The Make or Delegate Economy – Why AI is changing the theory of the firm

Introduction 

An engineering team can generate a technical recommendation in minutes and still wait days for a decision. The delay often sits between systems and people: finding the current specification, checking a dependency, getting an approval, or working out who owns the exception. 

Agentic AI brings these delays into focus. When software can retrieve context, propose actions and use tools, leaders have to decide how much authority to give it. Which decisions can an agent execute within approved rules? Which need an engineer’s review? Which must remain with a named human owner? 

For a Global Capability Centre (GCC), this is an operating model question. The answer affects engineering quality, accountability, and the knowledge the organisation retains. A practical starting point is to separate decisions into three paths: delegate, co-pilot and retain. 

The theory of the firm meets agentic AI 

The make-or-buy question has long shaped how companies organise work. Ronald Coase’s work on transaction costs explains why firms coordinate some activities internally rather than purchasing every activity through the market. Searching, negotiating and coordinating all have costs. 

AI introduces another choice within that arrangement. A company can own a capability while delegating some of its routine decisions to software. It can also use an external partner while retaining approval rights over architecture, engineering standards, and product strategy. Capability ownership and decision authority need to be considered separately. 

The make-or-delegate economy is the perspective proposed in this article, rather than an established replacement for economic theory. Its argument is that AI changes the cost of gathering information and preparing decisions, while introducing new costs for validation, monitoring, and correction. 

Pratiti’s Build vs Borrow framework addresses which technical capabilities a business should own. The next question is how decisions should be allocated inside those capabilities. 

Attention limits engineering decisions 

Herbert Simon’s work on bounded rationality explains decision-making under limits of information and computational capacity. In an engineering organisation, those limits are familiar. An engineer may need to assess a drawing revision, compare supplier constraints and understand a customer requirement before deciding what to change. 

Agentic AI can assist with gathering that context and preparing an evidence-backed recommendation. Its usefulness depends on access to the right records, clear permissions, and a way to identify uncertainty. A fluent answer based on an obsolete specification can make a decision harder to validate. 

Pratiti’s article on Engineering Copilot explores how AI can support engineering decisions across the product lifecycle. The operating model still needs to define who authorises the resulting action. 

Three paths for allocating decisions 

The appropriate level of autonomy depends on the consequences of an error, how clearly the decision can be evaluated, and whether the action can be reversed. These paths are a practical framework for engineering teams, not formal industry classification. 

Delegate routine actions within defined limits 

Delegation is suitable for a narrow workflow with approved inputs, predictable outcomes, and observable failure conditions. Examples include routing routine tickets, retrieving approved documents, or initiating a predefined test suite after a code change. 

A configuration workflow might use an agent to retrieve customer inputs and invoke a validated rules engine. The rules engine should determine whether component combinations meet approved constraints. An open-ended model response should not become the authority for a safety-critical engineering rule. 

Before allowing autonomous execution, specify: 

• The systems and records the agent can access or change. 

• The rules and thresholds it must follow. 

• The conditions that require escalation to a person. 

• The logs, rollback process and named owner for the workflow. 

Start with a bounded action. If the agent encounters missing evidence, conflicting revisions, or a request outside its scope, it should stop and escalate. Delegation reduces routine handling only when exceptions remain visible. 

Copilot decisions that need engineering judgement 

Co-pilot workflows suit decisions where AI can organise evidence, but a qualified engineer must evaluate the recommendation. Examples include comparing architecture options, suggesting possible root causes, proposing test cases, or identifying change impacts across documents. 

The recommendation should show its sources, assumptions, and unresolved questions. Reviewers need enough context to challenge it. Approval becomes weak if the engineer sees only a conclusion and must rebuild the analysis to check it. 

For teams using digital product engineering services, this means designing review into the workflow. The value comes from reducing the effort of finding and comparing relevant evidence while preserving a meaningful engineering decision. 

Track how often reviewers accept, modify or reject recommendations, and why. A high acceptance rate alone does not demonstrate quality. Independent checks are needed to determine whether reviewers are accepting correct recommendations or simply trusting confident outputs. 

Retain decisions with strategic or serious consequences 

Product direction, safety-critical changes, significant commercial commitments, and major architecture decisions should have explicit human accountability. AI can contribute to analysis, but the designated owner must decide whether to proceed. 

These decisions often involve competing objectives that cannot be resolved from a single rule. A lower-cost component may affect reliability. A faster release may introduce unacceptable operational exposure. The organisation must decide which trade-offs it will accept. 

Human review also needs a defined boundary. State who approves the action, what evidence they require, and which conditions make the proposal unacceptable. Adding an approval button without these details does little to improve the decision. 

Match governance to the level of autonomy 

In its May 2026 guidance, Gartner recommends governance proportionate to agent autonomy and scope. It predicts that by 2027, 40% of enterprises will demote or decommission autonomous agents because of governance gaps discovered after production incidents. This is a forecast, not an observed failure rate. 

The practical implication is to apply different controls to a read-only assistant and an agent that can change production records. Permissions, testing, audit trails, and intervention mechanisms should reflect what each workflow can do. 

Autonomy should also be revisited. A workflow that performs reliably on one product family may need human review when requirements, regulations or supplier data change. The NIST AI Risk Management Framework provides a broader reference for managing AI risks throughout deployment and use. 

A manufacturing example 

Consider an illustrative manufacturer of configurable industrial equipment. A customer request triggers configuration checks, supplier comparisons, documentation updates, and production approval. This is a hypothetical workflow, not a Pratiti customer case study. 

The delegated path retrieves the approved specification, runs compatibility checks through validated rules, and prepares a record of the results. It escalates incomplete or conflicting inputs. 

The co-pilot path compares feasible configurations against customer requirements and current supplier information. An engineer reviews the recommendation and its tradeoffs. 

The retained path covers a configuration outside the validated design envelope, a safety-related change, or a significant commercial commitment. The accountable owner reviews the evidence before authorising action. 

All three paths need shared revision control and traceability. If each uses a different version of the same specification, faster execution may simply produce inconsistent decisions sooner. 

Why capability ownership matters for GCCs 

Agents depend on engineering knowledge, configuration logic, test evidence, and product context. If those assets are scattered across vendors and project teams, introducing agents can reproduce the same coordination problems in software. 

A capability-led GCC can establish ownership of that knowledge and the workflows that use it. This involves searchable records, access controls, maintained rules, and engineers who understand both the domain and the system. 

When evaluating GCC consulting services in India, enterprises should ask who will own the decision rules, source records, evaluation datasets, and escalation process. These responsibilities matter beyond the first AI deployment. 

Measure the quality and cost of decisions 

Start with a baseline for one workflow. Measure the elapsed time from request to approved action, the time spent gathering context, review effort and the rate of errors or reopened decisions. 

After introducing AI, track exception rates, human interventions, recommendation corrections and the cost of monitoring. Measure time savings after review and rework, rather than counting generated recommendations or prompts. 

The distribution across delegate, co-pilot, and retain paths is useful for understanding the workflow. It should not become a target to maximise autonomy. The right allocation is the one that improves outcomes at an acceptable level of risk and cost. 

Conclusion 

Agentic AI gives enterprises more options for preparing and executing decisions. Realising that value requires clear authority, reliable knowledge, and a review process suited to the consequences of each action. 

For GCC leaders, the starting point is one recurring engineering workflow. Map its decisions, assign accountable owners, and determine where bounded delegation or co-pilot support can remove delays. 

At Pratiti Technologies, we support GCCs across product engineering, data, AI and digital transformation. Speak with our team about designing an engineering workflow with clear boundaries between human judgement and AI execution. 

Frequently asked questions 

What is the make-or-delegate economy? 

It is the strategic perspective used in this article to examine how enterprises allocate decisions between people, AI agents, and combined workflows, alongside traditional make-or-buy choices. 

Which engineering decisions can be delegated to AI 

Start with repeatable actions that use approved rules and inputs, have limited consequences, and can be monitored and reversed. Escalation criteria and accountable ownership are prerequisites. 

Does agentic AI replace engineering judgement? 

It can support evidence gathering and routine actions. Decisions involving safety, strategy, or complex trade-offs still need qualified review and accountable authority. Its effect on individual roles depends on how the organisation redesigns work. 

How do GCCs support agentic AI? 

A GCC can own and maintain the domain knowledge, integrations, evaluations and governance that AI workflows require. Centralising delivery alone does not provide those capabilities. 

What should enterprises measure? 

Measure decision time, errors, review and rework effort, intervention rates, and total workflow cost. Assess improvements against a baseline with comparable decisions.