The Margin

In this episode of The Margin, MGI Research Co-Managing Directors Igor Stenmark and Andrew Dailey are joined by Research Associate Ethan Weiss to examine one of the most consequential questions facing the Contract Lifecycle Management market: How do rapidly growing AI-first legal tools fit alongside traditional CLM platforms? As solutions from companies such as Harvey, Legora, Ivo, and other AI challengers gain adoption and investment, enterprise buyers are being forced to determine whether these technologies represent alternatives to core CLM, complementary productivity tools, or the beginning of a broader convergence across the market.

Drawing on MGI Research's field research and Functional Capability Assessment across 77 distinct CLM capabilities, the panel compares traditional CLM platforms, AI-based CLM-lite products, and AI legal point solutions across MGI's six-stage CLM maturity model. The discussion explores where AI challengers are gaining ground in areas such as contract intelligence, negotiation, automated review, and redlining, while examining why core CLM platforms retain significant advantages in system-of-record functionality, workflow, integration, compliance, security, and post-signature execution. The conversation also addresses user adoption, the viability of vibe coding contract applications, the potential role of general-purpose LLM providers, and why market convergence appears increasingly likely. The episode concludes with practical guidance for developing a defensible AI and CLM strategy, evaluating vendors against specific use cases, and understanding the risks of both moving too quickly and doing nothing.

Key Analytical Takeaways

• Legal AI and Core CLM Are Not an Either-Or Decision: Why AI legal tools and traditional CLM platforms currently address different users, capabilities, and use cases, and why some organizations are already deploying both.
• AI Challengers Are Closing Important Capability Gaps: How emerging tools are competing aggressively in contract intelligence, negotiation, redlining, automated review, and legal productivity while core CLM platforms maintain broader lifecycle capabilities.
• Systems of Record Still Matter: Why enterprise contract management extends beyond reading and negotiating contracts to workflow, security, compliance, integration, repository management, and post-signature execution.
• Vibe Coding Has Limits in Enterprise CLM: Where AI-assisted development can address specific backlogs and use cases, and why building, maintaining, governing, and sustaining a core contract system presents a fundamentally different challenge.
• CLM Market Convergence Is Coming: Why the boundaries between traditional CLM, CLM-lite, and AI legal point solutions are likely to narrow as incumbents expand AI capabilities and challengers move toward broader contract management functionality.
• Buyers Need a Defensible Point of View on AI: How organizations should evaluate use cases, adoption speed, long-term architecture, vendor durability, ROI, and the potential cost of doing nothing rather than simply responding to pressure to adopt AI.

Featured Experts

Igor Stenmark | Managing Director, MGI Research

Igor Stenmark is a co-founder and managing partner of MGI Research. Igor brings his 30+ years of experience in entrepreneurial, strategic advisory, investment management, and executive roles in the technology industry to his clients. He serves as a strategic adviser to technology buyers, investors, boards, and management helping them make more informed decisions, enter new markets, optimize positioning, and build lasting value.

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Ethan Weiss | Research Associate, MGI Research

In this episode of The Margin, MGI Research Co-Managing Directors Igor Stenmark and Andrew Dailey are joined by Senior Research Analyst Elise Talley and Research Associate Ethan Weiss to examine the findings from the 2026 Agile Billing Buyer's Guide and discuss how artificial intelligence is transforming enterprise monetization. As organizations race to commercialize AI-enabled products and services, traditional billing systems are being pushed far beyond their original design assumptions. New pricing models built around usage, tokens, API calls, credits, and outcome-based pricing are forcing enterprises to rethink the technologies that underpin revenue generation.

Drawing on MGI Research's evaluation of 50 leading billing suppliers, the panel explores why usage billing has become a foundational capability, how market consolidation is reshaping the competitive landscape, and why agility has emerged as the defining characteristic of modern monetization platforms. The discussion also examines the widening gap between AI vendor marketing and customer adoption, the growing convergence of CPQ, billing, payments, revenue recognition, and CLM, and why buyers should evaluate vendors based on business outcomes, architectural flexibility, and long-term strategic fit rather than AI feature checklists. The conversation concludes with practical guidance for selecting billing platforms, interpreting MGI's quantitative vendor ratings, and preparing enterprise monetization stacks for the next generation of AI-driven business models.

Key Analytical Takeaways

• AI is Redefining Enterprise Monetization: Why AI is driving a new generation of pricing complexity through usage-based, tokenized, API-driven, and outcome-based commercial models that legacy billing platforms were never designed to support.
• Usage Billing Has Become Table Stakes: How usage billing has evolved from a niche capability into a core enterprise requirement as organizations seek to monetize AI-enabled products and services.
• The Monetization Stack is Consolidating: Why acquisitions across billing, CPQ, payments, CLM, and revenue recognition signal a broader shift toward unified quote-to-cash platforms, and what that means for enterprise buyers.
• Agility Now Matters More Than Efficiency: Why organizations should prioritize architectural flexibility, rapid pricing experimentation, and long-term adaptability over incremental operational improvements when evaluating billing platforms.
• Selecting Vendors for the AI Era: How buyers should balance product capability, financial stability, execution consistency, cultural fit, and ideal use case when evaluating enterprise billing suppliers, while recognizing that AI alone is not a substitute for deterministic financial systems.

Featured Experts

Igor Stenmark | Managing Director, MGI Research

Igor Stenmark is a co-founder and managing partner of MGI Research. Igor brings his 30+ years of experience in entrepreneurial, strategic advisory, investment management, and executive roles in the technology industry to his clients. He serves as a strategic adviser to technology buyers, investors, boards, and management helping them make more informed decisions, enter new markets, optimize positioning, and build lasting value.

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Elise Talley | Senior Research Analyst, MGI Research

Ethan Weiss | Research Associate, MGI Research

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with Todd McElhatton, Chief Financial and Operating Officer at Zuora, to examine how artificial intelligence is reshaping software business models, enterprise finance, and the economics of recurring revenue. While concerns about AI have largely centered on whether it will disrupt SaaS, McElhatton argues that the more immediate challenge for finance leaders is balancing innovation with precision. As organizations introduce AI-powered products, usage-based pricing, and increasingly complex monetization models, finance teams must ensure that systems responsible for billing, revenue recognition, collections, and financial reporting remain accurate, auditable, and trusted.

Drawing on executive leadership experience at Zuora, SAP, Oracle, and VMware, McElhatton explains why systems of record require deterministic outcomes rather than probabilistic AI, how enterprise software companies should evaluate monetization strategies for AI capabilities, and where finance organizations can realistically achieve productivity gains without compromising governance. The discussion also explores Zuora's transition from public to private ownership, how longer investment horizons are influencing strategic decision-making, and why operational discipline, customer value, and measurable business outcomes will ultimately determine which software companies benefit most from the AI transition.

Key Analytical Takeaways

• AI and the Future of SaaS Economics: Why usage-based pricing and AI-enabled products are creating new monetization opportunities without necessarily replacing traditional subscription models, and how software vendors should evaluate pricing based on measurable customer value rather than AI alone.
• Deterministic Finance in a Probabilistic AI World: Why mission-critical systems of record, including billing, revenue recognition, and financial reporting, require precision and auditability that generative AI cannot independently deliver, making human oversight an essential component of enterprise finance.
• Operational AI Beyond Product Features: How finance organizations can use AI to accelerate implementations, improve forecasting, automate reconciliations, enhance customer onboarding, and increase engineering productivity while maintaining financial controls and governance.
• Private Ownership and Long-Term Execution: How Zuora's transition from public to private ownership has enabled greater strategic flexibility, allowing management to prioritize multi-year customer outcomes and operational investments rather than quarterly market expectations.
• Funding AI Through Productivity: Why enterprise software companies should finance AI innovation by systematically reinvesting productivity gains across engineering, customer operations, professional services, and go-to-market functions instead of treating AI as a standalone investment initiative.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Todd McElhatton | Chief Financial and Operating Officer, Zuora

Todd McElhatton is Chief Financial and Operating Officer at Zuora, where he oversees the company's financial strategy, operations, and business transformation initiatives. Previously, he held senior finance leadership positions at SAP, Oracle, and VMware, bringing more than 25 years of experience guiding enterprise software companies through major technology transitions, financial transformation, and operational growth.

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with Mark Walker, CEO of Nue, to examine how artificial intelligence is reshaping enterprise monetization infrastructure and why the accelerating pace of product, pricing, and business model innovation is forcing organizations to rethink their quote-to-cash technology stack. As enterprises evaluate how to balance continued investment in existing SaaS platforms with growing pressure to fund AI initiatives, the conversation explores why flexibility has become a strategic requirement rather than a product feature.

Drawing on Nue's experience supporting both AI-native companies and established enterprise software vendors, Walker explains how AI is driving unprecedented experimentation in pricing models, product packaging, and commercial operations. The discussion examines why organizations must support multiple monetization approaches simultaneously, how agentic AI can reduce the operational burden created by constant product evolution, and why future-ready revenue systems must be designed for business models that do not yet exist. The conversation also explores the changing competitive landscape for enterprise software, the evolving role of implementation partners, and why customer agility may become the defining competitive advantage in the AI era.

Key Analytical Takeaways

• AI Is Accelerating Commercial Change: Why the rapid pace of AI innovation is forcing companies to rethink how quickly they can introduce new products, pricing models, and go-to-market strategies, placing new demands on quote-to-cash infrastructure.
• The Rise of Multi-Modal Monetization: How organizations are moving beyond traditional subscription pricing toward combinations of seat-based, usage-based, outcome-based, and hybrid commercial models, requiring significantly greater flexibility across the revenue stack.
• Agentic AI as a Revenue Enabler: Why the greatest near-term opportunity for AI may be helping sales organizations adapt to constant changes in products, pricing, and commercial policies without requiring continuous retraining.
• Enterprise Software's New Competitive Moats: How AI is changing the basis of competitive differentiation, shifting emphasis away from application functionality alone toward data, flexibility, domain expertise, and customer enablement.
• Building for Revenue Models That Don't Yet Exist: Why next-generation monetization platforms must prioritize architectural adaptability, allowing organizations to support future pricing strategies and business models that cannot yet be fully anticipated.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Mark Walker | CEO, Nue

Mark Walker is the CEO of Nue, a provider of agile quote-to-cash and revenue management solutions designed for modern subscription, consumption-based, and hybrid business models. His expertise spans enterprise software, monetization strategy, CPQ, billing, revenue architecture, and the application of AI to next-generation commercial operations.

Episode Overview

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with Noel Goggin, former CEO of Conga and board member at Anaplan and Auctane, to examine how artificial intelligence is fundamentally reshaping enterprise software companies. While much of the market conversation has focused on whether AI will disrupt the SaaS business model, Goggin argues that the more important challenge is organizational speed. As AI dramatically compresses product development cycles and lowers the cost of experimentation, software vendors must rethink pricing, product development, customer retention, and internal operations simultaneously.

Drawing on decades of executive leadership through major technology transitions, Goggin explains why incumbent software companies possess valuable advantages, including customer relationships, domain expertise, and mission-critical data, but must overcome organizational inertia to capitalize on them. The discussion explores the growing pressure on traditional seat-based pricing, the emergence of AI-first product strategies, the importance of creating organizational capacity through internal AI adoption, and why "clock speed" may become the defining competitive advantage of the next generation of enterprise software companies. The conversation concludes with Goggin's perspective on software valuations, M&A activity, enterprise architecture, and where AI-driven transformation is likely to create the greatest long-term value.

Key Analytical Takeaways

• The Pressure on Traditional SaaS Economics: Why seat-based licensing models are increasingly vulnerable as AI changes software utilization patterns, forcing vendors to rethink pricing, renewal strategies, and monetization before customers demand it.
• Clock Speed as Competitive Advantage: How AI compresses product development cycles and market experimentation, making organizational agility and cross-functional execution more important than the underlying SaaS business model itself.
• Creating Capacity Through Internal AI: Why successful software companies will use AI first to simplify operations, accelerate software development, improve internal workflows, and free resources that can be reinvested into innovation and growth.
• Balancing Innovation with Customer Retention: How established vendors must simultaneously protect renewal rates for existing products while building AI-first offerings capable of driving competitive wins and incremental revenue.
• The Next Phase of Enterprise Software: Why mission-critical enterprise applications remain well positioned despite AI disruption, as organizations increasingly combine packaged software with internally developed AI agents, faster development cycles, and new application architectures built around proprietary enterprise data.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Noel Goggin | Former CEO, Conga

Noel Goggin is an enterprise software executive and board member with decades of experience leading high-growth technology companies through periods of strategic transformation. As former CEO of Conga, he led the company's operational turnaround, product transformation, and acquisition strategy while achieving Rule of 40 performance. He currently serves on multiple technology company boards and advises organizations on AI strategy, enterprise software innovation, and executive leadership.

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with John Stame, enterprise architect and former business systems leader at Atlassian, to examine the architectural realities of scaling product-led growth (PLG) businesses. While PLG promises frictionless customer acquisition and self-service monetization, many successful software companies eventually confront a new challenge: introducing direct sales, channel partners, or enterprise selling without disrupting the systems that fueled their initial growth.

Drawing on more than three decades of experience designing enterprise business systems at Atlassian, Microsoft, and Chevron, Stame explains why supporting multiple sales motions requires more than adding new applications. It demands disciplined governance, careful architectural decisions, and a unified approach to quote-to-cash that balances innovation with operational consistency. The discussion explores the tradeoffs between best-of-breed and suite architectures, the growing complexity introduced by usage-based pricing, and why enterprise architecture remains a strategic capability rather than an academic exercise.

Key Analytical Takeaways

• The PLG Expansion Challenge: Why product-led growth companies often reach an inflection point where enterprise sales, channel relationships, and high-touch customer engagement require fundamentally different business systems and commercial processes.
• One Business, Multiple Sales Motions: Why maintaining a single quote-to-cash architecture becomes increasingly difficult as organizations introduce CRM, CPQ, contract lifecycle management, and enterprise pricing capabilities alongside frictionless self-service commerce.
• Governance Without Sacrificing Innovation: How high-growth organizations can establish architectural governance around critical assets, including product catalogs, pricing, contracts, and master data, while preserving the speed and agility that define product-led businesses.
• Enterprise Architecture as a Strategic Discipline: Why successful organizations continuously monitor the health, scalability, and operational limits of their monetization infrastructure instead of treating enterprise systems as static implementations.
• The Next Evolution of Quote-to-Cash: How AI assistants, usage-based monetization, and increasingly integrated commercial platforms are likely to improve enterprise selling workflows, while leaving the underlying architectural complexity of quote-to-cash largely intact.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

John Stame | Enterprise Architect and Consultant

John Stame is an enterprise architect and business systems advisor with more than 35 years of experience designing and scaling enterprise applications at companies including Atlassian, Microsoft, and Chevron. His expertise spans product-led growth, enterprise architecture, quote-to-cash systems, governance, and the operational design of high-growth software organizations.

In this episode of The Margin, Andrew Dailey, Managing Director at MGI Research, speaks with Dr. Michael Wu, Chief AI Strategist at PROS, about what other industries can learn from decades of airline experience in dynamic pricing and revenue optimization. Dr. Wu shares insights on building trust in AI, leveraging data to drive price precision, and accelerating quote times in complex B2B environments. They also explore the shift from black-box to glass-box AI, the power of 1% pricing changes, and how CFOs can start preparing now for AI-powered pricing transformation.

What you’ll learn in this episode:

• Why the airline industry became the blueprint for real-time pricing, yield management, and demand sensing
• How AI-powered pricing enables businesses to operate dynamically in volatile markets at scale
• Why data readiness and automation are prerequisites for effective AI-driven pricing decisions
• How trust, visibility into pricing decisions, and change management determine whether AI pricing actually delivers value
• Why pricing is the fastest and most powerful lever CFOs can pull to drive margin, revenue, and competitive advantage

Episode Overview

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with Praful Saklani, CEO and Co-founder of Pramata, to examine why contracts remain one of the largest untapped sources of enterprise intelligence. Despite years of investment in contract lifecycle management (CLM), e-signature platforms, and digital repositories, many organizations still struggle to answer basic operational questions about customer commitments, pricing agreements, renewal opportunities, and contractual obligations because the underlying information remains fragmented across documents and disconnected systems.

Drawing on decades of experience building contract intelligence platforms, Saklani explains why contracts should be viewed not as legal documents, but as strategic business assets that influence revenue growth, profitability, customer relationships, and operational execution. The discussion explores why traditional CLM implementations often fail to deliver enterprise-wide value, how generative AI is transforming the extraction and interpretation of unstructured contract data, and why successful AI adoption depends on transparency, governance, and domain expertise rather than simply deploying large language models. The conversation concludes with Saklani's perspective on AI-first enterprises, implementation transformation, and the future of contract-driven business intelligence.

Key Analytical Takeaways

• Contracts as Enterprise Intelligence: Why customer and supplier agreements contain critical operational, financial, and commercial information that extends far beyond legal compliance and should be treated as a strategic enterprise data asset.
• Why Traditional CLM Falls Short: How contract lifecycle management systems often succeed at document storage and workflow automation but struggle to capture the negotiated complexity, historical context, and interconnected relationships that drive business decisions.
• Generative AI Beyond Document Search: Why AI delivers the greatest value when it organizes, validates, and contextualizes contract data rather than functioning as a conversational interface over isolated documents.
• Trust Through Transparency: How enterprise AI applications require explainability, auditability, and validation mechanisms that allow users to trace conclusions back to their contractual source material instead of relying on opaque AI-generated answers.
• The Next Evolution of Enterprise Applications: Why AI has the potential to dramatically reduce implementation complexity, accelerate business configuration, and enable natural language interactions with enterprise systems, provided organizations redesign application architectures rather than simply layering AI onto existing software.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Praful Saklani | CEO and Co-founder, Pramata

Praful Saklani is the CEO and Co-founder of Pramata, a provider of enterprise contract intelligence solutions that help organizations unlock operational and commercial value from complex customer and supplier agreements. His expertise spans contract lifecycle management, enterprise AI, legal technology, contract analytics, and the application of generative AI to unstructured enterprise data.

Episode Overview

In this episode of The Margin, MGI Research Managing Director Andrew Dailey speaks with Puneet Gupta, Founder and CEO of Amberflo and former engineering leader at Amazon Web Services and Oracle, to examine why usage-based monetization has become a foundational capability for modern software companies. While consumption pricing is often viewed as a billing model, Gupta argues that its true value lies in enabling faster product innovation, richer customer insights, and more agile commercial strategies.

Drawing on his experience building metering and billing infrastructure at AWS during its formative years, Gupta explains why successful consumption businesses begin with instrumentation rather than pricing. The discussion explores the cultural and organizational shifts required to adopt usage-based monetization, the architectural importance of metering as a system of record, and why organizations that treat monetization as strategic infrastructure, not simply a finance project, are better positioned to capitalize on AI-driven products and evolving customer expectations. The conversation concludes with Gupta's perspective on the expanding role of CIOs in building the data foundations required for the next generation of enterprise software.

Key Analytical Takeaways

• Usage-Based Monetization as an Innovation Strategy: Why consumption pricing should be viewed as an enabler of faster product innovation, experimentation, and customer value rather than simply an alternative pricing model.
• Metering as the New System of Record: How modern monetization architectures depend on accurate, real-time usage instrumentation that serves as the foundation for pricing, billing, forecasting, and product analytics.
• Beyond Pay-As-You-Go: Why successful usage-based businesses increasingly adopt hybrid commercial models, including prepaid consumption commitments and flexible pricing structures, that combine financial predictability with operational flexibility.
• Monetization Requires Organizational Alignment: How product, finance, engineering, sales, and customer success must align around shared usage data and customer outcomes for consumption models to deliver their full strategic value.
• Building the Infrastructure for AI-Era Software: Why CIOs have an opportunity to establish enterprise-wide monetization platforms that support AI-powered products, continuous innovation, and future business models built around usage intelligence rather than static subscriptions.

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Puneet Gupta | Founder and CEO, Amberflo

Puneet Gupta is the founder and CEO of Amberflo and a pioneer in usage-based monetization infrastructure. Prior to founding Amberflo, he helped build large-scale metering and billing systems at Amazon Web Services and Oracle, where he gained firsthand experience scaling some of the world's largest consumption-based cloud platforms. His expertise spans usage-based pricing, metering architecture, cloud infrastructure, product strategy, and enterprise monetization.

Episode Overview

In this episode of The Margin, MGI Research Managing Directors Andrew Dailey and Igor Stenmark sit down with Dan Brown, Chief Product Officer at Celonis, to examine one of the most consequential questions facing professional services: Will generative AI fundamentally replace knowledge workers, or will it become another enterprise technology that augments rather than disrupts human expertise?

Drawing on leadership roles at Microsoft, Certinia, and Celonis, Brown separates the operational realities of AI adoption from the surrounding hype. While generative AI is proving highly effective at automating repetitive knowledge work, summarizing complex information, and accelerating project execution, it continues to struggle with judgment, counterfactual reasoning, opinion-based analysis, and the trust required for high-value advisory engagements.

The discussion also explores the practical challenges organizations face as they attempt to operationalize AI amid mounting executive pressure, fragmented technology environments, rising customer expectations, and uncertain economic conditions. Rather than treating AI as a standalone strategy, Brown advocates a "Pragmatic AI" approach centered on measurable business impact, rapid deployment, and continuous feedback loops that integrate AI directly into day-to-day operational workflows.

Key Analytical Takeaways

• The Accelerant vs. Replacement Debate: Why generative AI is unlikely to eliminate professional services but will fundamentally reshape how knowledge workers spend their time by automating routine project execution while increasing demand for higher-value advisory work.
• The Human Advantage in Judgment: Where AI continues to fall short, including opinion-based consulting, scenario planning, counterfactual reasoning, negotiation, and trust-driven client relationships, and why these capabilities remain difficult to automate.
• Pragmatic AI Over AI Theater: How organizations can avoid expensive experimentation by prioritizing AI initiatives that deliver measurable impact, are easy to deploy, and create closed-loop operational feedback rather than disconnected productivity gains.
• The Enterprise Adoption Challenge: Why organizational enablement, change management, and employee education may prove more difficult than implementing the underlying AI technology itself.
• Professional Services as AI Enablers: How consulting and services firms may become larger, not smaller, as enterprises struggle to integrate hundreds of existing applications alongside rapidly evolving AI capabilities. 

Featured Experts

Andrew Dailey | Managing Director, MGI Research

Andrew Dailey is a co-founder and managing partner of MGI Research. Andrew brings his 25+ years of diversified technology and financial services experience working in the enterprise software market and Fortune 500 firms to his clients.

Dan Brown | Chief Product Officer, Celonis

Dan Brown is Chief Product Officer at Celonis and has held senior product and strategy leadership roles at Microsoft and Certinia. His work focuses on enterprise software, process intelligence, professional services automation, and the practical application of AI to improve operational performance while maintaining trust and business outcomes.