Summary: Modern integration patterns and ownership models to reduce spreadsheet stitching and accelerate insight. The 84% of CFOs prioritizing data integration need practical approaches.
Outcome: Reduced manual data work and faster access to insights
Takeaway: Practical integration patterns finance leaders can implement
Key Discussion Points:
- The 84% problem: why data integration tops CFO priorities
- Common integration anti-patterns: spreadsheet stitching and swivel-chair
- Modern integration patterns: APIs, ETL, and data platforms
- Ownership models: who owns the data and the integration
- ERP as the system of record: integration best practices
- Real-time vs. batch: choosing the right approach
- Data quality at the integration point
- Building vs. buying integration capabilities
- Change management for new data workflows
- Measuring integration success: time savings and data quality
Summary: Core patterns that reduce manual workarounds across close, AP/AR, project costing, and reporting. Copilot AI features, real-time analytics, and integration with Power Platform.
Outcome: Improved finance operations through effective Business Central utilization
Takeaway: Practical patterns for maximizing Business Central's finance capabilities
Key Discussion Points:
- Core finance workflows: close, AP/AR, and project costing optimization
- Reducing manual workarounds through proper system configuration
- Copilot AI features for finance users
- Real-time analytics and reporting capabilities
- Power Platform integration: Power BI, Power Automate, Power Apps
- Multi-entity and intercompany capabilities
- Bank reconciliation and cash management
- Budgeting and financial planning features
- Common implementation patterns for mid-market
- Roadmap and upcoming finance features
Summary: Third-party risk is now one of the fastest-growing sources of operational disruption, data exposure, and audit pain—but most programs still rely on paperwork that doesn't reduce risk. This session reframes vendor risk as a speed enabler.
Outcome: Vendor risk becomes a business enabler
Takeaway: A short vendor scorecard CFO/CIO can operationalize
Key Discussion Points:
- Stop collecting questionnaires; start enforcing requirements
- Risk-tiering vendors by data + access + operational dependency
- What 'material vendor risk' looks like in finance and IT terms
- Contract clauses that matter (audit rights, breach terms, subprocessor controls)
- Vendor access boundaries: least privilege, segregation, API scoping
- How to read SOC reports quickly (what matters, what's noise)
- Understanding CUECs (complementary user entity controls) and why they bite you
- Continuous monitoring: what 'continuous' really means (cadence + triggers)
- Vendor drift: changes in scope, subprocessors, data use, and integrations
- Procurement acceleration: fast-track path for low-risk vendors + escalation paths
Kurt Manske - Cherry Bekaert
Summary:
Mid-market finance teams still spend 30–40% of their week on low-value work: chasing receipts, coding transactions, reviewing invoices, and prepping for close. This session shows how Ramp uses AI agents, trained on patterns from 50,000+ businesses, to eliminate that overhead. Not a future roadmap, but what's delivering ROI today.
Takeaway:
AI in finance isn't about replacing your team, it's about giving them back the hours lost to reconciliation and compliance so they can focus on strategy.
Key Discussion Points:
- AI agents that autonomously flag fraud, code expenses, and enforce policy and know when to escalate to a human
- Agentic workflows that auto-approve low-risk transactions and route exceptions for review, replacing manual approval chains
- Real-time fraud detection that scans for anomalies and AI-generated fakes
- AI-powered spend optimization: software negotiation benchmarking, automatic hotel rebooking when prices drop, and vendor payment timing to maximize cashback
- Measurable ROI: businesses saving an average of 5% on total spend through more efficient workflows and automated cost recovery
Summary: Resource-constrained finance teams are under growing pressure to deliver forward-looking insights - not just backward-looking reports. This session explores how lean FP&A functions can adopt predictive forecasting and driver-based planning without enterprise-scale budgets or headcount. We'll walk through practical frameworks for identifying the operational drivers that actually move the needle, building lightweight models that connect those drivers to financial outcomes, and layering in predictive techniques that turn your forecast from a static spreadsheet into a dynamic decision-making tool.
Attendees will leave with a clear, actionable roadmap for transitioning from static and manual planning efforts to a driver-based cash forecasting model - one that is right-sized for smaller teams and designed to improve forecast accuracy, shorten planning cycles, and elevate finance's role as a strategic partner to the business.
Summary: Today’s finance leaders need faster close cycles, sharper visibility, and greater efficiency without adding complexity. This session examines how mid-market organizations are using Sage Intacct to modernize finance operations, improve reporting, reduce reliance on spreadsheets, and generate measurable ROI. Attendees will leave with practical ideas for building a more agile, insight-driven finance function.
Outcome: Attendees will learn how Sage Intacct helps mid-market finance teams close faster, report better, reduce manual work, and generate measurable ROI.
Takeaway: A practical framework for identifying where finance modernization can create the fastest impact through efficiency, visibility, and scalable growth.
Key Discussion Points:
- Faster close cycles without adding headcount
- Better reporting with less spreadsheet dependency
- Real-time visibility across growing organizations
- Immediate ROI through automation and efficiency
- What CFOs should prioritize in modernization decisions
- How Sage Intacct supports scale, speed, and control
Summary: Cyber risk is no longer a technical issue—it's a business continuity and financial exposure issue. This session reframes cyber risk management around operational resilience, material impact, and decision-ready reporting.
Outcome: A modern operating model for cyber risk at executive speed
Takeaway: A CFO/CIO-ready cyber risk playbook tied to material business outcomes
Key Discussion Points:
- Cyber risk translated: downtime, fraud, data exposure, regulatory impact, reputational damage
- The 'business exposure' view: what could stop revenue, disrupt operations, or create material loss
- What matters most now: identity, ransomware resilience, vendor exposure, and recovery readiness
- Identity as the control plane: MFA/SSO, PAM, access reviews, least privilege
- Resilience by design: backups, segmentation, recovery testing, incident authority
- Detection that matters: signal vs noise, response timelines, operational readiness
- Security investment decisions: what reduces exposure fastest (and what doesn't)
- Board and CFO reporting: metrics that drive decisions, not vanity dashboards
- Incident readiness: decision rights, escalation paths, tabletop exercises that work
- Modernization without expanding blast radius: control patterns for cloud + SaaS + AI
Kyle Wehrli - Cherry Bekaert
Summary:
AI pilots are everywhere—but measurable operational impact remains elusive. This session explores how organizations can close the Operational AI Gap by combining Dynamics 365, Copilot, and AI agents with a modern, AI-powered integration strategy. We’ll show how Celigo enables businesses to go beyond the Microsoft stack to connect SaaS applications, EDI, APIs, and agentic workflows—ensuring AI operates on trusted, real-time data across the enterprise. Attendees will learn how to move from disconnected AI experiments to scalable, governed, and outcome-driven operations.
Takeaways:
- Understand why most AI initiatives stall before delivering operational value
- Learn why integration—not just AI—is the foundation for successful Copilot and AI agent adoption
- See how to extend Dynamics 365 AI capabilities across non-Microsoft systems
- Discover how AI-powered integration improves data quality, automation, and error resolution
- Walk away with a framework for building a scalable, future-ready integration strategy for AI
Key Discussion Points:
- What is the Operational AI Gap and why it matters
- Limitations of AI within siloed or Microsoft-only environments
- The role of integration in enabling AI (SaaS, EDI, APIs, and agentic flows)
- How Celigo supports cross-platform orchestration for Dynamics 365
- Embedding AI into workflows vs. using standalone AI tools
- AI-assisted integration: mapping, monitoring, and error management
- Real-world use cases of AI agents and Copilot in integrated environments
- Best practices for governance, scalability, and measurable ROI in AI initiatives
Summary:
Sustaining finance transformation requires more than new systems or one time process redesign—it requires a durable operating model. This session explores how leading finance teams sustain modernization by treating core finance capabilities (FP&A, reporting, analytics, close support) as owned services, with clear accountability, prioritization, and continuous improvement. By applying product style disciplines—defined owners, backlogs, service levels, and change communication—finance evolves from a reactive reporting function into a scalable, decision support engine that improves over time.
Outcome:
A sustainable finance operating model that embeds ownership, governance, and continuous improvement into day to day execution—ensuring transformation gains persist beyond the initial initiative.
Takeaway:
A practical operating model blueprint for sustaining finance transformation through clear ownership, service orientation, and continuous improvement.
Key Discussion Points:
- Why transformation fades without an operating model
- Why project based modernization efforts stall once implementation teams disband—and how operating models prevent regression.
- Operating model fundamentals for modern finance
- Defining roles, decision rights, and accountability across FP&A, reporting, analytics, and accounting support.
- Ownership as the anchor of sustainability
- Assigning clear owners for finance capabilities, not just processes or tools—and what those owners are accountable for.
- Applying product disciplines to finance services
- Using backlogs, prioritization, and service definitions to manage demand and focus improvement efforts.
- Continuous improvement in the finance cadence
- Embedding improvement into monthly and quarterly rhythms instead of relying on episodic transformation projects.
- Service levels, transparency, and expectations
- Establishing SLAs, change communication, and release style updates to build trust with business stakeholders.
- Engaging the business as customers, not requestors
- Shifting finance’s posture from reactive support to proactive internal consulting.
- Measuring whether transformation is sticking
- Metrics that signal sustainability: usage, decision impact, satisfaction, cycle time, and quality—not just output volume.
- Building the muscle, not just the model
- Developing the skills, behaviors, and culture required to sustain modernization over time.
Summary: Tax engine integration (Avalara, Vertex), nexus management, ERP connections, and ongoing compliance. How technology reduces audit risk and manual effort.
Outcome: Streamlined sales tax compliance with reduced audit risk
Takeaway: A practical approach to tax automation technology selection and implementation
Key Discussion Points:
- Tax engine options: Avalara, Vertex, and native ERP capabilities
- Nexus determination and ongoing monitoring requirements
- ERP integration patterns and data quality requirements
- Exemption certificate management and automation
- Rate determination and product taxability mapping
- Audit trail and documentation best practices
- Multi-state compliance complexity management
- ROI calculation for tax automation investments
- Common implementation pitfalls and how to avoid them
- Ongoing maintenance and rate update processes
Chris Grimes - Cherry Bekaert
Summary: Governance can either slow an organization down—or become the system that increases decision velocity and reduces rework. This session shows how modern organizations build trust programs that scale innovation safely.
Outcome: A modern governance operating model
Takeaway: A 'minimum viable governance' blueprint
Key Discussion Points:
- The old way: governance as a brake (late-stage approvals + rework)
- The new way: governance as a product team (policies-as-code mindset)
- Decision rights: who approves what, when, and based on what evidence
- 'Guardrails, not gates': standards that teams can reuse immediately
- Designing governance for velocity: embed controls where work happens
- Exception handling: managing risk acceptance like a backlog, not chaos
- KPI examples: cycle time, control exceptions, audit effort, rework reduction
- 'Trust signals' for leadership and boards: what to report and why
- Scaling across products: federated models vs centralized enforcement
- Governance maturity roadmap: what changes at 90 days, 6 months, 12 months
Dan Sembler - Cherry Bekaert
In June, the leading reporting tools that finance teams rely on become something fundamentally different.
Reporting Intelligence from insightsoftware adds always-on AI monitoring, automated insights, and 60-minute setup to the Excel-based reporting your team already uses. Built on the foundation of Jet Reports and Spreadsheet Server, with one critical addition: an intelligence layer that watches your financial data around the clock and surfaces what matters before you have to ask.
The cost of reactive reporting isn't inefficiency. It's missed decisions and your best people stuck in the data instead of on top of it.
In 30 minutes, you'll:
Learn how Reporting Intelligence shifts finance teams from running reports after the fact to acting on AI-driven insights the moment something changes.
See a live walkthrough connected to real ERP data, including alerts, autonomous insights, and the path from anomaly to action.
Leave with a clear next step for your business — whether you're a current user, a prospect, or planning an ERP migration.
Caria Collins - insightsoftware
Summary: Most SMBs have Microsoft Copilot licenses but report using only 10-20% of capabilities. Research shows 72% of users struggle to integrate Copilot into daily routines yet 90% would fight to retain access. This paradox reveals a usage problem, not a value problem.
Outcome: Maximized ROI from existing Copilot licenses
Takeaway: Top 5 finance and IT use cases with practical implementation guidance
Key Discussion Points:
- Top 5 finance use cases: meeting summarization, reconciliation in Excel, collections communication, variance analysis
- Top 5 IT use cases: helpdesk automation, security incident summarization, policy documentation
- Role-based deployment matrix: who gets what licenses
- Goal/Context/Expectations/Source prompting framework
- Building custom departmental agents in Copilot Studio
- Adoption metrics that matter
- Training approaches that drive usage
- Common mistakes in Copilot rollout
- License optimization strategies
- Roadmap for expanding Copilot capabilities
Summary: As finance organizations race to modernize, data integration and fragmented systems remain critical roadblocks. This 20-minute session explores the evolving mandate of the CFO to act not just as a financial steward, but as the Enterprise Data Architect. We will dive into how finance leaders can break down data silos and spearhead the establishment of unified KPI definitions, robust master data governance, and scalable reporting frameworks. Participants will discover how taking ownership of the data architecture is the essential first step to turning technology and AI investments into real-world strategic results.
Outcome: Attendees will gain a clear roadmap for bridging the gap between disconnected data processes and a unified, AI-ready reporting ecosystem, empowering the finance function to become the definitive source of truth across the enterprise.
Takeaways:
- Standardizing KPIs: Actionable strategies for aligning KPI definitions across departments to eliminate conflicting metrics and drive cohesive business goals.
- Mastering Data Governance: Practical approaches to implementing master data management that ensures data integrity and operational readiness for advanced automation tools.
- Modernizing Reporting: Steps to transition from manual, siloed data gathering to automated, streamlined reporting frameworks that accelerate strategic decision-making.
Summary: Privacy programs often become either checkbox exercises or roadblocks to analytics and innovation. This session focuses on building privacy programs that protect the organization while enabling AI and data-driven decision-making at speed.
Outcome: A modern operating model for privacy that enables growth
Takeaway: A privacy-by-design playbook for analytics and AI initiatives
Key Discussion Points:
- Privacy that accelerates: enabling trusted data use without constant escalations
- Data classification + inventory: focus on high-risk flows, not 'boil the ocean'
- Purpose limitation + minimization: collecting and using only what you need
- Retention and deletion discipline: reduce exposure and cost
- AI + privacy: what data is acceptable for AI use cases (and what isn't)
- DPIAs/PIAs that work: lightweight assessments with real value
- Vendor data processing controls: subprocessors, cross-border, and access boundaries
- Privacy incidents: how to contain, notify, and recover without business disruption
- Metrics that matter: approval cycle time, rework reduction, incident trend reduction
- Governance integration: privacy + security + compliance as a single decision system
Richart Ruddie - Captain Compliance
Summary: For many mid-market teams, the month-end is a two-week marathon of manual data entry and spreadsheet reconciliation. This session explores how to shift toward a more continuous accounting model, with the ultimate goal of shrinking the close cycle down to just three days. See how BILL removes the manual bottlenecks in AP through intelligent automation by capturing invoices, automating approvals, and maintaining a real-time sync with your ERP to ensure your books are always decision-ready.
Outcome: A practical roadmap for accelerating your close cycle and a live look at the automation tools that make it possible.
Takeaways:
- A framework for spotting the manual hurdles that prevent a faster, more accurate close
- A hands-on look at BILL’s intelligent capture, automated workflows, and ERP synchronization
- Seeing how AI-driven fraud detection works in real-time to prevent duplicate payments and protect your bottom line
Key Discussion Points:
- The "close cycle" maturity curve: Moving from reactive manual work to proactive, automated finance
- The integration advantage: How BILL acts as the connective tissue between your bank and your ERP
- Live demo: A step-by-step walkthrough of the touchless workflow in action
- Strengthening governance: Why automation offers better auditability and control than manual processes
- Scaling with confidence: How to implement these changes without disrupting your current operations
Summary: Most leaders at this conference have heard the AI pitch dozens of times. They’ve seen the demos, read the articles, and maybe even bought licenses. But they haven’t started. Not because they’re resistant—because nobody has given them a realistic, low-stakes way to begin. This session is built from 40+ weeks of one leader’s documented AI journey—what actually worked on day one, what didn’t, and the four skills that turned awkward first conversations into genuine productivity. No live demos. No tool comparisons. Instead: an honest account of what it feels like to start, a practical framework for the first conversation, and a specific challenge attendees can try tonight.
Outcome: Attendees leave with a clear mental model for their first productive AI conversation and a specific prompt to try before tomorrow morning
Takeaway: The Four Skills framework—Set the Stage, Read Like a Skeptic, Work the Conversation, Know When to Stop—and a concrete first-night challenge
Key Discussion Points:
- Why most AI adoption stalls at “I should try this” and never reaches “I actually did”
- The honest version of day one: what starting actually feels like for a business leader
- The Spectator vs. Participant trap: why waiting for the right use case guarantees you never find it
- The Four Skills framework: what 40 weeks of documented AI learning revealed as the actual working skills
- Set the Stage: the 30 seconds before your first prompt that determine whether the output is useful
- Read Like a Skeptic: why AI’s most dangerous failure mode is confident-sounding nonsense—and how to spot it
- Work the Conversation: treating every AI response as a first draft you shape through dialogue, not a final answer
- Know When to Stop: the skill nobody teaches—recognizing when AI is the wrong tool for the problem
- Why “try AI” isn’t a strategy—and what to do instead
- The Tonight Challenge: a specific prompt designed for finance and IT leaders that produces a surprising result on first try
Summary: Documenting software development, ERP implementations, cloud migration, and process automation as eligible R&D activities. Technology as a tax credit opportunity.
Outcome: Identify and capture R&D tax credits from technology investments
Takeaway: Documentation framework for qualifying technology activities
Key Discussion Points:
- What qualifies: the four-part test for R&D activities
- Software development as qualified research
- ERP implementations and customization eligibility
- Cloud migration and infrastructure modernization credits
- Process automation and AI development activities
- Documentation requirements and contemporaneous records
- Qualified research expenses (QREs) calculation
- State R&D credit variations and opportunities
- Audit defense strategies and common challenges
- Integration with finance modernization planning
Vivian Kohrs - Cherry Bekaert
Summary: Policies, bias considerations, security, data privacy, and responsible use frameworks. How to adopt AI without creating new risks.
Outcome: A comprehensive AI governance framework
Takeaway: Practical policies and controls for responsible AI adoption
Key Discussion Points:
- AI governance framework components and ownership
- Bias detection and mitigation strategies
- Data privacy considerations in AI training and inference
- Security controls for AI systems and models
- Regulatory landscape and compliance requirements
- Vendor AI vs. internal AI governance differences
- Transparency and explainability requirements
- Human oversight and escalation procedures
- Risk assessment methodology for AI use cases
- Monitoring and ongoing governance processes
Summary:
Sales tax compliance continues to be a growing burden for businesses, driven by complex regulations and constant change across jurisdictions. In this session, we’ll explore how automation simplifies compliance, reduces risk, and improves efficiency on Sage, Microsoft, and other platforms. We’ll also look at how Avalara is embedding AI into its solutions to further streamline and strengthen tax management.
Takeaways:
- Understand the true cost and risk of manual sales tax compliance
- See how automation can improve accuracy, efficiency, and scalability
- Learn how Avalara integrates with core business systems like Sage and Microsoft
- Gain insight into how AI is enhancing compliance processes and decision-making
Key Discussion Points:
- The increasing complexity and burden of sales tax compliance for businesses
- Common challenges with manual processes and fragmented systems
- Overview of Avalara's automated compliance solutions
- Seamless integrations with Sage, Microsoft, and other business platforms
- The role of agentic AI in driving smarter, more proactive tax compliance
Travis Hussey - Avalara
Summary: Strategic briefing for CEOs, CFOs and accounting leaders on the operating system behind useful AI.
Summary: Finance and IT leaders know inefficiencies exist but struggle to identify and quantify them. Process mining analyzes actual system logs to visualize real workflows—where deviations occur, which activities consume time, where rework happens. Customers typically uncover ~20% savings within 30 days.
Outcome: Data-driven identification of automation and improvement opportunities
Takeaway: How to identify good candidate processes and connect outputs to automation investments
Key Discussion Points:
- What process mining reveals that traditional process mapping misses
- Platform options: Power Automate Process Mining, ProcessMind, Apromore
- How to identify good candidate processes: 5+ activities, high volume, compliance requirements
- Connecting process mining outputs to RPA and AI automation investments
- Data requirements and system log access
- Privacy and security considerations for log analysis
- Quick wins: processes to mine first
- Interpreting process mining results
- Building the business case from mining insights
- Continuous process monitoring and improvement
Summary: A brand-agnostic decision framework: requirements gathering, fit-gap analysis, TCO modeling, change impact assessment, and implementation approach. How to avoid 'big-bang regret' and what mid-market CFOs get wrong.
Outcome: Confident ERP selection decisions with reduced implementation risk
Takeaway: A repeatable ERP evaluation framework for mid-market organizations
Key Discussion Points:
- Requirements gathering: distinguishing needs from wants
- Fit-gap analysis methodology and scoring
- Total cost of ownership (TCO) modeling beyond license fees
- Change impact assessment and organizational readiness
- Implementation approach options: big-bang vs. phased
- Vendor evaluation criteria and reference checking
- Contract negotiation considerations
- Internal team requirements and partner selection
- Common mid-market ERP selection mistakes
- Timeline and budget planning realities
Summary: The 'Cost Center to Strategic Partner' language directly mirrors how the CFO Survey describes finance's own evolution. As business leaders plan to modernize operations and shift from reactive to proactive company-wide transformations, developing a business component model gives companies a framework to connect business strategies to technology strategies.
Outcome: Business component model mapping business essentials to technology enablement.
Takeaway: Framework to begin to align business components and IT capability.
Key Discussion Points:
- CFO Survey related observations and insights
- The foundation for technology strategies
- How to define an enterprise business component model
- How to connect IT labor, infrastructure, software and budgets to a business component model
- How to articulate IT cost, functional fit and complexity of technologies in business terms
Summary:
A practical look at how governance, permissions, and content structure shape Copilot readiness and AI outcomes in Microsoft 365.
Outcome: Better understanding of how to reduce data exposure and improve AI readiness.
Takeaway:
You cannot scale AI successfully on top of unmanaged content and overshared data.
Key Discussion Points:
- Copilot readiness fundamentals
- Oversharing and permissions risk
- Legacy content and architecture challenges
- Visibility into data exposure
- Governance actions that improve AI outcomes
Ravinder Bhinder - Orchestry
Summary: See how Copilot's evolution from prompt-based content generation to independent agents unlocks new possibilities for enterprise automation. We'll break down the fundamental differences, showcase practical implementations, and help you determine which approach drives ROI.
Outcome: Clear understanding of when to use generative vs. agentic AI approaches
Takeaway: A decision framework for choosing the right Copilot capability for each use case
Key Discussion Points:
- Generative AI fundamentals: prompts, context, and content creation
- Agentic AI fundamentals: autonomous action and workflow execution
- Copilot's evolution: from chat assistant to independent agent
- Use case mapping: where generative AI excels vs. where agents win
- Practical implementation: building your first Copilot agent
- Integration patterns: connecting agents to business systems
- Governance considerations: controlling what agents can do
- ROI comparison: measuring value from each approach
- Common pitfalls and how to avoid them
- Roadmap: what's coming next in Copilot capabilities
Summary: Maturity assessments provide a structured, objective view of people, process, data, and technology capabilities—highlighting gaps that can derail transformation programs, such as ERP.
Outcome: Clear understanding of organizational readiness for transformation
Takeaway: A maturity assessment framework to identify and address transformation risks
Key Discussion Points:
- The PPDT framework: People, Process, Data, Technology dimensions
- Maturity model levels and what they mean practically
- Assessment methodology and stakeholder involvement
- Common gaps that derail ERP and transformation programs
- People readiness: skills, change capacity, and sponsorship
- Process readiness: standardization, documentation, and optimization
- Data readiness: quality, governance, and migration complexity
- Technology readiness: infrastructure, integration, and technical debt
- Prioritizing remediation efforts before go-live
- Using assessment results to set realistic timelines and budgets
Summary: AI governance should accelerate value delivery—not slow it down. This session focuses on how CFOs and CIOs can fund AI initiatives with clear ROI, measurable productivity gains, and built-in controls that reduce rework and risk.
Outcome: Real ROI framing, not hype
Takeaway: A practical scorecard for 'AI use cases worth funding'
Key Discussion Points:
- Where AI wins first: forecasting cadence, close acceleration, variance explanations, spend controls
- Defining success: 'fewer manual touches' + faster decisions + higher confidence
- Picking the right use cases: high frequency, high friction, high impact workflows
- Data readiness requirements: what 'decision-grade data' means for AI
- Human-in-the-loop design: where humans approve, override, and validate
- Guardrails without slowing delivery (policies, approvals, model usage boundaries)
- Model risk realities: hallucinations, drift, explainability, auditability
- Usage controls: data access boundaries, prompt governance, logging, retention
- A 90-day AI pipeline that doesn't turn into shelfware (pilot → measure → scale)
- Scaling responsibly: standard patterns, reusable controls, and change management
Merritt Baer - Enkrypt AI
Summary:
AI tools like ChatGPT, Claude, and Microsoft CoPilot are really smart, but they don't know anything about your company's customers, sales, or data. That information and context is locked inside your business software. Usually, connecting AI to your company's systems takes months of development work and costs hundreds of thousands of dollars. PopdockAI is the tool that you need to bridge the gap between AI and your business systems. It connects them in hours instead of months, keeps your data safe with strong security controls, and saves money by only sending AI the exact information it needs (not huge amounts of unnecessary data). In this session, you'll learn how companies are actually using AI in their real business operations with PopdockAI's easy-to-use platform that connects to over 100 different business applications.
Takeaways:
Attendees will learn how Popdock AI securely connects AI to their business systems, providing context-aware insights, protecting data, enabling fast integration with 100+ applications, and showcasing real-world use cases.
Key Discussion Points:
- The limitations of generic AI tools in understanding company-specific data.
- How Popdock AI connects AI to your business systems in hours, not months.
- Security and privacy controls that protect sensitive business data.
- Cost and efficiency benefits of sending only relevant data to AI.
- Case studies demonstrating AI in action with real business operations.
- Overview of Popdock AI’s platform and supported integrations.
- Tips for getting started and maximizing ROI from AI in your organization.
Summary: See how AI Agents built with Copilot Studio and Power Automate can transform capital expenditure approval workflows. This case study demonstrates how to connect approvals, remove manual handoffs, reduce errors, and accelerate cycle times.
Outcome: Understanding of how Copilot Studio and Power Automate can automate complex approval workflows
Takeaway: A practical reference architecture for AI-powered approval automation
Key Discussion Points:
- The CapEx approval challenge: manual handoffs, delays, and visibility gaps
- Copilot Studio fundamentals: building conversational AI agents
- Power Automate integration: connecting approvals across systems
- Designing the workflow: triggers, conditions, and escalation paths
- User experience: how approvers interact with the AI agent
- Error handling and exception management
- Reporting and visibility: real-time status and audit trails
- Implementation lessons learned: what worked and what didn't
- ROI measurement: cycle time reduction and error rates
- Extending the pattern to other approval workflows
Summary: As distributors expand across Amazon, Shopify, Walmart, and direct web channels, finance teams often lose visibility into true channel profitability, inventory carrying costs, and cash conversion cycles. This session demonstrates how to unify order-to-cash workflows within your ERP using the Microsoft technology stack.
Outcome: Clear framework for achieving financial visibility across multiple sales channels
Takeaway: Integration strategies and data quality requirements for multi-channel commerce success
Key Discussion Points:
- The multi-channel visibility challenge: Amazon, Shopify, Walmart, direct web
- True channel profitability: costs most finance teams miss
- Inventory carrying costs and allocation decisions by channel
- Cash conversion cycle differences across sales platforms
- Unifying order-to-cash workflows within your ERP
- Microsoft technology stack integration: Outlook, AI-assisted order creation
- Channel-specific margin analysis and reporting foundations
- Where DIY integrations break down and why
- Evaluating connector solutions: what to look for
- Data quality requirements that determine success or failure
Paul McLellan - Suite Engine
Summary: Organizations are increasingly dependent on vendors for privacy tooling, compliance platforms, cloud security, monitoring, and AI enablement—but many don't have a clear standard for what 'good' actually looks like.
Outcome: A modern operating model for selecting and governing external partners
Takeaway: A 'what good looks like' scorecard for privacy, compliance, and security vendors
Key Discussion Points:
- Vendor outcomes vs features: what should improve if the vendor is working
- The 'proof' standard: what evidence to require beyond marketing claims
- Security tooling effectiveness: coverage, visibility, and operational usability
- Compliance tooling effectiveness: workflow automation, evidence integrity, audit readiness
- Privacy tooling effectiveness: data discovery, classification, consent, retention support
- Integration reality: APIs, identity integration, data flow mapping, deployment complexity
- Total cost of ownership: people/time overhead, tuning requirements, false positives
- Operational maturity fit: don't buy 'enterprise complexity' for mid-market needs
- Vendor accountability: SLAs, reporting cadence, and measurable performance indicators
- Governance of partners: ongoing monitoring, scope drift, renewals, and exit planning
Brian Kirk - Cherry Bekaert
Dixon Wright - Riveron
Alicia Laing Clarke - PGATSS - In Transition
Summary:
This session will cover how Flowgear’s integration and automation platform can seamlessly integrate any Sage product, any Microsoft product, etc. with absolutely any 3rd party system, application, technology, database, on-prem system, proprietary system, absolutely anything at all. While there are native connectors and other tools that exist in this space, we will deep dive into our differences not only technically, but commercially as well to help scale your business. There will also be a live demonstration in the presentation.
Takeaways:
- Eliminate manual processes and data silos across Sage, Microsoft, etc. environments
- Why traditional integrations (custom code, point-to-point, native tools) break or don't work at scale
- How an iPaaS platform like Flowgear enables faster, more flexible integrations
- The ways we work with our partners to productize integration services alongside your existing ERP services
- Real examples of integrations that improve visibility, efficiency, and decision-making
Key Discussion Points:
- Flowgear Overview: Who Flowgear is and what we do
- High level understanding of our platform and how we have gone to market
- What problems are Flowgear solving for
- Flowgear’s Application Connector and Technology Connector Ecosystem
- Flowgear’s Powerful Orchestration Engine
- Real World Use Cases for Flowgear
- Flowgear Demonstration
- Flowgear’s Integration Maturity and Opportunity Assessment Program
Summary: Mid-market companies manage hundreds or thousands of contracts—vendor agreements, client MSAs, leases, employment agreements—often stored in scattered SharePoint folders, inboxes, and filing cabinets. AI contract review can cut analysis from 40 minutes to 2 minutes with 98%+ accuracy.
Outcome: Modernized contract management with AI-powered intelligence
Takeaway: How to run a 30-day pilot with 50-100 contracts
Key Discussion Points:
- Contract intelligence capability maturity model: repository to extraction to obligation tracking to risk analysis
- Comparison of mid-market tools: ContractPodAi, Ironclad, Juro
- Immediate-value use cases: auto-renewal alerts, obligation calendaring, clause identification
- How to run a 30-day pilot with 50-100 contracts
- Integration with existing document management
- Security and confidentiality considerations
- Change management for legal and procurement teams
- ROI calculation: prevented penalties, captured discounts, reduced legal spend
- Build vs. buy considerations
- Implementation roadmap and quick wins
Summary: A 90-day action plan to streamline month-end close, reduce manual touches, and move key KPIs (days-to-close, manual-touch rate, error rates). Quick wins and sustainable improvements.
Outcome: Faster, more reliable month-end close process
Takeaway: A 90-day action plan with prioritized quick wins
Key Discussion Points:
- Assessing your current close: bottlenecks and pain points
- Days 1-30: standardization and process documentation
- Days 31-60: automation of high-volume, low-complexity tasks
- Days 61-90: measurement, refinement, and sustainability
- KPIs that matter: days-to-close, manual-touch rate, error rates
- Technology enablers: reconciliation tools, close management
- Change management and team adoption
- Maintaining gains: governance and continuous improvement
- Common pitfalls and how to avoid them
- Building the business case for close modernization
Summary: Practical and comprehensive framing that incorporates fractional leadership (vCIO/vCISO) economics as a component rather than separate session—the fractional leadership question is part of the broader staffing model decision.
Outcome: Optimized IT staffing model balancing cost, capability, and control
Takeaway: Cost comparison frameworks and decision criteria for staffing models
Key Discussion Points:
- Assessing current IT capacity and identifying capability gaps
- Cost comparison frameworks: true cost of in-house vs. managed services
- When fractional leadership (vCIO/vCISO) makes sense vs. full-time hire
- Structuring co-managed arrangements that maintain internal control
- Transition planning: moving between staffing models
- Vendor selection criteria for managed services
- SLA design and performance management
- Knowledge retention and documentation requirements
- Building internal capabilities while using external support
- Success metrics for blended IT teams
Summary:
Mid-market finance teams are increasingly moving away from static annual budgets toward rolling forecasts and driver-based planning. Insights from the Cherry Bekaert Middle Market CFO Survey show that data integration is the top modernization priority, while lack of internal expertise remains a major barrier. Together, these challenges help explain why traditional budgeting processes are breaking down and why finance teams are looking for more flexible, continuous planning approaches.
Outcome:
An understanding of why annual budgeting is no longer effective for many mid-market organizations, and how continuous planning enables more agile decision making in a fast-changing environment.
Takeaway:
Modernizing planning is not just about changing the budget cycle. It requires better integration, the right tools, and a shift toward ongoing, scenario-based planning supported by purpose-built CPM technology.
Key Discussion Points:
- Why the Annual Budget Is Failing Mid-Market Finance Teams
- How to make the shift to Continuous Planning
- Improving Data Integration Without Replacing ERP
- Turning Ongoing Insight into Action with Prophix One
Summary: Overview and demonstration of how modern SaaS analytics platforms are embedding leading AI tools to improve usability and performance. This session will include a demonstration of practical use cases that improve analytic capabilities. Databricks will be profiled.
Outcome: Understanding of how AI-embedded analytics platforms accelerate insight generation
Takeaway: Practical use cases for AI-enhanced analytics in mid-market organizations
Key Discussion Points:
- The evolution of analytics platforms: from static dashboards to AI-assisted insights
- How generative AI changes the analytics workflow
- Databricks overview: architecture and AI capabilities
- Natural language queries: asking questions in plain English
- Automated insight generation: letting AI surface what matters
- Data preparation and quality: AI-assisted data cleaning
- Visualization recommendations: AI-suggested chart types
- Practical demonstration: real use cases in action
- Integration with existing data infrastructure
- Evaluating AI-embedded analytics platforms for your organization
Summary: Most AI conversations focus on doing existing work faster. That’s the wrong question. AI’s most distinctive value is making possible the analysis, investigation, and scenario modeling that never happened—because the manual effort couldn’t be justified. Every finance and IT team has questions they’ve stopped asking: the variance analysis nobody does at the SKU level, the vendor payment pattern review that could save six figures, the technical debt quantification that’s “too complex” for the current team. This session reframes the AI ROI question from “how much time does it save?” to “what decisions are you not making because the analysis was never worth doing?”
Outcome: Attendees identify specific high-value analyses in their own organizations that AI now makes feasible for the first time
Takeaway: A framework for identifying “below-the-line” work—analysis that was always valuable but never justifiable—and categories of hidden opportunity specific to finance and IT leaders
Key Discussion Points:
- The ROI trap: why “time saved” is the wrong measure of AI value
- Below-the-line work: analysis that was always valuable but sat below the threshold of justifiable manual effort
- Case study: a compliance analysis across dozens of inbox spreadsheets—work that would never have been approved manually—completed in one hour with AI assistance
- The questions you’ve stopped asking: how to recognize below-the-line opportunities in your own organization
- Finance-specific opportunities: SKU-level variance analysis, customer profitability segmentation, vendor payment pattern optimization, scenario modeling at granularity that was previously impractical
- IT-specific opportunities: license optimization analysis, integration failure pattern detection, technical debt quantification in financial terms
- Why “too complex for our team” is often a manual-effort barrier, not a capability barrier
- The forecasting problem: how one company’s request for AI help was really a request for analysis they couldn’t afford to do manually
- From “AI project” to “business question”: why starting with the problem instead of the technology changes everything
- Connecting below-the-line work to decisions: analysis without action is just expensive curiosity
Summary: The 'balance sheet' and 'interest rate' metaphor is the most CFO-friendly framing available. Treating technical debt as a compounding financial liability speaks the CFO's language directly.
Outcome: Ability to quantify and communicate technical debt in financial terms
Takeaway: Technical Debt Ratio calculation and prioritization framework
Key Discussion Points:
- Calculating your 'Technical Debt Ratio' and quantifying maintenance drag
- The compounding 'interest' of deferred maintenance
- Mapping technical debt to business risk and opportunity cost
- Prioritization frameworks: risk-based vs. opportunity-based modernization
- Building the CFO-ready business case for infrastructure investment
- Quick wins: where to reduce debt with minimal disruption
- Technical debt in M&A: due diligence considerations
- Communicating technical debt to non-technical stakeholders
- Creating a sustainable debt reduction roadmap
- Metrics to track technical debt over time
Summary: What if finance actually flowed—without disconnected tools, manual handoffs, or constant rework? This session shows how Truvio embeds automation inside Business Central to connect AP, expense management, banking, and commerce into one unified process from invoice to reconcile and order to cash.
Jasmine Leslie - Truvio
Heather Mitchell - Truvio
Summary:
The finance function is undergoing a fundamental shift. AI-powered automation is moving beyond basic process efficiency to enable truly autonomous accounting workflows — where transactions are classified, reconciled, reported, and analyzed with minimal human intervention. This session explores real-world use cases where AI is already transforming core finance operations, and what it takes to implement these capabilities inside your organization today.
Outcome:
Attendees will leave with a practical framework for identifying where AI-driven automation delivers the highest ROI in their accounting and finance function, a clear-eyed view of the technology landscape, and actionable next steps for piloting autonomous workflows without disrupting controls or compliance requirements.
Key Takeaways:
- AI is no longer a future-state concept in finance — it is deployable now across close, reporting, AP/AR, and FP&A workflows
- Autonomous accounting does not eliminate the finance professional; it elevates the role from transaction processor to strategic decision-maker
- The biggest barrier to adoption is not technology — it is data readiness, change management, and process standardization
- Controls and compliance frameworks can coexist with AI automation when implementation is approached thoughtfully
- Organizations that move now will build a compounding advantage in speed, accuracy, and finance talent retention
Summary: Mid-market companies accumulate disconnected systems: CRM, ERP, HRIS, support ticketing, project management. Data lives in silos. Employees manually copy information between systems. Reports require consolidating exports from multiple sources. Research shows growing SMBs are 2x more likely to have integrated tech stacks.
Outcome: Clear integration strategy to connect critical business systems
Takeaway: Decision framework for building vs. buying connectors
Key Discussion Points:
- Integration maturity model: point-to-point to hub-and-spoke to event-driven to real-time
- Platform comparison by company size: Zapier vs. Make vs. Workato vs. Celigo
- High-value integration patterns for finance-IT collaboration
- CRM-to-ERP integration: quotes, orders, and revenue recognition
- HR-to-finance integration: payroll, headcount, and forecasting
- Support-to-billing integration: usage tracking and invoicing
- Decision framework for building vs. buying connectors
- Data quality at integration points
- Security and compliance considerations
- ROI measurement for integration investments
Summary: AI hype makes it difficult to separate genuine opportunity from vendor marketing. Mid-market leaders face pressure to 'do something with AI' but lack enterprise research budgets to evaluate options. This session provides the strategic filter CFOs need.
Outcome: Clear-eyed view of AI opportunities vs. hype for mid-market
Takeaway: Decision framework for evaluating any AI investment
Key Discussion Points:
- Evidence-based assessment of AI categories with real ROI data
- What actually works: customer service AI, document processing, Excel productivity
- What to avoid: custom LLMs, fully autonomous decisions, massive data projects without clear use cases
- Warning signs of overhype: fully autonomous promises, vague ROI claims, 'transformational' language
- The 70/20/10 rule: 70% people/process, 20% technology/data, 10% algorithms
- Vendor evaluation: separating marketing from capability
- Build vs. buy considerations for mid-market
- Pilot design that actually tests value
- Realistic timelines and investment requirements
- When to wait vs. when to move
Summary: Tax and e-Invoicing challenges are a leading cause of delays and rework in Microsoft Dynamics 365 implementations. In this session, we’ll break down the five most common tax-related pitfalls teams encounter and how early ERP design decisions can either prevent or create long-term compliance and scalability issues.
Takeaways
- How to build a scalable, tax-ready foundation from day one
- Which master data elements matter most for tax outcomes
- How automation reduces ongoing maintenance and compliance risk
- How Vertex e-Invoicing supports global mandate readiness without ERP redesign
Key Discussion Points:
- The five most common tax and e-Invoicing pitfalls in Dynamics 365 implementations
- Early ERP design and data decisions that drive tax accuracy and compliance
- Why master data quality is critical and where teams often go wrong
- How automation helps prevent configuration drift and rework
- What e-Invoicing readiness means for future-proofing your ERP
Summary: 'Agentic AI'—systems that can autonomously plan, execute, and adapt multi-step workflows—represents the next wave beyond chatbots and copilots. But the market is flooded with 'agent washing'—only ~130 of thousands of claimed vendors offer legitimate agent technology.
Outcome: Clear understanding of agentic AI landscape and readiness considerations
Takeaway: Red flags for 'agent washing' and 2026-2027 planning guidance
Key Discussion Points:
- What 'agentic AI' actually means and how to identify vendor 'agent washing'
- Three agent categories ready for mid-market deployment
- Copilot Studio walkthrough: building a functional agent without coding
- Customer service agents: capabilities and limitations
- IT support agents: helpdesk automation
- Data retrieval agents: self-service information access
- Red flags to watch: multi-agent orchestration, fully autonomous decision-making, unclear ROI
- 2026-2027 planning guidance
- Infrastructure and data requirements
- Building vs. buying agent capabilities
Summary: Practical levers for DSO, DPO, and inventory days. Credit policy, collections operations, terms strategy, and treasury visibility as design constraints—not downstream outcomes.
Outcome: Improved working capital performance through intentional design
Takeaway: Actionable levers for DSO, DPO, and inventory optimization
Key Discussion Points:
- Working capital as a design constraint, not an outcome
- DSO improvement: credit policy and collections operations
- DPO optimization: terms strategy and payment timing
- Inventory days: demand planning and supply chain coordination
- Treasury visibility: cash positioning and forecasting
- Technology enablers for working capital management
- Cross-functional alignment: sales, procurement, operations
- KPIs and reporting for working capital performance
- Quick wins vs. structural improvements
- Building a cash culture across the organization
Summary: This is distinct from FinOps/cost sessions—it's about architecture decisions, benefits, risks and governance--not just spend management. With 70% - 80% of business using hybrid cloud environments this is a real operational reality that needs strategic guidance.
Outcome: Plan and approach to move, manage, and govern hybrid cloud environments.
Takeaway: Approach, decision management framework for hybrid cloud architecture operations
Key Discussion Points:
- Hybrid cloud overview
- Business case overview for hybrid cloud
- Key benefits and risk to plan for
- Sample migration adoption roadmap
- Workload placement criteria: what belongs on-premises versus the public cloud
- Governance Pillars & security framework
- Security and compliance in hybrid environments
- Cost framework & TCO model to measure hybrid cloud environments
- Recommended steps to begin adopting hybrid cloud environments
Summary:
The legacy SaaS approach to FP&A software will grow obsolete with the advent of AI models but Excel never will. Datarails helps bridge the gap by providing a layer of connectivity from your ERP and source data to AI, all while leveraging Excel-native architecture to optimize your FP&A processes.
Takeaways:
Financial Planning and Analysis needs to evolve in the world of AI, and legacy software solutions are no longer adequate for complex businesses demanding faster time to value.
Key Discussion Points:
- Financial analysis and reporting optimization
- Streamlining budgeting, planning and forecasting
- Excel is the heartbeat of finance - don’t move off it
- Increase time to knowledge with real-time dashboards and visibility
- Eliminating manual processes in FP&A
- Strengthening controls around spend
- Integrations with ERP, CRM, HRIS and more
- Connecting your LLM models to a trusted, governed data source
Mitch McCaw - Datarails
Summary: This session focuses on practical finance applications: turning messy data into AI-ready formats, building analysis tools for problems previously 'too expensive to explore,' and automating the manual work that finance teams have normalized.
Outcome: Identification of high-ROI AI opportunities in back-office operations
Takeaway: Two or three specific back-office AI applications to evaluate for your organization
Key Discussion Points:
- Why back-office AI delivers better ROI than front-office investments
- The hidden cost of manual work finance teams have normalized
- Data readiness: turning messy data into AI-ready formats
- Use case: automating data extraction and categorization
- Use case: exception-based processing and anomaly detection
- Use case: analysis tools for previously unexplored problems
- Building vs. buying: when to use off-the-shelf vs. custom solutions
- Change management: helping teams adopt AI-assisted workflows
- Measuring success: KPIs for back-office AI initiatives
- Case study: real-world back-office transformation results
Mike McDonald - Cherry Bekaert
Supreet Singh - Cherry Bekaert
Gagan Preet Singh - HP
Summary: You bought the licenses. You ran the training. You sent the encouraging emails. And adoption is flat. This session explains why—and it’s not because you chose the wrong platform. After 40+ weeks of documenting AI transformation across personal, team, and client contexts, one pattern is clear: the barriers that block AI adoption are rarely informational. They’re about values (“this feels like cheating”), identity (“this threatens who I am professionally”), and interest (“this threatens something I have”). When leaders treat every adoption problem as an information problem, they invest in training and communication that actively reinforces resistance. This session gives leaders a diagnostic framework to identify which barriers they’re actually facing—and why only one of the four types responds to better information.
Outcome: Leaders can diagnose whether their AI adoption barriers are informational, values-based, identity-based, or interest-based—and adjust their strategy accordingly
Takeaway: The Barrier Diagnostic framework and the uncomfortable truth that most AI adoption programs are solving the wrong problem
Key Discussion Points:
- The adoption paradox: why access and training don’t produce usage
- Real measurement data: what happened when one organization moved from “encouragement” to “expectation”—and why encouragement alone is abdication dressed as empowerment
- The Knowledge Trap: how AI makes leaders more knowledgeable but not more persuasive—and why that matters for adoption
- Four barrier types: informational (“I don’t know how”), values (“this conflicts with what I believe”), identity (“this threatens who I am”), and interest (“this threatens what I have”)
- The diagnostic question every leader should ask before designing an adoption program
- Why the person who resists AI loudest may understand it best—and what that means for your approach
- The Spectator vs. Participant divide: how organizations accidentally train their people to wait
- Literacy before agency: why leaders cannot effectively deploy AI tools for their teams without first experiencing personal AI fluency
- What actually works: removing the pressure to convert and focusing on problems people already care about
- The uncomfortable mirror: recognizing your own barriers before trying to address your team’s
Summary: Develop a framework to define emerging business needs, then value and prioritize those needs into proactive transformation initiatives.
Outcome: Definition of proactive and high value transformation initiatives
Takeaway: A practical business capability framework to move toward proactive transformation initiatives.
Key Discussion Points:
- CFO Survey related insights
- How to define emerging business needs
- Associating emerging needs with business capabilities
- How to scope the value and timing of business capabilities
- Prioritizing business capabilites
- Grouping capabilities into proactive transformation initiatives
- Defining proactive transformation initiative recommendations
Summary:
In this session, we’ll walk through the budgeting process from multiple perspectives, starting with department managers entering and reviewing budgets, then switching to the finance and CFO view for centralized oversight, forecasting, and last-minute adjustments. Attendees will also see how payroll budgeting scenarios are handled and how data is easily set up, imported, and exported. The session demonstrates how all roles collaborate effectively within a single, connected budgeting platform built specifically for Dynamics 365 Business Central and Sage Intacct.
Outcome:
A clearer understanding of how modern, role-based budgeting improves collaboration, visibility, and confidence across departments, finance teams, and executive leadership—without relying on spreadsheets.
Takeaway:
Dynamic Budgets delivers fast, intuitive budgeting, forecasting, and reporting. Designed by finance professionals who understand the real-world needs of organizations using Dynamics 365 Business Central and Sage Intacct.
Key Discussion Points:
- How department managers enter and review budgets confidently without Excel
- How finance gains centralized visibility, consistency, and flexibility
- Managing payroll as a structured, high-impact component of the overall budget
- Supporting collaboration, forecasting, and last-minute changes
- Simplifying data setup, import, export, and reporting
Summary: Join an interactive roundtable on what it really takes to make your data AI-ready—covering permissions, labeling, lifecycle, and risk. Bring your questions and experiences as we discuss practical governance patterns that unlock AI value.
Outcome: Clear understanding of data readiness requirements for AI initiatives
Takeaway: A data readiness checklist to assess your organization's AI preparedness
Key Discussion Points:
- What 'AI-ready data' actually means in practice
- Data permissions: who can access what, and how AI changes the equation
- Data labeling: classification and metadata requirements
- Data lifecycle: retention, archival, and deletion considerations
- Data quality: completeness, accuracy, and consistency standards
- Risk assessment: what can go wrong with AI and your data
- Governance patterns that enable AI without compromising security
- Compliance considerations: regulatory requirements for AI data use
- Quick wins: where to start improving data readiness
- Peer discussion: challenges and solutions from attendees
Summary:
Modern pricing failures rarely happen at deal close—they happen upstream, when governance, controls, and accountability break down between Finance, Sales, and IT. In this session, we walk through the Quote‑to‑Close lifecycle to show where margin leakage occurs, how pricing rules are routinely bypassed, and why most organizations lack visibility until it’s too late.
Attendees will learn how leading organizations establish pricing governance models, embedded controls, and financial accountability that protect margin without slowing down the sales engine. The session focuses on practical design patterns—not theory—so executives leave with a clear blueprint they can sponsor immediately.
Summary: IT operations and governance are often overlooked or treated as a black box during the acquisition diligence process. In many cases, the Target company's ERP platform is outdated, financial and customer data protections are lacking, and IT governance is either completely outsourced or handled only as needed. Recognizing what transaction metrics are important during the LOI or pre-close stage is vital to developing a success post-close integration program.
Outcome: Attendees will learn:
- Risks that will impact the transaction and build a mitigation strategy
- IT cost implications before the transaction closes
- IT and Cybersecurity governance metrics that matter the most
Takeaways:
- Define the scope and timing of IT operations diligence once the LOI period has begun
- Determine the impact of IT operations and governance on EBITDA
- Identify the investments and one-time costs necessary after the transaction closes
- Recognize when diligence is necessary and what initiatives are executed after acquisition
Key Discussion Points:
- Understanding the focus areas of IT governance, cybersecurity, and backoffice operations.
- Determining key staffing and cost metrics (benchmarks) that are useful during diligence.
- Identifying areas where subject matter specialists are used (and needed) for diligence and the metrics uncovered
- Defining the outcomes of diligence and what is communicated to other third parties, e.g. lenders, insurance carriers, investors
- Recognizing when you need to perform operations/cyber diligence and what you need for a successful transaction
Summary: This fast-paced session reveals how modern fraud exploits the blind spots in manual AP and why outdated workflows create unnecessary risk, bottlenecks, and operational waste. Through real examples and sharp insights, leaders will see how modern AP teams strengthen controls, accelerate approvals, and improve financial visibility by shifting to faster, smarter, automated processes.
Takeaways:
- A sharper view of where manual AP introduces avoidable risk and control gaps.
- How outdated workflows create bottlenecks that slow approvals and cloud cash visibility.
- How modernization cuts operational waste—rework, exceptions, and manual touchpoints.
- The visibility, speed, and confidence leaders gain when automation replaces inconsistent steps.
Key Discussion Points:
- The top fraud myths AP teams still believe.
- Manual workflow weak spots that attackers exploit.
- What “fast, lean, and fraud-resistant” looks like in 2026.
- Real examples of how automation closes the gaps that manual processes miss.
- How modernization improves accuracy, speed, and control for AP teams.
Jennifer Andreas - Yooz