Conga: Beyond Fragmentation: Building an Integrated Commerce Chain for AI-Driven Growth

Enterprises are increasingly focused on leveraging artificial intelligence (AI) to enhance commercial operations, seeking strategic pricing recommendations, predictive revenue forecasts, and automated contract analysis. The ambition for AI to deliver faster decisions and greater precision across the entire commerce chain—from initial pricing to quoting, contracting, and billing—is clear. However, a significant obstacle persists: the widespread fragmentation of the underlying systems and processes meant to govern these functions. This disconnection prevents AI from delivering on its full promise, creating what amounts to a hidden “tax” on growth and efficiency.

Conga’s The State of Commercial Operations: Fragmentation in the Age of AI report examines the critical challenges posed by fragmented commercial operations. It outlines how senior marketing and CX leaders can develop an integrated commerce chain, laying the foundational data and process integrity required to harness AI for measurable business outcomes and sustained competitive advantage.

The Hidden Tax of Disconnected Commercial Operations

Enterprise commercial operations, encompassing pricing, quoting, contract lifecycle management (CLM), and billing, are frequently disconnected, significantly impeding AI’s potential. According to Conga’s 2026 report, over half of surveyed organizations (55%) indicate their pricing, quoting, CLM, and billing systems are only partially integrated . This fragmentation extends beyond technology, reflecting competing priorities and a lack of shared commercial backbone across Sales, Finance, Legal, and IT teams . Critically, only 34% of respondents are confident their data is consistent, accurate, and ready to support AI at scale .

This disconnect creates system “islands” with disparate data definitions and workflows, resulting in a measurable “hidden tax” on growth and operational efficiency . The consequences are substantial. The report highlights that nearly three-quarters of non-CEO respondents (79%) find it challenging or very difficult to meet executive expectations for commercial operations and risk management . This struggle directly relates to the inability to achieve real-time visibility and proactive decision-making due to fragmented systems.

Fragmentation directly impacts key business outcomes and introduces significant risk. The research found that 45% of respondents lost a deal due to slow quote approval in the past six months, and 72% reported that slow contract processes increase business risk and compliance exposure . Specific financial and operational costs include:

• Manual work and rework: 41% of organizations experience increased costs due to data re-entry and discrepancy reconciliation across disconnected systems .
• Inaccurate revenue forecasts: 41% face challenges producing reliable revenue forecasts because pricing, contracts, and billing data do not reconcile in real time .
• Delayed customer response times: 39% report slower approvals and pricing adjustments, impacting agility and customer satisfaction .
• Lost or delayed revenue: 38% suffer from revenue leakage due to breakdowns during transitions from quote to contract to billing .

What this means: Disconnected commercial operations are not merely inefficient; they are a direct impediment to growth, effective risk management, and the reliable deployment of AI, leading to eroded margins, stalled deals, and compromised customer trust.

Strategic Pillars for an AI-Ready Commerce Chain

Achieving the promise of AI in commercial operations requires a foundational shift from fragmented processes to an integrated commerce chain. This involves aligning data, workflows, and incentives across critical functions such as pricing, quoting, and contracting.

Dynamic Pricing and Strategic Foresight

Traditional, periodic pricing models are no longer viable in volatile markets, necessitating dynamic, AI-informed strategies. The Conga report notes that supply chain volatility (52%) both as well as inflation or interest rate shifts (54%) are the primary factors threatening pricing stability . While 70% of leaders anticipate increasing investment in pricing technology within the next 12 to 18 months, only 8% can confidently measure the business impact of their pricing decisions, and a mere 4% currently use AI-powered, highly responsive pricing systems (p. 9-10). Fragmentation results in slow adaptation to market changes, inconsistent pricing across channels, and limited intelligence, which translates into slower deal velocity, margin compression, and reduced win rates .

What to do:

• Implement Pricing Simulation: Develop capabilities to model outcomes of price changes (desired by 46% of respondents0), allowing for testing of scenarios before deployment.
• Integrate Pricing Data: Ensure pricing data is seamlessly integrated with CRM, ERP, and CLM systems to maintain consistency across all sales channels and customer touchpoints (p. 4, 12).
• Leverage AI for Optimization: Utilize AI for advanced price optimization and customer segmentation (desired by 38% of respondents0), factoring in real-time market data and competitive intelligence.

What to avoid:

• Treating pricing as a static, periodic adjustment without real-time market responsiveness.
• Relying on siloed pricing data that prevents a holistic view of market dynamics or customer segments.

Confident Quoting and Streamlined Approvals

Quoting, as the customer’s first direct interaction with pricing, requires seamless, policy-driven processes supported by AI. The report indicates that 45% of organizations lost deals in the last six months due to slow quote approvals . Key barriers include approval friction (59%), manual workflow challenges (44%), disconnected systems (43%), and governance ambiguity (39%) . This signifies a lack of operational cohesion, where quotes move through fragmented processes, hindering sales velocity and increasing rework . AI is recognized as critical for assistance, with 75% of respondents believing it is essential for sales representatives during quote creation .

Operating Model and Roles:

• Define Clear Approval Workflows: Establish explicit roles and responsibilities for quote approval, with defined thresholds (e.g., discount limits up to 15%; policy exceptions requiring VP approval).
• Automated Approval Routing: Implement intelligent, automated approval routing based on predefined policy parameters (e.g., deal size, product type, discount level, customer segment) to reduce bottlenecks.

What ‘good’ looks like:

• Clear, contextual pricing guidance and intelligent product recommendations for sales representatives, reducing the need for manual overrides to below 5%.
• Streamlined, policy-driven approvals with full business context, enabling average quote approval times under 24 hours.
• Seamless integration across ERP, CRM, and contract management systems to ensure data consistency and accuracy .

Proactive Contracting and Risk Management

Contracts must evolve from static archives into active intelligence layers to manage risk and obligations proactively. A significant 40% of organizations are not confident they could produce a complete and accurate inventory of all active contracts within 48 hours . Moreover, 72% state that slow contract processes directly increase business risk and compliance exposure . This lack of visibility undermines accurate revenue forecasting, procurement leverage, and compliance control, leading to inconsistent terms and exceptions . AI-powered contract analytics are highly valued, with 95% agreeing on their utility for improved compliance, reduced manual effort, and better visibility across the contract library .

Governance and Risk Controls:

• Centralized CLM: Implement a robust, centralized Contract Lifecycle Management (CLM) system integrated with pricing and quoting platforms to ensure a single source of truth for all contractual agreements.
• AI for Risk Identification: Leverage AI to identify inconsistent commercial terms across deals (a challenge for 67% in Telecom and 69% in Manufacturing), track obligations, and detect potential compliance risks (e.g., non-compliance rates below 1%).
• Predictive Deal Risk: Utilize AI to predict deal risk based on contract terms (valued by 62% on the sell-side) and identify vendor consolidation opportunities (valued by 65% on the buy-side).

What this means: An integrated approach across pricing, quoting, and contracting, underpinned by connected systems and trusted data, transforms commercial operations from reactive archives into predictive, strategic control points.

Orchestrating the Commercial Backbone for AI-Driven Growth

The true potential of AI in commercial operations is realized only through a unified commercial backbone that connects data, workflows, and incentives across functions. The Conga report found that 93% of teams struggle to move deals smoothly across sales, legal, finance, pricing, and IT functions , primarily due to integration complexity, implementation time, and change management challenges . A fragmented environment fosters competing priorities, relies on manual coordination, and yields unreliable data, rendering AI initiatives largely ineffective . Therefore, organizations must prioritize foundational integration before scaling AI deployments.

What to do:

• Prioritize Data Harmonization: Establish a common data model and a single source of truth for all commercial data, including product catalogs, customer entitlements, pricing rules, and contract terms. This ensures data integrity and consistency across the entire commerce chain.
• Integrate Core Systems: Implement seamless data flow and process handoffs between critical systems such as CRM, CPQ (Configure, Price, Quote), CLM, ERP, and billing platforms. This reduces manual intervention and data inconsistencies.
• Foster Cross-Functional Alignment: Establish a dedicated steering committee comprising leaders from Sales, Finance, Legal, IT, and Pricing. This committee should define shared metrics (e.g., deal velocity, margin attainment, contract compliance rate), Key Performance Indicators (KPIs), and clear process ownership to eliminate silos.
• Invest in Domain-Specific Technology Partners: Select technology partners with proven industry expertise in commercial operations. As highlighted by the report, 77% of respondents consider industry expertise critical when choosing a technology partner .

What to avoid:

• Point Solutions and Tactical AI Deployments: Resist the temptation to implement AI in isolated pockets without first addressing the underlying system fragmentation. Such approaches deliver limited, non-scalable value.
• Underestimating Change Management: Do not neglect the cultural and organizational shifts required for new ways of working. Comprehensive change management programs are essential for adoption and success.
• Optimizing for Single Metrics: Avoid focusing solely on a single metric, such as sales containment or speed, at the expense of broader outcomes like margin integrity, compliance, or customer satisfaction. A balanced scorecard approach is crucial.

What ‘good’ looks like: A consolidated view of the entire commerce chain, providing real-time visibility into performance metrics (e.g., average quote approval time below 24 hours; contract processing time under 72 hours). It means proactive identification of risks (e.g., contract non-compliance rates below 1%) and data-driven insights that empower human decision-making, reducing manual overrides on pricing and contractual terms to below 5%.

Summary

AI’s transformative promise in commercial operations depends entirely on the degree of system and data integration within an enterprise. The findings from Conga’s report unequivocally demonstrate that fragmentation incurs a significant “hidden tax” on growth, operational efficiency, and risk management. By strategically addressing these disconnects and building an integrated commerce chain—where pricing sets intent, quoting operationalizes decisions, and contracting formalizes risk—enterprises can lay the essential groundwork for AI-driven insights. This shift from fragmentation to commercial cohesion is not merely an IT project; it is a strategic imperative that transforms commercial complexity into a sustainable competitive advantage, enabling predictive performance, tighter controls, and measurable growth.