WHAT ARE.....LARGE LANGUAGE MODELS?

Why have artificial intelligence (AI) chatbots like OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing become so sophisticated and usable? There’s a simple explanation: Large Language Models (LLMs).

LLMs have only been around for about five years, but recently these deep learning algorithms have advanced so rapidly that you can hold natural, human-like conversations with chatbots and actually get value from them.

In fact, even though ChatGPT — the flag-bearer for this technology — only came out in November 2022, more than half of US Internet users have tried it. Granted, most folks are just experimenting. But many others are finding value in these tools for research, generating ideas, and crafting remarkably proficient essays, articles, resumes, and emails. And Wall Street has been going gaga over anything even remotely AI related, in part, because of what LLM technology makes possible.

How it works

To understand how LLMs work, think about them like black holes. The more matter they consume, the bigger and stronger they become. LLMs have similarly insatiable appetites — for data. The more they’re fed, the better they function, which is why they are called “large” language models.

LLMs are built on neural network “transformers,” which detect how words relate to one another in order to process and understand natural language inputs and spit out human-like responses. But of course, as with any AI, you have to “train” the model to be able to access, understand, and transform data. The more learnable “parameters,” or variables, your LLM has for finding patterns in data, the more advanced its reasoning capabilities will be.

The a-ha moment

While LLMs have progressively improved since 2017, they took off like a rocket in large part because of generative AI, a type of artificial intelligence that can produce original content like text, images, and video on demand. Indeed, each version of the pre-trained transformer models underlying LLMs has added exponentially more parameter capability. For example, GPT-4, released by OpenAI in March, is thought to have around 1 trillion parameters compared to 175 billion for GPT-3.5, released in March 2022. 

There are three main types of LLMs: public models available to anyone; private models developed for use within enterprise organizations; and hybrid models that use both public and custom data. For example, there are four GPT models currently available from Open AI and Azure Open AI, and they are composed of four variants ranging from 350 million parameters on the small end to 175 billion parameters for the largest. 

What LLMs mean for everyday life

When talking about LLMs, we naturally turn to souped-up search engines like ChatGPT because, more than anything before them, they are consumerizing artificial intelligence.

Anyone can download these powerful apps for free and test them out. But they’re not perfect. They can (and do) make mistakes, such as plagiarizing content or making stuff up. But like any tool, if you use them as starting points or glorified copy editors, they can offer great time savings.

That’s today. Tomorrow, LLM-like models could very well influence other prolific digital technologies. Amazon, for example, is reportedly building a more “generalized and capable” LLM for its Alexa virtual assistant. LLMs are also  influencing the direction of existing search engines and could eventually completely change the way in which users interact with them. Opera and Microsoft Edge, for example, have announced browser features using LLMs, and others are expected to follow suit. LLMs will also likely transform many commonly used apps and tools, such as translators, spell checkers, and antivirus programs. 

What’s more, they could pave the way for a wide variety of new industry-specific applications. Google, for example, is reportedly testing an LLM-based medical chatbot called Med-PaLM 2 for answering patient questions more effectively. Heck, LLMs could even play a larger role in making humanoid robots like Ameca and Sophia smarter one day.

LLM technology is changing so quickly that they could be vastly different in just a few years. All we can say for sure is: Get ready. It’s going to be an interesting AI ride that will likely surprise us all.

NETFLIX: WINDING DOWN ITS DVD-BY-MAIL SERVICE FOR GOOD

The thing about Netflix is that it's maybe one of the best examples of a business that is known today as something very different from when it started. Most people think of Netflix as a streaming video service where you pay money to watch Ryan Reynolds movies on demand or binge-watch TV shows like Stranger Things and Ozark. 

Most people do not think of it as a service where you go to a website and tell it what you want to watch and it then sends you DVDs in the mail. That is, of course, how it got started, and it's how many people first knew and experienced Netflix. I can still remember the first time I received a red envelope in the mail. 

It was kind of absurd that you could pay a monthly fee and Netflix would just start sending you DVDs. When you finished watching whatever it sent, you would mail them back and the company would send whatever was next on your list. If you'd ever paid late fees because you'd forget to return a movie rental at Blockbuster, it was life-changing. 

Even though Netflix has mostly moved on from mailing DVDs--it's even tried to kill off the business several times--it's still technically a thing you can do. At least, for a few more weeks. 

That's because, after 25 years, Netflix is finally ending its DVD-by-mail business altogether. It's a bittersweet change for anyone who likes the healthy dose of nostalgia that comes from carefully choosing which movies to put in their queue and waiting for them to arrive. 

We've known Netflix was bringing its DVD business to an end, but this week the company sent customers an email with a delightful surprise:

After 25 years of movies in the mail, we're approaching the end of our final season. We really appreciate that you're sharing movie nights with us until the last day.

Let's have some fun for our finale!

If you click below by August 29th, you could find up to 10 extra discs in your mailbox. These finale discs will be sent out on September 29th, our very last shipping day. You won't know if any extra envelopes are headed your way until they arrive in your mailbox!

There are a number of things I love about this. The first is that Netflix basically has big warehouses with lots and lots of DVDs that are about to become obsolete. I don't know if it plans on having a big yard sale to get rid of them, but as someone who has tried to sell old DVDs to random strangers who show up at your house when you have a yard sale, I can tell you they're not worth much.

What is worth far more is delighting your customers. Technically, I guess Netflix still expects the DVDs back. Still, surprising the world's most passionate DVD renters--which is what you definitely are if you're still paying money to have them delivered in the mail--by sending them 10 extra DVDs so they can enjoy them before the whole thing shuts down is really something wonderful. 

The other thing I love is that Netflix doesn't have to do this. There was no expectation that it would do anything special as it winds down its oldest business. 

That really is the point--it wouldn't be remarkable or special if it wasn't a surprise. You see, surprising your customers by giving them something they love really is the most powerful form of delight.

I don't know how many people still get DVDs in the mail from Netflix, but those who do obviously care about it very much. Maybe they don't like streaming services, or maybe their particular taste in movies goes beyond what you can find on the various streaming services available. Regardless, Netflix is using something that could be a sad ending to do something that isn't just bittersweet, it's the perfect way to say goodbye.

LEADING CHANGE: WHAT WE CAN LEARN FROM BEZOS, GATES, HASTINGS, AND MUSK

Change! Change! Change! In today's fast-paced business environment, change is the only constant. It emerges from various directions: technological breakthroughs, shifting consumer behaviors, and unexpected market shifts, to name just a few. Leaders constantly find themselves at a crossroads, faced with a critical question: Should I drive change, adapt to it, or continue with business as usual?

As a leadership coach and business growth adviser, I help my clients answer this question. But there is no one-size-fits-all answer. Rather, there are multiple ways leaders can deal with change, as the stories of visionary leaders like Jeff Bezos, Bill Gates, Reed Hastings, and Elon Musk tell us. Their stories provide diverse strategies for tackling change and often inspire those I mentor to consider their own paths.

Here are the insights and core tenets that can guide you through the ever-evolving landscape of change and digital transformation.

Failure to Adapt: Leadership Lessons From the Past

The annals of business history are filled with examples of leaders who misread the signs of impending change, leading their companies to downfall. Kodak's leaders failed to embrace the digital revolution, missing the potential of technology they had invented themselves. The result? Bankruptcy and irrelevance.

Blockbuster's executives missed the rise of online streaming services like Netflix and passed up the opportunity to buy Netflix itself. Their failure to adapt led to the company's dramatic demise.

The leadership at Nokia, once a dominant force in the mobile phone industry, was slow to react to the smartphone era. Despite being technological pioneers, their reluctance to change led to a loss of market share and ultimate irrelevance.

These examples are not mere corporate failures; they are leadership failures, dire warnings for today's leaders. The key lessons I've drawn from these leadership failures are:

• - There is always someone coming after you.
• - Complacency and overconfidence in leaders can be catastrophic.
• - The key to success is to recognize the signs and adapt.
• - Organizations don't go through fundamental change unless their leaders create a sense of urgency.
• - Change often starts small but can escalate quickly.
• - In the face of change, doing business as usual is not an option.

While these cautionary tales provide important insights, it's equally instructive to study the companies that have successfully navigated change. One such example, offering valuable lessons in leadership and business growth, is IBM.

Adapting to Change: A Leadership Success Story

Louis Gerstner, who served as IBM CEO from 1993 to 2002, shifted IBM's focus from hardware to services, saving the company from near collapse. His successor, Samuel J. Palmisano (2002 to 2011), continued to transform IBM into a business solutions company, championing innovations like the "Smarter Planet" initiative. Later, Ginni Rometty (2012 to 2020) led IBM's foray into areas such as cloud computing, artificial intelligence, and quantum computing, although critics say she was too slow at adapting to change and creating a real champion.

However, there is a lot we can learn from IBM's enduring success in the rapidly changing tech industry. It is the result of purposeful transformation steered by visionary leaders. Gerstner's vision and strategic pivot guided the company's focus; Palmisano's innovative spirit and expansion embraced new horizons; and Rometty's risk-taking adaptation moved IBM further in the right direction.

The examples of these leaders have taught me:

• - When confronted with change, you'd better craft a forward-thinking vision of how to turn the threat of change into an opportunity to grow.
• - Promote and expect ongoing innovation! Don't rest.
• - Be willing and empower your teams to take risks.
• - Adapt with agility.

Driving Industry Disruption: A Visionary Leadership Approach

As the IBM story tells us, some leaders excel in adapting to change. However, visionary leaders like Jeff Bezos, Bill Gates, and Elon Musk have gone further by actively disrupting and redefining entire industries. Bezos transformed retail with Amazon and launched AWS (Amazon Web Services), altering not only the way we shop but also the infrastructure of the internet itself. Gates, through Microsoft, revolutionized the software industry and personal computing, setting standards that have shaped technology's role in our daily lives. And Musk has disrupted multiple fields, from automotive with Tesla's electric vehicles to aerospace with SpaceX's reusable rockets, challenging traditional business models and inspiring a new era of innovation and environmental consciousness.

The stories of these industry disrupters have taught me that visionary leadership means:

• - Not merely following trends or adapting to change but committing to a future-oriented vision and setting the agenda.
• - Thinking outside conventional paradigms and shaping change.
• - Relentlessly pursuing a vision, no matter how audacious or uncharted the territory may seem.

Embracing Self-Disruption: A Radical Leadership Paradigm

In the context of disruptive leaders, Reed Hastings stands out for a different kind of innovation: self-disruption. Hastings understood early on that the greatest threat to Netflix's DVD rental model was the looming advent of online streaming. Instead of waiting for the change to impose itself or for a competitor to seize the opportunity, Hastings chose to disrupt his own business model. With a visionary move, he transformed Netflix from a DVD rental company into an online streaming giant, leading the entire entertainment industry into a new era. But the transformation did not stop there. Netflix further innovated by creating its original content, becoming a significant player in the production of movies and series.

Hastings's leadership and Netflix's business success taught me:

• - In light of change, have the courage to make bold decisions.
• - Don't accept statements like, "We should not do this. If we did, we would cannibalize our own business."
• - Embrace the potential for self-disruption as an opportunity rather than a threat.
• - Courageously innovate, even if it means redefining your organization's core identity.
• - Cultivate a culture that encourages continuous evolution and transformation.

ChatGPT: A Contemporary Example

The frontiers of disruption continue to expand, and artificial intelligence (A.I.) is at the forefront. One salient example is ChatGPT. ChatGPT's prowess in crafting human-like text opens doors to transformative applications across diverse sectors. For instance, its impact reaches customer service, where scripted responses give way to dynamic conversations; content creation, where A.I. assists in generating rich and tailored materials; education, where personalized learning becomes accessible; health care, where diagnostics, patient engagement, and personalized treatment can be enhanced; and professional services like legal and management consulting, where ChatGPT offers the potential to automate routine tasks.

The arrival of ChatGPT and similar A.I. technologies is beneficial. But it must also ring a warning bell for businesses and leaders slow to adapt or mired in complacency. The stark reality is this: Persisting with business as usual in the wake of these technological leaps is a pathway to obsolescence. The wiser approach is to recognize that emerging technologies like A.I. can disrupt entire industries, and only those who act decisively to embrace these opportunities will thrive.

Guiding Principles for Dealing With Change

In our rapidly shifting business landscape, leading change is not just an option; it's a necessity. As you navigate the complexities of your industry, consider these guiding principles drawn from the successes of visionary leaders like Bezos, Gates, Hastings, and Musk:

• - Avoid complacency and overconfidence: Today's success doesn't guarantee tomorrow's. Stay vigilant!
• - Watch out for change: Keep an eye on industry trends and competition. Be prepared to act.
• - Embrace change proactively: Seek opportunities to innovate, and evolve.
• - Lead transformation in your industry: Be the disrupter and also disrupt your own model to stay ahead.
• - Be agile: If you cannot drive change, adapt with agility.
• - Create a culture of innovation: Encourage creativity, risk-taking, and continuous improvement.
• - Be a role model and never settle: Your commitment sets the tone for the entire organization.
• - Don't consider change a threat: See an opportunity in every change.

I hope these principles will also help you lead change in a world where change is the only constant. The choice is yours: Embrace change or risk irrelevance.

BY PATRICK FLESNER, INVESTOR, LEADERSHIP COACH, AND AUTHOR OF FASTSCALING AND THE LEADERSHIP HOUSE@PATRICKFLESNER

ZOOM REVENUE AND DEPLOYMENT OF A.I TOOLS

Shares of Zoom jumped in after-hours trading Monday after the company said it expects to rake in stronger-than-expected earnings in the rest of this fiscal year.

The company, which provides video and audio chat services, raised its outlook for profitability for the 2024 fiscal year in its second-quarter earnings report. It now expects revenue to be just under $4.5 billion. That represents approximately 2% growth year-over-year.

Zoom’s founder and chief executive officer Eric Yuan also touted the company’s rollout of recent AI features on a conference call with investors. Yuan said that the company’s “aggressive roadmap” when it comes to artificial intelligence is “aimed at empowering our customers to work smarter and serve their customers better.”

ZoomIQ, one recent AI-powered feature that the company expanded earlier this year, allows chat hosts to create meeting summaries powered by AI technology.

After discussing ZoomIQ on an earnings call Monday, Yuan said, “All of those generative AI features can make the platform not only more sticky, but also more valuable.”

Zoom (ZM) recently faced backlash on social media after it updated the wording of its terms of service to potentially allow the company access to user data to train AI technology. However, Zoom’s chief product officer, Smita Hashim, assured customers earlier this month in a blog post that it would not use customer data to train AI models.

WHAT IS...….COMPUTER VISION?

Before the Facebook app could automatically tag your friends in a photo, computers had to be taught to “see.” The road to artificial intelligence must pass through perception, the ability for machines to have vision. The field of computer vision enables such sight — it’s the science of extracting information from visual data, including images, videos, and scans. It’s computer vision that helps you deposit checks at an ATM or through your mobile phone and tags your friends’ pictures on social media.

How it works

Humans make sight look effortless. A two-month-old infant can recognize caregivers. A toddler knows the difference between a four-legged chair and a four-legged dog. But computers have only recently been able to recognize things in images.

Most of today’s computer vision algorithms are based on a branch of artificial intelligence (AI) called “machine learning,” in particular, “supervised learning.” To teach the computer to recognize a chair, you label thousands of images of chairs in all kinds of angles, and images that don’t contain chairs, and feed them to a statistical model. With each example image, an algorithm trains the model, so that it can later identify a chair correctly in other images that it has never seen before.

The a-ha moment

Computer vision has been used in some form since the 1950s, when machines could process checks. For this process, called magnetic ink character recognition (MICR), to work, the numbers had to be printed along the bottom edge of the check in a specific shape and size. The advent of more muscular computing power in the 1970s enabled computer vision algorithms called optical character recognition (OCR) to recognize characters from scanned or photographed images.

Today’s machine learning models can recognize handwritten digits or faces in photographs. In the early 2000s, with phone cameras and the internet making a wealth of photographic images available, researchers built an online database of millions of everyday images, ImageNet. This library was used in an open contest to let researchers test the performance of their computer vision algorithms. In 2012, researchers from Canada debuted a deep learning model that aced the test and blew away the competition —  it could identify objects with nearly twice the accuracy of any other model. 

What computer vision is used for today

Today, computer vision enables the “lane assist” function on your vehicle and can find COVID-19 in X-Rays ten times faster than radiologists and with more accuracy. The technology also helps detect cancer earlier and enables automatic toll collection for vehicles without transponders by taking pictures of license plates and sending the owners an invoice in the mail. Computer vision helps HP find manufacturing defects on the production line and assemble the best photo collages before sending them to be printed.

How computer vision might change the world

Computer vision is the primary pillar for autonomous driving and will likely expand to more self-driving functions. Before we get there, scientists will have to understand how machines arrive at the decisions that they make and whether those decisions align with ours. In the future, expect drones equipped with computer vision to execute search-and-rescue missions in remote locations or help submarines track marine ecosystems autonomously. Computer vision might deliver more capable robots able to map their surroundings and perform basic functions with ease. A domestic robot butler? Why not?

HOW THIS FOUNDER BUILT AN A.I WRITING TOOL THAT IS ACTUALLY ACCURATE

Frustrated with A.I. hallucinations, Katie Trauth Taylor, co-founder and CEO of Narratize, built a generative artificial intelligence that helps innovators accurately articulate their ideas.

"A lot of generative AI models are criticized today because they hallucinate, they make up facts, and that is completely unacceptable inside of science, tech, and medicine, how are we going to use this exciting new technology if we can't trust what it outputs? I lead a generative artificial intelligence company in Narratize. The generative AI co-author for busy innovators doesn't take the thinking work out of writing, but it does help them articulate their ideas and bold, compelling, and mostly accurate ways. We figured out how to use calls into peer-reviewed databases and highly respected journalism databases to make sure that the content that's created is based on fact and not fiction. It's not just pulled from the wide web the way that other large language models are. That's been a piece of the innovations in figuring out how to solve for the evidence. We've truly saved lives through that work. We've just transformed health systems. We've got green lights for innovations around diabetes research. We've taken veterans off the waitlist. We've worked with scientists and helped NASA get by in for new research. It sets me on fire those sparks, those aha moments you know where ideas get told and they're told well and it's clear. And it's accurate and evidence-based and it's meaningful and it makes an impact on the world. That's what it's all about". 

HOW A.I COMES TO THE OFFICE

Until a couple of months ago, creating marketing messages for global surrogacy agency ConceiveAbilities was madness. Its staff would spend hours, even days, individually crafting and refining emails, blog posts, and other communiques, taking the organization’s main message and repositioning it to four distinct audiences: target egg donors, surrogates, intended parents seeking an egg donor, and those looking to match with a surrogate, says CEO Cathy Kenworthy. 

Then came generative AI: Now the marketing team just asks it to rewrite the statement for each subgroup, using topical contexts. Kenworthy loves incorporating generative AI into her organization’s everyday tasks, since it saves her marketing staff hours of time “so we are able to focus on pressing initiatives.”

Meanwhile, Taj Reid, global chief experience officer at Edelman, a global communications firm, says that many companies are integrating generative AI into their daily work. His firm recently partnered with a mayonnaise brand to create a campaign driven by AI. Customers could type in what was lurking in their refrigerator, and the AI would generate recipes that aimed to reduce food waste. To emphasize the human element, Edelman partnered with chef and non-chef TikTokers to lead live cooking demos of those very dishes. “That was a fun application of what AI can do,” he enthuses.

He’s also seeing companies use AI to summarize large volumes of data and creatives turning to AI to eliminate that dreaded blinking cursor. “If you’re freestyling, AI can really provide context. If you wanted to pitch a story idea to the New Yorker, for instance, you can tell the AI to pretend it is a writer for the magazine, whose audience is a busy executive. Then you can ask it to take a passage you wrote and make it more eloquent, in the New Yorker style... and it returns a polished pitch.” And, he adds, AI capabilities and use cases are expanding every day. “Today is the worst AI will ever be,” he says. 

“I don’t believe that AI will take your job. I believe that people who know how to use AI will take your job.”

— Taj Reid, global chief experience officer, Edelman

It seems like every day there’s a new generative artificial intelligence, from Bard to Bing to another iteration of OpenAI’s ChatGPT, coming to market — along with countless, breathless headlines hyping its benefits and infinite possibilities. And while asking an AI trained on a large language model (like ChatGPT’s use of the entire internet, through May) to compose a poem for Father’s Day may feel magical, less media real estate has been devoted to workplace use-cases of this emergent technology. 

Sure, generative AI can be glitchy and certainly requires some company-wide guidelines, but this creative tool est arrivée, and thought leaders need to be considering how to employ it for the enterprise — whether that means generating meeting agendas, turning notes into presentations, creating sales or marketing assets, framing a pitch, or outlining a business plan in seconds. They also need to recognize its potential pitfalls and set some ground rules.

Old technology, new use cases

“AI is not new, it’s been around for decades,” says Scott Hallworth, HP’s chief data and analytics officer. “Chatbots and Siri and Alexa and Grammarly came about a long time ago. What’s different now is the speed and scale, and the ability to optimize the training model.” Hallworth spends his days thinking about AI and has found a few talents at which technology excels. 

Firstly, he says, generative AI — which essentially recognizes patterns — is very good at search, especially within a locked company ecosystem. “Documents, code, libraries, whatever it may be, you can now search for that fast and with precision,” Hallworth explains. Content creation is the second-best skill of a generative AI. “It’s very, very good at translation, for example, which once took hundreds of hours for an international company like HP, especially when trying to get the colloquialisms right.” Coding, too, can easily be generated by an AI as a starting point, since code is typically black and white, right or wrong, says Hallworth. 

While companies are finding massive time-saving use cases for AI, individuals in advanced roles, too, are finding ways to enhance their own skillset, he says. “It’s an augmentation and an accelerant… you still have to edit and review its output, but the speed in which I can get something done is a lot faster than if I’m sitting there staring at a blank sheet of paper.”

Pitfalls on parade

Scores of journalists have reported on the “hallucinations” that an AI can suffer, which is a consequence of the tech having such a talent for guessing. Less discussed are privacy issues. While Hallworth and Reid are big advocates for using generative AI to speed up and simplify work tasks, both believe companies need to set some rules of engagement before letting employees run amok with the tool. “I wouldn’t put any sensitive information into an AI like ChatGPT now, because you are offering that up to an open community,” says Reid. It’s not as though the AI companies are actively stealing your intellectual property, he explains, “but there is potentially an engineer who might be looking at inputs of large datasets and come upon your next, secret product launch. So be very careful when you use tools that are open source. My recommendation is not to plug in personal or sensitive intel into an AI until someone in your company has vetted that specific AI and whatever it is you’re inputting.” 

Hallworth’s approach is more structured and safeguarded. He allows employees to request a private “sandbox,” to which each user has a specific key, permission, and access rights. The data is synthetic or a sampling and used to see if an idea has any legs. “The key thing is that the sandbox operates like any other product funnel. You’re going to go from ideation to exploration to development to creating code,” he says. “And if there's any there there, you pass through a series of gates into a production environment to then be monitored and managed with all of the company’s cyber and legal controls.” If they don’t make it past that gate, the sandbox and all its data is permanently deleted after a relatively short period.

In the not-so-distant future

Reid foresees a time when an AI can generate designs that can be 3D printed at scale and deliver goods quickly to those who want or need them. He sees a nearer future when workers can use AI to understand the other side of an argument or ensure their product and practice and messaging adhere to the most recent laws, as ConceiveAbilities' director of content marketing Marci Hughes recently did. 

“I used a generative AI to share our blog about New York State laws, tying it in to Andy Cohen’s recent headline news about his journey with one of the first surrogates in New York state,” she says. “I typed ‘Andy Cohen Surrogacy Laws New York’ into an AI caption writer, then tweaked the post based on our needs.” 

The upside for workplace use cases is almost limitless, and nothing to be afraid of, insists Reid. “I don’t believe that AI will take your job. I believe that people who know how to use AI will take your job,” he says.

He reminds his team that we’ve been here before: new technology comes along, everyone panics that they’ll soon be irrelevant, and even better jobs are created. The most critical advice he has for leadership is to allow their teams to engage with this new tech, to get to know its best uses. 

“It’s critical that people know about AI and immerse themselves in it at work, though safely,” he says. “I see AI as an accelerant, of both good and bad ideas, but if you don’t participate, you will miss out. It is very much of the now.” 

BUILD A GREAT ONLINE CONTENT CREATION STRATEGY

"When you create social media content, what does your creative process look like?"

This anonymous question came in from a reader to my Q&A form. I'll happily answer because I suspect that others struggle with:

1. Overthinking this.

2. Spending too much time on it.

3. Not doing it enough/at all because of #1.

I've tried a lot of strategies over the years to do this and it's an ever-evolving process for me, so ask me again in six to 12 months, and I'll bet my answer will have changed as tools, platforms, algorithms, and priorities evolve (hint: yours should, too).

My current content creation process goes like this:

Start with the type of content that comes most naturally.

For me, that's long-form writing. Therefore, I write these precise emails first, one month at a time. Meaning: I block time once a month to write next month's content. I keep a running list of ideas and am very clear on my content pillars. Once those are written, I send them to my assistant to have them edited, formatted into our email platform, and sent as tests for me to review and approve.

Repurpose!

I've chosen two social media platforms on which to focus primarily because they align with where my audience is and because I enjoy them. Those are Instagram and LinkedIn. My assistant takes the lead on LinkedIn, asking if there are any themes or topics on which I want to focus. Otherwise, she knows my content and repurposes content from 12-plus months ago and freshens it up (past posts, posts from Instagram, past articles, newsletters, etc). She creates one running document and sends one month's worth of content to me for approval. She then schedules those to go out ahead of the next month. 

For Instagram, she takes my long-form writing, turns it into carousel posts using pre-approved brand templates we store in Canva, and makes me one month's worth of those. I write the captions and schedule them in Later (platform for content planning). I intersperse those with pictures and captions that are inspired by things that happen in real-time to keep the content fresh. I also leverage inspiration from client interactions and life in general to make Instagram stories. Those that perform well, in turn, often influence upcoming newsletters I write. And then the cycle begins again.

A few general tips to consider

• - Find a balance that works for you.
• - Start with one platform.
• - Don't be afraid to start, try, and iterate.

Repurposing can also look like:

• - Taking videos and cutting them into smaller ones
• - Splicing the audio out of a video
• - Taking the audio and transcribing it into written form
• - Making long-form short, or short-form long

Utilize tools that help (templates, A.I., outsourcing for the parts you don't like or where your skills are not as strong).

Figure out what balance of structure and pre-planning/scheduling works for you. Very few people are able to stay consistent with making content only when the muse strikes. 

Keep in mind that about 6 percent or less of your audience on Instagram see any given post (likely similar on other platforms). And your open rates on emails are likely between 20-50 percent depending on the size of your list. What this means is you shouldn't be afraid to use the same content again when time has passed, and/or to share the same message in a different way. 

You don't have to master a content creation system all at once. Take it one step at a time.

Make sure whatever you decide to do is because it supports a bigger strategy in your business, not because you think you 'should' do it.

I encourage you to take a moment to consider how, if at all, these insights might inform your strategy going forward, and then make a digestible action plan to make it happen.

BY DARRAH BRUSTEIN, 4X FOUNDER, AUTHOR, AND COACH@DARRAHB