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Last week I was talking to an entrepreneur who shared that, prior to AI software development tools getting so good, his team followed the standard two-week product sprint.

Every two weeks, they would sit down, map out the features, bug fixes, and roadmap priorities, and then the team would go off and do the work. They would write code, write tests, run everything through their internal processes, and then come back two weeks later to review what went well, what didn’t, where the different initiatives stood, and what should happen in the next sprint.

This type of two-week sprint is pretty common in the software development world.

Only now, with the advent of amazing tools like OpenAI’s Codex, SpaceX’s Cursor, and Anthropic’s Claude Code, the productivity of software developers has jumped dramatically. So much code is being written, so many tests are being created, so many features are being enhanced, and so many bugs are being solved that the head of product now does a daily stand-up with the head of engineering and the engineering managers every single workday.

Before, software engineers had to map things out, work through the code changes, write code, edit code, and test everything themselves. Now, much of that work is actually being done by AI. The software engineers are coaching the AI, providing feedback on what it writes, thinking through its recommendations on architectural changes, reviewing the code it produces, and orchestrating the different agents doing the work.

Of course, AI can write code much faster than a human. Software engineering is becoming much more about orchestrating agents than writing syntax in a particular language.

As AI coding continues to increase the pace of development, the biggest issue becomes curating what should actually be done.

When there are big initiatives underway, this is less of an issue. But when tweaking features and polishing different modules, it becomes easy to run fast and build things that might not be needed, don’t fit the overall vision, or are simply built because they are easy to build.

That makes staying close to the customer even more important. Teams need to understand the customer’s needs, how customers work today, and how they want to work in the future.

With two-week sprints, there was more time to gather feedback directly from customers, through customer advisory panels, customer success calls, or other channels. Now, if the product is changing on a daily basis, the customer feedback loop and the company’s opinionated vision of the future both have to evolve as well.

Ultimately, this all bodes well for startups delivering better products and customers getting more of what they need faster.

Entrepreneurs would do well to consider shrinking the cycle time of their sprints from two weeks to one workday, or some similarly short interval. But at the same time, they should dramatically increase the frequency and scalability of customer feedback so the team continues working on the most important things instead of adding more functionality simply because it is easy to do so.

We’re in a new age of software development. The startups that adapt best will balance dramatically faster innovation with equally fast customer feedback.

Last week, I had the opportunity to speak with the YPO Next Generation (YNG) Atlanta chapter about startups and entrepreneurship. YNG is a Young Presidents’ Organization program for members’ children between the ages of 18 and 30. Like the broader YPO community, the group regularly comes together through programs, retreats, and small-group forums to learn, share, and grow.

My talk focused on three themes.

The first was the unpredictability of life’s journey. I shared stories about startups I’ve been involved with and how often we began with one idea, worked hard at it, and then discovered a better direction. Almost every successful company I’ve helped build started by doing something other than what ultimately made it successful.

Pardot, for example, began by generating leads on behalf of B2B technology companies. We later pivoted to marketing automation, supplying those companies with the tools to manage their own lead generation and nurturing. We started in a promising area, but our first product wasn’t the one that worked.

The second theme was the human side of entrepreneurship. The familiar adage still holds: If you want to go fast, go alone; if you want to go far, go together.

In the startup world, as in nearly everything else, success comes down to people and teams. I shared stories about our focus on building a culture of positive, self-starting, and supportive people, as well as the two overarching mantras that guided us: Be the best place to work and the best place to be a customer.

This human-centered approach influences everything. When you talk with someone at a startup, or any company, you quickly get a sense of how they operate. Are they easygoing or cutthroat? Are they interested in a transactional relationship, or do they want to build trust for the long term? When something goes wrong, as it inevitably will, do they acknowledge it? How do they make it right?

The people side of business is, and always will be, one of the most important.

The third theme was market timing and the importance of following emerging trends. When something is white-hot and everyone is talking about it, it’s often too late. Ideally, you want to be three to five years early, early enough to build a working product, assemble a strong team, and establish a solid foundation.

Being a little early, but not too early, puts you in a powerful position. When the market arrives, you have the opportunity to grow even faster than the rapidly expanding market around you.

I enjoyed sharing these stories with the YNG group. If I had to distill the talk into three lessons, they would be these: Your original idea will often be wrong, people are at the center of everything, and in the startup world, it pays to be a little early when the next big trend arrives.

One of the things I enjoy doing is researching and coming up with new startup ideas. It’s fun to brainstorm and imagine what could be. Of course, there’s a big gap between ideation, execution, and ultimately building a successful company.

For many people who want to be entrepreneurs, there’s a natural feeling that the idea needs to be perfect. The reality is, it’s hard enough to find a great idea, let alone a perfect one.

A great starting point is simply talking to people. Reach out to friends, mentors, and colleagues and ask them what they would like to see exist in the world, in their home, or in their business. What frustrates them? What takes too much time? What do they wish worked better?

One of the best ways I’ve found to discover new ideas is to start doing something new yourself. Pick up a new hobby. Start a new side hustle. Make a new investment. Get involved with a nonprofit. Ideas become much easier to find when you put yourself in new situations.

Research fast-growing trends. Study lists like the Inc. 5000. Look at the companies on those lists and analyze their industries. Are they still early and growing quickly, which usually means there’s plenty of opportunity? Or are they growing quickly in an industry that has already matured, meaning there’s likely more competition and less white space?

My new favorite piece of advice for ideation is to combine AI with scheduled tasks.

Fire up ChatGPT, Claude, Grok, Gemini, or one of the newer automated AI agent products like Grok Bot, Meta Muse, or Instinct. Better yet, use several of them.

Then have the AI generate a new startup idea for you every hour, every day, or every week and notify you when the idea is ready for review.

Each time it sends you an idea, provide feedback. Do you like the category? Do you like the industry? Do you like certain characteristics of the business? What don’t you like? Over time, that feedback helps steer the next ideas in directions that are more interesting to you.

For me, I’ve turned this into a simple daily routine. Every night, ChatGPT is scheduled to generate a new startup idea and send it to me. Then every morning at 8 a.m., Grok Bot generates a second startup idea. That means I start each day with two fresh startup ideas to review.

As I go through them, I give feedback on what I like and don’t like: the market, the customer, the business model, the level of complexity, the size of the opportunity, and anything else that stands out. The goal is for that feedback to continuously improve and refine the types of ideas being generated.

Most of the ideas won’t be great. That’s the point. Instead of sitting down occasionally and trying to force myself to come up with a great startup idea, I have two AI systems continuously feeding me new possibilities every single day, seven days a week.

For entrepreneurs looking for new ideas and inspiration, talking to people about their problems and using AI on a scheduled basis to continuously generate ideas creates a tremendous number of possibilities to contemplate, combine, refine, or turn into the next big startup.

Every year, I look forward to reading the latest Inc. Magazine Inc. 5000 list of the fastest-growing private companies in the United States.

When I was a kid, I would grab Inc. magazine off the shelf at the local Publix and scour the list, studying the company names, stories, trends, ideas, and entrepreneurs behind some of the fastest-growing private companies in America.

Over the years, I’ve had the opportunity to create a few companies from scratch, as well as co-found a few more. Even after all these years, it’s still a thrill to build a fast-growing company and see it rank among private companies from across the country.

Here’s a list of the 2026 Inc. 5000 companies in Atlanta.

Inc. Rank	Company	3-Yr Growth	Industry	Leadership
14	Impiricus	11,691%	Artificial Intelligence & Data	Sandy Donaldson, Osama Hashmi
35	The Salon Suite Model	7,210%	Education	Patrice McKinney
127	Saleo	2,361%	Software	Justin McDonald, Daniel Hellerman
142	Carpool Logistics	2,201%	Logistics & Transportation	Michael Malakhov
149	Freestyle Snacking	2,103%	Food & Beverage	Nikki Seaman
171	Thrivemodal	1,928%	Human Resources	Oliver Ong
182	Document Crunch	1,888%	Construction / Software	Josh Levy
225	ZonPrep	1,521%	Logistics & Transportation	Kayvon Nazarian
234	My CPA Coach	1,459%	Financial Services	Krystal Standberry, Sherman Standberry
237	SCW.AI	1,432%	Software	Evren Ozkaya
269	Tractian	1,251%	AI & Data	Igor Marinelli
504	Trilogy Investment Company	684%	Real Estate	Jason Joseph
531	Heathos	655%	Insurance	Todd Baxter
537	Empify	647%	Financial Services	Ashley Fox
565	Anaptyss	617%	IT Services	Anuj Khurana
576	Cyble	609%	AI & Data	Beenu Arora, Manish Chachada
581	Art of Drawers	606%	Consumer Services	Allan Young
601	Financial Cents	578%	Software	Shahram Zarshenas
630	Granum	570%	Software	Mark Sedgley
640	Flock Safety	537%	Security	Garrett Langley
929	ketteQ	371%	Business Products	Mike Landry
953	We Mail America	363%	Advertising, Marketing & PR	Gary Pizzati
1,012	Cognosos	346%	Logistics & Transportation	Heath Thompson
1,035	Intown Golf Club	335%	Hospitality	Michael Williamson
1,238	ValidiFI	282%	Financial Services	John Gordon
1,308	AdvizorPro	269%	IT Services	Michael Magnan
1,351	Ruby-Collins	261%	Construction	Scott Cline
1,416	Advanced Tech Placement	250%	IT Services	Maggie Bray, Grace Denyer
1,478	RS Group	238%	Logistics & Transportation	Brent Staton
1,527	Manay CPA	229%	Financial Services	Burcu Bree Manay
1,558	Bellwood	225%	IT Services	Rob Kischuk
1,624	Docufree	214%	Software	Brad Jenkins
1,646	Vintage Modern	212%	Automotive	Chau Nguyen
1,671	Codoxo	207%	Healthcare & Medical	Musheer Ahmed
1,714	ChronicCareIQ	203%	Healthcare & Medical	Matt Ethington
1,777	Southworth PC	196%	Legal	Shaun Southworth
1,863	Southern Reins Logistics	186%	Logistics & Transportation	Jake Mathis
1,911	Cogitate	180%	Software	Arvind Kaushal
1,994	GOCare	172%	Software	Mike Roddy
2,056	Merit Financial Advisors	166%	Financial Services	Rick Kent, Kay Lynn Mayhue
2,181	Pye-Barker Fire & Safety	155%	Business & Corporate Services	Bart Proctor
2,279	LoadUp	148%	Environmental Services	Greg Workmon
2,359	Kahua	142%	Software	Scott Unger
2,386	Alloy	140%	Advertising, Marketing & PR	Raj Choudhury
2,420	Vantage Point Consulting	137%	IT Services	Krish Subbiah
2,447	20North Marketing	135%	Advertising, Marketing & PR	Connor Dunaway
2,590	Groth, Makarenko, Kaiser & Eidex	125%	Legal	Joseph Kaiser
2,626	Giant Sports Cards	123%	Consumer Services	Jon Butts
2,738	Canopy	116%	Software	Suzanne Lane, Steve Latham
2,864	Groundfloor	109%	Financial Services	Brian Dally
3,159	ImagineX	95%	IT Services	Shaun Bank
3,165	Protomiq	95%	Automotive	Jorge de Castro
3,287	TriData	90%	Financial Services	Christian Ranke
3,321	Tier4 Group	88%	Human Resources	Betsy Robinson
3,465	Dunamis Woman Enterprise	82%	Consumer / Education	Davetta Collins
3,515	PEPL	80%	Human Resources	Jennifer Vilines
3,785	TaxConnex	69%	Financial Services	Robert Dumas
3,793	Vayman & Teitelbaum	69%	Legal	Esther Vayman, Greg Teitelbaum
3,891	BeyondTrust Corporation	64%	Security	Janine Seebeck
3,913	Atlanta Divorce Law Group	64%	Legal	Sara Khaki, Shawna Woods
3,918	Procure Analytics	63%	Business & Corporate Services	Suja Katarya
3,983	Inteleca	61%	Computers & Technology	Paul Hogg
4,110	Innovien Solutions	56%	IT Services	Camryn Mastel
4,224	The Shelf	52%	Advertising, Marketing & PR	Lauren Jung, Atul Singh
4,265	Aggregate Singularity	50%	Software	Christy Williams
4,318	Community Construction Group	48%	Construction	Ross W. Haynes
4,363	Flex HR	47%	Human Resources	Jenny Morehead
4,486	YouDecide	41%	Insurance	Peter Marcia
4,687	WRK Marketing	31%	Advertising, Marketing & PR	Sesealy Owens
4,991	OTR Solutions	12%	Financial Services	Fritz Owens

Congratulations to all the entrepreneurs who made the list and are building dynamic, fast-growing businesses.

With all the talk of AI agents and vibe coding new software, one issue that keeps coming up is the system of record.

Historically, the system of record was the main software-as-a-service application a company used. Think Salesforce.com or HubSpot in CRM, Workday and Rippling in HR, and Pardot and Marketo in marketing automation.

As part of the SaaSpocalypse beatdown, followed by the slight improvement, one of the big ideas was that companies charging based on users or seats would come under pressure. If an AI agent is one seat but can do the work of 10 or 50 humans, the number of seats a company needs could go down dramatically over time.

Combine that with much better tools for building one-off applications and moving data from system A to system B, and there may also be less lock-in from legacy systems of record.

All of that might be true.

But there’s another idea that hasn’t been discussed enough: the potential for an entirely new system of record to emerge.

Imagine a startup that provides the backend, database, data lake, and underlying infrastructure, but also includes roles, groups, responsibilities, governance rules, workflows, required approvals, and clear designation of who owns each piece of data, who can change it, and who can’t.

Right now, when you vibe code a new CRM, go-to-market tool, HR application, or almost any other piece of software, the AI typically creates a new database with relatively limited rules around it. That becomes more complicated once multiple products and systems are reading from and writing to the same data. There are second- and third-order effects around permissions, ownership, governance, consistency, and control.

This creates an opportunity for a new startup.

It wouldn’t be an incumbent with existing pricing power and an innovator’s dilemma problem. Instead, it could provide a data store with a governance engine on top at a much lower cost than interfacing with a headless Salesforce.com or another legacy application.

Then, through open source libraries, great APIs, and excellent engineering, it could position itself so that the agents deciding which products and modules to use naturally select it as the system of record.

The goal would be to create a new industry-standard system of record that is AI-agent native, easy to drop into existing systems, compatible with applications that previously wrote to legacy systems, and available through a self-service freemium model without an expensive go-to-market motion.

For enterprise-focused entrepreneurs looking for a potential business idea, being the system of record has historically been incredibly valuable. It’s also clear that many legacy incumbents face the traditional innovator’s dilemma.

That creates an opportunity to build the next-generation system of record: AI-agent native, optimized for vibe coding, neutral across applications, easy for developers and agents to adopt, and priced like modern infrastructure instead of legacy enterprise software.

Recently, I’ve been using the new Grok Bot product from SpaceX AI via the Cursor team. The big idea for Grok Bot is that it’s a dedicated computer in the cloud that can do actual work by way of chatting with it. Of course, it has all of the power of AI and large language models, so the possibilities are endless.

I started by following a few different guides online to get going with Grok Bot and quickly set up a Chief of Staff bot to interface with the other bots. Then I set up individual bots to start doing work around some of my common day-to-day activities.

The easiest way to start with it is to think of it like a system that would be an employee. What would you assign as a task to an employee? Please build this spreadsheet. Please assemble the latest metrics and analysis for this report. Please review and provide recommendations for our next social media post. Please analyze what our competitors are doing in other markets. Please research the latest trends and best practices in our industry and summarize them daily.

Again, the key is to go from doing something like research or analysis to actually producing work from it and then putting that work product in the right place. The work product could be something like a new Google Doc with research. It could be a new Google Sheet with financial analysis. It could be ten new email drafts written in your tone and voice right inside your Gmail drafts folder.

Next, think of it like an employee where you give it work, it produces output, and it sends it to you, puts it in the cloud, sends it to your colleagues, or posts it in a Slack channel. It’s truly an AI teammate.

For me and my Grok Bots, I followed some of the best practices online and went with the Chief of Staff bot to interface with the other bots. Then I went with individual bots for things like monitoring all of the startups that I work with and sending me a weekly summary of anything new related to them, anything new on their respective websites, and any new comments or sentiment online about them. Stick it in a Markdown file and summarize it with a weekly email.

This example is similar to different products out there on the research side that will research things on a recurring basis and send you a summary. Nothing too fancy, but again, having the power of a large language model and AI means this can be made much more powerful over time.

As a simple example, anytime one of the startups that I work with issues a new press release, I can have the Grok Bot queue up an email draft in my Gmail account saying, “Congrats on this,” include a link to that press release, and then notify me that the draft is ready for my review.

Yes, I could do this by hand using some other tools. I could copy and paste the email draft by hand. I could have these pieces as part of my manual workflow. But by using AI plus a computer in the cloud, it’s always running. It never forgets. It does more research and has more access. The ultimate result is that it’s a repeatable workflow that slots right into my day-to-day. I get tremendous value from it, and it was low effort to set up and configure.

The next bot that I set up was the local startup activity bot. In a similar fashion, it reads all the websites that cover the local startup scene, summarizes what’s going on, and then sends me a weekly email with that information. Nothing fancy, but it’s an area of interest and focus of mine.

The next bot that I’m working on is around the individual startups that I work with. It aggregates all of their board decks, weekly updates, monthly updates, investor presentations, strategy docs, and anything and everything that I’m provided. It then uses the information from these proprietary sources and looks for its own trends and analysis, along with its own opinions on what it thinks the entrepreneur is doing well, what it thinks the entrepreneur is not doing well, its opinion on the market they’re in and the segment of the market, and any other nuggets or tidbits that it comes up with.

You could think of this as an analyst employee on the team, but again, constantly monitoring everything, constantly analyzing, constantly doing research, and then making it all actionable and feeding it up to me on a silver platter with minimal effort.

Entrepreneurs would do well to experiment with Grok Bot, start using their own AI employees, think through the rest of the team having AI as a team member, and implement this into the daily workflow.

Continuing with last week’s post on doing a voice dump into AI tools, another theme I’m bullish on is using voice in general.

One of the most popular products changing the way people work through voice is called Wispr Flow. Unlike voice transcription of the past, where the accuracy was much lower, tools like Wispr Flow feed the voice transcription into AI. The AI makes sense of what you were trying to say, fixes grammar, punctuation, and capitalization, understands things like proper nouns, and dramatically improves the quality of the output.

If you don’t want to download and try a new product just for voice, the great news is that ChatGPT, Claude, and Grok all have voice interfaces built in. You can simply start talking, and the AI will clean up what you say.

What I’ve found personally is that when I’m working on presentations, doing research, or thinking through ideas for my writing, I can do a voice dump straight into an AI tool or something like Wispr Flow. Within minutes, I have the beginnings of a foundation in clean, plain text. From there, I can whittle it down, refine it, reorganize it, and edit it.

Entrepreneurs would do well to start thinking about voice as much more than a faster way to dictate a text message on their iPhone. Voice is becoming a new way to interface with computers. Voice is a way to think with AI. Voice is a way to write.

And, of course, most people would rather talk than write.

If you’re not using voice multiple times per day to interface with your phone and laptop, you’re missing out on one of the best productivity gains AI has created.

A few months ago, I read a post arguing that one of the best ways to interact with AI chatbots like ChatGPT, Claude, and Grok is to do a complete stream-of-consciousness dump of everything in your brain. Good, bad, ugly, directly related, loosely related, just get it all out there.

And do it by voice.

Just start talking.

Before this, I was handcrafting prompts, trying to make them as precise and specific as possible. I was copying and pasting prompts from other people. I was carefully typing out what I wanted from the AI, usually in a fairly simple and structured way.

After a few months of using stream-of-consciousness voice interaction, I can never go back.

The quality of the results is dramatically better. The AI has more context. The responses are more thoughtful. The follow-up questions are better. The entire interaction is more productive because I am giving the AI far more information than I ever would if I were typing.

For entrepreneurs, the applications are endless. Use it for market research, customer analysis, product ideas, organizational changes, strategy, hiring, sales, or anything else happening in the business.

AI should become a thought partner that gets the full conversation about what is going on, what is going well, what is not going well, what you are worried about, and what opportunities you are considering.

Think about how you would talk through an issue with a co-founder. You would not carefully craft a perfect three-sentence prompt. You would provide context, jump between ideas, explain the history, debate the trade-offs, and use the other person as a sounding board.

AI can increasingly fill that role, with the added benefit of having access to an enormous corpus of information and the ability to research additional information online in real time.

My recommendation for entrepreneurs is simple: stop spending so much time hand-typing pretty prompts.

Hit the voice button and mind meld with the AI. Talk through anything and everything that comes to mind, then go from there.

The results are incredible.

Voice is the most natural interface for AI, and AI is finally the technology that makes voice truly powerful.

With each passing day, it becomes clearer that AI is making the intelligence side of startups and entrepreneurship more accessible, distributed, and abundant. As that happens, the real differentiators for success will increasingly come down to two things: agency and social skills.

The more computers can do what we previously thought of as thinking and intelligence, the more it falls on founders and team members to initiate action, make things happen, and get things done. At the same time, the human side of business becomes more important, whether that is corporate culture, relationships, trust, or the intangible feeling you get from working with colleagues, customers, and vendors.

Being smart and making good business decisions will increasingly become a commodity.

Consider agency and bias toward action. When the computer tells you the next best step, analyzes the market, identifies the trends, points out a problem, or highlights an opportunity, it is still up to the humans to act.

One of the arguments for people working less in the future is that AI will do more of the work. In the startup world, that can actually create an advantage. The more people at established companies work fewer hours, the greater the opportunity for startups to work harder, move faster, provide more value to customers, and gain ground.

If old-line companies work less, that creates an opening for upstarts to work more.

But taking advantage of that opportunity requires teams with high agency. They need to be self-starters with a strong bias toward action. They cannot simply wait for someone, or some AI, to tell them what to do next.

A similar dynamic will play out with social skills. If AI can give you a great answer, make a good recommendation, and analyze all the different trade-offs, everyone else will eventually have access to the same capabilities.

When intelligence becomes abundant, other forms of differentiation matter more.

For purely commodity or self-service transactions, much of business will become technology driven, with AI agents increasingly interacting with other AI agents. But high-consideration purchases will be different. Consulting engagements, large enterprise deals, M&A transactions, partnerships, and other high-stakes decisions will continue to involve people.

If everyone involved has powerful AI, along with some level of human expertise layered on top, the people who win the business will increasingly be the ones with the better soft skills. They will be the people who are enjoyable to work with, who build trust, who communicate well, and who others genuinely want to spend time with.

For AI-pilled entrepreneurs, this should be encouraging. The future is becoming even more oriented toward high-agency people. People who do not need to be told what to do. People who enjoy going above and beyond. People who find new opportunities and get after them.

At the same time, as intelligence becomes abundant, the things AI cannot do autonomously, or the situations that still require an expert human in the loop, will place an even greater premium on soft skills, people skills, and social skills.

Entrepreneurs would do well to invest in both sides of the equation: increase their agency and deliberately improve their ability to work with people.

Last week, I was talking with an entrepreneur who shared how he was using AI to do a much better job of identifying which prospects he should pursue.

It is easy to get a list of names and companies. It is much harder to determine which ones are the best fit for your product or solution.

This reminded me of one of our projects at Pardot 15 years ago. With a thousand customers, we knew there were certain characteristics that made a company much more likely to be a good fit for our product.

Here were the top three.

First, did the company advertise its own product through Google AdWords?

If so, that meant the company was already spending money on online marketing and direct response demand generation. Online advertising was a strong signal that the company could benefit from marketing automation.

Second, did the company have sales representatives listed on LinkedIn?

A sales team usually implied a more consultative and complex sales process involving multiple steps and touch points. Marketing automation was particularly valuable for companies with this type of sales motion.

Third, did the company’s website include a newsletter signup box or another form of email interaction?

This suggested that the company was already using email marketing, landing pages, automated responses, triggers, or similar tools. It demonstrated a basic level of marketing sophistication. Marketing automation could take those existing efforts and make them much more effective.

If a company was running Google Search Ads, employing sales representatives, and collecting email addresses on its website, the probability that it was a good fit for Pardot went through the roof. It became an obvious prospect.

What did we do next?

We built our own internal software to identify new companies and evaluate lists of potential prospects.

For example, the software would collect links from every article published on sites such as TechCrunch and TechMeme and add them to a database. It would then identify the companies mentioned in those articles, review Google Search results, analyze LinkedIn, and crawl each company’s website. It could even inspect the website’s source code for commonly used email marketing tools.

Finally, the system would produce a prioritized list of companies and make it easy to synchronize those prospects with Pardot.

We spent a good amount of time on this side project, and it was worth every penny.

Today, AI and large language models make this type of work dramatically faster and easier. They also make it possible to perform a much deeper level of analysis on every potential customer.

What words or ideas on a prospect’s website indicate that the company might be a strong fit for your product?

What broader concepts or signals can be identified through social media, job postings, press releases, customer reviews, or other public information?

What additional data points can AI triangulate at scale in an automated way that would have been impractical or impossible before?

My recommendation to entrepreneurs is simple. Study what your best customers have in common. Identify the characteristics shared by your ideal prospects. Then build tools and systems that continually find, evaluate, filter, and prioritize companies based on those characteristics.

You should never cold call or cold email a company again unless it has first been evaluated using your own set of characteristics.