advikipedia
Joined 1 year ago
Joined 1 year ago
The most successful deployment strategies we’ve seen started with:
simple and specific use cases with clear value drivers, that were low risk yet medium impact;
weren’t majorly disruptive to existing workflows;
preferably automating a task that the human user dislikes (or was outsourced);
the output of the workflow can be easily/quickly verified by the human for accuracy or suitability; and
demonstrated clear ROI quickly
Given the current levels of technological development, AI Agents work best when narrowly applied to very specific tasks and operating under a specific context. For instance, we’ve seen this in healthcare with revenue cycle management processes (claim and denial management) that health systems were already outsourcing to third-party providers.
The land-and-expand strategy for AI agents is very different to traditional SaaS. Given enterprises are increasingly under pressure from the C-Suite to incorporate AI into their work, there are plenty of opportunities for startups to “land” but it’s much harder to “expand” – and not only that, it’s taking much longer to expand even when they want to expand, because it’s a use case by use case rollout.
Much like the iconic Volkswagen ad, sometimes it’s better to “Think Small” and build trust first, rather than attempt too many use cases (and excessively complex use cases) right off the bat.
A few patterns emerged that might be relevant to anyone working on applied AI or automation:
- The main blockers aren’t technical. Most founders pointed to workflow integration, employee trust, and data privacy as the toughest challenges — not model performance.
- Incremental deployment beats ambition. Successful teams focus on narrow, verifiable use cases that deliver measurable ROI and build user trust before scaling autonomy.
- Enterprise adoption is uneven. Many companies have “some agents” in production, but most use them with strong human oversight. The fully autonomous cases remain rare.
- Pricing is unresolved. Hybrid models dominate; pure outcome-based pricing is uncommon due to attribution and monitoring challenges.
Infrastructure is mostly homegrown. Over half of surveyed startups build their own agentic stacks, citing limited flexibility in existing frameworks.
The article also includes detailed case studies, commentary on autonomy vs. accuracy trade-offs, and what’s next for ambient and proactive agents.
If you’re building in this space, the full report is free here: https://mmc.vc/research/state-of-agentic-ai-founders-edition...
Would be interested to hear how others on HN are thinking about real-world deployment challenges — especially around trust, evaluation, and scaling agentic systems.