Dr. Maya Chen – VP of AI, Fortune 500 Enterprise

On leading AI transformation in a 40,000-person organization

Q: What is the single biggest mistake enterprises make when adopting AI?

A: “They treat it like a technology project when it is really a change management project. I have seen companies spend millions on AI infrastructure and then wonder why adoption is 5%. The technology works. The bottleneck is always organizational: training, incentives, and cultural willingness to change workflows that have been in place for decades.”

Q: How do you measure success for enterprise AI?

A: “We track three tiers. Tier 1 is adoption: what percentage of employees use AI tools weekly? We are at 62% now, up from 8% eighteen months ago. Tier 2 is productivity: we measure time saved per employee per week, currently averaging 4.2 hours. Tier 3 is business impact: revenue influenced by AI decisions, which we can now trace to about $120M annually. Most companies only measure Tier 1 and wonder why leadership is not excited.”

Q: What advice would you give to someone starting an enterprise AI program?

A: “Start with a problem, not a technology. Find the process that everyone complains about, that costs the most, or that has the longest queue. Apply AI there first. One visible win does more for adoption than any town hall presentation.”

Alex Rivera – Founder & CEO, AI-Native Startup (Series B)

On building a 200-person company where AI does the work of 1,000

Q: What does “AI-native” actually mean in practice?

A: “It means every process is designed around AI capabilities from day one. Our customer support is 90% AI with human escalation. Our engineering team uses AI to write 70% of their code. Our marketing team produces 50 pieces of content per week with 3 people. We did not bolt AI onto existing processes. We designed processes assuming AI would handle the volume and humans would handle the judgment.”

Q: Does this model work for all businesses?

A: “No. It works best for digital-first businesses where the inputs and outputs are information rather than physical goods. A software company can be fully AI-native. A construction company probably cannot, though they can be AI-augmented in planning, scheduling, and documentation. Know where you sit on that spectrum.”

Q: What is your biggest challenge as an AI-native company?

A: “Quality control at scale. When AI produces 50 articles, 200 support responses, and 30 code commits per day, you need sophisticated review systems. We built internal tools that flag AI outputs below confidence thresholds for human review. Without that, you get quantity without quality, which destroys trust faster than manual processes ever could.”

Dr. Amara Okafor – AI Ethics Researcher & Policy Advisor

On navigating the ethical and regulatory landscape of business AI

Q: Is AI regulation helping or hurting innovation?

A: “Both, and that is the point. Regulation forces companies to think about consequences before deployment, which slows down some innovation but prevents the worst outcomes. The EU AI Act is imperfect, but it has pushed every major AI vendor to improve transparency, documentation, and bias testing. Those are good things. The companies complaining loudest about regulation are often the ones with the most to hide.”

Q: What is the most underestimated ethical risk in business AI?

A: “Automation bias: humans over-trusting AI decisions because they come from a machine. A credit analyst who overrides their own judgment because the AI model disagrees is a bigger risk than the model itself being wrong. We need to train people to use AI as input, not as authority. That is a cultural challenge, not a technical one.”

Q: What should a small business do about AI ethics if they have no dedicated ethics team?

A: “Three things. First, document every AI system you use and what decisions it influences. Second, ask yourself: if this AI decision were wrong, who would be harmed and how would we know? Third, create a simple review process where a human checks AI decisions on a random sample basis. You do not need a PhD in ethics. You need basic accountability structures.”

Jordan Park – Managing Partner, AI-Focused Venture Fund

On what investors look for in AI companies and where the market is heading

Q: What separates AI startups that succeed from those that fail?

A: “Distribution, not technology. I have seen brilliant AI technology fail because the founders could not reach customers, and mediocre technology succeed because the founders understood their market deeply. In 2026, the technology layer is increasingly commoditized. The defensible moat comes from proprietary data, domain expertise, and customer relationships.”

Q: Where are the biggest AI investment opportunities right now?

A: “Vertical AI applications. Horizontal AI tools (general chatbots, general writing assistants) are a red ocean with razor-thin margins. But AI built for specific industries – legal, healthcare, construction, agriculture – commands premium pricing because domain expertise is hard to replicate. We are backing companies that combine strong AI capabilities with deep industry knowledge.”

Q: What is your most contrarian AI prediction for the next 2-3 years?

A: “Most AI startups will not survive the margin compression coming as foundation model prices drop 10x every 18 months. Companies built as thin wrappers around APIs will get squeezed out. The survivors will be those who use AI to build something genuinely new, not those who reskin existing AI capabilities with a nicer interface. The market will consolidate significantly by 2028.”

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