Three-day virtual conference for AI builders, technology leaders, founders and investors, covering AI strategy and governance, startups, and production AI engineering.
When
Oct 14, 2026 – Oct 16, 2026
Where
Online event
Attendance
Virtual
Status
scheduled
Event type
Conference · Published by organizer
Registration
Free Explorer access to selected main-stage keynotes, ceremonies and community chat; paid passes for broader sessions and workshops. See the ticket comparison for current terms.
Organizer
AI Builders Network
AI executives, practitioners, founders and investors; worldwide virtual participation including North America.
Lightning talk · Editorial classification · Date to be announced
Most AI products are built to handle the case when the AI is right. The hard part is designing for when it's not.
I'm a UX designer at Bevaya, an AI-powered insurance automation platform. My job is to design the screen where trained reviewers catch, correct, and override AI extractions in real time, at volume, under time pressure, with real liability. That screen is the last line of defense between a hallucinated field value and a bad insurance decision.
Over the past year I've learned things about designing for human-AI collaboration that I couldn't have learned anywhere else. Show the AI output even when it's wrong, blank fields are harder to correct than bad ones. Confidence signals work, but the moment you show a percentage you lose the reviewer's trust. Hard stops before submission feel safe but kill throughput at scale. The document is evidence; the extracted data is the work.
These aren't UX opinions. They're decisions we made, broke, and remade with real reviewers on real workflows.
If you're building an AI product, at some point your users will need to catch what your model missed. This talk is about what happens in that moment, and what the UX of that experience determines about whether people trust your product or abandon it.
Talk · Editorial classification · Date to be announced
Every day, new AI products launch but only a small percentage achieve meaningful customer adoption or sustainable revenue. Building an impressive AI model is no longer the competitive advantage. Winning customers, earning trust, proving ROI, and creating a repeatable go-to-market engine are.
In this session, Shweta Agrawal shares a practical playbook for transforming AI innovation into scalable business growth. Drawing from her experience advising startups, mentoring founders, and leading AI product and go-to-market initiatives, she will explore why many AI companies struggle after launch and what the highest-performing teams do differently.
Attendees will learn how to identify true product-market fit, position AI around customer outcomes instead of technology, design pricing and commercialization strategies, accelerate enterprise adoption, and build a repeatable revenue engine that scales.
Whether you're launching your first AI product or scaling an enterprise AI platform, this session provides practical frameworks, real-world case studies, and actionable strategies you can apply immediately
Lightning talk · Editorial classification · Date to be announced
AI has collapsed the cost of building a product — but distribution hasn't gotten any easier, and most technical founders have no idea where to start. Drawing on my background running growth and lifecycle marketing across marketplace and consumer businesses, and recent conversations with dozens of early-stage AI founders, this talk breaks down how AI founders can get their product in front of real customers and start gaining traction. I'll share a concrete framework for building a lightweight growth/CRM foundation from day one — what tools to use (and what to skip), what actually moved metrics at scale, and what I got wrong. Attendees will leave with a practical checklist they can apply to their own product that week, not just a theory of growth.
Talk · Editorial classification · Date to be announced
Delphi builds digital versions of people. The first answer is the product: if someone's Delphi cannot speak from their life on day one, trust never recovers. We scaled content training from about 5K to about 100K items per day at 99.8% workflow success using Temporal durable workflows so crashed workers resume instead of losing half-trained minds. Attribution is the silent failure: multi-hour podcasts mix speakers, so we use onboarding voice clips to train only on the right segments. Agents grow a temporal knowledge graph for retrieval, and we borrow stylometry to score whether a Delphi matches its person (about 70% on real conversations vs about 3% chance). A practitioner talk on pipelines, attribution, graphs, and identity evals.
Talk · Editorial classification · Date to be announced
AI world is evolving fast, from prompt engineering to context engineering, then harness engineering to loop engineering. This also shows a clear advancement in industry use cases.
The next generation of agents will not be improved by better prompts alone. They will improve through harness evolution: better tools, skills, memory, compaction, artifacts, evals, observability, and feedback loops.
In this talk, I’ll show why many production agents plateau even as models improve. Their prompts, tool lists, evals, and orchestration were often built for older model behavior. A naive model swap can look underwhelming because the harness is hiding the improvement.
Talk · Editorial classification · Date to be announced
AI demos are easy. Shipping something a CFO will actually rely on is not.
I spent the last year building an agentic copilot for corporate treasury — the kind of system that has to answer "what's our cash position in EMEA right now?" without making anything up, without leaking data, and without falling over when an Azure region has a bad afternoon. This talk is about what I learned along the way.
I'll walk through the real architecture: how we wire up tool-calling LLMs, why we ended up with a mix of RAG and direct database queries, how multi-region failover on Azure OpenAI actually works in practice, and what it took to plug all of this into Microsoft 365 Copilot as a declarative agent. I'll also spend time on the boring-but-critical stuff — auth, audit logging, observability — because that's usually where enterprise AI projects quietly die.
If you've ever built a slick prototype and then watched it struggle the moment it met production, this one's for you. You'll leave with a blueprint you can steal, a list of traps to avoid, and a clearer sense of where today's agent frameworks help and where you're still on your own.
Talk · Editorial classification · Date to be announced
Everyone is talking about what Agentic AI can do. Far fewer are asking what organisations need in order to deploy it successfully at scale.
As AI systems evolve from assistants to autonomous decision-makers, enterprises face a new challenge: creating the governance, accountability, and trust frameworks required for AI to operate safely and effectively. The organisations that succeed will not necessarily have the most advanced models—they will be the ones that build the strongest operating systems around them.
This session explores why trust is becoming the most underappreciated competitive advantage in AI adoption and how leading organisations are designing governance frameworks that accelerate innovation rather than slow it down. Attendees will gain practical insights into the future of enterprise AI, the rise of agentic systems, and the operating principles organisations need to thrive in an AI-first world.
Talk · Editorial classification · Date to be announced
Your next customer won't visit your website. Their AI agent will buy on their behalf - either inside LLM apps like Gemini, ERP systems like SAP or inside chatbot on merchant's website. The impact of agentic commerce is different for a consumer whose Gemini is browsing for a laptop and for an enterprise procurement team whose SAP buyer agent is reordering hardware against a negotiated contract.
This session takes the merchant's platform perspective: the product feeds and commerce APIs that make agentic purchasing possible across the first-party and third-party surfaces and focuses on the three strategic decisions that determine whether a merchant can serve the user journeys effectively. It also surfaces two structural failure modes to be aware of.
Talk · Editorial classification · Date to be announced
Everyone's building AI agents. Few are shipping them into enterprise production with governance, cost control, and user trust intact.
As the Global Rovo AI Practice Lead at Eficode, I've spent the last 18 months helping organisations move from sandbox experiments to governed, production-running agents, from 50-user pilots to 25,000-seat rollouts. I've watched the same failure patterns repeat across every engagement, and none of them are the ones people expect.
This talk shares the honest, field-tested lessons from deploying AI agents in regulated enterprise environments: what breaks when you hand agents real workflows, why the "blank agent problem" kills more initiatives than technical complexity ever does, and what actually happens when every team starts building their own agents without a shared governance model.
I'll cover:
Why 95% of enterprise AI agent investments show no measurable return and the three patterns that separate the ones that do
The governance gap: what happens when every team builds their own agent with their own knowledge sources, and you realise nobody owns the question "what can this agent see?"
A real customer case where we turned scattered compliance inputs across 70+ attachments into structured, actionable planning (including what failed first)
The blank agent problem: why giving people a powerful agent with no structure is worse than giving them nothing
Adoption patterns that survive contact with real users vs. the ones that collapse after the workshop high
No product demos. No theory. Just the patterns, anti-patterns, and hard-won lessons from the field.
Talk · Editorial classification · Date to be announced
Over the past year I've turned my own operating rhythm into a stack of AI agents: one finds and qualifies small business leads while I'm teaching a class, one drafts and formats keynote decks in my brand voice, one tracks every open loop across a Salesforce role, a consulting practice, and two university teaching gigs so nothing falls through. This isn't a case study about a department rolling out AI — it's a builder's field report on what happens when you hand your own calendar, pipeline, and content engine to agents and watch where they succeed, where they quietly break, and what that teaches you about designing agents for other people. I'll walk through the actual systems (NadiaOS, my BDR workflow, my content pipeline), what failed first, and the design principles that emerged from running agents against the messiest, highest-stakes dataset there is: my own life.
Panel discussion · Editorial classification · Date to be announced
Everyone is talking about building AI. Far fewer people are talking about what it's like to buy, evaluate, govern, and deploy AI inside a real business.
As Chief Operations & Technology Officer at Face Reality Skincare, I sit on the customer side of the table. Over the past year, I've evaluated AI vendors, led internal AI adoption efforts, launched an AI Council, tested emerging tools, and worked to identify where AI can drive meaningful business impact across operations, customer experience, supply chain, and technology.
This session will share lessons learned from the perspective of an end user and business leader rather than an AI vendor or product builder. We'll discuss what business leaders are actually looking for, where AI creates the most value, why many organizations struggle to move beyond experimentation, and what technology providers can learn from the realities of implementation inside growing companies.
Attendees will leave with a clearer understanding of how AI decisions are made inside organizations and how to bridge the gap between AI capability and business value.
Talk · Editorial classification · Date to be announced
AI coding tools can generate a working interface in minutes, but working code is not the same as production-ready code. Generated UIs can introduce accessibility failures, fragile interactions, inconsistent components, and testing gaps that surface only after development moves downstream. This session presents a practical engineering approach for adding verification to AI-assisted frontend development. We will look at how design-system constraints, automated accessibility checks, test scaffolding, CI quality gates, and human review can work together to catch problems early without losing the speed benefits of AI. Attendees will leave with a framework they can adapt to teams already using AI coding assistants.
Talk · Editorial classification · Date to be announced
Most large companies now have a Responsible AI policy. It lives in a PDF, it leans on words like fairness and accountability, and almost no engineer has read it. Meanwhile the person shipping an LLM feature has to decide, in code, what the model is allowed to say and do. This talk is about closing the distance between those two documents.
Drawing on work building AI systems inside a regulated bank, I'll follow one principle from the policy statement down to the running system: what it claims on paper, and what it has to become in code before anyone trusts it. Expect real controls you can picture implementing. A vague rule like "no unfair outcomes" has to turn into a test that fails the build when the numbers drift. A model that saw sensitive data in training has to be blocked from repeating it to the wrong person. The interesting part is the mechanics, the actual point where a value becomes a line of code.
Policy and production are usually owned by different people who rarely sit in the same meeting. The space between them is where Responsible AI quietly succeeds or fails. This talk is for anyone on either side of that space who wants to understand how the other half works.
Fireside chat · Editorial classification · Date to be announced
The more AI we build, the more AI incidents we will create, and our existing operating models aren’t prepared for the volume, complexity, or speed of what’s coming.
Join Alex Layng, VP of Product, and Zach Burnett, CEO of RadarFirst, for a practical conversation about building a scalable approach to AI incident management. They’ll explore how organizations can define and identify AI hazards, determine when an incident creates reporting, regulatory, or litigation risk, and turn evolving requirements into consistent, defensible decisions.
Drawing on regulations and frameworks including the EU AI Act, GDPR, and the NIST AI RMF, Alex and Zach will examine what privacy, legal, compliance, and AI governance teams can learn from established regulatory practices and where new operating models are needed.
They’ll also share how RadarFirst is helping organizations codify complex laws, apply practical best practices, and manage privacy and AI incidents confidently in a rapidly evolving landscape.
Talk · Editorial classification · Date to be announced
Organizations around the world are investing heavily in AI, yet many struggle to move beyond pilots, proofs of concept, and isolated use cases. While the spotlight is often on the technology itself, the biggest barriers to success are rarely technical.
In this session, I will explore why AI is often the easiest part of the journey and why true transformation requires organizations to rethink leadership, operating models, processes, skills, and culture. Drawing on real-world experience leading technology and transformation initiatives, I will share practical insights on bridging the gap between AI ambition and business impact.
Attendees will leave with a clearer understanding of the organizational challenges that determine AI success, common pitfalls that slow adoption, and actionable strategies for building an AI-ready organization that can turn experimentation into lasting value.
Talk · Editorial classification · Date to be announced
AI systems are easy to demo but hard to trust in production.
As teams move from prototypes to real deployments, they hit the same wall. Models generate outputs, but production systems require guarantees. Hallucinations, inconsistent behavior, data leakage, and uncontrolled agent actions are not edge cases. They are structural gaps in today’s AI stack.
The problem is architectural.
In this talk, I introduce the concept of a Trust Layer, a missing runtime control plane between models, agents, and applications that makes AI systems reliable, enforceable, and safe to integrate into real workflows.
At the core is a practical pattern that brings control to non-deterministic systems by:
- validating and constraining inputs and outputs
- enforcing policy at runtime, not just in documentation
- coupling retrieval and grounding with enforceable constraints
- continuously evaluating and monitoring behavior in production
- maintaining consistent identity and trust boundaries across systems
Drawing on real-world architecture, I will show:
- where traditional LLMOps approaches break down
- how to design validation and evaluation pipelines beyond offline benchmarks
- how trust mechanisms reshape both single-agent and multi-agent systems
- the key trade-offs between centralized control and decentralized trust
You will leave with a practical blueprint for building AI systems that are not just deployable but observable, testable, and trustworthy by design.
Fireside chat · Editorial classification · Date to be announced
Most AI conversations happen in industries where speed matters more than scrutiny. Healthcare and Pharma / MedTech are the opposite: every model, every chatbot, every automated decision has to survive regulatory review, clinical accountability, and patient trust before it ever reaches a real user. In this session, Sam Kwan shares firsthand experience building and deploying AI inside pharmaceutical marketing and commercial operations, including a machine learning model that changed how a global pharma company allocated its field force, and one of the first PAAB-approved AI chatbots in Canada. Sam breaks down what actually changes when you build AI for a regulated industry: the tradeoffs, the stakeholders you don't expect, and the discipline it takes to move from prototype to something that survives legal, compliance, and medical review. For builders working in any high-stakes or regulated space, this session offers a grounded, practitioner's view of what it takes to ship.
Panel discussion · Editorial classification · Date to be announced
Artificial intelligence and energy are becoming two sides of the same coin. As AI drives unprecedented demand for power, data centres, grids, and cooling infrastructure, AI is simultaneously transforming how energy is generated, managed, and consumed. This session explores where the biggest opportunities lie across both "Energy for AI" and "AI for Energy," highlighting emerging technologies, investment trends, and the startups shaping the next wave of industrial innovation.
Sub sectors I'd underwrite today:
Advanced Cooling Hardware
40% of data center energy waste, defensible IP window, sticky enterprise lock-in before commoditization hits.
DC Power Architecture
Every internal conversion point is wasted energy; architectural shift is structural and creates years of enterprise switching costs.
Waste Heat Monetization
Turns data center thermal liability into revenue and anchors customer relationships at gigawatt scale.
Sub-sectors I wont:
Software-Only Energy Optimization
Commoditizing fast, no switching costs, crowded field already decided—margin compression incoming.
Speculative Generation Tech (Fusion, Advanced Geothermal)
2028 power crunch won't wait for 2030+ CapEx-heavy deployment cycles and regulatory limbo.
Grid-Scale Storage Without Locked Off-take
Merchant power kills unit economics; utility adoption is dead without data center anchors already committed.
Talk · Editorial classification · Date to be announced
Recent developments in machine learning have enabled the creation of highly realistic synthetic voice systems capable of imitating specific individuals. Contemporary voice synthesis technologies typically rely on deep neural network architectures trained on large datasets of speech recordings.
However, the training process underlying these systems raises significant legal challenges. Training datasets often contain large volumes of voice recordings that may simultaneously qualify as copyrighted sound recordings or performances and as biometric personal data capable of identifying a specific individual. Despite this overlap, existing academic literature has generally examined copyright law and data protection as separate regulatory domains.
This speech adresses this intersection by proposing a governance model inspired by Mireille Hildebrandt’s concept of legal protection by design. The model explores how copyright licensing and biometric consent could be implemented within the same technical infrastructure through automated licensing mechanisms and smart contracts.
Talk · Editorial classification · Date to be announced
Product management is going through its most significant shift in a generation. First drafts of PRDs, research synthesis across thousands of data points, and working prototypes built without engineering support have all become dramatically faster and cheaper. For experienced product managers, this raises an exciting question: when production work takes minutes instead of days, where does your time — and your value — go next?
This story-driven keynote offers a practical answer: the judgment, context, and taste you've built over years of product work are not made obsolete by AI. They are amplified by it. Experience is the multiplier on everything these tools produce. We'll begin with a simple mental model — three levels of AI adoption in product management. Level 1 is productivity: drafting, summarizing, and rewriting. Level 2 is insight: synthesizing months of user interviews, support tickets, and survey data to surface patterns no individual could find alone. Level 3 is strategy: building AI into your product and using rapid prototypes to strengthen roadmap conversations. Through real case studies, we'll explore what Levels 2 and 3 look like in practice and how PMs are using them to grow their influence and their careers.
We'll also have an honest conversation about the human side of this shift: which capabilities remain distinctly yours — stakeholder trust, product taste, organizational context, the courage to say no — and the practical guardrails every PM should know around accuracy, data privacy, and maintaining your own voice.
Attendees will leave with three things: a self-assessment model for their own AI adoption, a lifecycle map of practices and prompts they can apply immediately, and a 90-day plan for moving from everyday productivity gains to genuine strategic leverage — positioning themselves as the person their organization turns to as AI reshapes product work.
Talk · Editorial classification · Date to be announced
Healthcare payer organizations are increasingly turning to cloud and AI to reconcile member identities across enrollment, claims, pharmacy, and care management systems. Identity resolution errors create asymmetric risk: false negatives fragment member histories, while false positives can expose protected health information and trigger HIPAA privacy incidents. This session introduces the Probabilistic Sentinel System (PSS), an AI-driven scoring architecture for privacy-safe member identity resolution, grounded in the Fellegi-Sunter record linkage framework and extended through differential attribute weighting, a negative SSN conflict penalty, ROC-calibrated thresholds, sensitive-condition override logic, and auditable lifecycle governance suited to cloud-native MDM platforms.
Attendees will walk through the PSS scoring model, threshold routing design, audit controls, and statistical validation methodology, including precision, recall, F1 score, ROC-AUC, subgroup fairness analysis, and drift monitoring. The talk reframes identity resolution from a technical data quality task into a governed, cloud-scalable AI decision framework where every match is traceable, reviewable, and calibrated against organizational risk tolerance, delivering measurable gains in accuracy and reduced privacy exposure.
Designed for healthcare data leaders, cloud and AI architects, and compliance professionals, this session offers a practical, defensible approach to identity resolution balancing automation with regulatory accountability, aligned with modernizing healthcare through digital health, cloud, and AI.
Talk · Editorial classification · Date to be announced
AI builders working in regulated healthcare need more than accurate models. They need reliable, traceable, and machine-readable data foundations. This session explains how statistical programmers prepare FDA review-ready clinical data packages across NDA, BLA, IND, sNDA, and rolling-review pathways, and how these practices can support responsible AI adoption in regulatory workflows.
Attendees will follow the submission lifecycle from Pre-IND activities through NDA or BLA filing and Complete Response Letter support. The session covers programmer responsibilities within eCTD Modules 2.7 and 5, CDISC implementation using SDTM and ADaM, Define-XML 2.1, and commonly used analysis datasets such as ADSL, ADAE, ADLB, ADTTE, and ADRS or ADTR.
The presentation also examines validation and quality controls involving SDRG, ADRG, Pinnacle 21, Analysis Results Metadata, and dual-programmer review. It connects these established practices with emerging AI-ready submission capabilities, including Dataset-JSON, growing use of R alongside SAS, and machine-readable metadata. Participants will leave with a practical framework for building compliant, validated, and structured clinical data foundations that can support automation and future AI-enabled regulatory processes.
Talk · Editorial classification · Date to be announced
Tax systems impact every individual, making the integration of AI into this space both highly influential and deeply complex. In this session, we will dive into the architecture of the traditional Retrieval Augmented Generation(RAG) and Agentic RAG systems designed to tackle these intricate tax workflows. We will focus heavily on the critical methodologies required to ensure strict factual accuracy, model grounding, and safe reasoning when deploying Generative AI in such a high-stakes enterprise environment. To close, I will share how we leverage large-scale user feedback and model performance traces to establish a continuous improvement loop, ultimately shaping the future of tax research.
Talk · Editorial classification · Date to be announced
AI agents don’t fail like traditional software. They don’t just throw exceptions, they drift, misinterpret tools, invoke the wrong functions or behave differently across environments. When deploying Arm’s Open-Source custom MCP server to power AI assistants for architecture development, migration, & optimization, we faced a critical question: how do we test a system built for nondeterministic interaction? In this talk, I’ll share how we moved from manual validation to a repeatable, CI-enforced testing strategy using Pytest & Testcontainers. We spin up real MCP server in Docker during tests, validating tool discovery, invocation, & protocol compliance end-to-end.
This isn’t about mocking LLM output. It’s about testing the contract between agents & tools. The key insight: treat your MCP server like production infrastructure, not experimental glue code. Because “it worked on my machine” is not a deployment strategy.
This session contributes practical, open, & reproducible testing approach that any project can adopt to improve reliability & trust in Open-Source MCP implementations. Thus, making agent infrastructure production-ready.
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