Do we need a Readiness Review first?

Not always. If roles and workflows are already chosen, you can start here. If they are not, a Readiness Review is usually the better first step.

How many people are involved?

Usually two to five selected participants. The program is deliberately focused so the team can adopt what it learns.

What do we keep at the end?

Role playbooks, configured projects or assistants, templates, review checklists, a before-and-after scorecard, an adoption report, and a prioritized automation backlog.

Can it cover sensitive work?

Where the systems and agreements support it. Sensitive work begins with explicit decisions about access, vendors, storage, logging, and human review.

30-Day AI Productivity Program

Flagship engagement

Make key roles measurably better at real work in 30 days.

Selected employees learn and adopt repeatable AI methods on actual recurring tasks—with configured tools, review rules, and a measurement baseline. Not a generic company workshop.

Typically $6,000–$9,000 · Four focused weeks

Plan a 30-Day Program See the week-by-week plan

MIT education · Former Amazon product leader · Longtime CTO · You work directly with Jason

A four-week timeline of printed deliverables—baseline scorecard, role playbooks, a configured checklist, and a before-and-after comparison—threaded by an orange workflow line

Who the program is for

For teams that have chosen the roles and problems—and now need adoption.

The Program fits organizations ready to act, often after a Readiness Review, a strong referral, or a discovery conversation.

Leadership has selected the roles and recurring work to improve
Employees are already experimenting with AI informally
Recurring knowledge work in sales, operations, administration, reporting, or client service
A need to move from scattered experiments to repeatable, measured methods
Willingness to use real company work in supervised labs
An executive sponsor to protect time and remove blockers

What changes in 30 days

Repeatable methods, configured tools, and a measured baseline.

Role-specific playbooks

Practical methods for the selected roles, built from real recurring tasks.

Configured tools

Projects, assistants, templates, and review checklists your team keeps.

Human-review rules

Explicit guidance for checking, approving, escalating, and protecting sensitive information.

Adoption baseline

A before-and-after view of task time, editing burden, output usefulness, and use.

Failure modes named

Where the method breaks, and how staff recognize and handle it.

Automation backlog

A prioritized list of what better tool use solved and what merits configuration, integration, or a build.

How roles and workflows are selected

A small number of roles, chosen for value and feasibility.

Start from leadership's priorities and competitive pressure
Choose recurring tasks with recognizable inputs and outputs
Favor work with enough volume to matter
Confirm a human owner for each workflow
Establish current time, quality, and review burden as a baseline
Limit scope so the team can actually adopt what it learns

Week-by-week program

Baseline, methods, repeatability, measurement.

1
Week 1 — Baseline and role selection
Confirm outcomes, inventory tools and licenses, select roles and recurring tasks, and establish current time, quality, and review burden.
2
Week 2 — Real-work methods
Build instructions, examples, and review rubrics; test ChatGPT, Claude, voice, browser, or suite features on real work; identify failure modes.
3
Week 3 — Repeatability
Configure projects, assistants, templates, and checklists; define human review and escalation; train on live work.
4
Week 4 — Measurement and handoff
Compare performance, assess adoption, document methods, prioritize the automation or build backlog, and deliver a leadership report.

Real-work labs

The team practices on actual company work—supervised.

Labs use real documents, meetings, research, communication, and reports, with a person reviewing outputs. Examples vary by role.

Drafting and review

Turn discovery notes into proposals, or compare long documents against a standard position for professional review.

Research and briefings

Prepare sourced client or prospect briefings before calls, meetings, or proposals.

Voice to structured work

Turn spoken field or meeting notes into records, summaries, and tasks with human review.

Supervised browser tasks

Gather information across approved sites and portals and prepare a report, with checkpoints.

What you keep

Configured outputs your team owns at the end.

Role-specific playbook
Configured project or assistant
Prompt and context templates
Review checklist and escalation rules
Before-and-after task scorecard
Adoption report
Prioritized automation backlog

Measurement and adoption

Measured behavior, not attendance.

Productivity

Compare task time, editing burden, and output usefulness against the week-1 baseline.

Consistency

Assess whether the method produces reliable results across people and cases.

Adoption

Track whether the selected roles actually use the configured tools on live work.

What you provide

The program works when leadership protects time and access.

An executive sponsor
Two to five selected participants, depending on scope
Access to representative recurring work
Existing policies or constraints on data and tool use
A list of major software and AI subscriptions
Protected time for labs and practice on live work
Participation in a final leadership report

Risks and boundaries

What the program does not do.

It is not a generic all-company workshop
It does not guarantee a specific return on investment
It does not include a production custom application unless separately scoped
It does not authorize unsupervised external communications or irreversible actions
Adoption depends on protected time and leadership support
AI outputs require review appropriate to the task
Sensitive workflows may require approved vendors, agreements, or additional controls

The goal is durable capability inside your team—methods, tools, and review rules that outlast the engagement.

Example role playbooks

What a configured role looks like.

Sales

Sourced prospect briefings, consistent proposal drafts, and tracked follow-up with human approval on outreach.

Operations

Voice-to-task workflows, exception routing, and document comparison with staff review.

Client service

Standardized intake and response drafting, escalation rules, and a shared knowledge assistant.

Leadership

Recurring management memos assembled from existing source material, reviewed before sending.

Price and scope

Typically $6,000–$9,000, based on roles and depth.

Scope is defined in writing before the program begins.

Price is affected by

Number of roles and participants
Number and complexity of recurring workflows
Tool and license landscape
Sensitive-data requirements
On-site versus remote delivery
Depth of measurement and reporting

Why The Tomorrow Machine

The person who designs the method also builds it.

You work directly with Jason Merkoski. The methods are built and tested on your real work, not delivered as slides. Jason combines an MIT education, early Amazon product leadership, and current hands-on work across ChatGPT, Claude, voice AI, supervised browser automation, and production systems.

That means the program can teach what is genuinely useful now and prioritize automation only where the evidence supports it.

Configured on your real recurring work
Human review built into every method
Measured adoption, not attendance

FAQ

Questions buyers ask first.

Training teaches concepts. This program configures tools on your real work, establishes review rules, and measures whether adoption actually improves speed, consistency, or capacity.

Before and after the program

How the Program connects to the rest of the ladder.

AI Productivity & Readiness Review

The right first step when roles and workflows have not been selected yet.

See the Readiness Review

Workflow Upgrade

For a proven process that needs configuration or supervised automation.

See the Workflow Upgrade

Fractional AI Operator

Ongoing ownership and continuity after the program ends.

See the Fractional Operator

Plan a 30-Day Program

Turn scattered AI experiments into measured capability.

Bring the roles and recurring work you want to improve. We will confirm fit, define the smallest useful scope, and plan the four weeks.

Free 20–30 minute AI Fit Call · No sales team · You speak directly with Jason

Prefer email? Reach Jason directly at hello@tomorrowmachine.ai.

Based in Santa Fe, New Mexico. U.S. Mountain and Pacific working hours.

Book an AI productivity call

Pick a time for a free 20–30 minute AI Fit Call. We will look at how your team works today, where existing AI tools could help, and the smallest sensible next step.

What happens on the call

1.You describe the recurring work, pain point, or competitive pressure on your mind.
2.We decide together where existing AI tools could help—or whether a simpler fix comes first.
3.If there is a fit, we identify the smallest sensible next step: an Executive Session, Readiness Review, or 30-Day Program.
4.No sales team, no pitch deck. You speak directly with Jason.

Useful to bring

The recurring work or decision you want to improve
The AI tools and software your team already uses
What would make a first step worth doing