Three immediate priorities for buyer-ready IT operations

1) Govern agentic Copilot capabilities in productivity apps; 2) audit AI compute and energy exposure with MSPs; 3) harden detection and authentication against AI-enabled scams.

Why agentic copilots and new AI chips matter for SMBs

Large technology vendors are moving agentic capabilities from research demos into mainstream productivity tools and specialized AI hardware. Microsoft has announced agentic features in Word, Excel, and PowerPoint that can take multi-step actions in documents and workflows, and cloud vendors are shipping new generation chips explicitly designed for autonomous agents. For a small or mid-size business that relies on cloud productivity suites and occasional custom AI, those changes translate into new operational surface area—not only compute costs but also application permissions, data access, and auditability.

That combination matters for buyers of managed IT and cybersecurity services because it converts previously manual workflows into automated ones and creates concentrated compute demand. Expect higher and more unpredictable cloud spend tied to model runs, and new vendor controls to manage what an agent is allowed to do in your tenant. Treat the arrival of agentic AI as an operations and procurement problem first: you need governance, capacity planning, and monitoring before you scale.

Practical governance for agentic Copilot features in Microsoft 365

Start by locating where agentic features are enabled and apply a conservative pilot. Use tenant-level controls to restrict who can create or grant agent permissions, and configure Data Loss Prevention (DLP) and Conditional Access around any agents that can access sensitive files or connectors. For Microsoft 365 customers, enable unified audit logging and retain activity logs for an initial 90-day pilot so you can observe agent behaviors and false positives before changing longer-term retention or automation thresholds.

Operationally, define an approvals workflow for any agent that will act on behalf of users—especially if it can send emails, move files, or call external APIs. Require an owner, a test plan, and explicit data scopes. Work with your MSP to include these controls as a managed service item: policy templates, periodic reviews, and an emergency kill switch the MSP can trigger if an agent behaves unexpectedly.

Control compute and energy exposure with capacity and FinOps practices

The next-generation AI chips powering agentic systems also increase power draw and rack density. That has real budget implications: cloud egress, model hosting, and on-prem power and cooling can all drive unexpected bills. Begin with a simple inventory: which workloads use GPU/TPU-class compute, how often they run, and who is billed. Add cost-to-serve metrics into your monthly IT report and require change-control approval for any new AI workload that materially increases compute hours or data transfer.

If you host on-prem or colocate, ask your MSP or datacenter partner for a power-capacity report and projected heat load for any planned AI deployments. Negotiate SLAs that address unexpected power-related outages and include clauses for cost overruns tied to AI bursts. For cloud-first setups, require tagging of AI workloads and integrate those tags into your FinOps dashboard so you can cap budgets or schedule non-critical runs during off-peak pricing windows.

Harden against AI-driven scams and protect user trust

AI-generated social engineering is now more convincing: voice and text models can mimic tone and context at scale. Tighten authentication and detection now: enforce phishing-resistant multi-factor authentication for privileged accounts, enable mailbox protection and URL rewriting in email gateways, and apply real-time link analysis. Add behavioral detection rules that flag unusual agent activity (mass file-sharing, atypical API calls, or automated outbound messages).

Train employees on the specific risks of automated agents. Run focused phishing simulations that include AI-augmented lures and capture lessons learned. When you evaluate MSPs, ask for evidence of simulated-attack programs and incident response playbooks that cover deepfake and AI-augmented fraud scenarios, not just legacy phishing vectors.

Selecting an MSP: experience, controls, and measurable outcomes

When you shop for outside IT support, prioritize MSPs that can demonstrate three capabilities: governance around agentic features, measurable FinOps and capacity planning for AI workloads, and modern threat detection for AI-based scams. Ask candidates for a short engagement pilot (30–90 days) that includes an initial risk assessment, a configuration hardening roadmap for Microsoft 365, and a cost forecast for AI compute under projected usage.

Require deliverables and KPIs: reduced blast radius for agent permissions, measurable decrease in false negatives from threat detections, and a cost-per-model-run baseline for AI workloads. These concrete outcomes turn the abstract promise of AI into a measurable operational program you can manage and renew.