Evangelos Simoudis on Adoption Trends, Use Cases, Success Factors & Challenges.
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Evangelos Simoudis is Managing Director at Synapse Partners, a firm that helps corporations apply AI and invests in startups developing data-driven AI applications. This episode explores the current state of enterprise AI adoption, distinguishing between the steady progress of traditional AI and the experimental phase of generative AI. It highlights key success factors like long-term experimentation and process change, identifies leading use cases (customer support, programming, documents), and discusses challenges in data strategy, model selection, and operations. The conversation also touches on the nascent state of agentic systems and the outlook for near-term production deployments.
Interview highlights – key sections from the video version:
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- Market data sources and overall AI-adoption trends
- Agentic-AI pilots and the enterprise experimentation bottleneck
- Top early Gen-AI use-cases: customer support, coding & “intelligent documents”
- Which industries are (and aren’t) moving Gen-AI to production
- Attributes of companies that make the jump from POC to production
- Redesigning business processes to become “AI-first”
- Data-strategy overhauls and the open-vs-proprietary model dilemma
- Hyperscalers vs. “best-of-breed” – where enterprises source their models
- The ML-Ops / LLM-Ops tooling gap & risk-management opportunities
- Production architectures: RAG’s dominance and early talk of Graph-RAG
- Enterprise curiosity about “agents” – definitions and real capabilities
- Orchestration & tooling challenges for agent workflows
- Near-term outlook: scaling the three leading Gen-AI use-cases
- Autonomous-vehicle debate: camera-only vs. multi-sensor systems & tele-ops
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Related content:
- A video version of this conversation is available on our YouTube channel.
- Nestor Maslej → 2025 Artificial Intelligence Index
- Evangelos Simoudis → From Hype to Reality: The Current State of Enterprise Generative AI Adoption
- Robert Nishihara → The Data-Centric Shift in AI: Challenges, Opportunities, and Tools
- What AI Teams Need to Know for 2025
- AI Unlocked – Overcoming The Data Bottleneck
- AI Agents: 10 Key Trends & Challenges You Need to Know
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Transcript
Below is a heavily edited excerpt, in Question & Answer format.
Current State of AI Adoption
What is the current state of AI adoption in enterprises, particularly regarding generative AI versus traditional AI approaches?
There’s growing interest in AI broadly, but it’s important to distinguish between generative AI and discriminative AI (also called traditional AI). Discriminative AI adoption is progressing well, with many experimental projects now moving to deployment with allocated budgets.
For generative AI, there’s still a lot of experimentation happening, but fewer projects are moving from POCs to actual deployment. We expect more generative AI projects to move toward deployment by the end of the year, but we’re still in the hype stage rather than broad adoption.
As for agentic systems, we’re seeing even fewer pilots. Enterprises face a “bandwidth bottleneck” similar to what we see in cybersecurity – there are so many AI systems being presented to executives that they only have limited capacity to evaluate them all.
How do you gather data to form your view of enterprise AI adoption?
We triangulate four information streams:
- Firsthand data from our AI-focused portfolio companies and their enterprise clients
- Confidential advisory work Synapse Partners does with large corporations
- Conversations with sell-side investment-bank analysts tracking public and late-stage private firms
- Industry analysts who cover both private and public markets
By fusing these four lenses, we can see past isolated anecdotes and identify real adoption patterns.
Successful Use Cases & Leading Industries
In which business functions are generative AI projects successfully moving from pilots to production?
Three major use cases stand out:
- Customer support – various types of customer support applications where generative AI can enhance service
- Programming functions – automating software production, testing, and related development activities
- Intelligent documents – using generative AI to automate form-filling or extract data from documents
These three areas are where we see the most significant movement from experimentation to production, both in solutions from private companies and internal corporate efforts.
Which industries are leading the adoption of generative AI?
Financial services and technology-driven companies are at the forefront. For example:
- Intuit is applying generative AI for customer support with measurable improvements in customer satisfaction and productivity, reporting 4-5× developer-productivity gains
- JP Morgan and Morgan Stanley are seeing productivity improvements in their private client divisions, where associates can prepare for client meetings more efficiently by using generative AI to compile and summarize research
- ServiceNow is having success in IT customer support, reporting over $10 million in revenue directly attributed to AI implementations and dramatic improvements in handling problem tickets more efficiently
Interestingly, automotive is not among the leading industries in generative AI adoption. They’re facing more immediate challenges like tariff issues that are taking priority over AI initiatives.