Choose an AI consulting company that can understand the business problem, explain why AI is appropriate, implement what it recommends, test difficult cases, protect the information involved, measure the result, and leave your company with clear ownership.
The best AI consulting company is not automatically the one with the longest list of models, platforms, and certifications.
Those things may matter. But they do not tell you whether the firm can understand your operation and produce something the business can actually use.
I once advised a prospective client not to hire Losung after our discovery conversations.
She wanted help improving processes, reducing dependence on herself, strengthening handoffs, connecting systems, and introducing more automation. All things we do.
But as we went deeper, it became clear that a proven, standardized product and another consultant’s established operating approach were a better fit for where the company was at that time.
So that is what I recommended.
Your consulting partner should be able to make that decision even when it costs them the project.
Use this scorecard when comparing AI consulting companies
| Area | What a strong firm should show |
|---|---|
| Problem discovery | How it studies the workflow, users, systems, data, exceptions, current cost, and intended outcome |
| Solution judgment | Why AI is better than a process change, existing feature, integration, or normal automation |
| Implementation ability | What the firm can design, build, integrate, deploy, and support—not only recommend |
| Security and governance | How access, permissions, vendors, logging, retention, human review, and incidents will work |
| Testing | How normal cases, difficult exceptions, uncertain outputs, and failed integrations will be evaluated |
| Measurement | Which baseline and business outcome will determine whether the work succeeded |
| Ownership and handover | What your company owns and how your team will operate the system after launch |
Start with problem-first discovery
A strong firm should ask how the work happens today before discussing a solution.
Who owns the process? Which systems are involved? Where does information get entered again? What happens when something does not follow the normal path? What is the delay, error, or manual work costing?
Be cautious when a consultant recommends a chatbot, agent, or platform before understanding those things.
The technology may be impressive. That does not mean it belongs in your process.
Confirm what the firm can implement
“AI consulting” can mean several different services.
Some firms produce strategy and roadmaps. Some configure existing tools. Some build custom software but do not redesign the process around it. Others can take the work from discovery through integration, deployment, adoption, and handover.
None of those models is automatically wrong.
The proposal should make clear which one you are buying and what your team must provide to complete the rest.
Ask what will be working at the end—not only what will be presented.
Make security specific
“Enterprise-grade security” is not an answer by itself.
Ask:
- What information will the system access?
- Which vendors or models will receive it?
- How are existing permissions preserved?
- What actions and outputs are logged?
- What must a person review?
- What happens when something goes wrong?
- Who can approve new uses or changes?
The required controls will depend on the process and consequence. A drafting assistant and a system influencing a financial or customer decision should not be governed the same way.
Inspect the testing plan
A polished demonstration normally uses clean examples.
The useful question is what happens with missing information, contradictory records, unusual language, expired access, a failed connection, or an output the model is uncertain about.
The firm should be able to explain what a correct result looks like, which test cases will be used, what is escalated, and who decides whether the system is ready.
Demand a measurement and ownership plan
The engagement should identify a baseline and a small number of business measures such as handling time, backlog, errors, throughput, capacity, cost, or customer response.
Prompt counts and generated words are not useful measures when the project was approved to improve an operation.
Also ask who owns the accounts, code, configuration, data, prompts, documentation, monitoring, vendor relationships, and future changes.
Your company should not discover after launch that it cannot operate the system without the consultant.
Warning signs
- The solution is selected before discovery.
- The proposal describes AI capabilities but not the business outcome.
- Security and governance are deferred until after the pilot.
- Only perfect demonstration cases are shown.
- There is no named owner for adoption or post-launch operation.
- Value is promised without a baseline.
- The company cannot explain what you will own after handover.
Questions to ask before signing
- When would you advise us not to use AI?
- What do you need to understand before recommending a solution?
- What will be working at the end of the engagement?
- How will difficult cases and failures be tested?
- What will require human review?
- How will we measure the business result?
- What will our company own and maintain?
- What assumptions and exclusions could change the scope?
Evaluate Losung the same way.
Our job is not to find a way to sell you AI. It is to understand the problem well enough to recommend what actually makes sense—even when the right answer is a tool you already own, another consultant, or leaving something alone because it is not broken.