(Hint: It Starts With Using AI Properly)
I’ve never seen a moment when so many long-accepted assumptions have become so unglued. One of them is that growth requires hiring. Whether you are a railroad, a bank, or a brand manufacturer, the prevailing wisdom for decades has been the assumption that you need more warm bodies to grow the top line.
But that underlying premise has been shattered by AI. No longer does growth require more sales and ops people. Similarly, more clients don’t immediately call for more account managers. Larger marketing budgets can be scaled without more people and without higher agency costs. This Great Decoupling can happen across all business functions.
Understanding Why Growth Stalls
What I just described is a fundamental change in the way companies can grow. And it’s because of AI.
Growth doesn’t usually stall because there aren’t enough people. Growth freezes because the handful of genuinely valuable people you have aren’t able to accomplish everything their talent makes possible. And it’s usually because they are stuck performing manual work like stitching together spreadsheets or pulling data out of unwieldy systems instead of doing the value-added work you want them to do.
A healthy exercise is to step back and consider whether the ops lead who knows every customer by name and the finance person who understands the business better than the spreadsheet does are buried in manual work.
Talent that can drive your business forward is being gruntified. They are stitching together data from several tools, rebuilding the same report every Monday, copy-pasting between systems that should have been built to talk to each other, but are technologically incommunicado.
Faced with this, many business leaders’ first instinct is to hire people to lighten the load by taking work off the plates of your most valuable people, worrying they will leave if you don’t hire more people to share the misery. Most often it isn’t another person that’s needed; it’s fewer broken processes. And this is where AI can help significantly.
Let’s End Soul-Deadening Work
No one stands up in school and says, “When I grow up I want to spend my days reconciling spreadsheets.” Yet those are the kind of jobs many talented people have fallen into.
AI has arrived to rescue them from drudgery, and to simultaneously rescue business leaders from the assumption that more growth demands more people. What’s called for is a thoughtful path to leveraging AI as the unlock that the enterprise has been waiting for.
It is especially middle-market (companies between $25M and $1B) who are most caught in the growth vs. headcount vise and who are the least well served by the current options for successful AI deployment.
These businesses, with the right AI solutions, can actually move faster than larger enterprises who are saddled with cumbersome decision-making, governance, and procurement processes. By appropriately leveraging AI, middle market challenger brands can jump ahead of their much larger competitors.
Augment Your Best People With the Right Tools
I’ve seen what happens when the most valuable people are given back the time to do high-level, skilled work. They love their job again, and the spring in their step is palpable.
AI is best at tasks that involve volume and repetition — not replacing the judgment instincts of someone who’s done the job for years. So, the critical part is selecting the right AI tools, the same way you would assess any other tool for your team.
Get the People Part Right First
While getting the technology right is a risk (data shows that up to 85% of AI deployments fail), getting the internal culture right is even more critical. In many companies, AI is a shorthand for fear. In fact, AI could just as easily stand for “Anxiety Intensified.”
If people believe AI is there to replace them, they’ll quietly work against it. Address that fear directly. The goal is fewer tedious tasks, not fewer jobs. Find the person genuinely excited to try AI and let them lead the first process. A peer champion earns more trust than a top-down mandate from leadership.
These are new skills, so it’s worth bringing in a coach to teach them properly so they can replace dread with delight.
3 Levels To Deploy AI, and Most Companies Miss Out on the Most Important Third Part
The most successful AI deployments use a three-step process.
Level One Is Self-Service Experimentation
Give people access to simple tools like ChatGPT, Claude, or Gemini. Let them explore and dabble till they’re dazzled. It can be on their own real work, or a wedding they’re planning. Motivation is a key driver of adoption. Avoid prescribed use cases that feel like homework.
People will naturally migrate to the tasks they perform most often: drafting an email, cleaning up a spreadsheet, responding to an RFP.
The first real opportunity for culture change is leveraging use cases that the team finds for themselves. These may be tools like dashboards to capture frequently used marketing data, financial data summaries, and prompts to research prospective customers. Company leaders can see who is using the tools the most — typically a clue to where the champions sit in the organization.
If it’s not happening fast enough, don’t be reluctant to bring in outsiders for training and support — after all, AI is like any new skill and sometimes needs additional support to be learned. The mistake is staying at level one indefinitely.
Level Two Is Building Something the Team Can Rely on To Go Deeper
One person’s clever chatbot trick lives in their head and disappears the day they leave. Turn it into something shared, a prompt template, a lightweight tool, a prototype automation that runs the same way no matter who’s using it. These could include tools like a content calendar planner that generates topic ideas and scheduling suggestions, a scorecard to rate suppliers on quality, delivery, and cost, or a customer service tool that transforms support tickets into searchable knowledge articles. The trick here is to have these be owned by IT and ensure they are reliable and reusable by several people or a whole department.
Level Three Reimagines the Process Itself and Builds and Embeds an Application Into the Company Tech Stack
I’ve seen companies get excited by the possibilities of AI and convince themselves that a prototype at Level 1 or an agent at Level 2 is equivalent to a production solution.
That’s a fatal trap. For example, that customer service tool needs live integrations, feedback loops, and version control. The supplier scorecard needs collaborative scoring and integration with procurement systems. The content calendar only works for one person without approval workflows, integration with brand assets, and connection to marketing analytics systems.
Getting something to work once on your champion’s laptop is only the first quarter mile. The mistake that prototypes often make is automating a broken process exactly as it stands or not recognizing the real work needed to embed a new solution into the current tech stack.
That spreadsheet the ops team relies upon might be disguised as solid infrastructure, but it is often a monument to a workaround, patched together for years to avoid solving the real problem.
My advice is to map every manual step with the person doing the job, probing for which ones only exist because there was no better option before. When leaders do that with piercing objectivity, often half the process will disappear.
As companies move into Level Three, they will find situations of real complexity, as in when a fix needs to plug into a CRM, accounting, or scheduling system and nobody internally has that skill set.
Don’t ask the ops lead to be a system integrator too. Bring in expertise from outside, but I urge you not to be trapped into hiring expensive consultants who can’t build and integrate the solution, or dev shops who fail to see the holistic picture.
The First Real Scale Unlock
AI is decoupling long-held assumptions like growth requires more headcount, as it breaks other dependencies. Like quality and cost. Customization and efficiency. Speed and accuracy.
This gives leaders who recognize the unlock AI offers a huge advantage. Stop making decisions based on correlations that no longer apply. Grow faster with fewer people. Build your moat on the new architecture of redesigned processes, a leaner team, and a sophisticated understanding of how to unlock the operational promises of AI.
Lauri Kien Kotcher is the CEO & Co-Founder of Different Day. She brings a singular set of career experiences to her role, rendering her uniquely positioned to lead an AI solutions company focused on enabling rapid and scalable transformation to the middle-market.
Soon after earning a JD and MBA at Stanford, Lauri joined McKinsey and became a partner with domain expertise in retail and consumer products. After more than a decade, she left McKinsey for CMO roles at the consumer health business at Pfizer and Godiva.
Lauri’s CMO tenure was marked by great success at bringing innovation and share gains to legendary American brands. Her next step was entrepreneurial life, becoming CEO at hello products, which was acquired by Colgate for 7x revenue. From there, she moved on to the Shade Store, where she brought technological innovation to this leader in home decor.
Before co-founding Different Day, Lauri was CEO at quip, which earned TIME’s Best Invention of 2025.
Photo courtesy Galina Nelyubova for Unsplash+
