AI advice from people who build it.

Most AI consulting services end with a slide deck full of possibilities and no owner for any of them. Ours ends with decisions. You get a scored list of use cases, a recommendation for each (build custom, buy a tool, configure what you already pay for, or wait), a data policy your staff can follow, and a dated plan. If you want, the same team then builds the first pieces through our custom AI tools and AI agents practice.

We have been doing this since before it was called AI. Unified has run marketing and software for more than 1,100 businesses since 2006, and we built our own platform, UnifiedIQ, where 29 AI agents watch client websites, calls, CRM data and ads while people review and approve the work. That matters for you: we advise from what has held up in daily use, not from vendor demos.

Who hires an AI consultant, and when

• Owners who keep hearing "use AI" and want to know which two or three jobs in their business would actually pay off first.
• Teams already paying for AI seats (ChatGPT, Copilot, Gemini) that nobody uses past the first week.
• Operations leads with one painful process (intake, quoting, follow-up, reporting) who need to know if AI fits or if a plain automation would do.
• Regulated businesses (healthcare, insurance, legal, finance) that need guardrails before anyone pastes client data into a chatbot.

If you are earlier than that, start with our free AI readiness assessment scorecard or the owner's map in AI for small business. Both are written to help you decide whether you need outside help at all. Plenty of businesses do not, and we will tell you so.

At a glance. AI consulting is a short, fixed-scope engagement that ends in four artifacts: a use-case scorecard, a build-buy-configure decision per use case, a written data and risk policy, and a 30-60-90 day rollout plan with named owners. If an engagement does not produce those, it was a workshop, not consulting.

Readiness is less about technology than people expect. The businesses that get value from AI quickly have three things in common: a process someone can describe step by step, data that lives in a system rather than in someone's head, and one person who owns the outcome. Missing any of the three does not rule you out. It just tells us what to fix before spending on software.

The five readiness questions we ask in the first meeting

1. Can someone on your team write down the job, step by step, including the exceptions?
2. Where does the information for that job live today: a CRM, a shared drive, email, paper?
3. How often does the job happen: daily, weekly, a few times a year?
4. What does a mistake cost: an awkward email, a lost lead, a compliance problem?
5. Who will check the AI's work for the first month, and how much time can they give it?

Then, as with all our AI consulting services work, we score every candidate use case on the same five factors, each from 1 to 5. Value and frequency push a use case up. Data readiness tells us whether it is buildable now. Risk and effort pull it down. The point of the table is not precision. It is getting the whole leadership team to argue about the same numbers instead of the newest demo someone saw.

The scores above are an illustration of how a typical service business might rate itself, not data from a study. Yours will differ. Notice the last row: fully automated outbound email often scores high on excitement and low on everything else, because it is risky to your sender reputation and needs clean lists most businesses do not have. Lead reply drafting usually wins because it is frequent, valuable and easy to supervise.

Anything scoring 20 or more becomes a candidate for the first 90 days. Between 15 and 19, we usually fix the data or the process first. Under 15, we park it and revisit in six months. The same method sits behind our long-form guide to custom AI solutions, if you want to run it yourself.

Every scored use case gets one of four answers. Buy when a mature product already does the job and your process is normal. Configure when you already pay for software (your CRM, Google Workspace, Microsoft 365) that has AI features nobody has turned on. Build when the job depends on your own data, rules or workflow and no product fits without bending your business. Wait when the data or the process is not ready.

Because we have no reseller deals, our AI consulting services can tell you to buy the off-the-shelf product when it is the right call. When building is right, the ownership question matters most. Anything we build for you runs on your accounts, with your data and your code, so you are never renting your own workflow back from us.

Three builds that show the range

For HonestyInsure.com, a health insurance site, we put a 24/7 AI agent chat in the hero so visitors get answers at any hour, alongside 144 indexed pages and guides. For HuntReserve, we built a custom CRM and booking platform for guided-hunt outfitters, with nine drag-and-drop pipeline stages and digital waivers that replaced paper entirely.

For the Center for Spiritual Living in Fort Lauderdale, we migrated 732 events to a custom AI-assisted events platform. Each started as a consulting conversation about one painful job. None started with "we should use AI." We also have a live call assistant in pilot with a South Florida wellness practice, which suggests responses from a playbook the practice approved while a person runs every call.

The fastest way to get hurt by AI is staff pasting customer data into a free chatbot account. So every engagement produces a one-page data policy in plain English: which tools are approved, what data may go into each, what must never go in, who reviews outputs, and how to report a problem. We base the structure on the NIST AI Risk Management Framework, a voluntary framework built around four functions: govern, map, measure and manage.

The guardrails we set on every build

• Data classes. Public, internal, confidential and regulated. Each tool is approved for specific classes only.
• Business accounts only. Consumer chatbot accounts can have different data terms, so staff use workspace or API accounts with training on your data switched off where the vendor allows it.
• A human checkpoint on anything that leaves the building under your name: emails, quotes, public replies.
• Grounding and citations. Assistants answer from your documents and show where the answer came from, so staff can check it.
• Security testing against the OWASP Top 10 for LLM Applications, which lists risks such as prompt injection, sensitive information disclosure and excessive agency.
• Logs and a kill switch. Every action an agent takes is recorded, and someone can turn it off in one step.

Regulated businesses need more. If you are a HIPAA covered entity, any AI vendor that handles patient information generally needs a business associate agreement, and HHS publishes sample business associate agreement provisions that show what one covers. Our healthcare marketing agency work follows the same rule. This is practical guidance, not legal advice: your counsel signs off on the final policy.

The rule we never break: nothing goes out under a client's name without a person approving it. That is how UnifiedIQ runs for our own clients, and it is the default we design into every system we build for yours.

The plan is where AI consulting services turn advice into work. It names one owner per use case, a start date, the measure of success, and the point where we stop and decide whether to keep going. We keep it short enough to fit on one page, because a plan nobody reads is just a longer deck.

Questions to ask any AI consultant before you sign

1. Have you shipped software that is in daily use, and can I see it working?
2. Do you take referral fees or reseller margins from any tool you might recommend?
3. Who owns the code, prompts and accounts at the end?
4. What will you tell me not to do?
5. How will we measure success at day 90, in numbers my team can check?

Red flags in AI consulting services are easy to spot once you know them: a consultant who recommends a platform before hearing about your process, promises to replace staff, cannot explain where your data goes, or measures success by the number of tools deployed. Good AI work is usually smaller and duller than the pitch, and it pays back faster for exactly that reason.

If AI is part of a wider growth question, our consulting and strategy team can fold the plan into your marketing roadmap, or a fractional CMO can own it alongside everything else. See the full AI software practice, or talk to us about your first use case. We will tell you honestly if you are ready.