Many AI startups launch first in English because the models, benchmarks, developer tools and enterprise buyers are easiest to find there. That approach can help a company reach the market quickly, but it can also cause founders to overlook a much larger multilingual opportunity.

According to the International Telecommunication Union, 2.2 billion people remained offline in 2025, most of them in low- and middle-income countries. As more of these users come online, many will expect digital products to work in the languages they use every day — not merely offer translated versions of English-first experiences. They represent a significant growth opportunity for companies prepared to build multilingual products.

But founders cannot simply plug a translation API into an English-based model and instantly go global. Many general-purpose models provide uneven performance across languages. Some languages are represented by more tokens for equivalent content, which can increase cost and reduce the effective amount of text that fits into a context window. Lower-resource languages may also receive weaker results because they have less high-quality training and evaluation data.

I have seen this challenge firsthand through my contributions to the Government of India’s BHASHINI and BhashaDaan initiatives and as an expert contributor to C-DAC’s Vikaspedia. BhashaDaan crowdsources speech, text, translation and image-labeling contributions for Indian-language technologies, while Vikaspedia provides knowledge across social-development sectors in India’s scheduled languages. Contributing to these initiatives reinforced a consistent lesson: You cannot serve a multilingual market by treating language support as a translation feature added at the end.

Stop paying the invisible language tax

“Tokens” are the fundamental billing unit of generative AI. Tokenization varies by model, and even English words do not always map to one token. However, multilingual studies have found that equivalent content can require materially different numbers of tokens across languages. When a tokenizer fragments a target language more heavily, the application may pay for more input and output tokens to communicate the same meaning.

A simple first estimate is: Estimated multilingual text cost = Comparable English text cost × Token-count multiplier.

This estimate does not include differences in model pricing, caching, output length or infrastructure. A language-focused tokenizer or model may materially reduce inference costs, but founders should benchmark it using representative conversations in each target language before making a platform decision.

Engineering teams should compare general-purpose and language-focused models using representative regional inputs. Evaluate token count, response quality, latency, safety, licensing and total cost together. A model that uses fewer tokens is not a better business choice if it produces less reliable answers.

Leverage sovereign and institutional language resources

High-quality digital and training resources are distributed unevenly across languages, leaving many lower-resource languages with less material for model training, retrieval and evaluation. When startups implement Retrieval-Augmented Generation (RAG) for regional languages, their systems may produce weaker or less grounded results when suitable localized retrieval and evaluation data is sparse, outdated or poorly translated.

Founders should evaluate sovereign and institutional language resources before paying to recreate equivalent data. Before using any resource for retrieval, fine-tuning or commercial deployment, verify its license, provenance, update history, quality, privacy conditions and permitted uses. Government backing should not replace technical and legal due diligence.

Properly licensed, relevant resources can improve language coverage and reduce the amount of data a startup must collect independently, but their quality and suitability must still be tested.

Architect for vernacular-first interfaces

When building for the U.S. enterprise market, the default user interface is often a text box and a keyboard. However, mobile-internet research indicates that reading, writing and digital-literacy difficulties are major barriers to mobile-internet adoption.

In markets where user research identifies typing, literacy or script entry as meaningful barriers, founders should evaluate voice-enabled and visual interfaces rather than assuming that a text box is sufficient. As I explained in my earlier analysis of conversational AI and “Zero-UI” systems, reaching the next billion users often requires fitting technology into their existing communication habits rather than forcing them to navigate a conventional app

If voice is central to the target workflow, design and test the audio pipeline early. It should be evaluated using representative accents, dialects, noisy environments and code-mixed speech rather than added as an untested wrapper at launch.

Multilingual expansion should also begin with one narrowly defined market rather than a simultaneous global launch. Choose a high-value workflow, test it with native speakers, measure task completion and support costs, then use that evidence to decide whether the architecture is ready for the next language.

The real opportunity is outside the echo chamber

Multilingual markets contain users whose needs are poorly served by English-first products. Capturing this opportunity requires treating localized AI as a core engineering and product discipline.

Audit your token economics to protect your margins, verify the legal and technical quality of regional datasets and design interfaces around how target users actually communicate. An English-only architecture may prevent an otherwise strong AI company from reaching users who prefer to speak, search and transact in other languages.

Many AI startups launch first in English because the models, benchmarks, developer tools and enterprise buyers are easiest to find there. That approach can help a company reach the market quickly, but it can also cause founders to overlook a much larger multilingual opportunity.

According to the International Telecommunication Union, 2.2 billion people remained offline in 2025, most of them in low- and middle-income countries. As more of these users come online, many will expect digital products to work in the languages they use every day — not merely offer translated versions of English-first experiences. They represent a significant growth opportunity for companies prepared to build multilingual products.

But founders cannot simply plug a translation API into an English-based model and instantly go global. Many general-purpose models provide uneven performance across languages. Some languages are represented by more tokens for equivalent content, which can increase cost and reduce the effective amount of text that fits into a context window. Lower-resource languages may also receive weaker results because they have less high-quality training and evaluation data.

Netflix has a major problem, according to the $281 billion company’s co-CEO, Ted Sarandos. 

“Overall, we’re not growing as fast as I want us to, and we’re working on making that move faster,” Sarandos said at a Bloomberg conference in Los Angeles this week. “We are, though, also doing things that create a lot of headwind to that number.”

Sarandos disclosed that Netflix’s global engagement rose just 2% in its latest reported period, even though revenue continued to grow at double-digit rates in every region. Netflix is the world’s largest streaming service, according to Forbes. 

To rekindle growth, Sarandos told Bloomberg that Netflix is pushing beyond its traditional on-demand mix of scripted films and television and pushing into live entertainment, including sports, wrestling, comedy and major cultural events. He said that the company devotes 5% of its approximately $20 billion annual content budget, about $1 billion, to live programming.

The strategy is not designed to maximize hours watched; live shows account for only about 1% of Netflix viewing. Instead, Sarandos said that they serve a different commercial purpose. They attract new subscribers, give existing customers a reason to stay and create more valuable inventory for advertisers.

Live shows can help Netflix feel less like a library people visit intermittently. They offer a potential way to reduce churn and broaden the business beyond its historical reliance on movies and series. 

Other aspects of Netflix’s strategy to grow quickly

Netflix is also widening its theatrical ambitions and promoting movies with major built-in audiences. 

Sarandos said Greta Gerwig’s Narnia: The Magician’s Nephew will receive a wide theatrical release in 2027 before arriving on Netflix, followed later that year by the animated Charlie and the Chocolate Factory. 

According to Deadline, Netflix has planned longer periods of time that movies will be exclusively showing in theaters before they become available on the streaming platform. Netflix plans to exclusively show Narnia in theaters for 50 days and Charlie for 47 days, substantially longer than the limited runs traditionally associated with Netflix originals. 

Sarandos said the sequel to KPop Demon Hunters will get an even larger rollout. He told Deadline that audiences should expect a “very broad” theatrical debut for the follow-up, which he said would be a “big, broad, global” release.

Netflix appears to see four-quadrant movies, or films capable of attracting children, parents, younger adults and older viewers, as especially suited to the big screen. KPop Demon Hunters falls under that category, Sarandos told Variety last month. 

He added that Netflix released more than 30 films in theaters last year, tailoring each run by title, city, marketing spend and number of days in theaters. 

AI investments

Netflix is expanding its use of AI to make film and TV production faster and less expensive.

In March, the company acquired InterPositive, an AI filmmaking technology firm founded by Ben Affleck, for $587 million. The technology targets mainly post-production work, such as adjusting color, adding visual effects and reframing shots. It isn’t capable of generating an entire film from scratch. 

On Netflix’s second-quarter earnings call in July, Sarandos said Netflix had used AI on about 300 titles for planning and visual effects.

Netflix has a major problem, according to the $281 billion company’s co-CEO, Ted Sarandos. 

“Overall, we’re not growing as fast as I want us to, and we’re working on making that move faster,” Sarandos said at a Bloomberg conference in Los Angeles this week. “We are, though, also doing things that create a lot of headwind to that number.”

Sarandos disclosed that Netflix’s global engagement rose just 2% in its latest reported period, even though revenue continued to grow at double-digit rates in every region. Netflix is the world’s largest streaming service, according to Forbes. 

A forklift tips over in the warehouse. A customer slips on a wet floor. A company van rear-ends someone on the highway. In the moment, these events feel like isolated crises, but the fallout rarely stays contained. Downtime piles up, revenue stalls, key employees are suddenly unavailable, and insurance adjusters start asking questions the business wasn’t ready to answer.

That’s what owners often miss: An accident isn’t just a safety problem; it’s a continuity problem. Treating legal preparedness as one piece of a broader risk management strategy, alongside safety compliance, gives a company a real shot at bouncing back quickly instead of scrambling for months.

Understand the legal risks before an accident happens

You can’t prepare for a risk you haven’t named. Start by mapping out where accidents are most likely to originate in your business:

• Employees performing daily tasks or operating equipment
• Customers and visitors on company property
• Company-owned or leased vehicles
• Contractors and vendors working on-site
• The physical premises itself, including parking lots and entrances

A periodic liability and safety assessment, done annually or after any major operational change, helps surface blind spots before they turn into claims. Bring in an insurance broker, safety consultant or attorney to walk through your actual exposure rather than guessing.

Create a clear accident response plan

Once you know your risks, build a plan employees can follow without hesitating. Panic makes people forget procedures they’ve never practiced, so assign responsibilities ahead of time rather than figuring it out mid-crisis.

A solid plan should cover securing the scene, reporting the incident internally and to regulators when required, contacting emergency services, preserving evidence and documenting what happened before details fade. Beyond the ethical obligation to your team, neglecting workplace safety carries real financial risk, including penalties steep enough to strain a small business’s cash flow.

The goal isn’t a binder nobody reads. Run through the plan during onboarding and safety meetings so employees know their role before anything goes wrong.

Document everything after an incident

Good documentation is what separates a manageable claim from a drawn-out dispute. Immediately after an incident, gather:

• Photographs and video of the scene
• Contact information for witnesses
• A written incident report
• Maintenance and inspection records tied to the equipment or property involved
• Employee training records
• Any relevant emails, texts or internal communications

Resist the urge to speculate about fault while you’re gathering facts. Assigning blame too early, even informally, can complicate things later if the incident escalates.

Know when to seek legal guidance

Not every scraped knee or fender bender needs a lawyer involved. But serious injuries, disputed liability, multiple affected parties or any hint of pending litigation changes the calculus fast.

That’s the point where it pays to know when to seek legal guidance, since companies facing significant injury claims are often better served by consulting attorneys experienced in the litigation process rather than navigating it alone. This isn’t about lawyering up defensively; it’s about understanding how these cases unfold before the other side does.

Coordinate legal preparedness with insurance coverage

Legal strategy and insurance coverage should move in step, not in separate lanes. Review your policies before an accident, not after, so you actually know what’s covered and what isn’t. Pay close attention to notification deadlines: Many policies require prompt written notice of a claim, and missing that window can jeopardize coverage regardless of how valid the underlying claim is, a point emphasized in the American Bar Association’s claim-filing guidance.

Keep policy documents, claim correspondence and adjuster contacts organized in one place so nothing slips through during a stressful week.

Protect business continuity during recovery

While legal and insurance matters unfold, the business still has to run. Build contingency plans before you need them: Line up backup suppliers and transportation options in advance, identify which processes are truly critical versus which can pause temporarily, and prepare a communication plan for employees, customers and vendors.

Companies that treat business continuity planning as a standing practice, rather than something invented on the fly, tend to recover with far less financial damage.

Review what went wrong, and improve

Once the dust settles, resist the temptation to move on without a real post-incident review. Look honestly at gaps in safety procedures, training, documentation habits and emergency response, using resources like OSHA’s incident reporting guidance as a benchmark for what should have been in place.

Every accident, however unwelcome, is also a data point. Businesses that mine it for lessons tend to face fewer repeat incidents down the road.

Prepared businesses recover faster

Accidents will happen. No amount of planning eliminates that reality entirely. What legal preparedness changes is what happens next: how fast the business stabilizes, how well it protects employees and customers, and how much control it keeps over the outcome.

Pairing solid safety procedures with documentation habits, insurance coordination and a clear sense of when to call a lawyer doesn’t just limit damage. It keeps the business operating while everything else gets sorted out, which is really the whole point.

A forklift tips over in the warehouse. A customer slips on a wet floor. A company van rear-ends someone on the highway. In the moment, these events feel like isolated crises, but the fallout rarely stays contained. Downtime piles up, revenue stalls, key employees are suddenly unavailable, and insurance adjusters start asking questions the business wasn’t ready to answer.

That’s what owners often miss: An accident isn’t just a safety problem; it’s a continuity problem. Treating legal preparedness as one piece of a broader risk management strategy, alongside safety compliance, gives a company a real shot at bouncing back quickly instead of scrambling for months.

Understand the legal risks before an accident happens

You can’t prepare for a risk you haven’t named. Start by mapping out where accidents are most likely to originate in your business:

When growth stalls, almost every business owner reaches for the same levers. You know the ones. More content, more ads, more discount codes. Maybe even more texts blasted to a list that stopped opening them months ago. 

In my practice, these tactics are effective. But could they be more effective? Yes, they could. Not by spending more, but by clarifying what you already have.

Before you spend an additional dime on more marketing, examine your brand’s positioning first.

The root problem that looks like a marketing problem

A marketing problem and a positioning problem can look very similar. Growth plateaus, and your cost to acquire a new customer increases. But the fixes are completely different, and mixing them up wastes time and money.

A marketing problem means people understand who you are and what you offer. They just are not hearing about it enough, or in the right places. The message works, and it thrives on distribution, frequency and targeting.

A positioning problem is quieter and more stubborn. People are hearing about you, but it does not elicit an action, because they cannot answer two questions fast enough: What do you actually do, and why does that matter coming from you instead of the next person?

If a stranger cannot repeat your value back to you in one sentence after landing on your website from an ad, you do not have a visibility gap. You have a clarity gap. More content will not fix that problem.

Meet the surgeon who did not need a bigger budget

A few years ago, a cosmetic surgeon, Dr. Gina Maccarone of The Surgeonista, came to my agency wanting to grow her new practice more quickly. Her website was strong. Her content calendar was full. Her social presence looked, by every surface measure, like it was doing everything right. People were seeing her brand. Now what she needed were consultations.

We spent our first week together on one question: Why choose this surgeon over the 20 others within a 50-mile radius?

In a saturated market, differentiation is key to elevating your brand. More noise isn’t going to differentiate you. Better positioning will. We uncovered that her messaging described procedures instead of describing why she was uniquely positioned to solve her patients’ root problem: not feeling confident in their own skin.

A peer-reviewed study tested different positioning strategies using real brands. Researchers evaluated them based on brand favorability, differentiation and credibility. The findings showed that benefit-based positioning and user-based positioning generally performed better than feature-based positioning.

It took a few conversations to get there, but we uncovered her category of one. She was the only female surgeon in the region who was triple board certified in her specialty. Once we repositioned around that one fact, everything else got easier: the content, the press pitches, the partnerships — because for the first time there was something specific for people to remember her by. 

The signals that mean it is time to reposition

I have started noticing the same handful of signals everywhere, not just in medicine. If you are constantly explaining what you do as a job instead of mirroring that you understand your clients’ problem and educating them on the best solution for that problem, positioning might be the issue — not your marketing.

If your sales process feels heavier than it should, that is another signal that positioning can be improved. If you keep attracting people who like you but are not quite right, pay attention to that too. And if your business has grown but your messaging has not caught up with it yet, that is usually the biggest tell of all.

None of that means you failed. Most of the time it means you grew faster than your positioning could keep up with. Repositioning is a growth strategy.

What happened when I applied this to my own business

My agency has been around for a decade. For years, our positioning stayed broad, because it could. We worked across beauty, home and wellness, and the work was good. But sales conversations could run long. Proposals got customized over and over. We were explaining ourselves constantly instead of being instantly understood — the exact signal I now tell clients to watch for. That is usually the first sign that something is not broken. It has just outgrown its container.

So last year we repositioned the whole company. Instead of trying to be a great agency for a lot of categories, we decided to become a category of one for a very specific audience. We stopped saying we offered public relations services and started owning a specific, named framework instead, called our Path to PRominence. We narrowed our focus from beauty, home and wellness down to women’s health, wellness and aesthetics, full stop. 

What happened next? Qualified leads went up. Competition went down. Sales conversations got shorter, because people showed up already understanding what we did and why it mattered to them specifically. Inquiries through AI became warmer (if you can call anything from artificial intelligence “warm!”). That did not happen with vague positioning. It happens when your expertise is obvious enough for a machine to place you correctly, let alone a person. And best of all, our revenue doubled in one year.

How to find your category of one

If you think you have a positioning problem rather than a marketing problem, start smaller than feels comfortable. Write down the one thing you want to be known for. Something true, specific and hard for a competitor to claim. Not your mission statement. Not your list of services. The one line a client could repeat back to you, word for word, six months after working with you.

Then pull up your last 10 pieces of content, your homepage and your bio, and hold them up against that one sentence. If most of what you find is generic or interchangeable with a competitor’s, you have found your answer.

Our client did not need a bigger ad budget. She needed sharper positioning — one that attracted her ideal patients, enhancing her efforts when pursuing them. My own agency did not need a new marketing plan. It needed a narrower, truer answer to the question every buyer is asking: Why you, and why now?

Before you spend another dollar on visibility, ask whether what you already have is clear enough to be worth seeing.

When growth stalls, almost every business owner reaches for the same levers. You know the ones. More content, more ads, more discount codes. Maybe even more texts blasted to a list that stopped opening them months ago. 

In my practice, these tactics are effective. But could they be more effective? Yes, they could. Not by spending more, but by clarifying what you already have.

Before you spend an additional dime on more marketing, examine your brand’s positioning first.

Most entrepreneurs spend a lot of time thinking about networking, and it makes sense. Industry events, conferences, panels, coffee meetings and dinners can lead to customers, partners, hires and investors that would never come through a cold channel.

But I think many founders misunderstand what kind of networking actually pays off.

In my view, the best networking is a byproduct of doing excellent work, serving customers well and becoming known for something specific. When that happens, the right relationships tend to find you. Investors reach out because the company is working. Customers refer you because the experience was great. Other operators want to meet you because you’ve built something worth understanding. The strongest networks rarely come from handing out the most business cards. They come from trust earned again and again over time.

The problem with networking before you have traction

Networking can become a distraction, especially early on. It can look like spending time at panels and conferences hoping one meeting will fix the underlying challenge of building the business, while the fundamentals, such as the product, customer experience and follow-up, aren’t where they need to be.

The uncomfortable truth is that no amount of networking will make up for a weak product, inconsistent service or a lack of customer trust. If the product isn’t working, more introductions usually won’t fix it. If customers don’t feel taken care of, more surface-level relationships won’t create a lasting business.

I’ve been there myself. A coffee meeting can feel productive because it creates motion. You had a conversation, made a new contact and maybe got some advice. But motion isn’t progress, and it’s easy to confuse the two when the real work is harder to face.

For a founder, the real work might be talking to customers, improving the product, tightening the business model or making a hard hiring decision. These tasks may not feel as exciting as meeting new people, but they’re usually what make networking work later.

Why founder friendships are different

I don’t want to dismiss relationships altogether. I’ve benefited enormously from a core group of founder friends, and that’s very different from general networking.

These are people who know me, understand my business and have lived through enough of the founder journey that I can be honest with them in ways that are harder with employees, investors or board members. They understand the pressure of making decisions with incomplete information, being responsible for other people’s livelihoods and pushing forward when the answer isn’t obvious. Peers like that challenge your thinking and share what they’ve learned, which is incredibly valuable.

What makes these relationships work is depth, trust and shared context. They aren’t random contacts collected at events. They’re people who have seen how you work, understand what you’re building and have a reason to stay invested in your progress.

How to build the network that actually matters

For some people, that network already exists through school, past companies, former colleagues or years in the same industry. Others have to build it more intentionally.

If you’re an early founder without a real network, I recommend spending time in a high-talent environment before starting your own company. Work at a great startup. Join a team with excellent engineers, operators, salespeople and product thinkers. Learn how strong people work, build trust with them and form relationships based on doing hard things together.

The same principle applies in any industry: the best relationships come from real collaboration and follow-through. Take real estate. The agents with the strongest referral networks usually aren’t the ones at every event. They’re the ones who make the lender’s job easier, communicate well with attorneys and vendors, and protect the client experience when a deal gets stressful. Over time, people remember who made the work smoother and who handled pressure well.

Targeted outreach can work

Targeted outreach still has value when it’s done well. If you reach out to someone a few years ahead of you, in a relevant market, with a specific reason for wanting their perspective, many people will respond. Most founders had help along the way, and there’s a natural instinct to pay it forward when a request feels genuine.

A thoughtful, specific question will usually get a better response than a vague request to “pick your brain.” Someone who studies how another founder built their business and asks about one relevant decision is far more likely to make a real connection than someone sending the same message to 50 people.

Follow-through is what turns that first conversation into something meaningful. When someone asks for advice, acts on it, reports back on what happened and keeps the other person updated, the relationship changes. That’s the kind of networking that works: specific, earned and grounded in action.

The best network is usually a byproduct of doing the work so well that people want to be close to it. Build something strong, serve people well, follow through and become known for a clear standard. The relationships that matter will follow.

Most entrepreneurs spend a lot of time thinking about networking, and it makes sense. Industry events, conferences, panels, coffee meetings and dinners can lead to customers, partners, hires and investors that would never come through a cold channel.

But I think many founders misunderstand what kind of networking actually pays off.

In my view, the best networking is a byproduct of doing excellent work, serving customers well and becoming known for something specific. When that happens, the right relationships tend to find you. Investors reach out because the company is working. Customers refer you because the experience was great. Other operators want to meet you because you’ve built something worth understanding. The strongest networks rarely come from handing out the most business cards. They come from trust earned again and again over time.

AI promises to make employees more productive. It may also turn them into managers of AI bots — without a promotion or pay increase. 

According to a recent Business Insider report, AI is changing the workday for many employees. Employees face pressure to use the technology and consequently spend more time managing AI bots that do tasks humans were initially hired to do, like teaching, coding, writing or designing. They now spend less time on work responsibilities related to their professions and more time managing bots that do work for them. 

For many workers, AI now comes with management responsibilities. They must tell agents what to do, review their output, ask for changes and step in when something goes wrong. Other employees spend much of their time checking AI-generated work and remain accountable for everything that gets published or sold. 

The idea that AI could turn every worker into a manager has been around for years. Now, companies are putting it into practice.

Salesforce recently introduced “job-ready agents” for sales, customer service, ecommerce and internal operations. The company is also giving employees tools to train those agents and improve their results.

At the same time, IT service provider Wipro has seen massive productivity improvements due to AI. The company’s chief technology officer, Sandhya Arun, said in an interview with Business Insider that AI has added the productivity equivalent of 20,000 workers. She imagines a future in which one engineer can manage a group of agents.

Meanwhile, at Daytona, a 30-person AI startup, CEO Ivan Burazin recently told Business Insider that each of its 16 engineers oversees about five AI agents on average. He said employees there no longer write code themselves.

The pros and cons of managing AI

Sinda Khenine became a software engineer because she wanted to build things. But today, much of her job involves overseeing the AI systems that do much of that building for her, she told Business Insider.

Khenine, a software and AI engineer at Electrolux who is also starting her own AI business, says that on the pro side, agents have helped her write more code and build a company on her own. That’s something that would have been “10 times harder” before, she acknowledged.

However, the trade-off is that she spends less time on the hands-on engineering work she originally loved. Instead, she picks the models, gives them context and rules, checks their work and pieces together output from several agents into a finished product.

“The effort is more focused on the coordination itself rather than the engineering problem that we are solving in the first place,” Khenine told Business Insider. 

Khenine also manages people at Electrolux, but she said overseeing AI agents can be even more demanding. Agents need extensive setup, and they require continued attention after they start working.

“It’s like a loop,” she says. “You need to monitor the results, you need to see the progress. You need to decide if you need to rerun or finish the job.”

AI promises to make employees more productive. It may also turn them into managers of AI bots — without a promotion or pay increase. 

According to a recent Business Insider report, AI is changing the workday for many employees. Employees face pressure to use the technology and consequently spend more time managing AI bots that do tasks humans were initially hired to do, like teaching, coding, writing or designing. They now spend less time on work responsibilities related to their professions and more time managing bots that do work for them. 

For many workers, AI now comes with management responsibilities. They must tell agents what to do, review their output, ask for changes and step in when something goes wrong. Other employees spend much of their time checking AI-generated work and remain accountable for everything that gets published or sold. 

Danielle Scott had spent years working in franchise development when she got an opportunity that appeared too good to be true and almost impossible to pass up: buying six tutoring centers in Central Florida.

She was successful, ambitious and, by her own description, “pretty arrogant.” The chance to become a franchise operator felt like the next logical move. So Scott assembled funding, drawing from her own savings and money from her family and friends, and bought the portfolio for more than $3 million in the early 2000s.

“Everybody could get money back then,” Scott says in a new interview with Entrepreneur. “Money was very easy and very cheap, and that was the problem.”

In hindsight, Scott saw warning signs she didn’t fully investigate. She says she should have asked why the tutoring company was selling off corporate-owned locations. At the time, however, she interpreted it as an extraordinary opportunity rather than a possible indication of risk.

“I got very excited,” she says. “I was very young, and I was very successful, and I pretty much had the world in the palm of my hand at that moment.”

Then the Great Recession happened

The 2008 Great Recession hit Scott’s businesses hard. When the economy worsened, many households began cutting discretionary spending. Tutoring, music lessons and extracurricular activities were often among the first expenses to go.

The business suffered an immediate exodus of customers. Scott says one child’s program could cost nearly $5,000 per year, and as parents withdrew their children, the revenue loss piled up rapidly.

“Everybody was pulling out their kids,” Scott says. “We lost $180,000 in one week. We didn’t have enough money to keep things afloat; we had to start closing the centers.” 

The centers were in Central Florida, a market Scott says was hit especially hard by the downturn. Her recollection of the period is defined by incomplete housing developments, closed businesses, empty malls and a swift increase in crime.

“There were roads that were being built into neighborhoods that would just stop,” she says. “You would see a road going up a hill with street lamps, no houses, just a road, and it just stopped.”

The conditions were devastating not only because of the macroeconomic collapse, but also because Scott had built a business model that depended on customer spending that could be postponed or eliminated. In a downturn, many families didn’t view tutoring as a necessity, even if Scott believed in the service and the company’s mission.

Closing the centers

As the centers’ finances deteriorated, Scott began closing them. She ultimately lost all six businesses.

The loss was financial, professional and personal. Scott employed more than 100 people, some of whom had spent a decade or more with the tutoring centers. Some blamed Scott for their lost jobs. She received death threats. And for a time, she believed the collapse had ended her career in franchising.

Eventually, she realized that “this situation would have happened whether I was standing there or not,” she says. “There was nothing that anybody could do.”

Before the collapse, Scott believed that she had reached the top of her professional game. She had corporate experience, operational knowledge, a growing portfolio and the confidence that comes with early success. Losing the businesses forced her to reassess her identity as a leader. 

“It humbled me completely,” Scott says. “I mean, beyond humbled me.”

Reflecting on that time, Scott now believes she should have paid closer attention to the broader business environment and demanded answers about why the franchisor was divesting corporate-owned units. She had been senior enough to recognize that the decision warranted scrutiny, but she did not pause long enough to conduct the level of due diligence she now considers necessary.

“If I had, I probably wouldn’t have bought them at all,” she says. 

The emotional toll was equally severe. Scott feared that the loss had permanently damaged her standing in franchising.

“It made me feel like I had ruined my career,” she says. “It made me feel as though I would never work in franchising again because who does something like this at the top of their career and then fails so massively?”

Returning to the franchise industry

Her return was not immediate. Scott says it took about three years after the collapse for her to feel that she had truly come back to the franchise industry. 

“It took a lot of people telling me that it wasn’t my fault and that they still wanted to work with me and that I was still amazing and that I still had a lot to offer to the industry,” she says.

Scott dipped her toe back into the franchise industry by helping a franchise owner pursue growth and secure an equity partner. She was initially hesitant, but the relationship became a successful experience.

Today, as chief development officer of Alliance Franchise Brands, Scott helps guide franchise growth, legacy ownership transitions, business sales and acquisitions across brands including graphics and signage franchises Allegra and Image360.

Her advice to entrepreneurs is not to minimize hardship or pretend that failure is painless. Her story makes it clear that when a business collapses, it can carry economic consequences, damaged relationships, public embarrassment and grief. But she believes that leaders cannot afford to remain trapped. 

“Don’t sit in it too long,” Scott says. “Let it be what it is, because the failures and the hits, they’re just going to keep coming — and all it does is make you better.”

For Scott, that perspective was hard-won. The crisis had made her feel exposed and ashamed, especially when employees blamed her for the closures. But over time, she came to see the difference between a flawed decision and a permanent personal failure.

“Just because you failed at something doesn’t mean you fail at everything,” she says. “And just because this happened doesn’t mean that it’s a reflection upon you.”

There’s a lot of noise around AI right now, and most of it describes a familiar pattern: a chatbot layered on top of existing software, an automation added to a workflow or a feature that helps someone finish a task a little faster.

Some of those features are genuinely valuable. If AI can reduce manual work, make information easier to find or help someone move through a task more efficiently, that’s real utility. But useful and AI-native aren’t the same thing.

Much of what gets called AI-native today is really AI-assisted. The old workflow is still there, the same person handles the same sequence of steps and the same assumptions shape the product. The AI simply sits on top and speeds up parts of it.

That creates incremental value, but it has a ceiling. When you assume the existing workflow is fixed, you limit yourself to improving the work as it is instead of asking whether it should be redesigned altogether. The companies that build truly AI-native products will think very differently.

AI-native design starts with the work, not the feature

The most important question isn’t “How can we add AI to this product?” It’s “If we were designing this workflow from scratch, knowing what AI can and can’t do, what would the best version look like?”

Those questions lead to very different products. Start with the existing workflow, and you’ll likely end up with a better tool: a few faster steps, some automated tasks, easier access to data. The product improves, but the user’s day-to-day work looks largely the same.

Start with the work itself, and you’re forced to ask more fundamental questions. What outcome is the user trying to achieve? Which parts of the work require human judgment, taste, context or relationship-building? Which parts are repetitive, research-heavy or data-driven, and better suited to AI? Where should the human stay in control, and where are they doing work software can now handle better?

The best AI-native products may even feel surprisingly quiet, because the value comes from redesigning the workflow beneath the surface rather than adding something flashy on top.

How we applied this to our CRM

At Luxury Presence, we recently went through this exercise while building our new customer relationship management (CRM) product.

Our customers are professionals whose businesses run on personal relationships. The best of them stay in touch with their contacts, follow up at the right moments, remember client preferences, track life events, maintain referral relationships and make clients feel cared for long after a deal closes.

That work is valuable, but it’s time-consuming. Most of our customers know they should reach out to past clients and prospects more consistently, but doing it well takes research, context, timing, writing, personalization and follow-through. When they’re also serving clients, closing deals and running a business, relationship-building is often the first thing to slip.

We could have asked how to make the existing CRM experience better by adding AI-generated email copy, a chatbot or a feature that made the current workflow slightly faster. Instead, we asked what relationship management should look like now that AI can already handle parts of the process extremely well. Three areas stood out:

• Researching contacts. AI can pull together relevant signals, identify useful context and surface timely reasons to reach out faster and more consistently than a person manually combing through a database.
• Filling in missing information. AI can find third-party data, fill gaps and organize information around each contact, making the whole system more useful.
• Drafting personalized messages. With enough context, AI can produce a strong first draft, especially when the alternative is that the message never gets written.

Where the human still matters

For our customers, the personal relationship is the business. They know things about their clients that no system may capture: the nuance of a relationship, the right tone, the history that matters and context that never makes it into a database.

That’s why we chose a human-in-the-loop model. AI does the research, fills in the contact record, flags the opportunity and drafts the message, but the user reviews it, customizes it if needed and decides when to send it.

The goal isn’t to replace the relationship. It’s to remove enough manual work that people can show up more consistently and thoughtfully in the relationships that already drive their business. A fully automated message may be technically possible, but possible doesn’t always mean valuable. In a relationship business, the user’s judgment is part of what clients are paying for.

How to apply this to your business

The same exercise works for almost any product or team. Before adding AI to anything, try this:

1. Define the outcome. Ignore the current process and name what the user or employee is ultimately trying to achieve.
2. Break the work into parts. List every task involved, including the ones that tend to get skipped because they take too long.
3. Sort each task. Decide which parts depend on human judgment, taste or relationships, and which are repetitive, research-heavy or data-driven.
4. Assign the work. Give AI the tasks it handles well, and design the product so people stay in control of the moments where their judgment creates the most value.

Most companies are still in the AI-feature stage, adding useful tools to existing systems and calling it transformation. Some of those tools will help, but the bigger advantage will go to companies willing to redesign their workflows from the ground up.

The most valuable AI products won’t just make yesterday’s work faster. They’ll help people do the right work better.

There’s a lot of noise around AI right now, and most of it describes a familiar pattern: a chatbot layered on top of existing software, an automation added to a workflow or a feature that helps someone finish a task a little faster.

Some of those features are genuinely valuable. If AI can reduce manual work, make information easier to find or help someone move through a task more efficiently, that’s real utility. But useful and AI-native aren’t the same thing.

Much of what gets called AI-native today is really AI-assisted. The old workflow is still there, the same person handles the same sequence of steps and the same assumptions shape the product. The AI simply sits on top and speeds up parts of it.

When budgets get tight, most companies make the same move: they cut brand marketing and put every dollar into lead generation. It’s easy to see why. A paid search campaign that brought in 200 demo requests last month has a clear return you can show your CFO. A podcast sponsorship that makes future buyers recognize your name doesn’t. But cut brand long enough, and those leads get harder to close, more expensive to win and slower to come back when the market recovers.

When performance marketing eats the entire budget, a company stops creating demand and starts harvesting only what already exists in the market. On a spreadsheet, cutting upper-funnel spend looks like prudent governance: pause expensive media campaigns, put every dollar toward immediate conversions and present a clean return to leadership. Repeat that decision cycle after cycle, though, and you’re trading long-term market share for short-term efficiency.

The cost of harvesting without planting

Over my years leading marketing through shifting market cycles and economic headwinds, I’ve watched this tension play out again and again. During extended downturns, the instinct is to pull spending deeper into lead capture. For a public company under pressure to show profitability, the urge to cut anything without immediate, line-item attribution is almost irresistible.

The risk comes when a company over-indexes on demand capture for too long: it hits a wall of diminishing returns. Inbound form fills might hold steady for a quarter or two, but lead quality drops sharply. Prospects arrive without context or familiarity, so sales teams spend twice the effort explaining who you are and why you matter, burning valuable capacity on cold prospects who don’t yet trust the company.

Worse, pulling back on brand during a downturn costs you your place at the starting line when demand rebounds. Brand awareness isn’t a light switch you can flip back on when the economy loosens. If you go dark while conditions are tough, competitors who maintained their presence capture most of the recovering demand, and you’re left rebuilding recognition from scratch at a much higher cost.

Build a return case your CFO will accept

Breaking this cycle requires marketing leaders to change how they frame brand value in the boardroom. Asking a CFO to trust intuition or fuzzy ROI will fail every time. Brand investment can’t be presented as a leap of faith; it has to be tied to concrete proxy metrics that reflect real pipeline acceleration.

When my team set out to rebalance our marketing portfolio at Ryder, we stopped trying to prove direct attribution for broad awareness, which is nearly impossible. Instead, we focused on localized correlation and deal velocity, establishing a clear link between upper-funnel presence and bottom-of-funnel conversion.

We worked with partners to tag digital touchpoints during active brand campaigns, tracking regional website traffic jumps of more than 20% within a five-second window of campaign airtime. Then we mapped how that heightened visibility affected active deal cycles.

The data revealed a pattern even the most numbers-focused executive team could respect: when brand messaging is active in a market, prospects move through the sales pipeline significantly faster. Visibility validates your story before the first sales call, reducing friction and shortening sales cycles. Pairing those traffic spikes with annual brand perception studies gave our executive team clear evidence that brand spending isn’t a discretionary luxury. It’s the infrastructure that makes lead generation efficient.

Rebalance without breaking the budget

Rebalancing doesn’t require a multimillion-dollar broadcast buy in the middle of a lean cycle. If budget pressure rules out major broadcast channels, marketing leaders can shift a portion of performance dollars into targeted digital brand presence. Placing story-driven content on the specific channels where key decision-makers spend time keeps your brand visible without an outsized budget line.

Maintaining steady brand investment also keeps a company from yanking spending up and down with every quarterly shift. Expecting bottom-of-funnel tactics to drive sustainable revenue without brand equity is like asking someone to sign a marriage certificate before you’ve taken them to dinner.

Sustainable growth belongs to leaders who treat brand building and lead generation as two halves of the same engine. Lead generation captures today’s business; brand investment ensures tomorrow’s pipeline exists at all. Winning the budget argument isn’t about abandoning financial accountability. It’s about making sure your company stays top of mind long after the current quarter ends.

When budgets get tight, most companies make the same move: they cut brand marketing and put every dollar into lead generation. It’s easy to see why. A paid search campaign that brought in 200 demo requests last month has a clear return you can show your CFO. A podcast sponsorship that makes future buyers recognize your name doesn’t. But cut brand long enough, and those leads get harder to close, more expensive to win and slower to come back when the market recovers.

When performance marketing eats the entire budget, a company stops creating demand and starts harvesting only what already exists in the market. On a spreadsheet, cutting upper-funnel spend looks like prudent governance: pause expensive media campaigns, put every dollar toward immediate conversions and present a clean return to leadership. Repeat that decision cycle after cycle, though, and you’re trading long-term market share for short-term efficiency.

The cost of harvesting without planting

Over my years leading marketing through shifting market cycles and economic headwinds, I’ve watched this tension play out again and again. During extended downturns, the instinct is to pull spending deeper into lead capture. For a public company under pressure to show profitability, the urge to cut anything without immediate, line-item attribution is almost irresistible.

When revenue growth becomes harder to predict, cost discipline matters more. But as the founder of ButterflyMX, I’ve learned that the most durable opportunities do not always come from cutting headcount or delaying investments. They often come from reducing the cost and complexity of growth itself.

I see this clearly in proptech, where scaling can involve hardware, software, onsite deployments, integrations and ongoing customer support. But the underlying lesson applies to many businesses: Growth gets expensive when every new customer requires you to reinvent how the company operates.

Here are three ways founders can make growth more repeatable and protect margins in the process.

1. Turn successful pilots into repeatable playbooks

A successful pilot proves that a customer wants what you are selling. It does not prove that you can deliver it profitably at scale.

The problem often becomes visible after the first few wins. Each new customer requires a slightly different onboarding process, configuration, implementation workflow or support model. Individually, those exceptions may seem manageable. Across dozens or hundreds of customers, they create more labor, longer deployments and higher costs.

The lesson I’ve learned is to treat scaling like product development. Examine what worked, identify what truly needs to be customized, and standardize everything else.

Start with the customer profile. Define which types of customers are most likely to see value, what problems they are trying to solve and what conditions make implementation successful. Then build a repeatable sales process around the business case rather than the product alone.

Apply the same discipline to delivery. Standardize configurations, implementation requirements, documentation and handoffs between sales, implementation and customer success. A customer may have unique needs, but the internal process for serving that customer should not be entirely unique.

The goal is not to eliminate flexibility. It is to eliminate unnecessary variation. Every custom workflow and manual handoff adds cost, and those costs compound as the business grows.

2. Remove integration friction before it becomes a scaling tax

The second problem is fragmentation.

Most businesses eventually accumulate technology from different vendors, departments and stages of growth. When those systems cannot communicate easily, every new implementation can create another integration project.

I see this frequently in real estate. A single property might have access control, energy management, maintenance software, sensors, resident applications and other systems operating alongside one another. The same challenge exists in other industries wherever companies rely on a growing collection of specialized technology.

Founders should think about interoperability before fragmentation becomes expensive. That starts with consistent data. Information should use common definitions and structures so it can move between systems without requiring teams to repeatedly clean, translate or reconcile it.

Open interfaces matter, too. Application programming interfaces and other common standards make it easier to connect new systems without rebuilding the technology environment around them.

In real estate, that principle can extend from access control and energy management to in-unit thermostats. The point is not to connect technology simply because it can be connected. It is to make sure adding another system does not create another isolated source of data and another layer of operational work.

This is ultimately a margin issue, not just a technology issue. When integrations are repeatable, implementations require less custom work and customers can expand without forcing the company to solve the same technical problems again.

3. Turn customer success into a revenue engine

Customer success is often viewed primarily as a retention function. I think founders should also ask a different question: What expertise is the company already providing after the sale that customers would value as an ongoing service?

Customer-facing teams have a particularly useful vantage point. They see where customers struggle, which workflows consume the most time and which problems continue after implementation. Those insights can reveal opportunities for new services, product improvements and expansion.

The first step is to identify recurring work that produces a clear customer outcome. Depending on the business, that might include monitoring, optimization, reviews, training or proactive support.

Then make the offering repeatable. Define what customers receive, how frequently the service is delivered and how success will be measured. Avoid vague promises or outcomes you cannot substantiate. Build measurement into the service so customers can evaluate the value themselves.

Customer success teams can also help expansion happen more naturally. When customers have simple ways to understand and communicate the results they are seeing, it becomes easier for them to make the case for expanding the relationship internally.

The broader lesson is that customer success should not only solve problems after the sale. Done thoughtfully, it can help identify what customers value enough to keep buying.

The bottom line

When growth slows, protecting margins does not have to mean indiscriminate cost cutting. One of the most valuable things founders can do is examine the cost of growth itself.

Make successful processes repeatable. Remove technical friction before it multiplies. Look for recurring value in the work your teams are already doing for customers.

The companies that do this well do not simply spend less. They build businesses in which each new customer becomes easier to serve than the last.

When revenue growth becomes harder to predict, cost discipline matters more. But as the founder of ButterflyMX, I’ve learned that the most durable opportunities do not always come from cutting headcount or delaying investments. They often come from reducing the cost and complexity of growth itself.

I see this clearly in proptech, where scaling can involve hardware, software, onsite deployments, integrations and ongoing customer support. But the underlying lesson applies to many businesses: Growth gets expensive when every new customer requires you to reinvent how the company operates.

Here are three ways founders can make growth more repeatable and protect margins in the process.