Automatic cover art

Automatic

Automatic

Written by: Eric Lamanna
Listen for free

About this listen

 Podcast for Automatic.co and LLM.co, the AI automation specialists.2026 Automatic.co
Episodes
  • Multimodal Private LLMs: Why They’re Becoming the Enterprise Standard

    Multimodal Private LLMs: Why They’re Becoming the Enterprise Standard
    May 15 2026
    Episode summary: Multimodal private LLMs are quickly moving from experimental concept to enterprise priority. In this episode, we expand on the LLM.co article “Why Multimodal Private LLMs Are Becoming the Enterprise Standard” and explore why business leaders are increasingly interested in AI systems that can process text, images, audio, video, and structured data inside secure, internally governed environments. The conversation is aimed at executives, operators, technical leaders, and buyers trying to understand why the next phase of enterprise AI will be defined not just by model intelligence, but by multimodal reasoning, privacy, and governance.The core argument is straightforward: most enterprises do not operate on text alone. Their most valuable signals are scattered across screenshots, dashboards, contracts, maintenance logs, support transcripts, meeting recordings, product demos, voice notes, spreadsheets, diagrams, and structured operational data. A unimodal model can be useful, but it can only understand one narrow slice of that environment at a time. A multimodal private LLM changes the equation by allowing the organization to bring those signals together into one reasoning layer without sending its most sensitive information outside the company’s own security perimeter.That matters because the real business value of multimodal AI is not just that it can look at an image or listen to audio. The value is that it can connect multiple data types into a richer, more useful context. When a system can align a screenshot with a support transcript, a thermal image with maintenance notes, or a meeting recording with slides and chat activity, it starts generating operational insight that is difficult to achieve through manual synthesis or text-only AI. This is where multimodality becomes multiplicative rather than merely additive.What this episode coversWhy enterprise AI is shifting from text-only productivity tools to multimodal reasoning systems.How multimodal models combine text, audio, visual, and structured signals into denser operational context.Why private deployment is becoming critical for regulated, sensitive, or strategically valuable enterprise data.How governance, permissions, logging, and policy enforcement must be built into the model workflow itself.The role of multimodal AI in meetings, internal knowledge work, training, product development, support operations, and cross-functional coordination.Why modularity and open standards matter when making long-term enterprise AI architecture decisions.A major theme throughout the episode is that privacy is not separate from capability. For enterprise buyers, the most powerful AI system in the world is still the wrong choice if the governance model is unacceptable. That is why private multimodal LLMs are so compelling. They make it possible to pursue higher-value use cases — including those involving internal audio, image, design, operational, legal, or financial data — without creating the same level of risk that often accompanies public model usage. For leadership teams, this is what moves AI from curiosity to procurement-ready infrastructure.The episode also explores why governance is becoming part of the product itself. In enterprise settings, it is not enough to bolt on compliance after deployment. Models working across multiple modalities need policy controls that apply to every type of signal they touch. Permissions, auditability, review rules, logging, and data handling controls must be native to the workflow. The more capable the model becomes, the more important those controls become. Done well, governance should not feel like friction. It should quietly make ambitious AI use cases safe enough to scale.We also examine some of the most practical use cases. One is meeting intelligence: systems that listen to calls, transcribe them, interpret slides and chat messages, and generate structured summaries with action items while the conversation is still fresh. Another is product and engineering coordination, where a multimodal model can compare mocks, requirements, user feedback videos, and implementation changes in one loop. We also talk about internal training, where companies can create adaptive learning from their own recordings, support cases, and documentation rather than relying on generic slide decks that employees ignore.Another key idea is that multimodal private LLMs may become the connective tissue for enterprise knowledge. In many organizations, the problem is not lack of data. It is that useful information lives in too many formats and too many systems. Multimodal reasoning helps turn those fragments into a coherent operational narrative. That has implications for faster root-cause analysis, better internal search, stronger compliance review, improved knowledge transfer, and more consistent decision-making across teams.The episode also addresses future-proofing. Enterprise buyers should not think about this category as...
    Show More Show Less
    16 mins
  • Agentic AI for Healthcare and Life Sciences: From Copilots to Workflow Ownership

    Agentic AI for Healthcare and Life Sciences: From Copilots to Workflow Ownership
    May 14 2026

    In this episode, we break down Automatic.co's report on agentic AI for healthcare and life sciences and explore where the category is moving from simple AI assistance toward true workflow ownership.

    The report's core idea is that healthcare does not just need smarter tools. It needs systems that remove operational steps. That is why the conversation focuses on agentic AI as a move from software as a tool toward software as labor in carefully bounded, high-value workflows.

    We cover how this shift applies across:

    • clinical workflow automation, including documentation, chart summarization, inbox management, and discharge coordination
    • administrative and operational automation, including prior authorization, denial management, claims support, referral routing, and scheduling
    • patient coordination and navigation, where agents can help move people through fragmented care workflows
    • life sciences operations, including trial matching, recruitment workflows, pharmacovigilance, medical writing, and regulatory support

    A major theme in the article is that the market is no longer asking only whether AI can generate useful output. It is asking whether AI systems can manage workflow states, coordinate next steps, and reduce human friction across real operating systems.

    That is why integration depth matters so much. In healthcare, the most valuable systems will not necessarily be the ones with the most impressive standalone models. They will be the ones that connect into EHRs, revenue-cycle systems, patient communication channels, payer workflows, research operations, and documentation processes in ways that are safe, auditable, and operationally useful.

    We also talk through the three major reasons the market is moving now:

    • model capability has improved enough to support more reliable multi-step work
    • enterprise healthcare infrastructure is more digitized and interoperable than it was a decade ago
    • staffing pressure and labor shortages are forcing organizations to find leverage

    One of the most important takeaways is that administrative work is often the best first wedge. Revenue-cycle, authorization, denial, and operational workflows can deliver visible ROI with lower clinical risk than many direct-care use cases. That makes them attractive early deployment targets for agentic systems.

    At the same time, the episode explores why trust, auditability, and compliance are not optional extras in this market. They are adoption requirements. In healthcare and life sciences, AI systems only become valuable when teams can understand what the system did, what information it used, and where human review must remain in place.

    The broader takeaway is that agentic AI in healthcare and life sciences is not just a better chatbot story. It is an attempt to redesign how work moves through clinical, administrative, and research systems. The biggest winners will likely be the companies that remove steps, integrate deeply, and prove value within real workflows rather than abstract pilots.

    Referenced links:

    • Agentic AI for Healthcare & Life Sciences
    • Automatic.co
    Show More Show Less
    17 mins
adbl_web_anon_alc_button_suppression_c
No reviews yet