In the first post in our AI & Outsourcing series, we observed how artificial intelligence (AI) is transforming the outsourcing industry in ways that extend far beyond operational efficiency. This second post in the series discusses the need to rethink legal and commercial terms that govern outsourcing relationships, as companies increasingly incorporate AI-enabled tools and automation into outsourced services.
Traditional outsourcing agreements were designed around human-delivered services, predictable workflows, and established technology models. AI introduces new variables, including autonomous decision-making, evolving algorithms, data governance concerns, and intellectual property complexities, that require businesses to revisit long-standing contractual assumptions.
Traditional Outsourcing Contracts Are No Longer Enough
Historically, outsourcing agreements focused heavily on staffing models, performance metrics and FTE- or transaction-based pricing structures. While those provisions remain important, AI-enabled outsourcing arrangements require additional scrutiny around technology usage, accountability, and risk allocation. This is especially important since many of the major outsourcing service providers are differentiating themselves by incorporating their own proprietary AI platforms and automation capabilities into the solutions they are developing for customers.
Companies are increasingly revisiting contract provisions in fundamental areas, including:
- Intellectual property rights
- Usage and ownership of data
- Liability, compliance, and governance frameworks
- Pricing
As AI tools become embedded in outsourced operations, customers require greater visibility into how those tools function, what data they rely upon, and how risks are managed.
Intellectual Property and Data Rights Are Taking Center Stage
One of the most significant areas of negotiation involves intellectual property rights and data usage and ownership. AI systems often rely on large datasets to train and improve performance, raising important questions about how customer data may be used within (and outside of) outsourced environments. Companies are increasingly seeking contractual safeguards to prevent providers from using proprietary or confidential information to train generalized AI models without authorization.
Key issues now frequently addressed in outsourcing negotiations include:
- Whether customer data can be used to train AI systems
- Ownership of AI-generated work product
- Rights to improvements derived from customer data
- Restrictions on cross-client data usage
- Data localization and retention requirements
- Protection of trade secrets and sensitive business information
These concerns are particularly significant in highly regulated industries such as financial services, healthcare, life sciences, and legal services, where data governance obligations are especially stringent.
Liability, Compliance, and Governance Are Expanding
AI also changes how parties think about liability and operational oversight. Traditional outsourcing contracts often assumed that human judgment would remain central to service delivery. AI-enabled services introduce new risks involving inaccurate outputs, algorithmic bias, cybersecurity vulnerabilities, and regulatory uncertainty.
As a result, businesses are increasingly negotiating provisions related to:
- Human oversight requirements for critical decisions
- AI testing, validation, audit, and monitoring obligations
- Compliance with evolving global AI laws and standards
- Incident response and remediation procedures
- Insurance coverage for AI-related risks
- Indemnities tied to AI performance failures or regulatory violations
In many cases, customers are requesting stronger governance frameworks and more detailed reporting obligations to ensure ongoing visibility into AI-enabled operations.
Pricing Models and Workforce Assumptions Are Evolving
AI is also reshaping the economic structure of outsourcing deals. Traditional pricing models often were based on labor inputs, headcounts, or transaction volumes. Automation changes those assumptions by reducing manual work and increasing operational efficiency.
This shift is leading parties to reconsider:
- Outcome-based pricing models
- Shared savings arrangements
- Productivity gain allocation
- Automation-related benchmarking adjustments
- Workforce transition and reskilling obligations
- Change management provisions for evolving technologies
Providers and customers alike are seeking greater flexibility as AI capabilities continue to evolve during the life of long-term outsourcing agreements.
Transformation Projects Are Becoming Central to Outsourcing Transactions
Another significant shift in AI-enabled outsourcing deals is the growing emphasis on transformation projects designed to implement and integrate AI technologies into the customer’s operations. Increasingly, outsourcing transactions are no longer limited to transferring existing services to a third-party provider; they also involve large-scale operational transformation initiatives aimed at modernizing processes, deploying automation tools, and redesigning workflows around AI-enabled capabilities. In many cases, these transformation efforts are a fundamental component of the business case supporting the transaction itself.
Because these initiatives can significantly impact timelines, pricing models, operational responsibilities, and risk allocation, companies are recognizing the importance of addressing transformation requirements early in the transaction process rather than treating them as secondary implementation matters.
Key considerations often include defining transformation milestones, allocating responsibility for technology deployment and integration, establishing governance and change management frameworks, identifying data and infrastructure dependencies, documenting post-implementation service levels and fees, and determining how other performance improvements and cost savings will be measured. Early planning is particularly important given the evolving nature of AI technologies and the operational disruption that large-scale transformation projects can create if expectations and responsibilities are not clearly defined from the outset.
How We Can Help
Morgan Lewis’s outsourcing and AI teams help clients navigate the rapidly changing legal and commercial landscape surrounding AI-enabled outsourcing arrangements. The firm advises companies on structuring and negotiating complex outsourcing transactions, drafting AI governance and risk allocation provisions, addressing intellectual property and data rights concerns, and managing regulatory compliance obligations across jurisdictions. We also help organizations develop practical governance frameworks for the responsible use of AI technologies within outsourced services, enabling clients to balance innovation, operational efficiency, and risk management as AI continues to reshape the outsourcing market.