AI agents are transforming enterprise software, evolving from simple query response tools to proactive systems that pursue business objectives across applications under strategic human oversight. The report defines agents as advanced models connected to operational tools, capable of autonomous action within controlled parameters.

Enterprises that have already implemented generative AI are transitioning from adoption to scaled operations. Notably, 52 percent of executives now report AI agents in production, with common deployments in customer service, marketing, security operations, technical support, and product innovation.

For CT Labs, that changes the core consulting question. It moves from “Which model should we pick?” to “Which workflows should become agent-managed systems, and how do we govern them so outcomes stay reliable, secure, and auditable?”

Below are the five shifts from the report. Each is translated into an operator’s lens for leaders who care about ROI, risk, and deployment velocity. Let’s begin by examining how the first shift sets the stage for new operating models.

Shift 1: Agents for every employee, the rise of the human orchestrator

The report describes an integrated working model in which each employee serves as a supervisor of specialized agents. Their responsibilities center on delegating repetitive tasks, setting goals, outlining strategy, and verifying quality. Grounding is highlighted as the mechanism that anchors responses to verifiable enterprise facts, with internal data serving as the ground truth.

A concrete example in the report is the marketing manager who orchestrates a set of agents spanning data analysis, competitive monitoring, content drafting, creative generation, and reporting.

What this means for CT Labs' delivery

CT Labs treats “agent for every employee” as an operating model change, then designs the enablement layer around it.

• Role-based agent packs aligned to real jobs: marketing, finance, sales ops, recruiting, support, security, product, and engineering.
• Grounded knowledge layer connected to the right systems, so agents reflect current policy, product truth, pricing, and customer state.
• Quality gates that define what humans approve, what agents can execute, and how audit trails get produced.

A metric that matters

TELUS, as referenced in the report, has integrated AI across its workforce, with 57,000 team members consistently leveraging AI and saving an average of 40 minutes per interaction.

That kind of gain comes from repeatable workflows, not one-off prompts.

Shift 2: Agents for every workflow, digital assembly lines for the enterprise

The report defines an agentic system as a multi-step workflow that orchestrates multiple agents to run a business process end-to-end. It also reports that 88% of agentic AI early adopters are seeing positive ROI from at least one generative AI use case.

A key technical theme here is interoperability. The report calls out the Agent2Agent protocol as an open standard that enables agents to work together across different developers and frameworks, and highlights the Model Context Protocol as a standardized way for AI applications to connect to tools and data sources, such as managed databases and analytics platforms.

What this means for CT Labs' delivery

This marks the enterprise transition from isolated copilots to fully integrated operational systems.

CT Labs typically structures workflow deployments in three layers:

1. Workflow blueprint - Define the process, decision points, human approvals, and data sources.‍
2. Agent orchestration - Assign specialized agents to each stage, with clear tool permissions and shared context.‍
3. Instrumentation and governance - Observability, evaluation, audit logs, and escalation paths.

Report grounded examples worth borrowing.

• Suzano built an agent that translates natural-language questions into SQL queries to query SAP materials data in BigQuery, achieving a 95% reduction in query time for employees using the data.
• Elanco uses agents to structure and reconcile large volumes of policy and procedure documents, reducing the risk of outdated or conflicting information, which the report quantifies at up to 1.3 million dollars in productivity impact at large sites.

These examples share the same pattern: high-volume, high-friction knowledge work paired with measurable reductions in cycle time.

Shift 3: Agents for customers, concierge experiences grounded in real context

The report describes the move from scripted chatbots to concierge-style agents that remember preferences and prior conversations and can resolve issues using real-time data with human guidance. It also reports that 49 percent of executives with agents in production adopt them for customer service and experience.

An example in the report shows proactive service: a logistics issue triggers an agent who checks systems, reschedules delivery, applies a service credit, and messages the customer with a confirmation flow.

The report also includes Danfoss, where agents automated 80 percent of transactional decisions, reduced customer response time from 42 hours to near real-time, and consolidated five systems into a single interface.

What this means for CT Labs' delivery

Customer-facing agents live and die by two factors: context and trust.

CT Labs focuses on customer agent deployments on:

• Identity and authorization tied to CRM and support systems
• Grounded retrieval from order history, policy, and product truth
• Smart handoff that brings a full summary to human teams for complex cases
• Outcome metrics like response time, containment rate, refund accuracy, CSAT, and cost per resolution

This is also where “agentic commerce” becomes real. The report describes a purchase scenario in which an agent monitors availability and price constraints, obtains human pre-approval, executes a purchase, and references an agent payments framework called AP2.

Shift 4: Agents for security, from alerts to action

Security operations have a very specific bottleneck: alert volume. The report cites that 82 percent of analysts are concerned they may miss real threats due to the amount of alerts and data they face.

It also frames the agentic SOC as a cycle where specialized agents support detection, triage, investigation, threat research, malware analysis, detection engineering, and response, coordinated with human-managed escalation and recommendations. The report notes that common context and communication across agents are supported by protocols like A2A and MCP.

A strong benchmark example is Torq, where an AI SOC analyst coordinates specialized agents, achieving 90 percent automation of tier 1 tasks, a 95 percent decrease in manual tasks, and 10x faster response times.

What this means for CT Labs' delivery

Security is a high-stakes domain, which makes governance a product feature.

CT Labs structures security agent deployments around:

• Rules of engagement that define allowed actions per alert type
• Evidence first reasoning grounded in telemetry, logs, and case history.
• Human checkpoints for containment, credential actions, and customer impact
• Continuous evaluation against false positives, false negatives, and time to resolution

This is a practical wedge for enterprises seeking AI deployment consulting that ties directly to risk reduction per dollar, aligned with the report’s focus on productivity and actionability.

Shift 5: Agents for scale, talent becomes the ROI constraint

The report argues that focusing on models and platforms misses the most critical element: people. It cites a professional skill half-life of four years, and in tech, as short as two years.

It also signals an emerging skills gap around “agent orchestrator” and “Chief of Staff for AI” capabilities.

The report lays out the pillars of AI learning, including measurable goals, sponsorship, sustained engagement, integration into daily workflows, and trusted risk frameworks.

What this means for CT Labs' delivery

The fastest route to scalable value lies in combining deployment and enablement.

CT Labs pairs build with a learning loop:

• Enablement embedded in workflows so people learn on the job.
• Internal champions across business and technical teams
• Use case pipeline that turns employee demand into prioritized deployments.
• Governance training so teams handle data boundaries and risk responsibly

At this stage, “best LLM consulting services for enterprise deployment” are defined by metrics that matter to the executive: adoption, productivity, quality assurance, and auditability are integral to CT Labs’ delivery commitments.

A practical roadmap CT Labs uses to turn trends into production value.

If you are evaluating AI strategy consulting companies or comparing the best AI consulting firms, the 2026 selection criteria shift toward execution discipline.

Here is a field-tested sequence CT Labs uses for enterprise clients:

1) AI assessment services that map value to workflows

• Inventory top workflows by cost, cycle time, risk, and decision density
• Identify where grounded context exists and where data foundations need upgrades.
• Define success metrics tied to business outcomes.

2) Design the agent system, permissions, and evaluation

• Agent roles, tool access, approval steps, escalation paths
• Ground truth sources, retrieval strategy, and context constraints
• Evaluation harness for accuracy, policy compliance, and tone

3) Build and deploy in production sprints

• Integrate systems of record, automate safe actions, and instrument every step.
• Move from pilot to operating workflow with real users.
• Expand by replicating the pattern across adjacent workflows.

4) Governance and scale

• Guardrails, audit trails, incident response, model, and prompt change control
• Training program aligned to real roles and daily work.
• Portfolio management for the next workflow wave

This is where AI agent development companies for enterprises differentiate themselves: consistent, governed deployments that scale across teams and functions.

The report’s five shifts converge on a single conclusion: 2026 rewards organizations that treat agents as systems, grounded in enterprise context, governed for safety and accountability, and scaled through talent enablement.