Tech & Sourcing @ Morgan Lewis

Organizations are increasingly leveraging a range of delivery models to provide business services, including internal teams, Global Business Services (GBS), Global Capability Centers (GCCs), outsourcing providers, and managed services. As organizations combine these approaches with AI-enabled delivery, important governance considerations include accountability, decision making, performance, and contractual obligations across an interconnected ecosystem of suppliers, internal teams, and AI systems.^1,5

AI adds another dimension to these decisions. As AI-enabled tools become embedded in business processes and services, organizations may need to revisit governance structures that traditionally focused on contracts, suppliers, personnel, and technology. Oversight now may also need to address the use of AI by suppliers and internal teams, the allocation of decision-making authority, and the processes for monitoring performance and addressing issues that arise across organizational and contractual boundaries.

Existing governance frameworks were designed to oversee people, processes, and technology services, not ecosystems that include AI-enabled workflows, autonomous agents, software-based delivery, and dynamic multi-provider structures.^2,3 As adoption accelerates, leaders are confronting new questions:

• Who is accountable when an AI-enabled process spans multiple suppliers, internal teams, and automated agents?²
• How should performance be measured when outcomes depend on both human and digital workforces?³
• What monitoring, validation, and escalation processes are appropriate when AI-generated outputs inform operational, financial, customer, or compliance results?²
• How should legal, regulatory, operational, and contractual responsibilities be addressed across multiple parties?⁵

Organizations that establish clear oversight structures, define decision rights, maintain transparency into AI-enabled operations, and align accountability across their ecosystems will be better positioned to accelerate innovation, improve resilience, manage risk, and realize greater value.^2,4 Fragmented governance, by contrast, creates compliance exposure, slower decisions, and barriers to transformation.⁴ Effective governance can help organizations realize the benefits of AI-enabled operating models.²

Why the Governance Imperative Is Accelerating

• Delivery ecosystems are growing and becoming more interconnected: Organizations may rely on combinations of outsourcing providers, managed services, GBS, GCCs, internal teams, and digital platforms. The structure of these operations continues to influence cost, access to talent, regulatory obligations, data transfers, resilience, and oversight needs, while creating new accountability and performance management challenges.^1,4
• AI is becoming embedded in service delivery: As AI moves from experimentation to enterprise scale across finance, procurement, customer operations, human resources, information technology, and analytics, governance may need to extend beyond technology oversight to accountability, transparency, monitoring, and control.^2,3
• Regulatory expectations continue to evolve: Organizations should assess the laws and regulatory expectations applicable to their AI use cases, service providers, data, and operating locations, including privacy, cybersecurity, and sector-specific requirements. Developing AI governance expectations require models that can adapt to changing legal, privacy, cybersecurity, and compliance obligations. ⁵
• Leaders are demanding outcomes, not activities: Focus is shifting from labor arbitrage and service-level attainment to business value, resilience, customer experience, and measurable results.^1,3

The Accountability Gap

A business function may involve a range of internal and external delivery partners, each with different performance measures, reporting structures, and escalation paths.⁴ Relevant information can also sit across procurement, finance, legal, operations, cybersecurity, and other functions, making coordinated oversight important.⁴

Few governance models were designed for hybrid ecosystems requiring alignment across providers, internal organizations, automation, and AI.¹ Many organizations also lack a consistent framework for deciding when to insource, outsource, automate, establish a GCC, or redesign work.¹ The resulting friction extends beyond administrative inefficiency, affecting performance, responsiveness, innovation, and value realization.⁴

What changes when AI enters the delivery model?

Governance frameworks are expanding to address how and where AI is used within outsourced services; who owns accountability for AI-enabled decisions; how agents and digital workers are inventoried, monitored, and governed; what data trains and operates the models; how outputs are validated for bias and accuracy; and how AI incidents are escalated and remediated.^2,3 These considerations extend beyond the traditional outsourcing manager, requiring coordination across procurement, technology, legal, compliance, cybersecurity, privacy, risk, and business leadership.^2,6

For critical AI-enabled processes, organizations should identify an accountable business owner, define where human approval is required, and allocate responsibility for handoffs among delivery teams. Supplier contracts should preserve the supplier’s agreed responsibility for the services and the technology dependencies it selects.⁶

The Regulatory Perimeter Has Moved

The use of AI in service delivery introduces legal and regulatory questions that should be assessed in light of the particular technology, use case, delivery locations, and allocation of responsibilities. Depending on the circumstances, obligations may apply to AI providers, deployers, and other participants in the service chain. For example, the European Union AI Act distinguishes among different roles and imposes requirements that depend on the system and the role of the organization involved. Organizations operating across jurisdictions should account for differences in applicable requirements and implementation timelines.⁵

Contractual and legal considerations to keep on the radar include obligations to comply with applicable law; allocation of responsibility for AI uses and outputs; disclosures, approvals, and transparency; audit and information rights; subcontracting and fourth-party oversight; data privacy, security, and cross-border transfers; intellectual property and data-use rights; incident response; and appropriate remediation, transition, and termination rights. Because technology and legal requirements can change during a relationship, parties should also consider processes for reviewing and updating relevant controls and contractual arrangements.²

Visibility and Control Become Strategic Assets

Unlike traditional software, AI-enabled systems may continuously evolve and produce outputs that are difficult to explain, creating challenges for organizations monitoring performance, assessing compliance, or investigating issues.² In response, organizations are starting to negotiate enhanced governance and transparency provisions, AI-specific reporting and performance metrics, model-change disclosure, documentation and audit access, incident notification procedures, human-in-the-loop requirements, AI oversight committees, and expanded monitoring obligations.^2,6 For material changes, the parties to a contract should specify when notice, testing, approval, or fallback arrangements are required, rather than treating every routine update in the same way.

Commercial arrangements will need to evolve in parallel. At the contracting stage, parties should evaluate the criticality of the use case, allocation of accountability, permitted data uses, intellectual property rights, performance measures, pricing adjustments, change management, and mechanisms for implementing innovation. Traditional service levels may remain appropriate, supplemented where useful by measures of quality, accuracy, turnaround time, customer outcomes, and productivity improvements.^1,3,6 Where fees or savings are linked to improvements, the parties should agree on and document appropriate baselines, data sources, and measurement methods. ⁶

Governance should also track the total cost of AI-enabled delivery, including customer-paid cloud and usage charges, and address consumption approvals and how productivity gains affect fees. ⁶

From Supplier Management to Ecosystem Governance

Supplier management is evolving into a broader governance discipline that manages performance, value, accountability, risk, and relationships across internal and external delivery models.⁴ A unified model gives leadership consistent visibility, comparable performance measures, and clear decision rights across suppliers, GCCs, GBS organizations, and internal teams.⁴ Core elements include supplier segmentation, governance routines and executive reviews, defined roles and decision rights, performance and value tracking, standardized data and reporting, and technology-enabled insight generation.⁴

Where that foundation exists, AI becomes a meaningful accelerant—identifying risks, monitoring obligations, detecting recurring performance issues, generating executive insight, and prioritizing action.⁴ Where it does not, AI tends to magnify existing governance problems rather than resolve them.⁴ Human oversight remains essential, because these decisions involve commercial judgment, legal exposure, risk tolerance, and stakeholder expectations.⁴

The Path Forward

The future of AI-enabled delivery models will be defined less by technology adoption than by governance effectiveness.² Contract provisions are one component of effective AI governance. Leading organizations are building enterprise-wide capabilities that align outsourcing governance, supplier management, AI risk management, compliance, cybersecurity, privacy, and business leadership within a common framework; including cross-functional governance committees, enterprise AI policies and standards, third-party risk management, AI inventory and monitoring, employee education, defined decision rights, ongoing compliance assessment, and resilience planning.²

Commercial models will need to evolve alongside them, supporting clearly allocated responsibilities, outcome-based performance measurement, innovation incentives, and collaborative relationships across the ecosystem.^1,3,6

A practical starting point is to map a critical process, identify its AI tools and dependencies, assign decision rights, and test continuity arrangements. Exit planning should address access to usable data, workflows and documentation, necessary transition-use rights, agreed assistance and pricing, and periodic testing of the ability to move the service. ⁶

Looking Ahead

The next phase of enterprise transformation will be defined by how effectively organizations govern interconnected delivery ecosystems where people, technology, providers, and AI systems collectively influence business outcomes.^1,2

Organizations making the most progress are not waiting for regulatory certainty or industry standards to mature. They are proactively reassessing governance frameworks, redefining accountability models, and building the capabilities required to oversee increasingly complex delivery environments.^2,4 For many leaders, the next questions are no longer whether governance should evolve, but where to begin, how accountability should be structured, and which capabilities will create the greatest strategic advantage. As AI becomes embedded throughout modern delivery models, governance will increasingly determine whether organizations merely adopt new technologies or successfully transform how they create, protect, and sustain business value.²

How We Can Help

Morgan Lewis advises organizations on structuring and negotiating outsourcing and AI-enabled services arrangements, including issues relating to AI governance, contractual allocation of responsibilities, pricing and performance, data use and intellectual property, privacy, cybersecurity, regulatory compliance, and oversight of complex delivery models.

This blog post is a joint perspective from KPMG LLP and Morgan Lewis. KPMG LLP helps organizations navigate the evolving delivery ecosystem, from sourcing strategy and operating model design through governance implementation, supplier management transformation, AI-enabled oversight, and value realization.

1. KPMG LLP, The Future of Outsourcing: Rethink Everything, webinar, November 24, 2025.
2. Morgan Lewis, AI & Outsourcing Series: Rethinking Outsourcing Governance – Compliance and Control in the AI Era, July 2, 2026.
3. KPMG LLP, Rewriting the Outsourcing Playbook | AI, Automation, and Platforms.
4. KPMG LLP, Supplier Management Strategy and Governance.
5. Morgan Lewis, Outsourcing Trends for 2026: AI, GCCs, and Global Delivery Models, January 20, 2026.
6. Morgan Lewis, AI-Enabled Outsourcing: Key Contract, Pricing, and Governance Considerations, July 2026.