This session opens with the first look at the WILMAH Career Pulse Survey, launching exclusively at AHNTI. It's the inaugural edition of WILMAH’s measure of where women in animal health actually stand, on progression, support and the barriers still getting in the way.

The findings set up a facilitated panel discussion asking the harder question: given what the data shows, what should animal health companies actually be doing about it? The conversation moves from diagnosis to action, real steps companies can take on talent development and structural support for women moving into senior roles.

This session is open to everyone. Women in leadership, those supporting women in leadership, and anyone simply interested in what the future of animal health looks like.

Places are limited. APPLY HERE

Affordability can sometimes hide a broader conversation about identifying what’s important to the pet owner. What does the data tell us about pet owner preferences for the delivery of care?

• Communicating value and price
• Financing, insurance, wellness plans and flattening out the lifetime cost of ownership
• Capturing and using sentiment analysis
• Chronic disease management and the complexity of long term care
• Communication prior to in-clinic visits

Great science doesn't guarantee investment. Funding decisions happen based on factors that rarely make it onto slides: market validation, team composition, regulatory strategy, and business models that actually work. This panel explores what investors are really evaluating when they assess animal health opportunities.

• What separates fundable companies from well-pitched ones
• Market validation: what investors need to see before they believe the opportunity is real
• Regulatory strategy as an investment signal: how pathway choices affect risk and return
• Team composition and what investors are actually backing
• Business model scrutiny: the unit economics and revenue assumptions that get stress-tested hardest

The science to select livestock for feed efficiency, disease resilience, heat tolerance, and productivity simultaneously already exists. Genetics may represent the single greatest untapped opportunity to transform livestock production, moving from herd-level averages toward individual animal intelligence that connects breeding decisions to productivity. The barrier is not scientific. It is commercial: the architecture needed to verify, attribute, and reward genetic value across the supply chain is still being built. This session explores what unlocking that transformation requires. We will discuss:

• Why genetic value fails to translate into financial reward at producer level and what business models and data infrastructure are needed to change that
• What the shift from herd-level to individual animal management requires in terms of traceability, shared frameworks, and aligned incentives
• How fragmented systems, proprietary platforms, and misaligned stakeholder interests get overcome to create shared, verifiable value
• What downstream financial signals from processors, retailers, and buyers are needed to make producer-level genetic investment rational

The technology available to veterinary practices has never been more capable. But capability does not always directly correspond to performance. Open API access remains inconsistent, integrations break quietly after go-live, and the real cost of connecting systems is rarely what anyone discussed. The gap is not just adoption. It is the absence of a true orchestration layer that makes the pieces work reliably in a clinical environment.

This session will address the real performance and interoperability challenge head on: what it actually costs to connect systems, why integrations fail in practice rather than in demos, and what measurable value looks like across different practice types and workflows.

• The Integration Reality: Why does API access remain inconsistent, and what does the absence of a true orchestration layer cost practices operationally and financially?
• Who Pays: What are the real economic costs of integration in markup, staff time, and implementation burden?
• Performance Over Capability: How do practices evaluate technology against clinical outcomes rather than feature lists?
• Proving Value: What are the concrete metrics, documentation time, charge capture rate, staff retention, and revenue balance, that define success and hold partners accountable?

The rise of biologics has introduced manufacturing complexity that didn't exist a decade ago. Monoclonal antibodies, stem cell therapies, and mRNA platforms all demand specialist capability. This session explores how manufacturing is moving from a back-office function to a strategic differentiator.

• The biologics manufacturing challenge: what's required for mAbs, stem cells, and mRNA, and who can deliver it
• API sourcing and supply chain resilience: where ingredient-level decisions create downstream risk
• Manufacturing strategy for start-ups: why your CDMO relationship is a founding decision, not an operational one
• COGS and commercial viability: how unit economics at the manufacturing stage determine whether a product can reach market
• Manufacturing as a diligence factor: what acquirers and investors look for in your CMC package

Livestock technology has never been more capable. Yet a consistent pattern repeats: innovations that succeed in controlled settings fail in commercial production. The challenge is not innovation. It is translating data into simple, actionable decisions that deliver visible ROI within the real constraints of commercial livestock environments, where labor is scarce and untrained, infrastructure is hostile, genetics and climate vary enormously, and workflows leave no room for complexity. This session explores what designing for actual operational reality requires. This session will explore:

• Why turning data into decisions is the defining ROI question, and what tools that genuinely change producer behavior look like
• How workflow fit, simplicity, and the variability of real production environments determine whether technology delivers on its promise or fails at deployment
• How data quality, fragmentation, and interoperability determine whether technology scales within a single operation and across a diverse producer base
• What a commercially credible validation pathway looks like and where the roadblocks are