produce quality code faster using various AI tools, often cutting coding time by well over 50% on tasks. The result? The productivity gap narrows. The advantages of technology aren’t confined to an elite few – they’re scaled to everyone.

Real-World Wins Beyond Engineering
We’re already seeing AI-driven productivity boosts across roles. Small business teams are using AI for content creation, data analysis, and marketing at remarkable rates: 44% of SMBs use AI for content, 41% for analysis, 39% for marketing. Importantly, these gains don’t require enterprise budgets. Many companies report spending under $50 a month on AI tools, showing that even lean startups can afford to supercharge their people. Consider a mid-size travel company that rolled out an AI assistant across departments: adoption hit 95% of employees, who saved an average 2+ hours each per week, speeding up coding tasks by 75% and freeing 500 hours weekly overall. That kind of broad-based efficiency would have been unimaginable from one “hero” employee alone.

AI-First Thinking as a Cultural Mindset
Becoming a “10x everyone” organization isn’t just about tools – it’s a cultural shift. It means encouraging every individual, from HR to Finance to Sales, to ask: “How can AI help me achieve more?” Leaders must foster an AI-first culture where experimenting with new AI solutions is welcomed and training is provided to boost confidence. We’ve found that when teams view AI not as a threat but as a collaborator, engagement soars. People start proactively sharing AI tips with colleagues and weaving AI into daily workflows. Over time, this mindset compounding effect means innovation coming from all corners of the company. It’s no longer top-down optimization – it’s bottom-up, continuous improvement powered by AI in every function.

Leadership’s Role in Empowering 10X Teams
To truly get 10X output from everyone, leadership has to set the tone. This starts with leading by example – when founders and executives openly use AI tools for their own tasks, it signals that it’s encouraged (and safe) for others to do the same. It also involves investing in your people’s AI skills. Forward-looking companies are rolling out AI upskilling programs so employees can maximize these tools. (Even giants like PwC are investing $1B to train all 75,000 employees in AI, underscoring how crucial broad AI fluency is.) In a smaller organization, this might mean dedicating team hackathons to automate tedious work or creating an internal AI champion group to share best practices. The message from leadership should be clear: we have your back as you experiment and grow with AI.

10X Everyone: The New Competitive Edge
The payoff for cultivating an AI-empowered workforce is immense. Studies project that AI can improve employee productivity by up to 40% on average (more on how this compounds to 10X later) – an astonishing jump that no single hire could ever achieve alone. Companies that bake this into their culture will outpace those that merely hire more bodies. When AI-first thinking becomes second nature in your culture, you don’t just get more output from isolated stars; you get a cohesive team of “10X” contributors driving 10X results. This is how small, agile businesses are punching above their weight and competing with far larger players. They’ve unlocked the real secret: 10X growth comes from scaling smarter, not just scaling headcount.

The era of the lone 10x guru is giving way to the era of the AI-augmented team. By embracing an AI-first culture, you enable each person to operate at a level that multiplies their productivity and creativity. The winners in this new landscape will be those who turn this widespread potential into reality – who make “10X Everyone” their ethos. After all, leveraging AI isn’t just a new way of working… it’s a new way of thinking.

The adoption of artificial intelligence in businesses has surged, with 75% of business leaders now using AI in some capacity, according to Top 5 AI Trends to Watch in 2025 | Coursera. However, many companies struggle to move beyond pilot projects to achieve widespread, scalable AI adoption. Why is this the case, and what can be done to bridge the gap?

The challenge lies not just in the technology itself but in the organizational readiness to integrate AI into existing workflows and culture. AI pilots often fail to scale because they are treated as isolated projects rather than part of a broader strategic initiative.

At Chiri, we understand that scaling AI requires a holistic approach. It’s not enough to have a great AI tool; you need to ensure that it fits seamlessly into your organization’s operations and that your people are equipped to use it effectively.

One key strategy is to start with clear business objectives. AI should be seen as a means to achieve specific, measurable outcomes, such as increasing efficiency, reducing costs, or improving customer satisfaction. By aligning AI initiatives with business goals, companies can ensure that their AI investments deliver real value.

Another critical factor is change management. Introducing AI can be disruptive, and employees may resist if they feel threatened or unprepared. Therefore, it’s essential to involve employees early in the process, provide training, and communicate the benefits of AI clearly.

Governance is also crucial. As AI systems become more complex, companies need robust frameworks to manage risks, ensure ethical use, and maintain compliance. This includes establishing clear policies on data usage, model transparency, and accountability.

Moreover, fostering a culture of innovation and experimentation can help. Companies that encourage their teams to explore new AI applications and learn from failures are more likely to succeed in scaling AI.

At Chiri, we specialize in guiding companies through the journey from AI pilots to pervasive adoption. Our approach focuses on integrating AI into your functions and culture, ensuring that it accelerates workflows and multiplies your team’s productivity.

In summary, scaling AI is about more than just technology; it’s about people, processes, and culture. By addressing these elements, companies can unlock the full potential of AI and achieve sustainable, measurable results.

In the rapidly evolving landscape of artificial intelligence, companies are realizing that AI is not just a technological upgrade but a fundamental shift in how businesses operate. A striking example is Moderna’s recent decision to merge its HR and IT departments under a single leader, Tracey Franklin, as Chief People and Digital Technology Officer, as reported in Moderna’s HR-IT merger: Trend or exception to the rule? | CIO. This move underscores a growing recognition that AI adoption requires more than just implementing new tools; it demands a cultural transformation.

An AI-first culture is one where AI is embedded into the very fabric of the organization, influencing decision-making, workflows, and even the company’s ethos. It’s about seeing AI not as a separate entity but as an integral part of every function, from product development to human resources.

At Chiri, we believe that when AI-first thinking becomes part of your culture, you don’t just get 10X engineers; you get 10X everyone. This means that every employee, regardless of their role, can leverage AI to enhance their productivity and impact.

But how do you cultivate such a culture? It starts with leadership. Leaders must champion AI not just as a tech initiative but as a business strategy. They need to communicate the vision clearly and ensure that AI initiatives are aligned with business goals.

Moreover, it’s crucial to involve non-tech stakeholders early in the AI adoption process. HR, for instance, plays a pivotal role in reskilling and upskilling the workforce to adapt to new AI-driven roles. By merging HR and IT, Moderna is signaling that people and technology are two sides of the same coin in the AI era.

Another key aspect is governance. With AI comes the need for robust ethical guidelines and data privacy measures. Companies must ensure that their AI systems are transparent, fair, and compliant with regulations, especially in highly regulated industries like finance and healthcare.

At Chiri, we guide companies to the most impactful AI solutions by weaving their adoption through functions and culture. We focus on data, adoption, and results, ensuring that AI becomes a natural part of how your company works.

In conclusion, cultivating an AI-first culture is about more than just deploying AI tools; it’s about transforming how your organization thinks and operates. It’s a new way of working and a new way of thinking. By embracing this transformation, companies can unlock unprecedented levels of productivity and innovation.

The sales pitch for generative AI was a dream. It was going to be the ultimate productivity hack, the magic wand that would finally slay the dragon of boring, repetitive work. The AI would handle the first drafts, the meeting summaries, and the routine emails, freeing up our teams for the deep, strategic, creative work that actually moves the needle.

And companies bought in, big time. 97% of business owners believe tools like ChatGPT will be a good thing for their business.

But now, a weird and troubling reality is setting in at a lot of companies. Instead of feeling liberated, many of your best people just feel…busier. They’re trapped in a new kind of digital hamster wheel: prompting, reviewing, editing, and fixing the endless stream of content the AI churns out. They’re spending more time babysitting the machine and less time thinking.

This is the new productivity paradox: the very tool that was supposed to save us time is just making us more frantic.

This happens when leaders mistake AI-generated activity for actual human productivity. You’re celebrating the volume of stuff the AI creates, not the value of the problems your team solves. Escaping this trap requires a total rethink of how you weave AI into your workflows.

The “Good Enough to Be Annoying” Trap

One of the biggest drivers of this paradox is that a lot of AI-generated content is just “good enough.” It can write a report that’s 80% correct, which sounds great. But as any writer or analyst knows, that last 20% is the hardest part.

Hunting down the factual errors, the weirdly robotic phrasing, and the logical gaps in an AI’s work requires intense focus. It forces your employee to switch from being a creator to being a critic. That constant context-switching is exhausting and can actually take more mental energy than just starting from scratch. The result isn’t less work; it’s just a new, more annoying kind of supervisory work.

Drowning in a Sea of AI-Generated Noise

Generative AI lets you create content at a scale that was previously unimaginable. Your marketing team can now generate 100 social media posts in the time it used to take to write five. Your sales team can blast out thousands of “personalized” emails a day.

This feels like productivity, but it’s often an illusion. It’s just more noise. The marketing team is so busy reviewing and scheduling the 100 posts that they have no time to think strategically about the campaign. The sales team is buried under a pile of low-quality replies from their email blast instead of having a few deep conversations with real, high-intent prospects.

In this scenario, the AI isn’t a productivity tool. It’s a distraction engine, pulling your team away from the work that creates real value.

How to Actually Get Productive with AI

To escape the paradox, you have to shift your focus from the AI’s output to the human’s outcome. The goal isn’t to generate more stuff; it’s to create more value with less human effort.

1. Automate the Scut Work, Not the Good Work: Instead of using AI for tasks that are creatively fulfilling (like writing a strategic plan), use it for the tasks that are pure friction. Use it to extract data from PDFs, summarize long research reports, or clean up a messy spreadsheet. Eliminate the work no one on your team wants to do, and you’ll free up real capacity for the work they love.
2. Go for 100% Automation, Not 80% Augmentation: For certain tasks, “good enough” is a trap. Instead of using AI to create a “first draft” that a human has to fix, find narrow, repetitive tasks where the AI can be 100% autonomous. Think invoice processing or data entry. This truly removes work from your team’s plate, rather than just changing the nature of it.
3. Measure Outcomes, Not Activity: Stop tracking how many articles are produced or how many emails are sent. Start measuring the things that matter to the business. Did the sales cycle get shorter? Did customer satisfaction go up? Did we reduce the error rate in our financial reporting? Focus on results, not volume.

Conclusion: It’s Just Not a New Way of Working, It’s a New Way of Thinking

Generative AI can be the most powerful productivity tool ever invented, or it can be a massive distraction. The difference isn’t in the technology; it’s in the strategy. By focusing on solving real problems and freeing your team from true drudgery, you can escape the productivity paradox. You can build a culture where AI makes your team not just busier, but genuinely better, more strategic, and more impactful. That’s how you get to 10X everyone.

As a founder or leader, you’ve probably gotten pretty good at using AI as a tool. You use it to draft emails, summarize meeting notes, and help your team brainstorm. You’re ahead of the curve.

But the next wave of AI is already here, and it’s going to make today’s tools look like toys. The conversation is shifting from AI as a tool to AI as a teammate.

We’re entering the age of Agentic AI.

These aren’t just chatbots that answer questions. AI agents are autonomous systems that you can give a goal to, and they will create a plan and execute a series of complex tasks to achieve it…all without you looking over their shoulder. This isn’t some sci-fi fantasy; Deloitte predicts that by next year, a quarter of all businesses using generative AI will have deployed AI agents.

For founders, this is the ultimate expression one of our core philosophies at Chiri: scale your output, not your headcount.

So, What Can an AI Agent Actually Do?

The difference between a generative AI tool and an AI agent is the difference between giving someone a hammer and telling them to build you a house. One is a useful tool; the other is an autonomous builder.

Let’s make this real for your startup:

• Your new “Sales Development Rep” agent: You give it the goal: “Generate 10 qualified meetings with VPs of Marketing at B2B SaaS companies.” The agent could then research companies, find contacts, draft and send personalized outreach, handle initial replies, and book qualified meetings directly on your calendar, updating your CRM the whole time.
• Your new “Supply Chain Manager” agent: You give it the goal: “Ensure we never run out of Part X.” The agent could then monitor your inventory, track shipments, analyze global risks, and if it detects a problem, automatically order from a backup supplier to prevent a stockout.
• Your new “HR Onboarding Coordinator” agent: You give it the goal: “Onboard our new engineer, Jane Doe.” The agent could then send the offer letter, handle benefits paperwork, order a laptop, and schedule Jane’s first week of meetings.

In every case, your role shifts from doing the work to defining the goal and managing the outcome.

The Rise of the “Orchestrator”

As AI agents take over more of the step-by-step execution, the most valuable human skills will change. The ability to process information quickly is becoming less important, while skills like coordination and strategic thinking are becoming more critical.

This gives rise to a new kind of leader: the Orchestrator.

The Orchestrator is a leader who is a master at designing and managing a hybrid team of human experts and autonomous AI agents. You’re like the conductor of an orchestra. You don’t need to know how to play the violin, but you need to know how to make it sound brilliant in harmony with the rest of the instruments. Your value is in your holistic vision and your ability to bring out the best in every player, human or digital.

As one smart leader put it, your job is to “Let AI be the legs. You be the brain.”

How to Get Ready for the Agentic Age

This transition won’t happen overnight, but you need to start laying the groundwork now.

1. Map Your Workflows: You can’t automate a process you don’t understand. Start by mapping out your most critical, multi-step workflows. Identify the repetitive, rule-based parts that are perfect candidates for an agent to take over.
2. Train Your Leaders to be Orchestrators: Your leadership training needs to evolve. Start teaching your managers systems thinking, process design, and data-driven oversight.
3. Build a Flexible Tech Stack: The agentic future requires a flexible technology foundation. Prioritize tools with open APIs that will let you easily plug in these new agent capabilities when they’re ready. A rigid, monolithic tech stack will hold you back.
4. Start Socializing the “Digital Colleague”: Begin talking about AI as a teammate, not just a tool. Run small experiments where teams collaborate with AI. The goal is to build a culture that sees agents as powerful partners that free up humans for more creative and strategic work.

Conclusion: A New Way to Scale

The arrival of AI agents is the ultimate tool for scaling a business smarter. By automating entire workflows, agents will allow companies to grow exponentially without their costs exploding. The founders who grasp this today and start building their organizations for this new reality will be the ones who build the defining companies of the next decade.

Do this and you can 10X Everyone.

If you’re a founder, CEO, CRO, or any other leader who doesn’t live and breathe code, the whole AI conversation can feel… intimidating. It’s a whirlwind of acronyms – LLMs, RAG, MLOps – that makes it tempting to just say, “That sounds like a job for the CTO.”

And it’s a massive strategic mistake.

An AI strategy is not an IT strategy. It is a business strategy, powered by technology.

Delegating your company’s AI strategy to the IT department is like asking the person who manages your office wifi to design your entire go-to-market plan. They’re essential and brilliant at what they do, but they don’t have the full business context. The success of your AI initiatives will have far less to do with the elegance of the code and far more to do with how deeply you weave it into your sales process, your marketing engine, and your company culture.

This is a business transformation, a way of thinking transformation, and it has to be led by all the business leaders.

Why the Business Has to Own AI

Here’s why the CEO, COO, CRO, and CHRO need to be in the driver’s seat on this:

• AI Solves Business Problems, Not Tech Problems: The goal of using AI isn’t to have cool tech; it’s to solve expensive, painful business problems. Only the business leaders on the ground truly understand where those problems are.
• AI Is a Change Management Nightmare (If You Let It Be): The biggest hurdle to AI success isn’t technology; it’s adoption. Getting your team to embrace a new way of working is a human challenge, not a technical one. That’s a job for business and people leaders, which is why our background in both People and Engineering is so crucial to making these projects stick.
• AI Doesn’t Stay in Its Lane: An AI tool for the sales team will inevitably impact marketing and customer support. A new automation in finance will touch operations. AI is a cross-functional sport, and it needs a business-led coach to manage the whole field.

Your Job: Be the Translator-in-Chief

As a business leader, your most important role is to be the bridge between the business goals and the tech team.

Your job is to define the “what” and the “why.” (“We are losing deals because our follow-up is too slow. We need to cut our response time in half because it’s killing our conversion rate.”)

Then, you empower your technical team to figure out the “how.”

Ask the right questions: “Will this solution be explainable enough for our compliance team?” or “How does this actually make life easier for our sales reps, not harder?” This ensures the tech serves the business, not the other way around.

Conclusion: You’re the Strategist. Own It.

Don’t let the jargon intimidate you into taking a backseat. You, the business leader, are the one who understands the business. You are the one best equipped to architect an AI strategy that drives real results. An AI strategy is a business strategy. And you are the strategist that can get to 10X Everyone.

The promise of AI was supposed to be freedom. Freedom from the boring, repetitive tasks that clog up our days. A future where our teams could finally focus on the big, strategic, creative work they were hired to do.

But for many, the reality feels… different. Instead of feeling liberated, your team might be feeling the pain of the “AI Tax”—a hidden burden of new processes, data-massaging chores, and workflow headaches that quietly eats away at all the promised benefits.

This tax gets levied when we drop a shiny new AI tool into an old way of working without thinking through the human consequences. It’s the extra hour your marketing person spends cleaning up an AI-generated blog post so it doesn’t sound like a robot. It’s the cognitive whiplash of switching between five different AI apps that don’t talk to each other. It’s the soul-crushing task of reviewing AI output that’s just good enough to be tempting, but not good enough to be trusted.

If your team feels like their new AI tools are making them busier, not better, you’re paying the AI Tax. And it’s a killer. Research shows that a top-down, “AI-first” approach that ignores how people actually work can backfire, hurting productivity and creating friction.

Is Your Company Paying the AI Tax? (4 Warning Signs)

The AI Tax doesn’t appear on your P&L. It shows up as friction, frustration, and a nagging sense that things should be easier. Here’s how to spot it:

• The “Process Bloat” Problem: Instead of simplifying a workflow, the AI tool has made it more complicated. Your team now has a five-step process involving copying, pasting, and checking the AI’s work, which replaced a simpler, two-step human process.
• The “Human Glue” Nightmare: Your team spends its days acting as the manual “glue” between disconnected AI systems, copying data from one platform to another because they don’t integrate. This isn’t the future of work; it’s a new kind of digital assembly line.
• The “AI Babysitter” Role: The AI is pretty good, but not great. So your team spends less time on their actual jobs and more time supervising the AI, correcting its homework, and cleaning up its messes.
• The “Another Freaking Tool” Fatigue: Your employees are drowning in new logins and dashboards. They’re so overwhelmed that they retreat to the old, comfortable ways of doing things, and your expensive new AI gathers digital dust.

How to Design a “Tax-Free” AI Rollout

Avoiding the AI Tax requires a shift in mindset. You’re not just deploying tech; you’re designing a better human experience. This is where our background in both People and Engineering is so vital—we know the tech has to serve the person, not the other way around.

1. Map the Human Workflow First: Before you even look at a tool, whiteboard the entire human process it’s supposed to improve. Every click, every handoff, every sigh of frustration. The goal is to simplify the entire journey, not just one little piece of it.
2. Make Integration a Deal-Breaker: When you’re looking at AI solutions, prioritize tools that play nicely with the systems you already have. A slightly less powerful tool that plugs seamlessly into your CRM is a thousand times more valuable than a “best-in-class” tool that creates a new silo.
3. Solve for the “Last Mile”: Many AI tools can get a task 90% done. But the real work, the real value, is in that last 10%. Have a clear plan for that “last mile.” Who reviews it? Who approves it? If you don’t solve for the last mile, you’re just creating more review work for your team.
4. Measure Total Time, Not AI Time: Don’t get impressed by how fast the AI completes its task. Measure the total human time it takes to get the job done, from start to finish. If that number goes up after you introduce the AI, you’re paying the tax.

Conclusion: True Productivity is Frictionless

AI has the potential to 10X your team’s capacity without 10X’ing your headcount. But that only happens when the technology is woven into your culture in a way that removes friction, not adds it. The goal is to create a work environment where AI is a seamless, invisible amplifier of human talent, liberating your team to do their best work.

To see how we’re putting these ideas into practice, learn more about Chiri’s approach.