Mark Makary Blog

Introduction

We spent decades generating value and revenue by providing services in the Integration space, and now AI is radically changing the landscape.

I’ve spent many years in this space — as a systems architect, as a CTO building an integration and fast data practice from scratch, and now leading a global Center of Excellence where I work with thousands of engineers and architects navigating this shift in real time. And I can tell you: what I’m seeing now is not another technology cycle. It’s a structural evolution.

Every major wave — cloud, mobile, SaaS, APIs, microservices — reshaped how professional services delivered value. We adapted, retooled, and found the next layer of complexity to own. But AI is different in a way where those waves weren’t. It doesn’t just accelerate delivery. It eliminates entire categories of work that our industry was built on.

The most profound shift ahead is simple but radical:

“Professional services will move from building systems to designing behaviors.”

This isn’t a slogan. It’s a structural change in how integration, automation, and enterprise architecture will operate in the next decade. Here’s what that is fundamentally changing in terms of providing value:

From Building Pipelines → Designing Behaviors

For decades, integration work meant stitching systems together. The professional services engagement was measured by the complexity of what was built:

• APIs and connectors
• ETL pipelines and data flows
• Streaming consumers
• Schema mappings and transformations
• Error handling and retry logic
• Documentation and runbooks

AI collapses all of this. Modern AI agents can already infer schemas, generate transformations, build connectors, monitor pipelines, and self-heal failures. The “plumbing” becomes automated — and the demand for humans to manually assemble it evaporates.

So what replaces all this? Behavior Design.

This is the new core competency for professional services. The consultant of tomorrow doesn’t configure a pipeline — they define the logic, boundaries, and intent that govern how autonomous systems act on behalf of a business.

What “designing behavior” actually means in practice:

Competency	Description
Intent Modeling	Teaching AI what the business means by “customer,” “priority,” “risk,” or “exception” — capturing nuance that no schema can encode.
Constraint Definition	Specifying what the AI must never do — e.g., delete financial records, escalate without approval, or override a compliance rule.
Interaction Rules	Defining how AI agents collaborate, negotiate, sequence tasks, and hand off work across systems and human touchpoints.
Fallback Logic	Designing how AI behaves when uncertain, when data is missing, when systems conflict, or when confidence thresholds are breached.
Ethical Boundaries	Embedding fairness, transparency, and regulatory compliance into agent behavior before deployment — not as an afterthought.
Optimization Preferences	Telling AI what to optimize for: cost, accuracy, speed, customer satisfaction, risk reduction — and in what priority order.

“We stop telling systems how to do things. We start telling them how to behave.”

From Writing Code → Supervising Autonomous Agents (The Trust Factor)

AI agents will write most of the integration logic. The question is no longer can we automate this? — the answer is almost always yes. The question becomes: who ensures the automation is safe, aligned, and trustworthy?

Humans will supervise the dimensions that matter most:

• Critical decisions and edge-case escalations
• Exception handling outside defined parameters
• Compliance drift and regulatory alignment
• Risk threshold monitoring and breach response
• Domain alignment — ensuring AI outputs reflect business reality
• Performance auditing and explainability requirements

This is similar to managing a highly capable, tireless junior engineer — except the engineer is autonomous, operates at machine speed, and is constantly learning from every interaction. The human role shifts from creator to steward.

The skill shifts from coding to judgment. From writing logic to evaluating it. From building systems to trusting — and verifying — them.

From Mapping Schemas → Defining Outcomes

Schema mapping is already a solved problem for AI. Field-level transformation, data type reconciliation, cross-system normalization — these tasks that once consumed weeks of consulting engagement are now handled in minutes by AI tooling.

What matters now is outcome definition: the ability to articulate, with precision and measurability, what success looks like. AI then builds the integrations, workflows, and logic needed to achieve those outcomes.

Examples of outcome-first thinking replacing schema-first thinking:

“Reduce customer onboarding time by 40% within the first quarter.”
“Guarantee 99.9% data accuracy across all integrated enterprise platforms.”
“Automate 80% of tier-1 support workflows without human intervention.”
“Ensure continuous SOC2 and HIPAA compliance across all data pipelines.”
“Eliminate manual reconciliation across finance, CRM, and ERP systems.”

Professional services becomes outcome architects, not integration engineers. The deliverable is no longer a connector — it’s a measurable business result, guaranteed.

From Fixing Bugs → Governing Systems

AI will fix most bugs automatically. Self-healing pipelines, automated root-cause analysis, and intelligent retry logic mean that the traditional “break-fix” engagement model for professional services is approaching obsolescence.

What humans govern instead is far more consequential:

• Safety — ensuring AI actions cannot cause irreversible harm
• Ethics — preventing bias, discrimination, and unfair outcomes
• Compliance — maintaining alignment with evolving regulatory frameworks
• Risk thresholds — defining when AI must pause and escalate to humans
• Transparency — maintaining audit trails and explainability for stakeholders
• Auditability — ensuring every AI decision can be reconstructed and reviewed
• Exception handling — designing human-in-the-loop processes for genuine edge cases

Governance becomes the new “engineering.” This is where deep domain expertise — legal, financial, clinical, operational — becomes more valuable than ever. AI amplifies the expert; it does not replace them.

Finally, we are switching from delivering labor to delivering an outcome

From Delivering Labor → Delivering Guarantees

The legacy professional services model is built on time and materials — hours billed, sprints delivered, resources deployed. That model is structurally incompatible with an AI-native world where the time-to-delivery collapses and the marginal cost of execution approaches zero.

Clients won’t pay for hours. They’ll pay for assurance. The new commercial model is built around guaranteed outcomes, not delivered effort:

Assurance Dimension	What Clients Will Pay For
Uptime	Guaranteed system availability and resilience commitments
Accuracy	Measurable data quality and decision-correctness thresholds
Automation Coverage	Defined percentage of workflows operating without human intervention
Compliance	Continuous regulatory alignment across jurisdictions
Cost Reduction	Contractual operational efficiency improvements with measurable baselines
Reliability	SLA-backed performance with defined remediation commitments
Predictability	Forecasting accuracy and variance reduction in business processes

Professional services becomes a guarantee business, not a labor business. This is the biggest business-model shift for the industry since the emergence of SaaS.

The New Professional Services Firm

The AI-native professional services firm does not look like a staffing agency for developers. It does not resemble the systems integrators of the 2010s. It is a fundamentally different kind of organization — one built on interdisciplinary expertise, strategic thinking, and the ability to translate business intent into autonomous system behavior, this is an example of what modern roles would look like:

Systems Architect — designing the foundational structures that AI agents operate within
Behavior Designer — defining the rules, constraints, and intent that govern autonomous agents
Domain Expert — providing the business, clinical, legal, or operational context AI cannot independently acquire
AI Supervisor — monitoring, auditing, and correcting autonomous system outputs at scale
Risk Manager — defining and enforcing the boundaries within which AI systems may operate
Outcome Guarantor — contractually accountable for measurable business results, not activity delivery

The Bottom Line

The question isn’t whether AI will transform professional services in the integration and streaming space. It already is. The question is whether the people and firms in this space will redefine their value before the market does it for them.

I’ve seen both sides of this. The teams that resist tend to double down on technical delivery — more connectors, faster pipelines, lower costs. The teams that thrive are asking a fundamentally different question: what does this business actually need to achieve, and how do we design systems that guarantee it?

The shift from labor to outcomes. From pipelines to behaviors. From code to governance. It’s not coming — it’s here.

The firms that thrive will stop thinking in terms of:

• Pipelines and connectors
• Integration hours and sprint velocity
• Schema mappings and ETL jobs
• Headcount and resource utilization

…and start thinking in terms of:

From Technical Delivery	To Strategic Value
Pipelines	Behaviors
Integrations	Outcomes
Connectors	Guarantees
Schemas	Governance
Code	Intent
Automation	Ethics & Orchestration

The human role in enterprise technology does not diminish in an AI-native world — it ascends. The questions that matter most are not “how do we build this?” but “what should this system do, how should it behave, and what does success truly mean for this business?”