The Real Reason Enterprise AI Projects Stall w/ Larissa Schneider Unframe | S2E12

Guest: Larissa Schneider — For years I watched smart companies invest in enterprise AI, then struggle to make it do anything useful. The tools were clunky, the integrations were painful, and the ROI was hard to find. That's why I co-founded Unframe: to build AI that goes live in weeks, not months, without ripping out the systems already in place. I spent a decade leading GTM and marketing at companies like Nutanix and Noname Security, working at the intersection of complex technology and the people who have to use it. At Unframe, we're building the Managed AI Transformation Platform: one foundation that compounds with every use case we deliver. We've now raised $100M, including a $50M Series B, because we believe the gap between AI's potential and its real-world impact is solvable.

In this episode, we break down why most AI projects fail, not because of the technology, but because of how they are managed. Larissa Schneider, Co-Founder and COO of Unframe, shares that the real gap is not in the models, but in execution. Many companies jump into AI without clearly defining the business problem they want to solve. Strategy decks look good, but when it comes to operations, projects stall. If there is no clear goal, no defined ROI, and no trust in deployment, AI will not deliver real value. We also talk about what actually works in enterprise AI. Start with the problem, not the tool. Measure outcomes. Focus on workflows that are broken, manual, or slow. The companies that succeed are not the ones chasing every new AI product, but the ones building systems that fit their business, their people, and their existing tech stack. There is also a strong reminder that AI is not just about automation. It requires alignment between IT and business teams, clear governance, and people who understand how to use it effectively. This conversation is a reality check for leaders. AI is not magic. It is a tool that only works when paired with clear direction, strong execution, and trust. The companies that treat AI as a business solution, not just a trend, are the ones that will move from idea to real impact. Tune in and learn what it really takes to deploy AI in the enterprise and get results that matter. -- TIMESTAMPS 00:00 Intro – The real gap in AI projects 00:12 Why business owners must expect ROI from AI 00:24 Deploy AI with trust or don’t deploy at all 01:12 Most enterprise AI pilots stall – here’s why 01:40 Welcome to The Human Protocol podcast 02:08 Why enterprise AI fails: organization, not technology 02:35 Larissa Schneider introduction – Co-founder & COO of Unframe 03:36 From Nutanix to cybersecurity to founding Unframe 04:24 Where real AI projects are failing in 2025 05:02 Stop buying hype tools – solve actual business problems first 06:40 Build vs Buy vs Consultancy – what actually works 09:15 The danger of strategy decks without operational reality 12:30 Common failure: no clear success criteria for pilots 15:50 How governance, risk & politics kill AI adoption 19:20 Real-world trust & security barriers in enterprises 23:40 Why most “cool” AI demos never scale 27:10 The power of starting small with high-priority pain points 31:50 AI agents – current reality vs future hype 36:20 Foundational models vs wrapper apps – where the value really is 40:10 Anthropic, cloud code, co-works – what matters right now 43:50 Guardrails for AI – what policymakers should focus on 46:00 Balancing regulation without killing competitiveness 47:48 When is efficiency enough? – The human side of AI 48:30 Final advice – start with your real business pain 48:53 Closing & call to action for AI leaders

Conversation Summary

In this episode of The Human Protocol Podcast, Larissa Schneider, Co-Founder and COO of Unframe, examines the impediments in enterprise AI projects, highlighting that the real challenges aren't technological but organizational. The conversation unveils how many companies approach AI by prioritizing tools over business goals, leading to stalled projects. For effective AI deployment, Larissa insists on starting with clear business problems, defining measurable outcomes, and aligning tools with existing workflows and structures. Moreover, she stresses the importance of integrating IT and business teams to foster trust and drive successful AI integration.

AI Success Hinges on Clear Goals, Not Tools

Larissa underscores that enterprises often focus on adopting AI tools without clearly defining the business problems they aim to solve. She advocates for starting with specific business objectives and measurable outcomes to ensure AI projects provide real value, rather than being influenced purely by trending technologies.

Navigate AI with a Build vs. Buy Strategy

Instead of defaulting to building in-house or buying off-the-shelf solutions, companies should evaluate their unique needs and capabilities. Whether building custom platforms or consulting external experts, aligning choices with organizational goals is critical to reducing the risk of fragmented systems and unmet expectations.

The Human Aspect of AI Adoption

For AI initiatives to succeed, organizational alignment is essential. Larissa highlights that success depends on robust collaboration between IT and business units, ensuring that solutions meet actual user needs and integrate seamlessly with existing systems, thus avoiding resistance and fostering trust.

Security and Governance Are Key in Enterprise AI

Larissa emphasizes that enterprises must prioritize internal benchmarks and regulatory compliance in AI deployments. By offering on-premise solutions and allowing customers to choose their preferred language models, Unframe addresses security concerns and aligns with corporate governance protocols.

AI Offers Solutions, Not Magic

AI implementations should be viewed as solutions to specific business challenges rather than catch-all remedies. Larissa notes that organizations must focus on leveraging AI where it delivers tangible business improvements, rather than pursuing it as a trendy initiative.

Final Thoughts

The discussion with Larissa Schneider offers a pragmatic view on deploying AI in enterprises, emphasizing the importance of strategic alignment and organizational readiness over technological novelty. Her insights urge companies to build on their strengths, linking AI tools directly to business objectives and workflows for sustainable success.

Key Takeaways

Frequently Asked Questions

What is the biggest reason AI projects stall in enterprises?

AI projects often stall not because of the technology itself, but due to poor alignment between strategic objectives and operational execution, as well as a lack of organizational readiness.

How can enterprises align AI initiatives with their business goals?

Enterprises should start AI projects by clearly defining the business problems they aim to solve and establishing measurable outcomes, ensuring that the tools and technologies they adopt are aligned with these goals.

Why is collaboration between IT and business teams crucial for AI success?

Collaboration ensures that AI solutions meet user needs, integrate with existing systems, and are embraced by both IT and business units, reducing resistance and maximizing impact.

What role does security play in AI deployment in enterprises?

Security is paramount, requiring enterprises to adhere to internal and regulatory benchmarks, and to choose deployment models—like on-premise solutions—that protect data integrity and privacy.

How can businesses effectively evaluate the ROI of AI projects?

Businesses should assess ROI on a project-by-project basis, gauging improvements in efficiency, customer response time, and manual task reduction, while ensuring these metrics align with broader business objectives.