Taking Your Product to Market Just Got Way Easier, Thanks to AI Ready to take a product to market, then 10x the results? Here is your guide.

Going to market used to be simple: Buy ads, hire a team of sales reps, and spend thousands of dollars on a PR stunt. Hell, just give away money if it means you'll sign up more users.

Today, all that has changed. Interest rates are no longer zero, and the cost of capital is much higher. Investors are more concerned about customer acquisition costs and unit economics. Startups need to stay lean, and founders need to be creative in how they reach, activate, and convert customers.

Incredibly, just as startups around the world were being forced to tighten their belts, they were given a magical new technology that stretches their go-to-market (GTM) dollars further than ever: generative AI.

Everyone knows about AI's ability to create text and images, but we're just starting to discover how transformative that can be for launching a business. I'm a Harvard Business School professor and cofounder of Flybridge Capital, an early stage venture capital fund with over $1 billion in assets under management, and I'm seeing it all the time now: Startups are using AI to completely rethink their GTM strategy — helping them reach more customers more efficiently, and quickly scale in ways that were nearly impossible before.

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To be clear, the fundamentals of a GTM strategy are still the same — and will likely always be the same. It's a mix of positioning and messaging, market selection, demand generation, distribution channels, sales funnels, and strategic partnerships, all working together to turn cold prospects into loyal, evangelical customers in the most cost-effective way possible. But these activities fall right into the sweet spot of generative AI as we know it today: It's a matter of content creation, lead nurturing and engagement, personalization, and data analysis.

In this article, I'll walk through the principles of a GTM strategy, and how to use AI to multiply your growth potential tenfold.

Let's start with a case study.

How one company went to market with AI

In 2018, cofounders Valentina Ratner and Kyle Dumont paired up to address a major opportunity: Technology companies were losing a combined $35 billion each year because of mistakes made while building hardware. It was a workflow issue: One team might change something in the development process, but that change wouldn't be properly communicated to other teams, which would lead to confusion and conflicting information. To solve this, Ratner and Dumont created a company called AllSpice — a workflow system specifically designed for hardware teams.

With approximately 300,000 electrical engineers working at 30,000 hardware companies, Ratner and Dumont estimated the total market opportunity to be over $5 billion to start. They released a beta version of AllSpice in 2020, landed a few hundred early customers, and saw initial signs of product-market fit: Top cohorts of users were spending nearly 40 hours per month on the platform, with over 100 interactions per week.

As they neared their official launch in 2022, Ratner and Dumont faced a difficult decision: Should AllSpice market itself to large or small customers? Enterprise-level customers pay high fees, and would have been the startup's first choice — but those customers are hard to land. They usually require hiring an expensive enterprise sales team, and then expect a lot of costly handholding and personalized attention. So AllSpice decided to target small-to-midsize teams instead, which had smaller budgets but were easier and more affordable to target.

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Then everything changed in late 2022, with the launch of ChatGPT. Suddenly, AllSpice could go after everyone at once.

Here's how.

AllSpice began using AI to nurture inbound leads, which is usually a time- and resource-intensive process. The startup's small sales team used tools like ElevenLabs and HeyGen to create thousands of customized demonstration videos based on a single recording. Not only is this radically more efficient, but AllSpice's customers (who are engineers themselves) often prefer these videos over a Zoom call with a salesperson.

AllSpice's team also used a sales execution tool called Outreach to automatically sequence outbound emails and follow up on tasks to move prospects through their pipeline. Outreach recently incorporated generative AI to help sales reps understand true buyer sentiment, summarize deals, generate personalized emails, respond to prospects, and forecast pipelines more accurately. These new workflows free up Ratner and her sales team to focus on the largest, most valuable clients while consistently converting small and midsize teams.

AI has helped scale AllSpice's customer support experience as well — a critical aspect of servicing large companies and enterprises. Ratner's team is using generative AI to automatically create video tutorials and documentation for every feature in their toolset. Crucially, AI helps them keep this documentation up-to-date as the AllSpice product rapidly evolves.

AllSpice is not alone here. As I work with startups, I'm seeing an explosion of AI tools across the go-to-market spectrum — from sales development reps (11x) to sales engineers (DocketAI) to customer data enrichment (Clay) to personalized videos (HeyGen) and more.

To be clear, AI will not make the difficult strategic decisions for you. It can't say who to target, how to sell them, and your best channels for growth. You must still understand GTM fundamentals yourself — but once you do, AI can become a supercharged sidekick to scale them.

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So before you use AI, let's polish up on those GTM fundamentals.

4 questions for your GTM strategy

In the early stages of a startup's life, the primary objective of GTM experiments is to discover a repeatable and scalable process for customer acquisition. There are four key questions to answer:

1. What is my initial market?

2. What is my growth and demand generation strategy?

3. What is my sales model?

4. Who are the best (if any) channel partners?

When you get this right, you'll build a playbook for consistent customer acquisition with favorable unit economics.

But even after you answer these four questions, there is still another decision to make: How quickly should you scale? When do you "pour fuel on the fire" to grow quickly? The answer depends on the strength of your product-market fit as well as market conditions. One of the greatest risks for a startup is scaling prematurely. Many startups have tried to force growth despite insufficient product-market fit and poor timing. Especially in an environment where capital is scarce, keeping "dry powder" and extending your runway is generally wise. Luckily, thanks to AI, you can do so much more with less.

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Now let's get back to answering the four GTM questions. We'll start at the top:

Question 1: What is your initial market?

In his 1962 book Diffusion of Innovations, author Everett Rogers developed a model for technology lifecycle adoption and defined five categories of customer profiles: innovators, early adopters, early majority, late majority, and laggards. (You may be more familiar with this concept from Geoffrey Moore's Crossing the Chasm, which built on Rogers' work.) Startups make the mistake of targeting early or late majority customers, who are more pragmatic and therefore difficult to please. Instead, founders need to identify and target innovators who are willing to adopt an incomplete or buggy product because it solves (though not perfectly) such a painful need in their lives.

Innovators and early adopters are more willing to provide valuable feedback. Startups should build a solid foundation of loyal customers who can provide essential insights for further product development and market expansion, while also serving as enthusiastic advocates.

It is surprisingly easy to choose the wrong initial market, and it's a mistake I see often. How do you ensure that you pick the right one? Here are a few criteria.

What your initial market needs

→ Passion and mission. Focus your energy on a market and customer type that you genuinely love. This will help you get through the ups and downs of the early startup journey. Plus, one way to find innovators is to look for the most passionate people in your space.

→ Small, yet broad. Select a market that is narrow enough to ensure focus, but large enough to attract capital and sustain multiple iterations.

→ Many prospective customers. Be sure your initial market has a set of requirements that are common to a broad set of prospective customers. That way, it will be easier to dominate your initial segment and then jump to the next.

→ A high willingness to pay. Ask yourself: Does your initial market have a vibrant ecosystem of free products? If so, customers may be accustomed to not paying for what you're offering — and that's a problem! Also, do your customers have the means to pay if you solve their problem?

→ No gatekeepers. Channels and partners are great for reaching more customers as you scale, but don't rely on these relationships to build your business in the early days. Choose an initial market where you have direct access to your customers, so you can learn from them. Keep control of your relationship with your initial market.

Your initial market should be full of innovators who desperately need your product. It's a jumping-off point to the next market, then the next, and then the next.

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Question 2: What is my growth and demand-generation strategy?

Stories of innovative marketing, demand generation, and "growth hacking" have been part of Startupland lore for decades: Richard Branson's PR stunts, PayPal's referral program, Steve Jobs' legendary product announcements, Duolingo's unhinged mascot — the list goes on.

These stories will pale in comparison to the growth "hacks" we'll see in the age of generative AI.

AI is already having a profound impact on the way companies gain attention — and their efficiency in doing so. Payments company Klarna is a good example of this. In their mid-year 2024 earnings report, they revealed that their average revenue per employee had grown 73% year over year. In May, they reported that operations costs were down 11%, and their sales and marketing costs were down 11% in the first quarter of 2024 — all of which they attribute to their use of AI. "We have built AI copilots for each of the parts of the flow," said David Sandstrom, Klarna's Chief Marketing Officer. "I think that the best marketers are going to 10x their impact and efficiency because they have these tools."

Experimentation has always been a crucial part of startup growth strategy. You need to design a series of experiments, place small bets, and see what sticks. Then you cut the losing experiments and double down on the winning ones. AI now allows you to run more experiments faster than ever before.

Here is an example: SEO content marketing. In markets where people are searching for immediate solutions (rather than idly browsing), Google is the best channel for reaching customers. For instance, people looking for a new job regularly use Google and other search engines to find job openings, job descriptions, and sample résumés or cover letters.

David Fano had this insight in 2019 as he searched for an effective growth channel for Teal, his AI-powered job search platform. Teal builds a suite of tools for job seekers, including an AI-powered résumé builder and job application tracker to run a streamlined, organized job search. Fano and his team experimented with several growth channels unsuccessfully, including social media ads and influencers. But most people saw those ads when they weren't actively searching for jobs. That's when Fano began to experiment with SEO. Fano hired content writers to create high-quality articles for job seekers. Teal saw promising results early, but their growth was capped by the cost of content marketing. Fano was paying $500 per article.

Then ChatGPT came out, and Fano decided to go all-in. He and his team built a low-touch AI writing system using OpenAI, no-code automation, and content management tools. Teal's GTM team created detailed article prompts and generated over three thousand articles tailored to specific job-related queries. For pennies, they created articles on specific career paths and job-specific résumé templates. Human editors reviewed each article, and after minor edits and prompt tweaks, they were good to go.

The results were dramatic. Teal's organic search traffic grew to over 100,000 page views per week and converted 3% of all visitors into paid users, driving their customer acquisition cost down to effectively zero.

Growth marketing is one of the fastest-growing sectors for AI tools. Founders need to keep their finger on the pulse of new tools and experiment constantly. You never know when the next 10x innovation will be released. Even when you find a successful growth strategy, be on the lookout for the next one.

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Question 3: What is my sales model?

Attention is one thing. Closing deals is another.

There are three basic sales models in business. Outbound sales describes when a team of salespeople reaches out to prospective customers to initiate the sales process. Inbound sales are when the company attracts potential customers through marketing and promotion, then salespeople complete the sales process. Product-led growth (PLG) is when the company attracts potential customers through marketing and promotion, and then the customer completes the sales process through a self-service portal.

In recent years, PLG has given startups a roadmap to rapidly scale with limited resources. We have all encountered (and likely used) PLG tools in our work: Calendly, Slack, Canva, Figma, and others. PLG tools tend to be inherently viral by promoting teamwork, and they make it easy to sign up by offering always-free (dubbed "freemium") plans. PLG companies have mastered the art of Software as a Service: They remove virtually all friction and risk from the buying process.

Still, it's not the perfect sales model for every company. PLG works well for Teal, but not AllSpice, which uses an inbound sales model. Why? First, the customers. Teal attracts job seekers from virtually every field of work, but the intensity of their job search varies. Some folks treat the job search like, well, a job, while others are just casually looking for work. But AllSpice does not want casual users. During its beta period, AllSpice tested out a freemium model — but found that paying users were much more engaged with the product than freemium users. AllSpice aims to be part of the daily workflow for hardware engineers, so they want users to commit with their credit card. As a result, it canceled the free plans.

Another difference is product complexity. AllSpice's freemium plan saw a lot of users sign up who were not a good fit for the product. By talking with each new lead, the company can ensure AllSpice is the right tool for them. Teal is designed to be used by every job seeker, from high schoolers to retirees. It's much more consumer-facing, while AllSpice is a business-to-business tool.

Finally, the business models and unit economics of each startup are different. AllSpice has fewer customers to target and therefore needs a higher customer lifetime value to build a big business. Teal's total addressable market is, again, all job seekers, which could be hundreds of millions of people around the world — but each customer's lifetime value is much smaller. Teal wants to activate as many users as possible and then convert them once they prove their value, which their freemium plan helps them do.

But as noted earlier, generative AI is starting to dramatically change this calculus. To start, Teal and other PLG companies need fewer low-revenue customers to be profitable and successful.

AI has saved Teal over $1 million a year on content creation alone. AllSpice is also growing more efficiently. They use AI to manage the majority of their inbound leads, leaving Ratner and her small sales team to focus on the high-revenue clients. Outbound sales have almost always been reserved for these select clients due to the high costs of fielding a sales team. But now, AI has made the outbound sales model cost-effective to attract smaller customers.

You are no longer bound by time and cost restraints. You now have the freedom to choose the best sales model for you and your initial market, whether that's PLG, inbound sales, or outbound sales. The cost of sales is rapidly declining, regardless of the sales model.

But the best overall sales model is a mix of approaches. PLG is still an effective and efficient sales model for many startups early on. However, as PLG companies scale, they tend to become less efficient overall compared to their peers who follow an enterprise sales-led approach. Every company that aspires to secure select, high-revenue relationships will have to eventually create an enterprise sales motion, a process that takes years to craft and perfect. Relying solely on PLG or inbound sales to take a bottom-up approach with enterprises can eventually lead to stagnation. To avoid this trap, structure the organization to operate a mix of sales motions after mastering the first one. Additionally, build the systems and expertise needed to service enterprise-level clients, including detailed and comprehensive documentation and a strong customer support program. Use AI across all these sales motions and post-sales functions to scale while staying lean.

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Question 4: Who are the best (if any) channel partners?

Channels and partners are third-party companies or platforms that give you easy access to your target customers. While they're often lumped together (into "channel partners"), there are key differences. Partners allow you to market and sell to your customers, while channels take on the responsibility of sales and marketing for you. Some channel partners are more possessive than others, meaning they keep control of the customer relationship.

Working with channel partners is a double-edged sword for both parties. There will always be a battle for customer loyalty.

Even if a channel partner doesn't aim to eliminate you, they will always try to charge higher and higher rents for access to their users. For example, Facebook was a fantastic growth channel for numerous businesses until they began to throttle organic traffic to grow their paid-ads business.

I always caution founders to be very selective in choosing channel partners — not only for the reasons I just shared, but also because any layer between you and the customer will slow down learning. Again, you do not want gatekeepers to control your access to your customer base.

There are three questions to answer when exploring channel partners:

1. Is this partner the best way to reach customers?

2. Can we afford the incremental economic burden required to incentivize the channel partner?

3. How do we build an independent relationship with our customers that doesn't rely on the channel partner?

Channel partnerships can be an enormous source of leverage, but they always come at a cost — both literal and in terms of customer connection.

Of all the phases of GTM, this is where AI will have the smallest role — for now. AI cannot choose a partner for you, nor can it manage that relationship. But in the long-term, AI might have the biggest impact on this phase of GTM — because it might cause the demise of channel partnerships entirely. Consider this: As it becomes easier to reach customers directly, startups will rely less on third parties for their GTM (a good thing, in my opinion). While channel partners will never entirely go away, startups that own their access to and relationships with customers will be more sustainable and profitable.

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Now go to market!

As you can see: Even in the age of AI, the principles of a go-to-market strategy have not changed. Business is always — and will always be — about building the right product for the right customer, and finding the most efficient way of reaching and acquiring them. But today, the means of reaching and acquiring customers is changing rapidly.

Customer acquisition costs are going down, which means that entrepreneurs' choices are going up. AI can open doors to new, better, and magical business models — and the only thing necessary is a willingness to experiment.

This essay was adapted from the new book The Experimentation Machine: Finding Product-Market Fit in the
Age of AI by Jeff Bussgang. Published by Damn Gravity Media.