#automation mistakes

[inklingdd]

7 Costly Marketing Automation Mistakes That Can Ruin Your Strategy

Marketing automation is a game-changer for businesses looking to streamline their marketing efforts, nurture leads, and increase conversions. However, if not implemented correctly, it can become a costly mistake that damages your brand, frustrates your audience, and wastes valuable resources.

To ensure your automation strategy is effective, avoid these seven common mistakes that could be ruining your marketing efforts.

1. Failing to Define a Clear Strategy

Many businesses jump into marketing automation without a well-defined strategy. Simply automating random tasks won’t yield results—it can actually create confusion and inefficiencies.

Why It’s a Problem

Leads receive uncoordinated and irrelevant messages.

Automation fails to align with overall business objectives.

Marketing efforts become scattered, reducing effectiveness.

How to Fix It

Clearly define your marketing goals before setting up automation.

Map out your customer journey and identify key touchpoints for automation.

Regularly review and adjust your strategy based on performance data.

2. Sending the Same Message to Everyone

One of the biggest advantages of marketing automation is segmentation, yet many businesses send the same generic messages to all their leads. This lack of personalization reduces engagement and conversions.

Why It’s a Problem

Customers receive irrelevant content and lose interest.

Engagement rates drop, leading to wasted marketing efforts.

Your brand appears disconnected from customer needs.

How to Fix It

Segment your audience based on demographics, behaviors, and interests.

Personalize messages by using lead data such as name, past purchases, and interactions.

Deliver targeted content that addresses specific pain points of each segment.

3. Overloading Leads with Too Many Emails

Automation makes it easy to send frequent emails, but too much communication can overwhelm leads and drive them away. Bombarding prospects with constant promotions can harm your brand’s reputation.

Why It’s a Problem

High email frequency leads to increased unsubscribe rates.

Your messages may be marked as spam, reducing deliverability.

Leads feel overwhelmed and disengage from your brand.

How to Fix It

Establish a consistent and balanced email schedule.

Focus on quality over quantity—only send valuable and relevant content.

Give leads control over their email preferences and frequency.

4. Ignoring Lead Nurturing

Not all leads are ready to make a purchase immediately. If you focus only on converting leads without nurturing them, you risk losing potential customers who need more time to build trust in your brand.

Why It’s a Problem

Leads who aren’t nurtured properly may never convert.

Your brand misses opportunities to establish long-term relationships.

Potential customers turn to competitors who offer better engagement.

How to Fix It

Set up automated lead nurturing campaigns with educational content.

Offer value through blog posts, webinars, case studies, and industry insights.

Track lead engagement and adjust content to fit their stage in the buyer’s journey.

5. Not Aligning Sales and Marketing Teams

Marketing automation works best when sales and marketing teams collaborate. If these teams aren’t aligned, leads may receive mixed messages or fall through the cracks.

Why It’s a Problem

Poor communication leads to lost sales opportunities.

Leads may get inconsistent messaging, leading to confusion.

Marketing teams may pass unqualified leads to sales, wasting time and effort.

How to Fix It

Establish clear lead qualification criteria agreed upon by both teams.

Use a shared CRM system where marketing and sales can track lead progress.

Hold regular meetings to discuss lead generation and conversion strategies.

6. Ignoring Analytics and Optimization

Many businesses set up automation workflows and forget about them. Without regular analysis and optimization, campaigns become outdated and ineffective over time.

Why It’s a Problem

Poor-performing campaigns continue running, wasting resources.

Opportunities for improvement are missed.

Leads receive outdated, irrelevant, or ineffective messaging.

How to Fix It

Monitor key metrics such as open rates, click-through rates, and conversions.

Conduct A/B testing on subject lines, content, and CTAs to improve performance.

Regularly update automation workflows based on data insights.

7. Not Cleaning and Updating Your Contact List

A common mistake in marketing automation is failing to maintain an updated contact list. Outdated or inactive contacts lower engagement rates and harm email deliverability.

Why It’s a Problem

Emails bounce or get marked as spam, reducing deliverability.

Sending messages to inactive contacts wastes time and money.

Poor list hygiene leads to low engagement and fewer conversions.

How to Fix It

Regularly remove inactive or unengaged contacts from your list.

Use re-engagement campaigns to win back dormant leads.

Ensure new leads are properly validated before adding them to your automation system.

Conclusion

Marketing automation can be a powerful tool when used correctly. However, making these costly mistakes can ruin your strategy and hurt your lead generation efforts. By defining a clear strategy, personalizing your messages, nurturing leads, and continuously optimizing your campaigns, you can maximize your marketing automation’s effectiveness.

Avoid these common pitfalls, and you’ll build a strong, automated marketing system that drives engagement, boosts conversions, and ultimately helps grow your business.

FAQs

1. How can I improve my marketing automation strategy?

Focus on audience segmentation, personalization, and data-driven optimization to refine your automation processes.

2. How often should I review my marketing automation campaigns?

It’s best to analyze performance metrics and optimize workflows every three to six months.

3. What’s the biggest mistake businesses make with marketing automation?

The biggest mistake is failing to personalize messages, leading to low engagement and ineffective campaigns.

4. How can I prevent my emails from being marked as spam?

Avoid excessive messaging, personalize your content, and clean your email list regularly to maintain good email deliverability.

5. Is marketing automation suitable for small businesses?

Yes! Marketing automation helps small businesses save time, nurture leads efficiently, and improve overall marketing performance.

By avoiding these costly mistakes, you can create a smarter, more effective marketing automation strategy that delivers real results for your business.

Avoid these 7 costly marketing automation mistakes that can ruin your strategy. Learn how to improve personalization, lead nurturing, and campaign optimization for better results.

[mostlysignssomeportents]

“Humans in the loop” must detect the hardest-to-spot errors, at superhuman speed

I'm touring my new, nationally bestselling novel The Bezzle! Catch me SATURDAY (Apr 27) in MARIN COUNTY, then Winnipeg (May 2), Calgary (May 3), Vancouver (May 4), and beyond!

If AI has a future (a big if), it will have to be economically viable. An industry can't spend 1,700% more on Nvidia chips than it earns indefinitely – not even with Nvidia being a principle investor in its largest customers:

https://news.ycombinator.com/item?id=39883571

A company that pays 0.36-1 cents/query for electricity and (scarce, fresh) water can't indefinitely give those queries away by the millions to people who are expected to revise those queries dozens of times before eliciting the perfect botshit rendition of "instructions for removing a grilled cheese sandwich from a VCR in the style of the King James Bible":

https://www.semianalysis.com/p/the-inference-cost-of-search-disruption

Eventually, the industry will have to uncover some mix of applications that will cover its operating costs, if only to keep the lights on in the face of investor disillusionment (this isn't optional – investor disillusionment is an inevitable part of every bubble).

Now, there are lots of low-stakes applications for AI that can run just fine on the current AI technology, despite its many – and seemingly inescapable - errors ("hallucinations"). People who use AI to generate illustrations of their D&D characters engaged in epic adventures from their previous gaming session don't care about the odd extra finger. If the chatbot powering a tourist's automatic text-to-translation-to-speech phone tool gets a few words wrong, it's still much better than the alternative of speaking slowly and loudly in your own language while making emphatic hand-gestures.

There are lots of these applications, and many of the people who benefit from them would doubtless pay something for them. The problem – from an AI company's perspective – is that these aren't just low-stakes, they're also low-value. Their users would pay something for them, but not very much.

For AI to keep its servers on through the coming trough of disillusionment, it will have to locate high-value applications, too. Economically speaking, the function of low-value applications is to soak up excess capacity and produce value at the margins after the high-value applications pay the bills. Low-value applications are a side-dish, like the coach seats on an airplane whose total operating expenses are paid by the business class passengers up front. Without the principle income from high-value applications, the servers shut down, and the low-value applications disappear:

https://locusmag.com/2023/12/commentary-cory-doctorow-what-kind-of-bubble-is-ai/

Now, there are lots of high-value applications the AI industry has identified for its products. Broadly speaking, these high-value applications share the same problem: they are all high-stakes, which means they are very sensitive to errors. Mistakes made by apps that produce code, drive cars, or identify cancerous masses on chest X-rays are extremely consequential.

Some businesses may be insensitive to those consequences. Air Canada replaced its human customer service staff with chatbots that just lied to passengers, stealing hundreds of dollars from them in the process. But the process for getting your money back after you are defrauded by Air Canada's chatbot is so onerous that only one passenger has bothered to go through it, spending ten weeks exhausting all of Air Canada's internal review mechanisms before fighting his case for weeks more at the regulator:

https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454

There's never just one ant. If this guy was defrauded by an AC chatbot, so were hundreds or thousands of other fliers. Air Canada doesn't have to pay them back. Air Canada is tacitly asserting that, as the country's flagship carrier and near-monopolist, it is too big to fail and too big to jail, which means it's too big to care.

Air Canada shows that for some business customers, AI doesn't need to be able to do a worker's job in order to be a smart purchase: a chatbot can replace a worker, fail to their worker's job, and still save the company money on balance.

I can't predict whether the world's sociopathic monopolists are numerous and powerful enough to keep the lights on for AI companies through leases for automation systems that let them commit consequence-free free fraud by replacing workers with chatbots that serve as moral crumple-zones for furious customers:

https://www.sciencedirect.com/science/article/abs/pii/S0747563219304029

But even stipulating that this is sufficient, it's intrinsically unstable. Anything that can't go on forever eventually stops, and the mass replacement of humans with high-speed fraud software seems likely to stoke the already blazing furnace of modern antitrust:

https://www.eff.org/de/deeplinks/2021/08/party-its-1979-og-antitrust-back-baby

Of course, the AI companies have their own answer to this conundrum. A high-stakes/high-value customer can still fire workers and replace them with AI – they just need to hire fewer, cheaper workers to supervise the AI and monitor it for "hallucinations." This is called the "human in the loop" solution.

The human in the loop story has some glaring holes. From a worker's perspective, serving as the human in the loop in a scheme that cuts wage bills through AI is a nightmare – the worst possible kind of automation.

Let's pause for a little detour through automation theory here. Automation can augment a worker. We can call this a "centaur" – the worker offloads a repetitive task, or one that requires a high degree of vigilance, or (worst of all) both. They're a human head on a robot body (hence "centaur"). Think of the sensor/vision system in your car that beeps if you activate your turn-signal while a car is in your blind spot. You're in charge, but you're getting a second opinion from the robot.

Likewise, consider an AI tool that double-checks a radiologist's diagnosis of your chest X-ray and suggests a second look when its assessment doesn't match the radiologist's. Again, the human is in charge, but the robot is serving as a backstop and helpmeet, using its inexhaustible robotic vigilance to augment human skill.

That's centaurs. They're the good automation. Then there's the bad automation: the reverse-centaur, when the human is used to augment the robot.

Amazon warehouse pickers stand in one place while robotic shelving units trundle up to them at speed; then, the haptic bracelets shackled around their wrists buzz at them, directing them pick up specific items and move them to a basket, while a third automation system penalizes them for taking toilet breaks or even just walking around and shaking out their limbs to avoid a repetitive strain injury. This is a robotic head using a human body – and destroying it in the process.

An AI-assisted radiologist processes fewer chest X-rays every day, costing their employer more, on top of the cost of the AI. That's not what AI companies are selling. They're offering hospitals the power to create reverse centaurs: radiologist-assisted AIs. That's what "human in the loop" means.

This is a problem for workers, but it's also a problem for their bosses (assuming those bosses actually care about correcting AI hallucinations, rather than providing a figleaf that lets them commit fraud or kill people and shift the blame to an unpunishable AI).

Humans are good at a lot of things, but they're not good at eternal, perfect vigilance. Writing code is hard, but performing code-review (where you check someone else's code for errors) is much harder – and it gets even harder if the code you're reviewing is usually fine, because this requires that you maintain your vigilance for something that only occurs at rare and unpredictable intervals:

https://twitter.com/qntm/status/1773779967521780169

But for a coding shop to make the cost of an AI pencil out, the human in the loop needs to be able to process a lot of AI-generated code. Replacing a human with an AI doesn't produce any savings if you need to hire two more humans to take turns doing close reads of the AI's code.

This is the fatal flaw in robo-taxi schemes. The "human in the loop" who is supposed to keep the murderbot from smashing into other cars, steering into oncoming traffic, or running down pedestrians isn't a driver, they're a driving instructor. This is a much harder job than being a driver, even when the student driver you're monitoring is a human, making human mistakes at human speed. It's even harder when the student driver is a robot, making errors at computer speed:

https://pluralistic.net/2024/04/01/human-in-the-loop/#monkey-in-the-middle

This is why the doomed robo-taxi company Cruise had to deploy 1.5 skilled, high-paid human monitors to oversee each of its murderbots, while traditional taxis operate at a fraction of the cost with a single, precaratized, low-paid human driver:

https://pluralistic.net/2024/01/11/robots-stole-my-jerb/#computer-says-no

The vigilance problem is pretty fatal for the human-in-the-loop gambit, but there's another problem that is, if anything, even more fatal: the kinds of errors that AIs make.

Foundationally, AI is applied statistics. An AI company trains its AI by feeding it a lot of data about the real world. The program processes this data, looking for statistical correlations in that data, and makes a model of the world based on those correlations. A chatbot is a next-word-guessing program, and an AI "art" generator is a next-pixel-guessing program. They're drawing on billions of documents to find the most statistically likely way of finishing a sentence or a line of pixels in a bitmap:

https://dl.acm.org/doi/10.1145/3442188.3445922

This means that AI doesn't just make errors – it makes subtle errors, the kinds of errors that are the hardest for a human in the loop to spot, because they are the most statistically probable ways of being wrong. Sure, we notice the gross errors in AI output, like confidently claiming that a living human is dead:

https://www.tomsguide.com/opinion/according-to-chatgpt-im-dead

But the most common errors that AIs make are the ones we don't notice, because they're perfectly camouflaged as the truth. Think of the recurring AI programming error that inserts a call to a nonexistent library called "huggingface-cli," which is what the library would be called if developers reliably followed naming conventions. But due to a human inconsistency, the real library has a slightly different name. The fact that AIs repeatedly inserted references to the nonexistent library opened up a vulnerability – a security researcher created a (inert) malicious library with that name and tricked numerous companies into compiling it into their code because their human reviewers missed the chatbot's (statistically indistinguishable from the the truth) lie:

https://www.theregister.com/2024/03/28/ai_bots_hallucinate_software_packages/

For a driving instructor or a code reviewer overseeing a human subject, the majority of errors are comparatively easy to spot, because they're the kinds of errors that lead to inconsistent library naming – places where a human behaved erratically or irregularly. But when reality is irregular or erratic, the AI will make errors by presuming that things are statistically normal.

These are the hardest kinds of errors to spot. They couldn't be harder for a human to detect if they were specifically designed to go undetected. The human in the loop isn't just being asked to spot mistakes – they're being actively deceived. The AI isn't merely wrong, it's constructing a subtle "what's wrong with this picture"-style puzzle. Not just one such puzzle, either: millions of them, at speed, which must be solved by the human in the loop, who must remain perfectly vigilant for things that are, by definition, almost totally unnoticeable.

This is a special new torment for reverse centaurs – and a significant problem for AI companies hoping to accumulate and keep enough high-value, high-stakes customers on their books to weather the coming trough of disillusionment.

This is pretty grim, but it gets grimmer. AI companies have argued that they have a third line of business, a way to make money for their customers beyond automation's gifts to their payrolls: they claim that they can perform difficult scientific tasks at superhuman speed, producing billion-dollar insights (new materials, new drugs, new proteins) at unimaginable speed.

However, these claims – credulously amplified by the non-technical press – keep on shattering when they are tested by experts who understand the esoteric domains in which AI is said to have an unbeatable advantage. For example, Google claimed that its Deepmind AI had discovered "millions of new materials," "equivalent to nearly 800 years’ worth of knowledge," constituting "an order-of-magnitude expansion in stable materials known to humanity":

https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/

It was a hoax. When independent material scientists reviewed representative samples of these "new materials," they concluded that "no new materials have been discovered" and that not one of these materials was "credible, useful and novel":

https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/

As Brian Merchant writes, AI claims are eerily similar to "smoke and mirrors" – the dazzling reality-distortion field thrown up by 17th century magic lantern technology, which millions of people ascribed wild capabilities to, thanks to the outlandish claims of the technology's promoters:

https://www.bloodinthemachine.com/p/ai-really-is-smoke-and-mirrors

The fact that we have a four-hundred-year-old name for this phenomenon, and yet we're still falling prey to it is frankly a little depressing. And, unlucky for us, it turns out that AI therapybots can't help us with this – rather, they're apt to literally convince us to kill ourselves:

https://www.vice.com/en/article/pkadgm/man-dies-by-suicide-after-talking-with-ai-chatbot-widow-says

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[teawizard]

Ooookay, I am finally unblocked (🥳🥳🥳) and to celebrate that, here, have triplets spending a totally calm and relaxing evening playing Codenames

[6mayhem]

character illustrators are calling themselves artists and talking about the soul of an artwork

[abowlofpetuniasandawhale]

“This is The Spreadsheet That Is Wrong And Everyone Hates!” They cry

“It is broken in strange and unusual ways that entrap good and wise men into stupid mistakes. Despite this, it is responsible for 80% of our kingdom’s profit somehow! This is why it is vital that you build a tool to replace The Spreadsheet That Is Wrong And Everyone Hates: because it is wrong and we hate it!”

But lo! Every time you present them with something that corrects one of the many issues of The Spreadsheet That Is Wrong And Everyone Hates, they complain that the numbers do not match The Spreadsheet That Is Wrong And Everyone Hates! You see, it must match The Spreadsheet That Is Wrong And Everyone Hates because The Spreadsheet That Is Wrong And Everyone Hates is responsible for 80% of our kingdom’s profit!”

[mistresscitrusslice]

My toxic trait is thinking I’m above using Grammarly. If I need grammar help, I’d rather look up the rules manually to understand it better next time.

[melisssg99]

Kill it. Kill AI with fire! Please!

You know AI guys are living in another reality bc the amount of terror I would feel if a computer sent me, unprompted, an image of a blank-expression copy of myself trapped in a endless hallway of mirrors is frankly indescribable

[rascheln]

It's funny, I started working here again in late January and some procedures did change since I worked here last year, but it's still insane to me that my coworker who has been working shifts here for way more months would still do shit that is absolutely not how it's supposed to be done.

[danniswrites]

One Mistake, 25 Hours, and 25 Employees Couldn’t Work!

It was a normal day at Northside Realty in Atlanta, GA. I was a computer operator. Half my day was data entry of insurance claims for our 2000 realtors. The other part was backing up the work of the 25 employees of the Accounts Payable Department. Our Data Processing department consisted of myself and my supervisor, David Van Zandt. We did everything from printing and distributing quarterly reports that were thousands of pages long and paychecks to troubleshooting five floors of terminals. We also transitioned from one computer system to another and tried to decipher the spaghetti COBOL code of a brillant programmer, who, very unfortunately, died of a heart attack. This was before the days of modular programming, and it is a very difficult task to figure out how to modify such programs. We would tackle this task on slow days.

On this day, my boss was in San Francisco for a conference.

My workstation at @northsiderealty in Atlanta, GA

One of my tasks was to run a UNIX shell program that was, I believe, somewhere around 256 characters long. It had to be typed in exactly as written. Maybe it was too long for a macro, so we couldn’t automate this.

My routine was to type it in and I could type 5 characters ahead in the buffer but no more than that. As I typed, I’d pause and check to make sure it was correct. I was very tired that night. Somewhere in the process, I mistyped and didn’t catch the mistake until the unholy thing was executing! In fact, I tried to interrupt it, but it was too late.

I tried to call my boss, then waited for a callback. California is 4 hours ahead of Georgia, so it was already pretty late. My computer operator duties began at 5 o’clock in the afternoon and went until around 9:30 or 10, so it was now around 6 or so.

While I waited, I went over the manuals for our software and looked up anything in the index to help me understand how bad this was. That was maybe an hour before I realized I wasn’t getting anywhere.

So, I tried my boss again, then called tech support.

They escalated me to one of the programmers, if not the programmer who wrote the shell routine. We discussed options. “You could restore from backup and start over,” he suggested.

I tried to call Dave again. It was getting pretty late and I was not allowed to work over 40 hours in a week, so I made a fateful decision.

I restored from backup. During this, I kept trying to call Dave, but thought surely this would fix my problem.

Finally, I had the backup finished and called again. This time I did get Dave. I explained.

“Tell me you didn’t restore from backup.”

I looked into the computer room where the 14-inch reel-to-reel tapes were still. The backup was finished. “Yes, I did.”

There was a pause, and an audible exhale. Oh, boy. I’m in for it now! But, Dave took a few seconds to collect his thoughts. I’m not sure I would have been as patient with my employee as he was. He didn’t get angry or say anything like I expected to hear, like, say, How could you be so stupid? I was sure saying it to myself.

Nope, he just said, “Let me think about this a minute. Maybe there are some things we can try to do together.”

And, that’s what we did. Dave suggested this and that, and I did it. For hours, he would suggest something and I would go do it, and come back to our black wall phone and report the result. At this point, I wasn’t as worried about going over 40 hours. I did take time to call my husband to tell him I would be late getting home, from another line. And, to cry for a minute.

Well, this process went on and one. Morning came, though our windowless space consisted of a temperature-controlled room that held the blue refrigerator-sized CS 200, our old minicomputer system, the backup system that took the 14-inch reel-to-reel tapes and storage cabinets with our old backups and fresh tapes. Then there was the beige cabinet about the size of a chest deep freeze. Today you probably have that much processing power in your cellphone.

All photos copyright Dannis Cole.

I called my pharmacist husband to let him know I was still at work and he’d have to get our 5 year old daughter ready for school.

At 8 am there was a knock, and I opened the door to find an incredulous worker or three. “Oh, my goodness! You’re still here, Dannis? None of our computers will come up.”

I had to explain things, which was extremely uncomfortable. Nobody got mad about it. But, my boss’ boss and his boss were very upset. They came next.

They had me explain in detail what I did, what I was doing to solve this problem, and talked to Dave on the phone. Of course, they were accounting folks and managers, not data processing people. What we did all day probably seemed like magic in a black box to them, just like their number-crunching and spreadsheets seemed like magic to me. The level of explanation they wanted was way beyond my league, but Dave was able to explain it very well. He has a Master’s in Computer Science, and is a very intelligent man, also very good with people.

Unfortunately, nothing we did worked. We were still trying things until at 10:30 am, Dave called it. I told his bosses, and one of them told me I needed to stay until I found a solution. Several ladies from AP went to him and convinced him to let me go home.

I was in tears. All 25 of those AP employees were going to have to repeat all their work from yesterday. I felt awful that my one-letter mistype caused this whole mess. But all of those employees gathered around me and told me they weren’t mad. Even though Dave’s bosses were very unhappy, they didn’t ever get rude.

I had been at work 25 hours straight. The exhaustion, the stress, lack of sleep, and my painful body got to me. Keep in mind, I was not a healthy person and had several un-diagnosed medical conditions. My disabilities also contributed to this in a big way. I wasn’t exactly the safest driver on the road, but my husband was now at work and couldn’t come get me. My commute took an hour, so I was extra careful.

I didn’t even get fired. Things got back to normal, though I quit typing ahead when doing the AP distro, even though it took longer.

Later, when I got a job at Georgia Tech Library in Systems, I told my boss about this. He laughed and said, “Oh, I did something similar at a job I had. I’ve heard a lot of stories from other people I’ve worked with, so I think everybody’s made a mistake that caused problems for other people.” That made me feel a little better about it, and other more minor mistakes as well. Everyone makes them, but if people aren’t admitting them, it’s easy to think you’re somehow a bad worker.

So, I’m writing this post for all of you out there who might’ve made a major mistake that affected other workers in your department. We’re human. We will make mistakes, sometimes big ones. What matters is your willingness to try to fix it, and how you treat your fellow workers when mistakes happen. My supers remembered to treat their human capital with respect, despite the extra stress, work and cost of my mistake.

We’ve probably all had a boss who lost their temper or wanted to place blame and shame. But, a good supervisor will realize that humans make mistakes and concentrate on fixing the problem, not getting revenge on the person who dared to make it.

Our department pulled together to cope with this mistake. Nobody yelled or got rude. People did express their frustrations, but in a humane manner. Note that there was considerable unhappiness. Nobody wants to repeat work they already did. Nobody wants extra work in a busy department.

But, Northside Realty had a very healthy work culture. The owner of those 22 companies was Johnny Isaakson, a congressman in Georgia for many years.

I didn’t see him often, but when I did, I saw why our work culture was so healthy. One day I came in and saw him greeting one of the custodians. By name. He also greeted me and asked for my name, since this was probably the first time he saw me.

Once, I had to go up to his office to trace down and document the wires from his terminal to where they entered the floor, then to the LAN. This involved crawling around under his desk on my hands and knees, which made me more than a bit nervous. I tend to be a bit timid around men. To my relief, Johnny Isaakson went about his work and phone calls. He wasn’t looking at me. He was friendly, but businesslike, which was exactly what I wanted. After I finished, he asked me some questions about my work. I left feeling like a valued employee. On his desk were pictures of his wife and family. I felt happy to work for this man, a person who treated all his employees well, from computer operator to custodian.

He opened his home to all of us for an annual Christmas party, and treated us to a picnic at Stone Mountain during the summer. Our families were welcome, the food was wonderful, and he provided these activities at his expense. These things really contributed to my happiness at work.

I still remember this job as one of my favorites. I enjoyed tackling problems, most of which we were able to fix, but I really enjoyed working with all these pleasant people. Every Secretary’s Day, like all our employees, I got a rose. Like the realtors, I was allowed to get the supply room to make me a name plate for my desk, which I still have.

A fun tradition we had was, everyone contributed a dollar every month for a communal birthday ice cream cake. The folks who had birthdays that month decided on what kind of cake they wanted, and everyone got a slice on the day nearest to all the birthdays.

Little things like that helped me feel like I belonged. A lot of workplaces could take some suggestions from my experience.

[levelupwithsam]

How to Build a Profitable Digital Business in 2024

The online business landscape is changing fast—are you keeping up?

If you’re struggling to get sales, visibility, or engagement, this guide will walk you through the exact steps to build a profitable digital business this year.

The biggest mistakes new digital entrepreneurs make (and how to avoid them).

How to identify your profitable niche and ideal audience.

The must-have tools and platforms for a smooth business operation.

How to automate key processes so your business runs 24/7.

💡 Want a step-by-step roadmap to launch your digital business? Download my free guide Making Money With Digital Products.

[firefox-enthusiast]

I really really hate hate hate conglomerates and monopolies, and I most assuredly loathe that the faceless entities hafe only automation for enforcing their terms of service and no button or contact info to talk to a human.

"we employ a mid sized city in our community centers around the world and we'd never give you a chance to talk to a human."

"sorry if being cut out from what is functionally our society is an inconvenience, hope you get well soon"

"also did you read the terms? Oh you did, well your content violated our terms of service anyway "

"you want to appeal? We can give you the option but we are not going to make sure the option is accessible, you can't expect us to offer you a choice AND make sure it is working for you"

"🙃"

[canadianmoneytalk]

Video: What I Would Tell My 15-year-old Self

What would I tell my 15-year-old self: have #goals, be careful about #marriage and #cohabitation, #kids cost 300K to raise, #invest in #risky and #volatile #assets, but #diversify and let #time do the work. Stay invested and #automate the investing, and change careers instead of quiting your job to #retire. Make #mistakes, consider #debt that makes you money but avoid #consumer debt. Have…

[stockexperttrading]

Maximize Your Forex Profits: A Guide to Margin Calculators for Funded Traders Global

Discover how margin calculations are essential for successful forex trading, especially for Funded Traders Global members. This article explores the significance of margin, the risks associated with margin trading, and the role of margin calculators in optimizing trading strategies. Learn how to use margin calculators effectively, choose the right type for your needs, and avoid common mistakes. Join Funded Traders Global and elevate your forex trading with precision and profitability.

[trans-leek-cookie]

crocheting in the dark has made me much more aware of the random shit I do to get proper tension on the yarn and like. I get why machines can't do this shit how the fuck would u program that

[txttletale]

are there any critiques of AI art or maybe AI in general that you would agree with?

AI art makes it a lot easier to make bad art on a mass production scale which absolutely floods art platforms (sucks). LLMs make it a lot easier to make content slop on a mass production scale which absolutely floods search results (sucks and with much worse consequences). both will be integrated into production pipelines in ways that put people out of jobs or justify lower pay for existing jobs. most AI-produced stuff is bad. the loudest and most emphatic boosters of this shit are soulless venture capital guys with an obvious and profound disdain for the concept of art or creative expression. the current wave of hype around it means that machine learning is being incorporated into workflows and places where it provides no benefit and in fact makes services and production meaningfully worse. it is genuinely terrifying to see people looking to chatGPT for personal and professional advice. the process of training AIs and labelling datasets involves profound exploitation of workers in the global south. the ability of AI tech to automate biases while erasing accountability is chilling. seems unwise to put a lot of our technological eggs in a completely opaque black box basket (mixing my metaphors ab it with that one). bing ai wont let me generate 'tesla CEO meat mistake' because it hates fun