ETTE's AI Practice

AI strategy and enablement for organizations ready to govern the work.

Outcomes-first, vendor-neutral, and security-aware — grounded in the same governance and audit discipline ETTE has brought to managed IT for over two decades. No hype, no lock-in. We start with your work, not a product.

Start With an Advice Session Read the AI Champions Case Study

Every engagement today runs through ettebiz.com — this site is the dedicated front door to ETTE's AI practice.

AI Advisory

Put AI to work without putting your data at risk.

Most teams already have shadow AI and no governance model. We start with the outcomes you care about, surface where AI is already in use, and build a right-sized governance and pilot program your team owns.

20+Years serving Washington, DC organizations

2002Founded — minority-owned MSP from day one

CISSP& CISA-certified engineers on staff

DC·MD·VAPlus nationwide client support

Find The Right Starting Point

Most teams should not start with agentic AI.

For most nonprofits, associations, and established private businesses, the right first question isn't "what can we build?" It's "what path is safe, useful, and realistic for our work?" ETTE uses three engagement levels so your organization can move at the right level of maturity.

Level 1

AI Foundations

For teams that need to understand what the AI path should look like before they make commitments.

Current use and risk discovery
Leadership goals and practical use cases
Data boundaries and governance basics
A first pilot design and 90-day review path

Best fit: leadership wants a credible plan and guardrails before broader rollout.

Level 2

AI Depth

For organizations ready to move from planning into full organizational implementation and adoption.

AI Champions cohorts and staff skill-building
Governed use-case intake and prioritization
Workflow redesign and adoption support
Quarterly governance and operating reviews

Best fit: teams have leadership buy-in and need the operating model to make adoption stick.

Level 3

AI Build

For later-stage teams considering agentic or custom AI workflows after readiness is proven.

Agentic workflow discovery
Security, data, and integration review
Build-vs-buy recommendation
Partner-routed implementation when appropriate

Best fit: later-stage teams with governance, clean data paths, and a proven use case.

Every engagement starts with an advice session and a scoped recommendation. Foundations and Depth are where most organizations should begin; Build is intentionally gated until the use case, data, and governance model are ready.

Who It's For

If AI is already in your org without a plan, start here.

Shadow AI already happeningStaff are using tools you have not vetted, and no one owns the risk.
Leadership pressureYour board or executive team is asking what the AI plan is, and you need a real answer.
Data-risk uncertaintyYou are unsure what is safe to put into AI, and which data should never go near it.
Opportunity, no governanceYou see clear use cases but do not yet have rules, approvals, or guardrails.

Beyond Basic Enablement

Policy and training are only the start. The value lives in the operating model.

Acceptable-use policies, staff awareness, and tool rollout are useful foundations. Organizations now need to decide which work AI should touch, what data is safe, who approves use cases, how pilots are measured, and what happens when a workflow is ready to scale.

Starting point

Basic enablement

Acceptable-use policy
Staff awareness & training
Tool rollout

ETTE AI Advisory

A working operating model

Process discovery
Use-case portfolio & prioritization
Data readiness
Governance operating model
Workflow redesign
Pilot design
Adoption measurement
Operating roadmap

How The Work Flows

From outcomes to evidence.

Discover work

Outcomes and where AI is already in use.

Prioritize use cases

Ranked by value and risk tier.

Prepare data & governance

Readiness scored; owners and rules set.

Pilot

A small, designed pilot with go/no-go criteria.

Measure

Adoption signals and outcome movement.

Scale or hold

A written recommendation, on the evidence.

Deliverables

An operating dossier you keep and run.

Every engagement leaves your team with the artifacts to run the program without us in the room — not a slide deck, a working operating model.

Use Case RegisterCandidates by risk tier
Approved Tools RegisterWhat's cleared, and for what
Governance Operating ModelOwners, approvals, exceptions
Data Readiness ReportReady / Address / Block, by domain
Pilot Design PlaybookScope, users, go/no-go criteria
90-Day Adoption ReviewEvidence and a recommendation
Acceptable Use PolicyOne component, in plain language
Operating RoadmapWhat to do next, and when

Proof, Not Promises

We tested this on ourselves first.

Between March and June 2026, fifteen ETTE team members ran the AI Champions Program across real ticket work, reporting, security analysis, and client communication — entirely in ETTE-governed tools, under an AI usage policy ETTE wrote for itself before writing one for clients.

15Team members enrolled

4Months from pilot to closeout

6Documented working sessions

6+Organizational skills deployed

"Multiple customers have now commented: 'Wow, you guys really know about this stuff — that's really impressive what you're doing.' That's a differentiator for us. We want to make sure we're pulling away from the pack of other MSPs."

— Program Lead, Closeout Session · June 30, 2026

Read the full AI Champions case study →

Why ETTE For AI

Security discipline, applied to AI.

We bring the same security discipline and practical governance we use across managed IT — built for nonprofits, associations, and established private businesses. Our guidance is grounded in our own internal AI Champions work and a structured fluency framework, so your team learns to run the loop without us in the room.

Let's Talk

Start with one good outcome. We'll handle the guardrails.

Start With an Advice Session Talk With an Advisor