The Margin

MGI Research
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The Margin is a podcast from MGI Research that explores the evolving world of business monetization. Hosted by MGI Managing Directors Andrew Dailey and Igor Stenmark, the show features candid conversations with founders, CEOs, product leaders, and industry experts at the forefront of pricing, billing, and revenue operations. Each episode dives deep into the strategies, technologies, and trends shaping how companies generate, capture, and grow revenue—from subscription and usage-based models to AI-driven monetization. Whether you're in finance, product, or IT, The Margin offers practical insights to help you navigate complexity and drive growth in the digital economy.

  1. 1d ago

    Objection Your Honor! Legal AI vs Classic CLM – Separating Fact from Perception

    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

  2. Jul 30

    Agile Billing Buyer’s Guide and Market Overview 2026

    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

  3. May 12

    Trust but Verify: Todd McElhatton on AI, Finance, and Systems of Record

    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.

  4. Apr 28

    Pricing in Motion: Mark Walker on AI and Revenue Reinvention

    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.

  5. Apr 14

    Speed Wins: Noel Goggin on AI and the SaaS Reset

    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.

  6. Feb 4

    Monetization at Scale: John Stame on Product-Led Growth and Direct Sales

    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.

    Monetization at Scale: John Stame on Product-Led Growth and Direct Sales

  7. Jan 26

    Pricing in Motion: Michael Wu on AI, Data, and Demand

    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 sensingHow AI-powered pricing enables businesses to operate dynamically in volatile markets at scaleWhy data readiness and automation are prerequisites for effective AI-driven pricing decisionsHow trust, visibility into pricing decisions, and change management determine whether AI pricing actually delivers valueWhy pricing is the fastest and most powerful lever CFOs can pull to drive margin, revenue, and competitive advantage

    Pricing in Motion: Michael Wu on AI, Data, and Demand

  8. Jan 8

    The Contract Intelligence Gap: Praful Saklani on Unlocking Revenue Hidden in Contracts

    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.

    The Contract Intelligence Gap: Praful Saklani on Unlocking Revenue Hidden in Contracts
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About

The Margin is a podcast from MGI Research that explores the evolving world of business monetization. Hosted by MGI Managing Directors Andrew Dailey and Igor Stenmark, the show features candid conversations with founders, CEOs, product leaders, and industry experts at the forefront of pricing, billing, and revenue operations. Each episode dives deep into the strategies, technologies, and trends shaping how companies generate, capture, and grow revenue—from subscription and usage-based models to AI-driven monetization. Whether you're in finance, product, or IT, The Margin offers practical insights to help you navigate complexity and drive growth in the digital economy.

Information

  • Creator
    MGI Research
  • Years Active
    2025 - 2026
  • Episodes
    18
  • Rating
    Clean
  • Copyright
    © 2025 MGI Research LLC
  • Show Website
    The Margin