comparison

consultance.ai vs traditional AI consulting.

AI consulting versus traditional consulting comes down to what lands at the end. Same starting line: a business gap and a budget. Different finish lines: one ships code, the other ships a roadmap. Side-by-side below.

Dimension
Traditional AI consultancy
consultance.ai
Deliverable
90-page roadmap, then a goodbye email
Working AI engine running on your real data
Who writes the code
Your team, an offshore agency, or nobody
We do. Same team as the audit.
Time to first working build
3-6 months of discovery
1-2 weeks proof of concept on your data
Time to production
9-18 months typical
6 weeks from kickoff
Cost model
Hourly billing, scope creep, multi-vendor
Custom, scoped to the workflow. Pays back inside a quarter.
Who owns the code
Often the vendor or unclear
You. Transferred at every milestone.
Handover risk
Strategy and build are different vendors. Things get lost.
One team audits, builds, deploys. Nothing gets lost.
Team size
Partner + 4 associates + 2 PMs + offshore devs
Founder-led, full-stack. No telephone game.
Maintenance after launch
Statement of work for v2, new pitch, new fees
Flat monthly retainer or you run it yourself
Lock-in
Proprietary platforms, retainers with notice periods
Month-to-month. Take the code and leave any time.
Specialization
Generic AI strategy applied across industries
Operator-built across finance, insurance, tax, and ops, with real dollar numbers rather than hypothetical ROI
Best for
Fortune 500 board reports
Founders and ops leaders who want shipped systems

FAQ

What is the difference between AI consulting and traditional consulting?

Traditional consulting sells analysis and hands you a decision. AI consulting, done properly, sells a system that runs after the engagement ends, which means the work has to include the build, the verification layer, and the handover. The distinction shows up in the deliverable rather than the pitch: ask whether you receive a recommendation or a repository, and who is on the hook when the thing has to work on a real month-end.

Is AI consulting replacing traditional management consulting?

Not replacing it, but taking the implementation half of it. The firms still win the board-level questions such as which markets to enter and how to reorganise. What they no longer own by default is the layer underneath, where a workflow gets rebuilt and has to survive contact with real data, because that work is now closer to software delivery than to advisory.

When does a traditional AI consultancy make more sense?

When the buyer needs a board-ready slide deck, not a production system. Big-four firms are optimized for governance and risk committees, not shipped code. If your goal is alignment across thousands of employees, hire them. If your goal is one workflow running by next quarter, hire an implementation partner.

Isn't an implementation partner just a dev agency?

No. A dev agency takes a spec and writes code. An implementation partner runs the audit, picks the workflow worth automating, designs the system, writes the code, deploys it, and trains your team. Strategy and execution from one team.

What about hiring an internal AI team?

Internal teams take 6-12 months to ramp. They learn on your time. A partner that has shipped 10+ AI engines gets you to working production in 6 weeks, then trains your team to run it. Hire internal after, not before.

Is custom pricing a red flag?

Fixed tier pricing prices the average customer. Most workflows are not average. Custom scoping means the build pays for itself, not so a tier looks tidy on a marketing page. We publish the engagement model and milestones, just not a sticker price for unique work.

Want to skip the deck?

Free 30-minute discovery call. Written audit if it makes sense to build.

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