Lyzr Research · October 2026 · A 40 minute read
Reimagine one workflow
at a time.
The Lyzr Method, from workflow to production workbench.
To the executive who sponsored an AI pilot and is waiting for the numbers
Your agents work. The workflow around them has not changed.
Eight in ten people feel faster.
McKinsey’s 2026 survey found 80% say AI has improved their individual productivity. Only 37% report a meaningful effect on earnings.
The gap is the workflow.
In McKinsey’s data, redesigning workflows had the biggest effect on bottom-line impact of 25 factors tested. Most organizations lay AI over the process they already had.
So reimagine one workflow at a time.
Redraw one workflow, agree the numbers, put agents into production, make sure people use them, and measure the result against where you started. Then pick the next room.
This is the argument
This guide explains why, and gives you a method you can run. Parts 3 and 4 describe the Lyzr Method and workbench and are labeled as Lyzr’s. The rest applies whatever you build with.
Lyzr Research, October 2026
The short version.
- The gapEight in ten feel faster with AI. Fewer than four in ten see a meaningful effect on earnings.
- The causeMost organizations lay AI over the old process. Workflow redesign is the strongest lever McKinsey found.
- The stakesGartner expects over 40% of agentic AI projects to be canceled by the end of 2027.
- The ideaReimagine one workflow at a time, like renovating one room while the house stays open.
- The Lyzr MethodRADAR, five stages. Reimagine, align, deploy, adopt and realize.
- The workbenchOne place to describe, build, test and ship the agents a reimagined workflow needs.
- The startBring one real workflow, a sponsor and a baseline. The rest follows.
What this covers
Eight parts, start to finish.
Why now
The gap
Eight in ten feel faster. Fewer than four in ten see it in earnings.
Do not automate
Reimagine
Four old ideas on why redrawing beats speeding up.
RADAR
The Lyzr Method
Reimagine, align, deploy, adopt and realize.
To production
The workbench
How a reimagined workflow becomes agents in production.
Choosing
Which room first
How to pick the first workflow, and check it is ready.
Who brings what
By seat
The sponsor, the owner, IT and the champions.
Predictions
What next
What we expect, and the strongest argument against.
Test drive
Where Lyzr fits
What Lyzr offers, and where to look elsewhere.
McKinsey, State of AI 2026
Eight in ten feel faster. Fewer than four in ten see it in earnings.
Of organizations say AI has a meaningful effect on earnings, while 80% say it has improved their individual productivity
The 6% McKinsey calls AI high performers are far more likely to have redesigned workflows. Nearly three quarters of them have, against about a quarter of the rest.
Feeling faster is not the same as being better
McKinsey State of AI survey, 1,719 respondents, as reported by Fortune, September 2, 2026.
The idea in one picture
Do not renovate the whole house at once. Redraw one room, then the next.
A workflow is a room. Redraw one, move in, learn what it taught you, then start the next. The house stays open and the other rooms keep running. This guide is the method for the first room, and for each room after it.
The gap.
Everyone feels faster, and the earnings have barely moved.
This part sets out the evidence that the gap between individual productivity and enterprise value is a workflow problem and not a model problem, and why so many pilots stall.
McKinsey, State of AI, March 2025
Only one in five organizations using generative AI has redesigned a single workflow.
Of respondents using gen AI say their organizations have fundamentally redesigned at least some workflows
Of 25 factors McKinsey tested, redesigning workflows had the biggest effect on seeing earnings impact. Most organizations are laying AI over the process they already had.
The strongest lever is the least used
McKinsey, The State of AI, March 2025.
What most pilots do
Automating a crooked path gets you to the same place, faster.
Most pilots add an agent to the existing steps. Each step gets quicker, and the handoffs, queues and approvals stay exactly where they were. Reimagining asks what the work is for and draws the shortest path to it.
Gartner, June 2025
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027.
Of agentic AI projects predicted canceled, for escalating costs, unclear business value or inadequate risk controls
Gartner also estimates only about 130 of the thousands of agentic vendors are real, a practice it calls agent washing. Most of the projects it sees are early experiments, often misapplied.
Cost, value and controls, not the model
Gartner press release, June 25, 2025.
MIT NANDA, The GenAI Divide, 2025
MIT found that 95% of enterprise generative AI pilots showed no measurable profit and loss impact.
Of enterprise generative AI pilots showed no measurable P&L impact, in a review of 300 initiatives and 153 executive surveys
The study’s method has been debated, so read the number as a signal and not a count. Its direction matches McKinsey and Gartner. The pilot works and the business does not change.
Read it as a signal
MIT NANDA, The GenAI Divide, State of AI in Business 2025, as summarized in secondary coverage.
BCG, the 10-20-70 rule
BCG puts 70% of AI success in people and process, 20% in technology and 10% in algorithms.
Share of AI value BCG attributes to people and processes, in its 10-20-70 rule
The model is the smallest part. The work around it, who does what, how decisions flow and how people adopt, is the largest.
The model is the smallest part
BCG’s 10-20-70 rule, as cited in secondary coverage.
METR, randomized controlled trial, July 2025
In a controlled trial, experienced developers were 19% slower with AI and believed they were faster.
Slower, as measured, while the developers believed AI had made them about 20% faster
Feelings are not measurements. The only way to know a workflow improved is to measure how it ran before you changed it.
Measure the baseline first
METR, randomized controlled trial with 16 experienced open-source developers, July 2025.
Gartner’s three reasons
Why agentic projects get canceled.
Escalating costs
Pilots built on top of old processes carry the old process’s cost and add their own.
Unclear business value
Without a baseline and an agreed measure, nobody can say what improved.
Inadequate risk controls
Agents that touch real systems need guardrails, and many pilots never design them.
Reasons are Gartner’s, June 2025. The explanations are ours.
The result
So the work to do is the workflow.
The model is rarely the constraint
The process around it usually is.
Speeding up steps is not the same as redesigning the work.
Pilots stall when nobody owns the outcome, only the tool.
Adoption, not deployment, is when the return begins.
Reimagine, do not automate.
Four old ideas about redrawing work.
Redesign is an old discipline. This part borrows four ideas, from factories, management, systems design and law, and turns them into questions you can ask of any workflow.
Instrument 01 of 04 · A model worth borrowing
The dynamo and the factory floor
Paul David, 1990
A powerful new technology pays off only after the work is redrawn around it.
Why it matters here
Factories began to electrify in the 1880s, and the big productivity gains arrived in the 1920s. Early owners swapped the steam engine for an electric motor and left the layout alone. The gains came when factories were redrawn, with a small motor at each machine and the floor arranged around the flow of work. Agents are a new motor. Swapping one in changes little.
Paul A. David, The Dynamo and the Computer, American Economic Review, 1990.
Instrument 02 of 04 · A model worth borrowing
Do not automate, obliterate
Michael Hammer, 1990
Do not automate the process. Obliterate it, and start again.
Why it matters here
Hammer argued that companies were using technology to speed up work that should not exist. His test was to ask what the work is for and design it from a clean sheet. Many steps exist because of an old tool, an old rule or an old org chart. An agent that does them faster has made a poor process more efficient.
Michael Hammer, Reengineering Work, Don’t Automate, Obliterate, Harvard Business Review, July to August 1990.
Instrument 03 of 04 · A model worth borrowing
Conway’s law
Melvin Conway, 1968
A system copies the communication structure of the organization that built it.
Why it matters here
If sales, operations and risk hand work to each other, the software will have three apps and three handoffs. An agent placed inside one of them inherits that structure. Reimagining means looking across the handoffs, and often deciding who should own the whole outcome.
Melvin Conway, How Do Committees Invent?, Datamation, 1968.
Instrument 04 of 04 · A model worth borrowing
Chesterton’s fence
G. K. Chesterton, 1929
Do not remove a fence until you know why it was built.
Why it matters here
Not every step is waste. Some approvals exist because of an audit, a loss or a regulation. Reimagining is not deleting steps at random. Map the work as it runs, learn why each step exists, and then decide whether to keep it, move it or retire it. The baseline is how you find the reasons.
G. K. Chesterton, The Thing, 1929.
The method in miniature
Five questions to ask of any workflow.
What is this work for?
State the outcome in one sentence.
Who needs the result, and when?
Start from their need.
Why does each step exist?
Keep the ones that protect a decision.
Which steps exist only because of an old tool?
Those are candidates to retire.
Where must a person decide?
Put the human exactly there.
Walls and handoffs
Every wall is a handoff, a queue and a wait.
Draw the workflow as rooms and walls. Each wall is a handoff. Some protect a decision, such as an approval or a control. Others exist because systems did not talk to each other. Keep the first kind and remove the second, and the work stops waiting.
Two ways to put an agent in a workflow.
The Lyzr Method.
RADAR, five stages from a problem to a result.
This part is Lyzr’s own method, described from Lyzr’s materials. RADAR stands for reimagine, align, deploy, adopt and realize. Reimaginers own the business outcome and forward-deployed engineers own the build.
The Lyzr Method
RADAR runs from a problem to a result in your own numbers.
Every Lyzr engagement follows the same five stages. It starts with a problem and a sponsor, and it ends when the outcome shows up in the business’s own numbers, measured against the baseline.
Two roles, one method
Reimaginers own the outcome. Forward-deployed engineers own the build.
Own the business outcome
They map the work, agree the design, drive adoption and measure the result.
Own the build
They design, build and ship agents into production.
Building is half the job
Lyzr’s CEO says return arrives when people use an agent, not when it is deployed.
Lyzr pricing page and Siva Surendira, LinkedIn, October 2026.
Stage 1 of 5, Reimagine
Reimagine starts with a baseline of how the work runs today.
Before anything changes, the Reimaginer maps the workflow as it actually runs, who touches it, where it waits and what it costs. That map is the baseline every later number is measured against. It is also where the team learns why each step exists.
Stage 1 of 5, Reimagine
What good looks like.
The work as it runs
Who touches it, where it waits, what it costs and where it fails, from the people who do it.
Why each step exists
Keep the steps that protect a decision. Mark the ones that exist for old tools.
A baseline you can measure
A small set of numbers, taken before any agent exists.
Stage 2 of 5, Align
Align agrees the design and the numbers before anyone builds.
The sponsor, the business owner, IT and security agree one picture of the new workflow, the agent’s role and the person’s decisions, and the targets that will count as success. Security and governance are settled here and not discovered at go-live.
Stage 2 of 5, Align
What good looks like.
One agreed picture
The new workflow, with the agent’s role and the person’s decisions drawn in.
Success in numbers
Speed, cost and quality targets, agreed with the sponsor before the build.
Security and governance
Data access, approvals and audit needs settled early, not discovered late.
Stage 3 of 5, Deploy
Deploy puts agents into production, connected to the real thing.
Forward-deployed engineers design, build and ship the agents. An agent counts as in production when it is connected to your production identity provider, tools and data. A sandbox pointed at a test system does not count.
Stage 3 of 5, Deploy
What good looks like.
Agents, not a demo
Built to the agreed design and tested against a sandbox of your systems.
Scored before it goes live
Simulated cases and a readiness score decide when an agent is safe to release.
Moved, not rebuilt
The tested agent goes to production connected to your identity, tools and data.
Stage 4 of 5, Adopt
Adopt continues until the new way is how the work gets done.
Agents that work are half the job. The other half is people using them. The Reimaginer works with team leads and champions on rollout, onboarding and follow-up until the new workflow is simply how the work gets done.
Stage 4 of 5, Adopt
What good looks like.
Where people really work
Understand how users actually work, and design the simplest experience for it.
With real users
Remove friction with the people who will use it, before rollout.
Rollout and onboarding
Champions, training and follow-up, until the new way is how work gets done.
Stage 5 of 5, Realize
Realize measures the result against the baseline, in your own numbers.
The same numbers are taken the same way, before and after. The sponsor who named the problem signs off the result. What worked becomes the pattern for the next workflow.
Stage 5 of 5, Realize
What good looks like.
Against the baseline
The same numbers taken the same way, before and after.
The sponsor signs off
The result is accepted by the person who named the problem.
The next room
What worked becomes the pattern for the next workflow.
Who brings what at each RADAR stage.
Lyzr pricing page, October 2026.
From workflow to workbench.
Where a reimagined workflow becomes agents in production.
This part is also Lyzr’s, described from Lyzr’s own pages. A workflow reimagined on paper still has to be built, tested and run. The workbench is where that happens.
The production workbench
A reimagined workflow becomes agents on one bench.
Architect turns a plain-language description into a working agent. Agent Studio is the pro-code workbench where engineers design, test and tune it, and test it safely against a sandbox of your systems. SuperFlow orchestrates agents into multi-step workflows. Building and testing is free, with no time limit.
What happens at each station.
Lyzr pricing page and docs.lyzr.ai, October 2026.
Closing the trust gap
Simulation closes the trust gap before go-live.
Agents are probabilistic, so business users need a higher bar of confidence before they sign off. Lyzr describes running thousands of simulated cases and scoring reliability before an agent touches real data or users. Failures feed back into the build.
The harness
Guardrails sit outside the model.
What the agent may do
Policy is checked before an agent acts, not after.
What it may touch
Identity, access and spend limits for each agent.
What it did
Audit trails a reviewer can read.
Lyzr describes the agent harness as guardrails, rules and controls outside the model.
Two ways to orchestrate agents.
Names from docs.lyzr.ai. The comparison is our description.
Which room first?
Choosing the first workflow, and checking it is ready.
The first workflow sets the tone for everything after it. This part gives four criteria for choosing it, shows what good and poor first workflows look like, and offers a readiness check against what a method like RADAR needs from you.
Choosing
Four criteria for a first workflow.
It hurts, and someone says so
A sponsor can name the problem in one sentence and the cost of leaving it.
It is a whole workflow
It has a clear start and end, an owner, and a result a customer or colleague receives.
The data and systems are within reach
Access can be agreed in weeks, not quarters.
You can measure it today
Volume, time and error rates exist, or can be sampled within a week.
A good first workflow, and a poor one.
Try it on your own workflow
Is your workflow ready for a first stage?
Six quick answers, and be honest. The weakest item is where to start.
Is this workflow ready for RADAR
Answer all six to see what to settle first.
A planning aid, not an assessment. Nothing you select here is sent anywhere.
Who brings what.
Five people, five stages.
A method is only as good as the people in it. This part sets out who is needed at each stage, what each person does, and what they have to decide.
Who you bring at each stage.
The first column of people is Lyzr’s list. What they do is our description.
The sponsor names one problem and signs off the result.
The business owner runs the new way of working.
IT and security sit in the room from day one.
Team leads and champions decide whether anyone uses it.
What comes next.
Five predictions, and the strongest argument against a method.
Predictions are cheap, so each one comes with what would prove it wrong and what to watch for. The strongest argument against this guide comes right after, because leaving it out would be dishonest.
Looking forward
Five things we expect to be held to.
A date
Each prediction names the year we expect it by.
A confidence
A rough number, so you can hold us to it.
Signals to watch
What would tell you early that it is happening.
What would prove us wrong
Written down before the fact.
Prediction 01 of 05 · Lyzr’s view
Workflow redesign becomes the standard question in an AI business case.
Why we believe it
McKinsey finds it the strongest lever, and boards will start asking whether a pilot changes the process or only speeds it up.
What would prove us wrong
If cheaper models make speed-ups good enough, boards may stop asking.
Signals to watch
- Business cases name the workflow being redrawn
- Boards ask for a baseline before approval
- Vendors sell redesign alongside software
Prediction 02 of 05 · Lyzr’s view
The outcome owner becomes a named role in AI programs.
Why we believe it
Pilots stall when nobody owns the result. Roles like the Reimaginer, a person who owns the outcome and not the build, are a response.
What would prove us wrong
If the role is absorbed into the build team, ownership stays blurred.
Signals to watch
- Job posts for outcome owners
- Programs that name a business owner at kickoff
- Adoption targets on executive scorecards
Prediction 03 of 05 · Lyzr’s view
Adoption, not deployment, becomes the milestone that counts.
Why we believe it
Gartner’s cancellations cite unclear value, and value starts with use.
What would prove us wrong
If usage is hard to measure, deployment stays the proxy.
Signals to watch
- Weekly active use reported to the board
- Contracts tied to adoption
- Champion programs funded
Prediction 04 of 05 · Lyzr’s view
Simulation before go-live becomes a normal release gate for agents.
Why we believe it
Agents are probabilistic, so confidence has to come from volume of tests. It is the same logic as testing software before release.
What would prove us wrong
If production monitoring replaces pre-release testing, the gate disappears.
Signals to watch
- Readiness scores written into contracts
- Regulators asking for test evidence
- Test suites shared across teams
Prediction 05 of 05 · Lyzr’s view
Most enterprises run agents in several functions, each reimagined one workflow at a time.
Why we believe it
The first room teaches the second, and the pattern repeats.
What would prove us wrong
If each function builds separately, the pattern will not travel.
Signals to watch
- A shared method across functions
- Reusable agents and building blocks
- One place to govern agents
The strongest argument against this guide
A method with people attached is just consulting by another name.
The argument
A five-stage method with people attached sounds like a services business, and services do not scale.
Where it is right
If every engagement is custom, it will not scale, and a buyer should ask what is reusable.
Where it stops
The reusable part is the harness, the templates and the platform. People supply what machines cannot, which is owning an outcome.
What would change our mind
Evidence that self-serve adoption reaches the same results without the people.
Where Lyzr fits.
A test drive, and when to look elsewhere.
This part describes what Lyzr offers around the method, from Lyzr’s own pages. Parts 3 and 4 covered the method and the workbench. Here we say what a first step looks like and where another approach may fit better.
Lyzr Agent Test Drive
Bring one real workflow and see it run.
Bring one real workflow
A real process with an owner, not a demo scenario.
Build it with our engineers
Working agents, built with Lyzr’s engineers.
Decide on what you saw
See the agents run on your own data before you commit.
Lyzr pricing page, October 2026. Offered to a select set of enterprise teams.
Where Lyzr fits, and where to look elsewhere.
About Lyzr
The full-stack agent infrastructure platform for enterprises and governments that want to own their intelligence.
Founded in New York in 2023. Agent Studio, Architect, Opencontroller, Open Cowork, Openfinetuner, Optimus and Madison cover building, governing, running and owning agents. Deploys in your AWS, Azure or GCP account, a dedicated tenant, SaaS, on bare metal, fully air-gapped, or on the Optimus appliance.
Backed by Rocketship.vc, BGV, Accenture Ventures, GFT, the Partnership Fund for New York City and Plug and Play.
Sources and notes
Every fact, and where it came from.
- McKinsey, The State of AI, March 2025, on workflow redesign and EBIT impact.
- McKinsey State of AI 2026 survey, as reported by Fortune, September 2, 2026.
- Gartner, Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 25, 2025.
- MIT NANDA, The GenAI Divide, State of AI in Business 2025, as summarized in secondary coverage. Its method is debated.
- BCG, the 10-20-70 rule, as cited in secondary coverage.
- METR, randomized controlled trial of 16 experienced open-source developers, July 2025.
- Lyzr, pricing page, RADAR, Reimaginers, Agent Test Drive and product descriptions, October 2026.
- Lyzr, homepage, Agent Studio and adoption steps.
- Lyzr documentation, deterministic and dynamic orchestration, evaluation and readiness scoring.
- Siva Surendira, LinkedIn posts on RADAR and Reimaginers. The platform blocks automated reading, so the wording comes from search excerpts.
- Persistent Systems, Re(AI)magine Conversations, Episode 12, on the agent harness and simulation-led testing.
Vendor claims are labeled as Lyzr’s. Statistics are labeled with their source, and secondary citations say so.
Sources and notes
The ideas, and who had them.
- Paul A. David, The Dynamo and the Computer, American Economic Review, 1990.The gains came after the floor was redrawn
- Michael Hammer, Reengineering Work, Harvard Business Review, 1990.Obliterate, do not automate
- Melvin Conway, How Do Committees Invent?, Datamation, 1968.The system copies the org chart
- G. K. Chesterton, The Thing, 1929.Know why the fence is there
The five questions, the readiness check, the predictions and the picture of the house are our own, offered to be argued with.
Your next step
Bring one real workflow.
Leave with a baseline, a design and a plan. Thirty minutes, no slides, and an honest view of whether Lyzr is the right way to run it.
01
Name it
We pick one workflow, an owner and a sponsor.
02
Map it
We sketch how it runs today and where it waits.
03
Test it
We scope a test drive on your own data.