So, I sent research agents through

recent surveys, marketplaces, job

listings, case studies, and online

communities. And there were five

workflows that just kept showing up. So,

in today's video, I'll show you the data

behind each one, what I'd build for a

portfolio, and why one relevant project

can beat 30 impressive demos. And if

you're starting from zero, I'll tell you

which one I'd build first. So, let's not

waste any time and just get straight

into today's video. Okay, so real quick,

I only counted workflows with recent

buyer demand, real deployments, a clear

buyer, and a result that they can

actually measure. So, this isn't a

market share census. It's a ranking

based on the strongest evidence that I

was able to find. Now, together, five

automations cover customer acquisition,

service, back office work, and employee

operations. So, if you learn these

patterns, you can genuinely start

mapping out opportunities for automation

across any business. Okay, so first up

is lead qualification and follow-up.

This is when a lead comes in and the

workflow enriches the lead, scores it

against the company's rules, and then

collects anything missing. It'll update

the CRM and then it can either start

following up or it can book a meeting.

The outcome that you're selling here is

fast responses to qualified leads

without making sales people dig through

a bunch of junk. Salesforce says that

Seammens is deploying this type of

system across 2,800 inbound leads a week

using about 50 verification rules. And

those 50 verification rules are where

the value truly lives because you have

to understand how that company defines

what a good lead is. So for a portfolio

project, pick one vertical, maybe

commercial cleaning or HVAC. Capture and

enrich the lead, score it, and create

the CRM record, then route it by

territory or by service, and give the

salesperson a short explanation of that

score. Now, to start, low confidence and

like unusual leads should still go to a

person rather than being completely

discarded, at least until you've done

enough testing to really dial in the

system. And for this type of automation,

I'd be tracking response time and the

percentage of qualified leads that

actually book. And one other thing here

is like don't go out and build 10 agents

talking to each other. You just need one

reliable workflow with clear rules, a

human handoff, and a clean CRM record,

and that can do the job. It's way

simpler. Just you don't need to

overengineer. All right, so number two

is customer support. This type of system

answers from approved sources. It'll

pull in relevant context and it will

complete a few safe actions when needed

and it will hand risky cases to a person

with a conversation summary. Salesforce

found that agentic AI adoption in

service organizations went from 39% in

2025 to 66% in 2026. And among teams

already using service agents, 70% said

that they saw measurable value within

just 60 days. So let's take like an

e-commerce demo for example. You would

handle order status, return eligibility,

and address changes. You would need the

system to pull in the real order and

policy and verify identity before

changing things and escalate refunds,

cancellations, suspicious requests, and

anything sensitive. The handoff to the

human should include a summary, the

sources that were used, the actions that

were taken, and basically like the

recommended next steps. Don't make the

human start from zero. But of course,

every company is going to have a

different process for customer support.

But anyways, this type of project, I

would think about measuring how many

tickets it actually resolves correctly

without a person. And then I'd also be

watching the reopen rate because if you

have a high resolution, that doesn't

really mean anything if the customers

keep coming back because the original

answer from the AI was wrong. All right,

real quick guys. Because I know that a

lot of you are building AI agencies and

managing agents for multiple clients and

that stuff can get messy pretty quickly

because every client needs their own

instructions, context, tools, and

connected accounts. So, Hyper Agent

gives you one place to build and manage

all of those agents. Each agent can have

its own system prompt, model, knowledge,

integrations, and automation settings so

that you can just set up a dedicated

agent for each client or each job. You

can also share agents through a team. So

your client can run the agent and keep

their own threads and outputs while you

maintain control of the configuration

behind it. So they get a clean way to

use what you've built without needing to

understand all of the technical setup.

And since Hyper Agent runs in the cloud,

agents can run off a schedule, they can

respond in Slack, they can be triggered

from web hooks, or they can just monitor

activity through live mode while your

laptop's actually closed and turned off.

So if that sounds useful for your

agency, then check out Hyper Agent

through the link in the description. Any

paid plan on there will get you $100 in

bonus credits. So, huge thanks to Hyper

Agent for sponsoring this part of the

video. Now, let's get back to it. All

right. And number three is a voice AI

receptionist for either missed calls or

after hours calls. So, missed calls.

This thing can qualify the caller. It

can book the appointment. It can update

the calendar, the CRM. In Fiverr's 2026

trend report, Fiverr compared two

six-month periods and found that

searches for AI voice agents were 49%

higher in the more recent 6 months. So

for an after hours build, I would

probably disclose that it's AI and I

would identify the service and the

urgency. The system can answer approved

questions. It can check the calendar. It

can book the appointment. It can send

confirmations. And anything uncertain or

urgent should always be routed to a

person. And if calls are being recorded,

make sure that you're following like

local consent and regulation rules and

privacy rules and things like that. And

make sure you're testing this thing in

the real world. It's messy. You know,

there's things like accents,

interruptions, background noises,

emergencies. There's a lot of stuff like

that. Now, the cool thing about this

one, and honestly, like every automation

that you'll ever build, is that you can

have a bunch of different agents

essentially stress test the automations

to find all of those edge cases that you

might not have thought about. Now, that

obviously isn't like 100% coverage of

all the edge cases, but it definitely

helps more than just what your brain can

do alone, especially because you can

have tons of different agents doing all

of that in parallel. And I think two of

the numbers that really matter here are

recovered bookings and cost per booking.

But I want you to notice the trend here,

which is that these automations have a

bunch of different metrics that they're

trying to move or that they ultimately

will move. But what I'm trying to do

here is dial in on one or two of each

because that makes the outcome way

easier to communicate to the business

owner. All right, so now number four is

documenttosystem processing. In a

Microsoft case study, tech custom

document workflow saves 40 hours a week

and has reduced errors by 99%. So

basically like a document lands in an

inbox or a folder or wherever the system

identifies it and extracts the correct

fields. Then it will basically validate

them against company records. It can

route the exceptions. It can create

drafts in the right systems. Zapier also

found that data entry and extraction was

the most common enterprise AI agent use

case at 47%. Now I will admit when it

comes to things like document

processing, they are a lot less

impressive of a demo compared to

something like a voice agent. but you

still have all the saved hours, the

fewer errors, and a smaller backlog. All

of those benefits make the ROI of that

system really, really easy for the

business owner to understand. And in

this whole AI automation space, I've

always said that boring is beautiful.

So, build something like invoice

processing. It'll pull invoices from a

shared inbox. It'll extract the vendor,

the line items, things like that. It

will validate the totals. It will then

match the purchase order. It will flag

discrepancies and create a draft bill.

Now, when you're first getting started,

keep these as drafts. So you're

basically letting the AI prepare the

transaction and then obviously you're

letting a human actually approve and

move the money around. And for this type

of system, I'd be watching things like

field accuracy and how many drafts need

no correction. All right, so number five

is employee service and onboarding. So

think about a new hire getting started,

but their laptop and system access still

aren't ready because everybody thought

somebody else had handled that. So this

type of automation basically keeps all

of those things from happening because

it'll answer policy questions. It'll do

HR and IT requests, approvals,

onboarding, offboarding, bunch of

different systems. McKenzie found that

agents show up most often in IT and

knowledge management, including things

like service desk work. And in a make

study, a Franklin CVY HR workflow went

from 30 days just down to 2 hours. So

try building an onboarding system. Once

the offer has been signed, route

approvals and create access requests

based on the person's role. Then you can

do things like assigning equipment,

assigning training, answer approved

questions, send reminders, and show the

manager what's still left incomplete.

But for this type of process, real

account creation would likely still need

manager and system owner approval. And

the sales cycle might just be a little

longer because of all those like

permissions. But this type of automation

really fits mid-size companies that

onboard people often but still miss

steps. And what does this automation

look like when it's successful?

Basically means new hires are getting

ready faster with fewer missed tasks. So

those are basically the five. But

knowing what to build is only half the

job. To sell one of these, you have to

understand the process behind it.

Because just copying one of these

diagrams or taking a random process off

the internet and assuming that all

onboarding is like that and putting it

into cloud or codeex, that's just not

enough. You have to interview the person

doing the work. You have to talk it

through with the stakeholders. You have

to walk through three recent examples at

least. Not just like the perfect SOP of

how it's supposed to go. You have to

look at how it really happens in the

real world. Map the triggers, the

systems, the data, the decisions that

follow rules versus decisions that need

AI judgment. Then you have to look at

things like exceptions, approvals,

success metrics. You will almost always

need to adapt the build to the company's

actual tech stack and permissions. It's

really hard to just have like a

one-sizefits-all solution. But once you

know the processes that well, the

business starts seeing you as a trusted

operator, a consultant instead of just

another tool builder. And if you're a

beginner, you don't need to access a

real company's data in order to start

building test projects and portfolios.

just use dummy data. So, if I were

starting out from zero right now, I'd

probably build something like document

processing just because it's super easy

to test and it's easy to get up and

running for a first project. But if you

really know CRM, then maybe build some

lead routing. If you really know local

service businesses, then build a missed

call recovery workflow. The point I'm

trying to make here is choose the

project closest to the people and the

processes that you already know because

you could have 30 impressive AI

automations in your portfolio and still

look risky if every demo feels generic

and it doesn't actually fit what the

customer is looking for. So, think about

if you wanted to go have an amazing

steak. You probably wouldn't choose the

restaurant that's serving sushi, tacos,

pizza, steak, and 20 other things. You

would choose the steakhouse because you

trust the specialist. An HVAC owner

thinks the same way. They trust the

person who understands emergency calls,

service areas, dispatch rules, and how a

booked estimate actually reaches their

system. So, a lot of times people ask me

like, "Oh, what's the best projects to

have in my portfolio?" And my answer

usually is whatever project solves the

pain of the person that you're actually

meeting with. Pick one buyer, one

business process, and one measurable

outcome. Because if you have a portfolio

with 50 things, but they're all

unrelated and they're just generic, it

might feel like that's more impressive,

but actually all you're doing there, I

think, is you're losing the trust of

that business even more. So, the five

automations, lead qualification and

follow-up, customer support resolution,

voice reception and booking, document

processing, and employee service and

onboarding. Together, these patterns let

you start mapping AI across basically

the entire business from front to back.

And I know we covered a ton of

information in today's video, so what I

did is I put all of this into a

completely free resource guide that you

can access in my free school community.

The link for that is down in the

description. But anyways, that is going

to do it for today. So, if you guys

enjoyed the video or you learned

something new, then please give it a

like. It helps me out a ton. And as

always, I appreciate you guys making it

to the end of the video. And I will see

you all in the next one.