Find out what's actually automatable.
Then build it.
Before recommending a single tool, I map exactly how an event moves through Health Dimensions today, client email through vendor confirmations through follow up, and flag precisely where an employee is doing something AI could do instead. Phase 2 turns that into a prioritized roadmap with real time, cost, and hours saved estimates on every item. Phase 3 builds it.
Three reasons AI consulting stalls before it ships.
Most engagements in this category fail for one of three reasons, and your posting already shows you're thinking about the first one.
A summary of everything solves nothing
Handing back a list of every task someone does just restates the problem in a spreadsheet. The hard part is deciding what actually deserves automation, and that's exactly the part most consultants skip.
Recommendations that never get built
A roadmap without an implementation phase is a slide deck, not a solution. The gap between here's what's possible and a system that actually runs is where most engagements quietly stall.
Event coordination lives in email
Client requests, vendor confirmations, and follow ups move through Outlook in a form that's easy for a person to read and easy for automation to get wrong, unless classification happens before anything gets acted on automatically.
Three phases. Learn, plan, build.
Phase 1 starts with the owner and your event coordinators, walking a real event from the moment a client email arrives through vendor confirmations, spreadsheet updates, and final follow up, so every recommendation is grounded in how Health Dimensions actually operates, not a generic template. Phase 2 turns that map into a prioritized roadmap, for each opportunity, what's done manually today, what could be automated, the recommended technology, how the new process would work, estimated implementation time and cost, estimated hours saved, and any real risk or limitation, so you're deciding with numbers, not guesses. Phase 3 is where it gets built and tested against your real email and event data, whether that's Microsoft Graph reading and classifying incoming client emails, Power Automate or n8n handling the vendor confirmation loop, or an AI agent drafting responses and tracking who still owes you an answer. The health fair example in your posting is exactly the kind of workflow this is built for, incoming email read and classified, event details extracted, the record created or updated, vendor outreach triggered, responses tracked, and a person alerted only when a real decision is actually needed.
What you get. Phase by phase.
Matches the three phases in your posting directly, with two more added for how the systems connect to your existing records and what happens after handoff.
- Meetings with the owner and event coordinators walking a real event start to finish
- Every touchpoint examined: client email, scheduling, vendor communication, spreadsheets, follow up
- The actual question asked at each step, why is a person doing this, could AI do it instead
- A clear picture of where the real administrative load actually lives
- Every opportunity documented: current process, what could be automated, recommended technology
- Estimated implementation time, cost, and employee hours saved on each item
- Risks and limitations called out honestly, not smoothed over to look better
- Recommended priority order so the highest value automation gets built first
- Systems built against your real email, event, and vendor data, not a demo set
- Microsoft Graph, Power Automate, n8n, Make, Zapier, or AI agents, chosen per opportunity
- Automated email reading, extraction, drafting, and follow up wherever it clears the reliability bar
- Tested against edge cases before anything goes live
- Event records created and updated automatically as client and vendor information comes in
- Vendor scheduling, confirmations, and response tracking wired so nothing waits on manual chasing
- Reporting and calendar management pulled from the same data instead of re-entered by hand
- Built to work with whatever system already holds your event and client data
- Documentation for every system so your team sees exactly what runs and why
- Employees alerted only when a real decision is needed, not notified on everything
- Ongoing support as adoption expands to more of the business
- Clear path to the next automation opportunity once the first ones prove out
Your questions, answered directly.
No generic proposal. Here's each question from your posting, answered in order.
A company where you analyzed operations and identified automatable processes. What did you automate?
For a client running B2B outbound campaigns, I built an Apify based Python scraper to source and qualify leads, then automated the outreach itself, roughly 1,500 leads generated, and up to 1,000 emails a day sent for 30 days straight through a 2 step sequence, while maintaining an above 3 percent positive reply rate. The analysis came first, mapping exactly where manual list building and manual sending were the actual bottleneck, before any of it got automated.
An example of an email-heavy business workflow you have automated.
That same campaign is the clearest example, a 2 step email sequence sending up to 1,000 messages a day for 30 days, with reply detection handled automatically so a positive response routed correctly instead of getting lost, and the above 3 percent positive reply rate held for the full run, not just an early spike.
Experience integrating AI with Microsoft Outlook and Microsoft 365.
I've built a Microsoft 365 triage and to-do system connected through Microsoft Graph with scoped permissions, read access to Outlook mail and nominated Teams channels, write access limited to Microsoft To Do or Planner only, so the system is structurally incapable of sending a reply or deleting anything even if something goes wrong. Incoming mail gets classified by action type, reply, decision, payment, scheduling, follow up, approval, or delegation, each with a priority score, plus a background sweep that catches messages that went quiet without a reply, the same dropped-thread problem event coordination runs into.
Which automation platforms do you work with?
Make and n8n are my daily platforms, but the right tool depends on what a business already runs on, and I regularly work in Power Automate, Zapier, and directly against APIs when that fits the stack better.
AI agents that perform multi-step business tasks, not just answer questions. An example.
I built a deal sourcing and CRM engine that runs the full chain end to end: scrapers and an email parser bring in new opportunities, Claude extracts and scores each one against acquisition criteria and flags risk patterns, qualified deals get enriched and routed into the CRM, and outreach sequences trigger automatically, without a person touching the middle steps. That's the same shape as your health fair example, read, extract, decide, act, and only surface a person when a real decision is needed.
What would you do during your first 10 hours?
Almost all of it goes to understanding the business before touching a single automation. That means sitting with the owner and event coordinators, walking a real event start to finish, client email through vendor confirmations through follow up, and asking why a person is doing each step manually before proposing anything gets automated. Building before that mapping is done is how automation ends up solving the wrong problem.
What is your hourly rate?
Happy to talk rate once we've scoped Phase 1 together on a call.
What country are you located in, and what hours are you available?
United States, with full overlap during US business hours.
Discovery to rollout. Each phase earns the next.
Click any stage to see exactly what lands before moving to the next phase.
What happens
- Meetings with the owner and event coordinators mapping a real event end to end
- Every touchpoint examined, email, scheduling, vendor communication, spreadsheets, follow up
- Why is a person doing this manually asked at every step, not assumed
- A clear map of where the actual administrative load lives
What happens
- Every opportunity documented with the current process and what could replace it
- Estimated implementation time, cost, and hours saved on each item
- Risks and limitations stated plainly, not glossed over
- A recommended priority order for what to build first
What happens
- Systems built against your real email, event, and vendor data
- Technology chosen per opportunity, Graph, Power Automate, n8n, Make, Zapier, or an agent
- Automated reading, extraction, drafting, and follow up where it's reliable enough
- Tested against edge cases before employees start relying on it
What happens
- Documentation for every system delivered so your team knows what runs and why
- Employees alerted only when a real decision needs a person
- Ongoing support as more of the business adopts AI
- Next opportunity queued once the first systems prove out
Health Dimensions, let's map the first event.
A short call to align on who to talk to first, the owner and which coordinators, and confirm the scope of Phase 1 before it starts. Happy to talk timeline and rates on the call.