➡️Founder @ https://xitter.cf/t.co/Kv8daIFW6N ➡️Leadership Team @ncbaptist
Wake Forest, NC
Joined June 2009
- Tweets4.8K
- Following1K
- Followers1.6K
- Likes6.5K
I think the fast pace of AI developments is fundamentally changing marketing in the tech sector. Do AI companies even make normal ad campaigns anymore? It’s just product announcements every other day.
✋🏼 Same ... in-person is time consuming, but different universe in terms of conversions for b2b. And for early stage it also provides real human feedback on which selling points work or not, which will then inform online marketing
Acre’s customers are hunting and fishing outfitters. It’s an overlooked and underserved niche market that’s often treated as a means to get to the hunter or angler.
We’re trying to break that mold and be obsessive about serving these small to mid size businesses. Traditional wisdom would suggest their standard transaction model (high-ticket invoicing) can’t be done with the same convenience and speed as common apps like Venmo.
We reject that assumption (and often think that maybe we’re crazy). So to see analytics like this on a $5k+ transaction is incredibly validating. When we say we save outfitters time and help them get paid faster, we mean it 💪🏼
For multiple reasons, I built Acre in front of my kids (17, 15, 11), entertained their questions, and explained business decisions as appropriate. There’s not a single question from customers, potential investors or industry partners that makes me more nervous than, “How’s Acre going, Dad?”
Before you ever put AI in front of your customers, ask yourself
Are your data, processes and product info structured and legible to agents? (prob not)
Are you using agents to improve your data, processes and product info? (prob not)
Do you clearly understand the marketing or customer service bottlenecks that an agent will open? (prob not)
Recently had a frustrating customer service experience with a very large telecom company. Here's what I took away:
Website appeared simple at face value, but only because the nav and CTAs were distilled beyond usefulness. Unclear action steps and disorienting workflow. The attempt at minimalism just produced vague corporate language that mocked simplicity.
🧵
The experience left me thinking, if they took half the budget of the design team, and half the budget of the AI team, and gave it to the UX team and Ops team, it would have been much easier for the customer (me) to give them more money
Use AI to work on the process before you deploy AI inside the process. A chatbot helping customers through a broken system might look like progress, but it's not.
Fascinating pattern to watch, spans a range of professions:
You know how a trend can be super viral among teenagers but the minute parents start doing it, it’s lame
Same with AI. Once an agent does a thing, everybody says wow that’s crazy but it’s immediately not cool or cutting edge anymore
This 100x
It's not sexy to say, but most of AI transformation has nothing to do with AI.
There are 10 steps in the sequence of making an internal process or external product AI-native.
Only 1 step is AI, and ironically, the other 9 steps are the far harder part.
Step 1: Identify the problem
- find the manual process worth automating. turn your brain off autopilot & turn on your "suck meter".
- funny enough, your company becomes more efficient just by mapping out your processes even if you don't introduce AI.
Step 2: Understand the workflow
- Map how people actually work today. grab an 8.5x11 piece of paper or @excalidraw and create a flow chart of the workflow from beginning to end.
- Least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time.
- Before you reimagine a process you have to become an expert in that process. Which means you either need to have the business context yourself or absorb it through osmosis (See what @DBredvick did at @vercel)
Step 3: Collect the data
- Gather sample inputs, documents, edge cases
- Example: for my content machine ai workflow, I gathered past slack messages/notion transcripts to test automated ideation & I pulled past X/linkedin posts to build .md files of my content voice
Step 4: Build the prototype [The AI Part]
- Whether its engineer-led or SME-led the goal is to test your hypothesis that there's a better way of doing things for yourself as customer zero. Don't worry about code cleanliness, don't worry about scalability, just worry about proving there's a there there.
Step 5: Test & iterate
- Validate with real users and edge cases
- Before you take the process from single player (only you using it) to multiplayer (many users), you want to beat it up with as many rounds of work & feedback + edge cases as possible. Turning every process into a self-improving loop before scaling is key.
Step 6: Integrate with systems
- Point-in-time data is good for testing the workflow, but live data is necessary before going into production.
- Example: for my content machine, i'm hooked up to notion/gmail/slack for content ideation & i'm hooked up to X & Linkedin to post content once it's ready to go.
Step 7: Roll out & train
- Whether the new process lives on a live link, on GitHub or an internal library, next step is hand-holding your peers/users through the onboarding process of your new workflow/product.
Step 8: Drive adoption
- It's actually pretty simple (just not easy). Introduce a new workflow that saves someone a lot of time and integrates with their already existing behavior so they don't have to deal with re-education.
- Embed the workflow in your culture where adoption is tracked, ideas & feedback are celebrated, and new/creative use cases become social currency in your business.
Step 9: Empower contribution
- Treat your new process like an opensource project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas/feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder.
Step 10: Measure & capture value
- Everyone is ROI obsessed atm. If you're in the experimental phase of AI adoption in your company, fuck ROI. The goal is to empower people to throw a lot of shit at the wall & see what's worth focusing on. You don't need to be scientific during this process. Intuition is more than enough in gauging what's working vs. not working.
- If you're in the scale-up phase of AI in your business, and you need to realize hard ROI, you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV/conversion rate/sales cycle speed.
For those who want to know the details ... it's a set of markdown files that have the events, programs, exhibitors, current book of reports, resolutions, etc. Tried to optimize for quick-strike info and navigation. Let me know if there's more you'd like to see in there.