Domino powers model-driven business for the world’s most advanced enterprises, including over 20% of the Fortune 100. Our Enterprise MLOps platform speeds up the development and deployment of data science work while increasing collaboration and governance, to scale data science into a competitive advantage.

Our platform enables thousands of data scientists to develop better medicines, grow more productive crops, adapt risk models to major economic shifts, build better cars, improve customer support, or simply recommend the best purchase to make at the right time. 

AI will accelerate discovery, streamline development, and transform compliance in the year ahead. In this session, you’ll hear predictions on:

The innovations poised to reshape early R&D and preclinical science.
How emerging AI systems could change clinical trial design and execution.
The new governance standards that will unify analytics and ensure trust in AI across the enterprise.

AI in life sciences: Five bold predictions shaping R&D and compliance in 2026

This session reveals the three critical shifts that determine who wins:

The great AI ROI reckoning: If you can't show business impact in three to six months, you're done.
The rise of true autonomous agents: Agents now make decisions and adapt without a human script.
The forward-deployed engineer boom: FDEs will be the highest-paid talent for integrating AI with legacy systems.

AI in enterprise: The three hardest truths shaping enterprise AI in 2026

Pharma and biotech organizations are under pressure to modernize their statistical computing environments (SCEs) to accelerate submissions, ensure compliance, and enable advanced analytics like AI/ML. But modernization requires more than technology. It demands thoughtful design, governance, and change management.

Domino and KSM Technology Partners share practical insights from real-world SCE implementations, covering the key drivers behind modernization, how to design a scalable and compliant architecture, and proven best practices for validation, governance, and stakeholder alignment.

You will get a clear understanding of how to:

Plan and execute a successful SCE modernization initiative
Bridge the gap between statistical teams and IT
Build an analytics environment ready for regulatory workloads and AI-driven discovery

Modern statistical computing environments

AI coding assistants like "vibe coding" are gaining popularity, but their limitations are quickly becoming apparent for enterprise-grade data science.

Relying on code authoring alone fails to address the full ML lifecycle, which includes data discovery, validation, and training—the parts that are often the most labor-intensive. Furthermore, without a robust platform, you face significant challenges in ensuring reproducibility and lineage, which are critical for effective collaboration and governance.

This webinar will explore vibe modeling, a new, comprehensive approach that addresses the shortcomings of pure coding assistants.

Watch this session to learn:

Why the traditional focus on "vibe coding" is insufficient for enterprise AI
Why a model-first workflow is essential for delivering real-world value.
How to extend your existing IDE and tools to manage the entire end-to-end data science lifecycle to increase productivity and reduce overhead.

Vibe modeling: The next generation of data scientist productivity

Model validation doesn’t have to be manual, fragmented, or reactive. In fact, it can be a competitive advantage—when it's automated, collaborative, and built into the model lifecycle from day one.

In Part 2 of our series, we show you how Domino helps financial services and insurance organizations modernize their model validation workflows from environment provisioning to automated audit trails. You’ll see how our unified platform enables validators, builders, and IT to work in parallel, not in silos cutting validation timelines by 50–70%.

We walk through:

- What validation looks like inside the Domino platform
- How to eliminate weeks of rework with one-click environment replication
- How automated documentation and real-time collaboration reduce audit findings
- Real examples of customers who’ve slashed validation timelines while enhancing compliance

Whether you're a model validator, IT leader, or data science exec, you’ll leave with a blueprint for bringing safe, scalable AI to production.

How Domino accelerates AI/ML validation at scale

Model validation is a bottleneck in your AI/ML workflow, delaying production and frustrating builders, validators, and business stakeholders. What if you could collapse six months of work into just six weeks?

In part one of this series, we get to the heart of what's slowing you down. We unpack the disconnect between data scientists, IT, and business leaders and explore how traditional, manual processes lead to "recreation hell" and prolonged approval cycles.

You'll learn:

- Why traditional validation methods don’t scale with today’s AI demands
- How leading teams are collapsing 6 months of work into 6 weeks
- Ways to resolve the tension between innovation and governance
- How to align your builders, validators, and IT teams into one efficient validation machine

Reduce model development and validation time from 6 months to 6 weeks

Why do so many AI initiatives stall in pharma and other regulated sectors? PharmaLex will share practical strategies to overcome governance gaps in data quality, ownership, and regulatory alignment. Learn how to build AI-ready data capabilities that scale across teams and across borders.

Overcoming AI & Data Governance Challenges in Pharma

With FY25 rapidly closing, federal and defense agencies are under pressure to transform AI priorities into production-ready solutions before budgets expire.

Watch this webinar and see how agencies can use Domino’s Zero to AI offering to deliver real-world outcomes, quickly. This session will break down how to bypass typical AI bottlenecks and stand up high-value, mission-aligned capabilities - on time and on target.

Understand how to:

- Accelerate the path from AI concept to production through expert-led implementation strategies.
- Equip internal teams with repeatable, high velocity development practices.
- Demonstrate immediate impact through incremental value delivery.
- Streamline acquisition using pre-approved, procurement-ready offerings tailored for federal agencies.
- Drive cultural adoption by aligning AI initiatives with mission priorities and day-to-day workflows.

Accelerate AI impact: Agency FY-end playbook

AI adoption is surging, and so is the need for robust AI governance. Many organizations struggle with fragmented policies, complex enforcement, and a lack of readiness, leading to wasted time and hindering AI project value.

Join Ahmet Gyger, Sr. Director of Product Management for AI Governance at Domino, to learn how to transform AI compliance into a powerful catalyst for productivity. This session will provide essential best practices for integrating, automating, and standardizing AI governance.

Attendees will discover how to:
- Integrate AI governance into existing structures for clear roles and responsibilities
- Automate and embed governance workflows to streamline compliance and reduce manual effort
- Standardize frameworks and processes, including templates and repeatable procedures, for scalable AI governance
- Drive innovation and ROI by transforming AI governance from a "tax" into a strategic asset

Applied AI governance: Turning compliance into a catalyst for productivity

GenAI shows promise in drug discovery, but cutting through the noise to understand where it’s truly making an impact can be difficult. This webinar will focus on how genAI is being applied in practical ways today, from designing novel molecules to predicting toxicity and extracting knowledge from complex datasets.

Asha Mahesh from Johnson & Johnson will share what it takes to make genAI useful and usable in discovery science. Join the webinar to:

- Learn which genAI use cases already deliver value across early discovery workflows and which remain aspirational
- Understand the foundational infrastructure, data strategy, and talent needed to support genAI at scale
- Get a realistic view of genAI’s limitations and what to watch for as the technology matures

Beyond the hype: Real-world GenAI use cases in discovery science

In part 2 of this vibe modeling series, you’ll get a walkthrough on how to use an MCP-enabled Domino, leveraging an example with a diabetes prediction machine learning project.

Vibe modeling: The future of data science (part 2)

Vibe modeling isn’t just about generating code with agentic dev tools, but about involving the AI agent in the full iterative development loop of understanding and transforming data, coding, evaluation, and optimizing a model. When pairing this process with the Domino platform, all the agent’s work is fully tracked, reproducible, and governable.

Vibe modeling: The future of data science (part 1)

Enterprises struggle between unregulated AI sprawl and innovation-stifling controls. Nick Elprin reveals how industry leaders balance responsible scaling with agility by unifying teams and embedding governance to accelerate transformation and deliver measurable business value.

The Modern AI Factory

This fireside chat explores the evolving AI regulatory landscape during administrative change. We examine shifting policy directions and how the changing political climate may reshape governance frameworks, innovation boundaries, and compliance requirements for the industry ahead.

Navigating AI Policy in the Trump Administration

As AI expands across life sciences, siloed tools, complex infrastructure, and compliance demands create costly sprawl. Experts from AWS, NetApp, and Domino will share how to align IT and data science to scale AI securely, efficiently, and with full traceability.

Tame AI Sprawl to Accelerate Innovation in Life Sciences

Financial institutions face new risks as AI advances from predictive models to GenAI. This session explores modernizing model risk management frameworks to address emerging threats, ethical concerns, and compliance requirements while maintaining trust and security in AI-driven transformation.

Model Risk Management in the Age of AI: Balancing Innovation, Ethics, and Compliance

Agentic AI holds the potential to transform enterprises by driving unprecedented gains in productivity, creativity, and automation. But alongside these benefits come serious challenges—misguided investments, endless proof-of-concepts that never reach production, and increasing scrutiny from regulators. As organizations race to experiment with autonomous AI systems, the need for a clear, actionable strategy has never been greater.

In this webinar, we move beyond the buzzwords and provide a grounded framework for understanding, deploying, and governing agentic AI in real-world environments. You’ll gain insight into how leading organizations are approaching this new frontier, what it takes to move from pilot to production, and how to stay compliant while driving measurable ROI.

You’ll learn how to:

- Identify the core capabilities and architecture required to successfully deploy agentic AI.
- Avoid common pitfalls such as PoC purgatory and unmanaged risk exposure.
- Build a governance framework that aligns innovation with security, compliance, and business value.

Watch now to learn how to confidently bring agentic AI from experimentation to enterprise value.

Agentic AI for discovery science

There is a dramatic shift in enterprise AI strategy – agentic AI and AI agents have become the new investment priority, outpacing previous generative AI spending.

Despite growing enthusiasm, organizations face significant challenges. Building AI agents is easy, operationalizing them is the hard part. Complexity and control -- including security, data integration, and multi-agent coordination – are the main issues.

Learn practical strategies for addressing the operational roadblocks of Agentic AI:

Deconstruct the agentic paradigm: Understand autonomous systems, multi-agent models, and governance-first trends.
Tackle complexity and control: Overcome challenges in system integration, orchestration, security, and AI ethics.
Establish best practices: Gain insights on reducing technical roadblocks and lowering high implementation costs.
Build a managed agent system: Learn to monitor, manage, and evaluate agents for enterprise alignment and compliance.

Operationalizing agentic AI: Strategies for production, governance, and control

Agentic

Artificial Intelligence

Data Science

Ethics

Governance

Scalability

Compliance

Enterprise IT

autonomous systems

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Data and Analytics

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Operationalizing agentic AI: Strategies for production, governance, and control

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