Too much manual work
Skilled people spend hours on drafting, research, reporting, and coordination that could take minutes.
Strategic AI consulting for growing companies
Become an AI-enabled organization—faster, smarter, more competitive.
The Tomorrow Machine helps leadership teams turn AI from scattered experiments into a real operating advantage. We start with the business problems that keep you up at night—wasted time, overwhelmed teams, manual work, competitors moving faster—and build the strategy, methods, and systems to solve them.
You do not need to become an AI company. But you will soon be competing with organizations that operationalized AI before you did.
MIT education · Former Amazon product leader · AI-native builder and longtime CTO
THE TOMORROW MACHINE. Put tomorrow to work.
- business problems, then tools
- First
- measure results
- Always
- strategy conversation
- Free
SignalSystemOutcome
MIT educationFormer Amazon product leaderAI-native builder and longtime CTO
The problem is not AI. It is where to begin.
You know AI matters. You do not know where it fits, what to trust, or what to do first.
Leaders do not wake up wanting AI. They wake up thinking their teams are overwhelmed, too much work is manual, and faster competitors are pulling ahead. AI can address all of that—but only with a clear strategy for which work to change, which tools to trust, and how to bring the whole organization along.
Teams are overwhelmed
Everyone senses AI could help, but no one owns making it real—and useful discoveries never spread.
Competitors are moving faster
The organizations that operationalize AI first respond quicker, protect margin, and win more work.
The opportunity is not another tool. It is a clear strategy for turning AI into a durable operating advantage across the business.
The competitive baseline is moving
Waiting does not preserve the status quo.
A competitor does not need a futuristic autonomous business to create pressure. It may only need salespeople who research and follow up faster, professionals who draft and review work more efficiently, managers who see current information sooner, and staff who spend less time on repetitive browser tasks.
Faster response wins work
A capable team can prepare a useful response, proposal, or analysis while a slower competitor is still gathering information.
Lower repetitive effort protects margin
Reducing avoidable drafting, searching, transcription, and data movement can change the cost of delivering the same service.
Better tools affect talent
Strong employees may prefer organizations that remove low-value work rather than asking them to endure it.
Unmanaged use is also a risk
Doing nothing does not prevent AI use. It may simply leave employees experimenting without approved methods or visibility.
The choice is not whether AI enters the organization. The choice is whether leadership shapes how it is used before competitors and employees shape the outcome for them.
Why The Tomorrow Machine
The question every leader asks: why us?
There is no shortage of AI consultancies. The difference is who does the work, how deep it goes, and whether it actually changes how your business operates.
Instead of a large consulting firm
You work directly with a senior operator, not a junior team learning on your budget. Fast decisions, real accountability, and strategy that turns into implementation—not a slide deck.
Instead of a generic AI consultancy
An MIT education, early Amazon product leadership, and 25+ years as a CTO and builder mean we can tell strategy from hype—and build the systems, not just recommend them.
Instead of an internal hire
You get breadth and current judgment across every major tool and workflow, kept up to date weekly, without the cost or commitment of a full-time AI executive.
The tools will change every few months. The advantage we build—a faster, smarter, more competitive organization—is what stays constant.
How we work
Business problems first. Strategy, methods, and systems that turn AI into an advantage.
Understand the work and competitive pressure
Identify recurring tasks, current AI use, existing software, and the productivity outcomes leadership values.
Choose the right existing capabilities
Select among ChatGPT/GPT, Claude, voice AI, browser automation, embedded suite tools, or simpler non-AI improvements.
Teach on real work
Create and test role-specific methods using actual documents, meetings, research, communication, and reports.
Make the method repeatable
Create instructions, examples, review checklists, projects, permissions, and ownership.
Measure productivity and quality
Compare time, editing burden, consistency, adoption, and failures.
Automate or build selectively
Engineer only the methods that have proven valuable and repeatable.
About
A firm built around senior judgment and hands-on implementation.
You work directly with our founder and principal consultant, Jason Merkoski—not a sales team or a junior delivery bench.
Jason Merkoski, founder and principal consultant
The Tomorrow Machine is a strategic AI consulting firm. Its founder and principal consultant, Jason Merkoski, has an MIT education and is a former Amazon product leader and longtime CTO. As one of the first employees on Amazon Kindle, he helped launch the first Kindle devices and publishing systems, and has spent 25+ years turning new technology into products, platforms, and operating systems.
The firm works hands-on across ChatGPT and GPT-based workflows, Claude and long-context document work, voice AI, supervised browser automation, model and tool evaluation, integrations, and production systems. That breadth means our recommendation follows your business problem instead of being constrained by a single vendor or delivery model.
We built The Tomorrow Machine for growing companies that need more than a generic AI workshop but do not have an internal AI strategy team. We help leadership become an AI-enabled organization—faster, smarter, and more competitive—and implement the deeper pieces only when they are justified.
- MIT BS in theoretical mathematics
- Years of experience at Amazon, Google, Intel, Motorola
- Multiple CTO and product/engineering leadership roles across healthcare AI, SaaS, adtech, creator marketplaces, fintech, and automation
- Hands-on AI-native builder across agents, LLM workflows, voice AI, ML/data pipelines, TypeScript/React, Python, and MLOps
- Author of Burning the Page and longtime applied futurist
- Hands-on from executive problem definition through production implementation
Results and measurement
Business problem → what changed → measured result.
Examples of the transformation work. Outcomes are anonymized and estimated or observed where exact client metrics cannot be disclosed.
Regional healthcare operator
Problem
Patient engagement relied on manual call review, inconsistent follow-up, and staff spending hours on transcription and note-taking.
Intervention
Built voice AI workflows for call summarization, task creation, and patient outreach—with human review before any external communication.
Result
Call review time dropped an estimated 60%. Follow-up tasks were created automatically for every flagged call. Staff reviewed and approved outreach instead of writing from scratch.
What made it trustworthy
HIPAA-conscious architecture, role-based access, audit logging, and explicit human escalation on every patient-facing action.
Founder-led SaaS company
Problem
Executive reporting took two days each week—pulling from five systems, reconciling numbers, and writing narrative summaries by hand.
Intervention
Built an automated weekly report that pulls from CRM, billing, and product analytics, then generates a plain-language summary of what changed.
Result
Reporting cycle reduced from two days to under two hours of review. Leadership caught a billing anomaly in week three that had been missed manually.
What made it trustworthy
Every metric linked to source data. Uncertain figures flagged for human verification. Report template owned and editable by the CEO.
Independent medical practice group
Problem
Prior authorization and intake paperwork consumed clinical staff time. New patient onboarding took 45+ minutes of manual data entry per case.
Intervention
Built document extraction and intake workflows that pre-fill forms from uploaded records, route exceptions to staff, and track authorization status.
Result
Intake prep time reduced by roughly half. Authorization status visible to front desk without calling back-office staff.
What made it trustworthy
No patient data sent to external models without BAA-covered vendors. All extracted fields require staff confirmation before submission.
Who we work with
Companies large enough to benefit from AI—small enough to lack an internal AI strategy team.
- Growing companies large enough to benefit from AI—but without an internal AI strategy team
- Leadership knows AI matters but has not figured out how to operationalize it
- Recurring knowledge work in research, drafting, reporting, and follow-up
- Scattered, informal AI use with no shared method or ownership
- Real pressure to keep pace with faster competitors
FAQ
The practical questions usually come first.
Because the relevant comparison is not AI versus no AI inside your company. It is your current way of working versus a competitor whose people can research, draft, respond, and coordinate faster. Waiting may increase the gap even if your own business feels unchanged.
No. Instruction is part of the work, but the product is measurable improvement in recurring work. We use real company tasks, configure the tools, establish review practices, and measure whether the method improves speed, consistency, or capacity.
The work may include ChatGPT and GPT-based tools, Claude, voice AI, supervised browser automation using ChatGPT Work agents, AI inside Microsoft or Google products, and specialized tools already used by the business. The choice follows the task, data, controls, and cost.
Yes. Casual individual use is different from a repeatable professional method. The work improves context, instructions, verification, reusable projects, review standards, and how useful practices spread across roles.
A supervised AI agent can perform approved steps in a browser—researching, entering data, moving between systems, or preparing forms—while a person reviews checkpoints and final actions. It is used only where permissions, platform rules, reliability, and risk make it appropriate.
Often, no. Existing models and business tools can handle substantial knowledge work. Custom development is recommended only after the recurring work and value are clear and existing products are insufficient.
We select a small set of recurring tasks, establish a baseline, and compare task time, editing burden, output usefulness, adoption, and failure modes.
The best fit is usually a growing company that is large enough to benefit from AI but does not yet have an internal AI strategy team—leadership knows AI matters but has not operationalized it. We work with clients across the United States, remote-first.
Yes, where the project, systems, and agreements support it. Sensitive work begins with explicit decisions about access, storage, vendors, logging, and human review.
Contact
Bring us your hardest recurring problem
Bring the business problem on your mind. We will give you an honest, specific point of view on where AI can create the most value—and the smallest sensible next step.
What happens on the first conversation?
- 1.You describe the business problem, recurring work, or competitive pressure on your mind.
- 2.We share an honest, specific point of view on where AI could create the most value—and where it should not.
- 3.You leave with a concrete sense of your best next move, whether or not you work with us.
- 4.You speak directly with Jason Merkoski, our founder and principal consultant—no sales team, no pitch deck.
Working with companies across the United States, remote-first. U.S. Mountain and Pacific working hours.
Book an AI strategy conversation
Pick a time for a free 30-minute AI Strategy Conversation. Bring the business problem on your mind—you will leave with an honest point of view on where AI can create the most value, and your best next move.
What happens on the conversation
- 1.You describe the business problem, recurring work, or competitive pressure on your mind.
- 2.We share an honest, specific point of view on where AI could create the most value—and where it should not.
- 3.You leave with a concrete sense of your best next move, whether or not you work with us.
- 4.You speak directly with Jason Merkoski, our founder and principal consultant—no sales team, no pitch deck.
Useful to bring
- The business problem or recurring work you want to improve
- The AI tools and software your team already uses
- What a meaningful first step would make possible