An AI startup strategy is the way founders build defensible businesses while the underlying platforms change by the day. At the 2025 Venture Atlanta Conference, the opening panel, “The Great Disruptor: AI and the Opportunities From Enterprise to Consumer,” brought together investors and enterprise leaders to unpack exactly that tension.
They discussed how startups can differentiate in a world shaped by OpenAI-scale platforms, why the traditional software-as-a-service contract is under pressure, and which metrics matter most when evaluating AI companies today.
Moderated by David Excell, Founder of Featurespace, the panel featured Gardiner Garrard, Co-Founder & Managing Partner at TTV Capital; Vanessa Larco, Co-Founder & Managing Director at Premise VC; and Ryan Sanders, Managing Director at Mercato Partners. Sponsored by CBRE, the conversation moved well beyond AI hype. Instead, it focused on the real decisions founders and investors are making right now around differentiation, pricing, adoption, capital efficiency, and long-term value creation.
Watch the Full Session
Key Takeaways
- Building an AI startup strategy requires resolving a real problem, not chasing every new model release.
- Founders should stop trying to compete head-on with OpenAI and instead build around context, contracts, control, and workflow ownership.
- Investors are looking beyond raw growth to usage, retention, gross margin, time to value, and the quality of revenue.
- Pricing models are shifting from flat subscription structures toward hybrid and outcome-based approaches, especially in customer support and agentic workflows.
- Atlanta is positioned well for this moment because of its enterprise access, technical talent, and capital-efficient startup culture.
- As AI adoption accelerates, founders and investors have a growing responsibility to think about ethics, governance, and human impact.
How AI is Reshaping Startup Strategy
One of the clearest ideas from the panel was Garrard’s framing of AI as a substrate.
“AI today is just your substrate, and you’re building an organization on top of it.”
— Gardiner Garrard, Co-Founder & Managing Partner at TTV Capital
In other words, AI is becoming the infrastructure that companies build on, but it’s not a strategy in itself. That’s what makes developing an AI startup strategy more demanding right now. Founders need to know what they are truly solving for, even as the underlying technology keeps changing.
What that means in practice:
- AI is becoming the baseline layer for modern software.
- The company’s “moat” is not just the model.
- Stronger AI startup differentiation comes from solving a real problem well.
- The best companies stay anchored to a mission instead of chasing every tool shift.
This also reflects broader AI venture capital trends. Investors are rewarding focus, commercial clarity, and adaptability more than novelty alone.
Featurespace is a strong example of that. Excell built foundational behavioral analytics technology, then applied it to fraud detection in financial services, creating an AI-native business with real enterprise value.
How Founders Can Compete Without Taking on OpenAI Head-to-Head
A major theme of the discussion was how startups can compete with OpenAI without trying to outbuild OpenAI itself.
“Stop trying to compete with OpenAI. Compete on context, on contracts, and on control.”
— Ryan Sanders, Managing Director at Mercato Partners
Larco compared this moment to the early days of mobile and cloud. At first, mobile apps and cloud software were dismissed as lightweight or easy to replicate. Over time, founders proved that entirely new businesses could emerge by building for new behaviors, new workflows, and new customer needs.
The panel’s advice for a founder’s AI startup strategy was clear:
- Don’t build around generic access to a model.
- Build around workflow, trust, and customer-specific context.
- Focus on markets where operational complexity creates defensibility.
- Remember that large platforms will not build every application.
That is where AI-enabled vs. AI native business models become more interesting. The market will support both, but founders still need a clear answer to one question: why this company, and why now?
The Metrics Investors Care About Now
An AI startup strategy is now judged by more than topline growth, because investors are digging deeper into the business fundamentals behind that growth.
The biggest metrics mentioned were:
- Usage
- Retention
- Quality of revenue of AI startups
- AI startup gross margin
- AI startup time to value
Usage may be the best early predictor of retentive power because it shows how attached customers are to the product. Likewise, customer success is one of the strongest current AI use cases, especially when teams use AI to identify risk, improve engagement, and support expansion opportunities.
The quality of revenue in AI startups now matters more than ever. Fast growth can look impressive on paper while still hiding renewal risk if the product is not becoming essential to the customer.
That’s why investors are looking more closely at who is buying, how the product is being used, and how likely that revenue is to hold over time.
Gross margin was another major theme. AI startup gross margin matters because it affects how much room a company has to reinvest, adapt, and scale. But there is some nuance here. Some founders are willing to accept lower margins early to prove demand, then optimize costs later once the market pull is real.
Why AI Pricing Models Are Changing
Another core part of a founder’s AI startup strategy is pricing. The panel argued that the traditional SaaS contract is under pressure as AI changes what customers expect from software and how quickly they expect value.
What is changing:
- Buyers want faster proof of ROI.
- Procurement teams are pushing back on long commitments.
- More founders are experimenting with hybrid models.
- Outcome-based pricing is gaining traction in some categories.
This is why SaaS pricing models for AI are starting to look different from earlier software eras. Pricing is still fluid, but the direction appears to be a combination of platform fees and usage-based pricing. In some categories, especially customer support, outcome-based pricing is becoming easier to justify because success is easier to measure.
That conversation also tied into the rise of the agentic AI business model. If an AI system can actually complete a task or resolve an issue, companies have a stronger case for pricing around outcomes rather than access alone. Outside of customer support, though, those models are still taking shape.
Why Atlanta Is Well-Positioned for the AI Era
Atlanta is in a strong position to lead the AI era. According to the State of Startups in the Southeast Report (2025) from BIP Ventures, deal counts are down, but average check size and overall capital are up across the region. Information technology and healthcare continue to lead, driven in part by growing AI adoption.
Here are more reasons that Atlanta stands out in the Southeast startup ecosystem:
- Strong access to enterprise buyers
- Deep relevance in fintech, logistics, and healthcare
- Proximity to Georgia Tech talent
- A culture of startup capital efficiency that AI founders can benefit from
- Stronger in-person networks in an increasingly noisy digital environment
As AI floods the market with more automation, outreach, and content, trust and real relationships become even more valuable. Atlanta offers founders direct access to customers, operators, and investors, where those connections can still create real momentum.
Atlanta’s history of capital efficiency may now be an advantage rather than a limitation. In an environment where smaller teams can build more, startups do not necessarily need Bay Area-style funding levels to create meaningful momentum. That makes the region especially well-suited for this next phase of developing a strong AI startup strategy.
How Investors Are Using AI Inside Their Own Firms
AI tools for founders and investors are already moving beyond experimentation. Members of the panel explained how they are already using AI across sourcing, diligence, note-taking, follow-up, legal review, and portfolio support.
The strongest AI use cases are often the ones that remove repetitive work quickly and let teams focus more on judgment.
Here are a few examples the panel shared:
- Filtering inbound opportunities
- Summarizing meetings and generating next steps
- Drafting memos and pulling public comps
- Reviewing legal language
- Supporting event planning and operations
Why Ethics Still Matter in an AI-Driven Market
The panel closed with a reminder that an AI startup strategy is most importantly about responsibility. Garrard urged the audience to think seriously about how AI shapes labor, society, and the kinds of companies getting funded in the first place.
The panel closed with a reminder: AI will change how value is created, but founders and investors will still shape where that value goes.
That is why ethics and governance can’t be layered in later. The strongest companies won’t just move quickly or generate financial upside; they will build something useful, responsible, and meaningful as the market evolves.
What Venture Atlanta Makes Clear About the Future of AI
Creating an AI startup strategy is not getting simpler. Founders have more tools than ever, but they are also navigating faster product cycles, changing pricing expectations, deeper scrutiny around revenue quality, and growing pressure to build responsibly.
The companies most likely to break through will be the ones that stay grounded in a real customer problem, build durable differentiation, and adapt as the technology keeps evolving. Just as importantly, they will understand what customers truly value and keep the human impact of their work in view.
That balance between innovation, discipline, and responsibility was at the center of this panel at Venture Atlanta, and it’s what will shape the next wave of standout companies.
Want to keep up with the conversations shaping the Southeast startup ecosystem? Follow Venture Atlanta on Instagram and LinkedIn and register for this year’s conference!
Frequently Asked Questions
How can AI startups compete with OpenAI and other large platforms?
The strongest AI startup strategy is not to compete with OpenAI head-on. As the panel discussed, founders are more likely to win by building around context, customer relationships, workflow ownership, and operational complexity. Large platforms may shape the foundation, but they will not own every use case or industry-specific application.
What is the difference between an AI-native and an AI-enabled business?
An AI-native business is built around AI as part of its core product foundation. The distinction between an AI-enabled vs AI native business becomes clearer when a company is adding AI to improve an existing product or workflow. Both can create value, but investors increasingly want to understand where the actual moat comes from and how durable that advantage will be as the technology evolves.
How are AI pricing models changing for startups?
The panel noted that SaaS pricing models for AI are becoming more fluid. Buyers want faster proof of value, and founders are experimenting with platform fees, usage-based pricing, and outcome-based structures. This is especially relevant in areas like customer support, where an agentic AI business model can tie pricing more directly to measurable results.
What metrics do investors prioritize when evaluating AI startups?
Investors are looking beyond topline growth. The panel emphasized usage, retention, quality of revenue, AI startup gross margin, and AI startup time to value as some of the most important signals. These metrics help investors understand not just how fast a company is growing, but how durable and efficient that growth really is.
Why is Atlanta a strong market for AI startups?
Atlanta stands out in the Southeast startup ecosystem because it offers enterprise access, strong fintech, logistics, and healthcare relevance, proximity to Georgia Tech talent, and a culture of capital efficiency. The panel also pointed out that in a market flooded with digital noise, Atlanta’s strong in-person networks may become even more valuable for founders building trust and momentum.
What are the biggest risks AI startups face right now?
The biggest risks discussed during the panel included weak differentiation, unclear pricing models, poor revenue durability, and moving too fast without thinking through governance or long-term implications. The conversation made clear that AI ethics startups need to think about responsibility early, not after the product is already in the market.
