78%
using gen AI in at least one business function
McKinsey reports that AI usage is now widespread across business functions.
Management consultancy with agentic engineering at the core
Management consultancy for the shift to agentic engineering.
We help leadership teams understand what agentic engineering means for their systems, teams, and operating model. Then we turn that into practical engineering change through system exploration and working demonstrations.
The AI transformation gap
Adoption is accelerating. Scale, value, and production readiness are still rare.
The problem is not access to AI.
It is redesigning systems, workflows, and operating models so AI can create value in production. That is where agentic engineering matters.
>80%
see no material earnings contribution
For most organizations, adoption is still moving faster than measurable business impact.
<10%
are scaling AI agents in any individual function
Experimentation is rising, but agentic use in production remains narrow.
74%
have yet to show tangible value from AI
BCG finds that only a minority have moved beyond proofs of concept into real value capture.
What we help with
Leadership decisions
The questions start in management, but the answers have to hold up in architecture, repositories, delivery workflows, and team reality.
Management advisory
Clarify where agentic engineering matters in your business, what it changes economically, and which decisions deserve leadership attention first.
System exploration
Surface system constraints, architecture realities, and where your current platform, repositories, and workflows enable or block the shift.
Working demonstrations
Build focused proofs in real contexts so leadership can evaluate opportunity, risk, and adoption before larger commitments.
Capability evolution
Translate insight into concrete changes across team design, engineering workflows, repository structure, and operating model.
Why management
Why management matters
Agentic engineering is not just a tooling shift. It changes team leverage, experimentation speed, system requirements, and the management decisions that shape delivery across the organization.
Smaller teams, more leverage
Leadership assumptions about team size, speed, and cost structure are changing as AI expands what focused engineering teams can do.
System quality becomes strategic
Architecture, documentation, and repository structure now directly affect how well agentic engineering can work inside the organization.
Abstract AI programs are not enough
Management needs evidence from real systems and working demonstrations, not generic transformation language.
How we work
How we work
We combine management consultancy with practical engineering work, so strategic recommendations are tested against the realities of the system.
Start with management questions
We frame the business and operating questions before prescribing tools, teams, or process changes.
Test in the real system
We explore the code, architecture, and constraints that actually determine what is possible.
Use working proof
We build demonstrations that make choices visible, testable, and easier to discuss at leadership level.
Stay hands-on and selective
We work directly with a small number of teams where practical change matters more than broad transformation theater.
Experience
Experience and fit
25 years of digital transformation and product engineering experience.
Deep roots in complex, large-scale, and safety-critical systems.
Comfortable in management discussions and deep technical contexts.
No staff augmentation, no generic AI theater, no slide-only transformation.
Contact
Start a conversation
If you need a clearer view of what agentic engineering means for your systems, teams, or operating model, we should talk.
Email hello@consistis.com