An AI-native consultancy is a consulting firm whose consultants are engineers: they build working AI software into your workflows in weeks, then stay to run and improve it. A traditional consultancy delivers analysis, a strategy and a roadmap, and someone else builds it. Choose by what you need at the end: a plan, or a working system.
What is an AI-native consultancy?
In an AI-native consultancy, the consultants are also the engineers. They work alongside your team, build inside your systems, and stay until the software does what it was meant to do. This is the forward-deployed engineer model, which started at Palantir and has spread quickly. McKinsey now publishes an explainer on the role,1 and job postings for forward-deployed engineers rose more than 800% between January and September 2025, according to Indeed data.2
The attention is practical. AI is easy to demo and hard to put into daily use, and embedded engineers exist to close that gap. The Pragmatic Engineer describes the same pull from the engineering side: the work sits between the product and the customer's real systems.3
How does an AI-native consultancy compare to a traditional consulting firm?
The biggest difference is what you have at the end: a plan, or a working system.
| Traditional consultancy | AI-native consultancy | |
|---|---|---|
| Team | Strategists and analysts led by a partner | Engineers embedded directly with your staff |
| First result | A plan; building starts afterward, often with another vendor | A first workflow live in weeks |
| Approach | Assess, recommend, then plan the rollout | Build the first workflow, learn from real use, then expand |
| Deliverables | Analysis, strategy, roadmap, business case | Working software in your workflow, plus monitoring and upkeep |
| After launch | Handoff to an implementer or your IT team | The same engineers support and improve it |
| Success measured by | Quality of the plan | Adoption and impact on the workflow |
What projects is an AI-native consultancy best for?
An AI-native consultancy is best at turning an existing manual workflow into an automated one. For Soren's clients at law firms, banks and healthcare organizations, that is usually back-office work such as client intake or a searchable internal knowledge base. These projects tend to share three traits:
- A defined workflow: clear inputs and outputs, so everyone can tell when the project is done.
- Existing systems: the software has to work inside them, which is where embedded engineers add the most value.
- Ongoing operation: AI workflows need monitoring and upkeep after launch.
How would each approach the same problem?
Take a hypothetical credit union whose staff handle loan applications by hand. Applicants send pay stubs, tax returns and identification. Staff check each package for completeness, key the data into the loan system, and chase anything missing. The process is slow, repetitive, and depends on a few experienced people.
| Traditional consultancy | AI-native consultancy | |
|---|---|---|
| First phase | Interviews staff and maps the intake process end to end, then assesses where AI could help across lending | Embeds with the lending team, watches real applications move through, and picks intake document review as the first workflow |
| What you receive | A findings report, a prioritized list of AI opportunities, and a business case for intake | A working tool in your environment that reads incoming documents, checks them for completeness, and prepares the data for your loan system |
| Who builds it | A separate vendor or your IT team, after the engagement ends | The same engineers who scoped it, working inside your systems |
| What comes next | Vendor selection, procurement, and implementation planning | Monitoring the tool on live applications, supporting your staff, and scoping the next workflow |
What does an AI-native engagement look like at Soren?
At Soren, you get the whole engineering team, typically 10 or more engineers, not one person assigned to your account. The first two weeks set the pattern for everything after.
- Weeks one and two: we meet your team on site or virtually, interview the people doing the work, and learn how it actually runs. By the end of week two there is a clear scope and project goal, and we are already building in the background.
- The first workflow: we pick low-hanging fruit with a return you can attribute directly, so success is clear rather than arguable.
- After launch: the same engineers stay to monitor, support and improve what they built. Engagements usually run a year or more, and you own everything we build.
The most common surprise in our assessments is how scattered the information is. Before any automation, most teams need their documents and data pulled into one place. More on the model in how we work.
When should you hire a traditional consulting firm instead?
Sometimes a traditional firm is the better call. Pick one when:
- The change spans the whole organization. Operating model redesign, restructuring and change management are people and strategy problems before they are software problems.
- The question is regulatory. If you need a defensible view of how a rule applies to your institution, regulatory specialists are the right resource.
- You need board-level strategy. A multi-year roadmap across many business units benefits from broad, cross-industry experience.
See what this looks like in your back office
Start with a 30-minute call with our team. We'll look at one of your workflows and tell you honestly whether building it is the right next step.
Frequently asked questions
Should we hire an AI-native firm or build an in-house team?
An in-house team gives you long-term ownership and deep knowledge of your business, but it means competing for engineers who are scarce: Indeed data showed forward-deployed engineer postings up more than 800% in 2025. A firm can get workflows live while you hire, and a good one leaves your team owning what was built.
Is an AI-native consultancy the same as a development shop?
Not quite. A development shop builds to a specification you provide, then moves on. An AI-native firm helps decide which workflow to automate first, builds it inside your existing systems, and stays accountable for how it performs after launch, including the monitoring and fixes AI workflows need.
What about a traditional firm with an AI practice?
Many large firms now have engineers, and some build real systems. Ask the same questions you would ask anyone: who writes the code, who is on call when it breaks, and whether the engagement starts with building or with a strategy phase.
How do you choose an AI consulting firm?
Ask four questions. Who writes the code, and who is on call when it breaks? What does your team own at the end? Whose platform is it built on, and what would leaving cost? How many clients does each engineer cover at once? A firm that answers all four plainly is building for you, not selling you a plan.
How long until the first workflow is live?
With an AI-native firm, weeks rather than quarters. At Soren, the first workflow is typically live within two to three weeks of getting access to the systems and information it needs. A traditional engagement usually delivers its plan first, and building starts after that, often with another vendor.
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