FOUR STRATEGIC-PLANNING QUESTIONS THAT WILL KEEP YOUR BUSINESS AHEAD OF THE A.I CURVE

When people talk about the growing use of A.I. in organizations, the focus is often on productivity gains, workforce restructuring, and ethical usage. All of this is extremely important, but A.I. can also have an enormous impact on dynamics within organizations, and these must be factored into any plan to adopt A.I. Otherwise, organizations may lose out on many of A.I.'s expected benefits and find themselves unprepared for future needs and shifts in the labor market. 

Eighty-three percent of executives say A.I. is a strategic business priority, meaning that how and why companies implement it will have far-reaching effects. Whether a business has already started using A.I. or has plans to adopt it more widely, it's essential to create an overarching, risk-aware A.I. strategy to avoid pitfalls and achieve the best outcomes. Before developing that strategy, leaders need a clear picture of where they're starting from and where they're hoping to go. These four questions can help bring that picture into focus.

1. Who in the organization is using A.I. and why?

While worker displacement is a common fear, generative A.I. could have an equalizing effect on the labor market, offering a solution to the talent gap between open jobs and available workers. 

In a recent study published by the National Bureau of Economic Research (NBER), less-experienced, lower-skilled customer service reps trained to use a generative A.I.-based conversational assistant saw larger gains in their job performance than more experienced or higher-skilled workers. This means companies that are having a hard time finding candidates may be able to lower skill and experience requirements for certain roles, train the people in those roles to use A.I. assistants, and achieve performance outcomes that meet or exceed their original expectations.

However, a recent BCG study revealed that 80 percent of leaders say they use generative A.I. regularly, while only 20 percent of non-management employees say they do. This suggests that overall, there is a disconnect between those who should use it and those who do. An A.I. strategy should go beyond figuring out what job functions can be replaced and enhanced by A.I. to include research-based assessments of which workers can benefit most, and achieve greater productivity gains, from investment in A.I.

To answer this question, organizations also need to know what they're starting with. A potential pitfall here is that workers may be hiding their use of A.I. and keeping their productivity gains quiet out of concern that they're training their replacements. The impact of these tools on performance could be transformative for a company, but no one is analyzing or measuring it. 

It also means no one is setting policies or standards for the use of the technology, which could have risky implications. A GitHub survey recently revealed that 92 percent of developers admitted to using A.I. coding assistants. At the same time, a November 2022 Stanford study showed that developers who use A.I. tools to help solve security-related tasks wrote significantly less-secure code than those who don't.

It's critical, therefore, that companies thoughtfully audit A.I. use throughout the organization, incentivize transparency, and embrace and reward productivity gains enabled by A.I. By doing this and getting the full picture, businesses can set appropriate policies, project realistic expectations for A.I. adoption, and develop a holistic strategy that deploys A.I. resources where they'll have the greatest impact. It's also crucial for organizations to manage the transition effectively, provide adequate support, and address any concerns or anxieties employees may have about implementing A.I.

2. What long-term business objectives is the company trying to achieve?

While much of today's focus is on the potential impact of generative A.I. on daily work, "more productivity" shouldn't be an end in and of itself. Organizations need to think about developing and implementing an overarching A.I. strategy that encompasses all business functions -- and how that can benefit workers and the business. 

It would also be unwise to implement A.I. to achieve short-term cost savings by lowering head count -- the real power of A.I. isn't in how well it can do a person's job for them, but in the potential for long-term gains in performance and innovation when people and machines work together. McKinsey forecasts that A.I. automation could eventually take over as much as 70 percent of worker hours, but it should be obvious that this does not mean replacing that proportion of the workforce -- it means extending what humans can do.

By automating mundane and repetitive tasks, A.I. allows employees to spend more time on meaningful and fulfilling work. This may require upskilling these employees to focus on the skills for tomorrow -- another win for the employee and organization. This could be particularly beneficial for workers who want to take on new challenges and contribute more but have become disengaged from their company for lack of those opportunities. Instead of letting those people go or waiting for them to move on, a company can re-engage these employees and the capabilities they were hired for, reducing turnover costs and retaining valuable institutional knowledge.

Rather than replacing humans, A.I. should augment human capabilities and enable collaboration between humans and machines. Jobs that require human judgment, creativity, emotional intelligence, and critical thinking are likely to be in higher demand as they complement A.I. capabilities. This can lead to the emergence of hybrid job roles that combine A.I. expertise with domain-specific knowledge and human skills.

3. What roles will the company need in the future to thrive?

As A.I. adoption grows, some roles will disappear, some will emerge, and others will evolve to account for A.I.'s impact. As companies develop their overall A.I. and workforce strategies, one of the biggest challenges will be to guess what the new roles will look like and how workers can be upskilled to fill them.

The company Eightfold A.I. used extensive workforce data to help decipher which roles will be in demand, which skills they'll require, and which adjacent skills give people the greatest potential to learn the skills needed for the rising role, thereby opening the talent pool for businesses to get the right talent. 

For example, we know that product owner will soon be a top role as it focuses on the tactical engine driving the development of A.I.-powered products. This role is a highly specialized version of a general software product owner role, and it's most common in organizations that use Agile methodology. According to Eightfold data, if machine learning, Hadoop, and AWS are primary skills for this role, people with any of the adjacent skills, like experience in algorithms or data science, could potentially upskill to do this role. 

4. How well does the organization understand the risks and challenges of A.I.?

A.I. programs are value-neutral, but how they're used isn't. Businesses must know how and where A.I. is used because under-informed or unethical use can expose a company, an industry, or a whole economy to serious legal and security issues, not to mention have a terrible effect on employee morale.

Organizations need to ensure transparency in A.I. decision making processes, address biases in A.I. algorithms, and establish guidelines for responsible A.I. use. Additionally, ethical challenges related to data privacy, security, and the impact on job security need to be proactively addressed.

A.I. combined with upskilling and strategic planning can future-proof a business, but the future is coming faster than we might think. No matter where an organization is in its A.I. adoption journey, it will be extremely important to proactively develop an overarching plan that accurately assesses needs and thoughtfully deploys resources for the best outcomes.

BY SANIA KHAN, CHIEF ECONOMIST AND HEAD OF MARKET INSIGHTS, EIGHTFOLD AI@SANIAKHANLAIQUE

WAYS CUSTOMERS CAN FINANCE YOUR BUSINESS

Do you need money to keep your business running? If so, who will provide it, and on what terms? In my 2012 book, Hungry Start-Up Strategy, I highlighted many answers to these questions. I think the best source of small-business financing is to sell a product customers find valuable enough to pay a price that enables your business to operate profitably.

As soon as you involve others -- such as venture capitalists or banks -- in your business, you give up control in ways that can put your company at risk. For example, venture capitalists often require you to put their partners on your board and to give them the power to replace you as CEO. Banks can charge high loan rates and liquidate your assets if you don't keep up with the payments.

It is not easy for business leaders to operate profitably from the start by selling their product at a price that ensures the company's positive cash flow. Happily, there are other ways that leaders can give customers an opportunity to finance their business.

Here are three other good -- if imperfect -- ways for customers to finance your business.

1. Sell equity to your customers.

While the details are hard to pull off, private companies can sell equity to their customers. 

A case in point is BrewDog, a Scottish beer company, whose CEO, James Watt, is a lawyer. As I wrote in 2010, BrewDog couldn't get a bank loan in 2009 because of the weak financial markets. So the brewer set up a website where BrewDog fans could buy shares at a minimum investment of $361. The idea ultimately raised $1.2 million from 1,500 investors after making the investment required to comply with British financial regulations, which included providing a full audit on its accounts.

By March 2012, BrewDog's products were available in over 27 countries, and 65 percent of its $11 million in 2011 sales came from outside the U.K. By 2022, its sales had risen to about $413 million, according to Scottish Financial News.

2. Encourage customers to participate in a crowdfunding campaign.

Crowdfunding, in which customers part with their money in exchange for early access to a company's products, has many advantages for companies. Here are some Fundera 2022 crowdfunding statistics I found interesting:

• Crowdfunding generates $17.2 billion in North America annually
• In 2022, funds raised through crowdfunding increased 33.7 percent
• 2022 hosted 6,455,080 worldwide crowdfunding campaigns 
• Successful crowdfunding campaigns raise an average of $28,656
• The average crowdfunding campaign raised $824 in 2022
• Only 22.4 percent of crowdfunding campaigns succeed

It also involves some disadvantages. Most notably, the customers who crowdfund a company that is acquired might feel tricked when they realize too late they never owned equity. 

According to USA Today, people who contributed $2.4 million to fund the September 2012 Kickstarter campaign for Oculus Rift's virtual reality headset were shocked in March 2024 to realize they would not receive any of the $2 billion Facebook spent to acquire Oculus.

Here is what John Wolf, Kickstarter contributor, wrote: "I would have NEVER given a single cent of my money to Oculus if I had known you were going to sell out to Facebook."

3. Sell bonds to customers without giving up equity.

Another way to finance your business is to sell "bonds to hundreds of customers and community members, with some investing as little as $10," according to the Wall Street Journal.

The good news about this approach is that local restaurants or retailers can raise capital from their customers and community members. What's more, if they buy regularly, they have an incentive to keep your company afloat.

The ability to raise money in this way is relatively recent. It took three years after passage of a 2012 Jobs Act provision for debt-fundraising platforms like Mainvest, Honeycomb Credit, SMBX, and WeFunder to get off the ground. Since 2021, more than 700 companies have raised money through debt or revenue sharing on these platforms.

The challenge for companies is the high interest rates they must pay on the bonds. For example, as the Journal reported, in April 2023, Palm City Wines raised more than $400,000 by issuing a small-business bond paying 9.5 percent interest on customers' investment monthly over five years. 

Palm City Wines co-founder Dennis Cantwell was happy customers could finance the retailer. "This gave an opportunity for people who supported us [by purchasing our product] to support us in another way," Cantwell told the Journal. 

If you can handle their negative aspects, these four approaches to customer financing may keep your business growing.

BY PETER COHAN, FOUNDER, PETER S. COHAN & ASSOCIATES@PETERCOHAN

HERE'S WHERE THE SMART A.I MONEY IS GOING NEXT

The crazy thing about what a lot of folks are calling artificial intelligence these days is that it's not so much intelligence as it is a question-and-answer routine with some very powerful "magic behind the curtain."

And there's nothing wrong with that. But let's peek behind that curtain a bit, because the magic is what makes the money.

The dumbest explanation of AI ever

My machine-learning friends will drag me for this, but the whole concept of artificial intelligence dumbs down to three simple actions: Gather input, make decisions, respond. 

The "intelligence" part is something we humans do a million times every day. For example, if you've read this far in this article, you made a decision to read it, then you'll either continue to read it or click away. 

The "artificial" part is something computers have been doing since they were invented. For example, you hit the power button, the computer receives binary input from a mechanical switch, then responds by firing up its bootloader to turn itself on. 

My computer scientist friends will probably drag me for that, too. 

However, the neat thing about human decision making is that it's inconceivably fast. You gather uncountable bits of input data while reading this article, from how much sleep you got last night to your own personal judgment call on every similar article you've ever read, and even the meaning you derive from each of the first few hundred words that I used to get you here. You do all this at blinding speed.

Today's computers are getting faster and more accessible. They can gather input, make decisions, and respond in nanoseconds. That's the magic currently printing the money. But if you want to follow current money to future money, the question is: How much data is being input?

And the answer is "excruciatingly little," at least when compared to our brains, our experience, and our environment. 

Better answers require better questions

When you think about "strong" AI versus "weak" or "narrow" AI, the difference comes down to the complexity of the question asked.

This is a concept I explored often at Automated Insights, where I helped invent the first publicly available Natural Language Generation engine in 2010, then spent the next seven years teaching machines to answer questions of increasingly broader complexity. 

We started in sports, so to give an example of the complexity scale, let's use once-in-a-lifetime baseball player Shohei Otani:

Did Shohei Otani hit a home run last night? That's an extremely simple question for a computer to answer, binary for the most part, and doesn't require any external or adjacent data. It's yes or no.

When we ratchet up the complexity -- How did Shohei Otani play last night? -- That requires much more processing. Did he play last night? Did he bat? Or pitch? Or both? How did he perform at each? How did that performance compare to his norm? How did it impact the game? 

Plus you need a lot more data to answer what seems like a simple change in the question. You need data from Otani's career, all the data from the game down to each pitch, all the data from all the players in the game, even adjacent data like injuries, time of day, and weather. 

Imagine the complexity needed to answer this much more important question (at least in a business context): What is Shoehi Otani's value as a baseball player? 

The answer to that question can mean big monetary differences for a lot of people, beginning with but certainly not ending with Otani himself. And I can assure you that you need a shedload of data to even begin to answer that question.

Getting to the right question

I advise entrepreneurs at startups and innovators at large companies. I've been doing this for decades, and about three years ago, I started trying to automate the question and answer part of giving advice (not automating the answers themselves, that's a whole other keg of fish). 

Anyway, I'm trying to solve one of the first tricky issues I learned about in my advising career, that coming up with the right answer is nowhere near as difficult as coming up with the right question. I get asked a lot of questions, and truthfully, most of the ones that come out of the blue are not especially valuable, because there is no complexity to them. 

Consider a question like, "How do I succeed with my startup?" That's too simple a question to be able to provide any helpful answers. But as the complexity of the question goes up, I can bring my wealth of data -- experience learned over decades -- to the forefront to generate a better, more helpful, more valuable answer. I can't do it in nanoseconds, but at this point, neither can any machine.  

The smart money is on asking the right questions

As people get better at creating long, complex, well-formed prompts for GPTs to respond to, that's starting to become a science in itself. But even now, the best advice you'll find on creating better prompts is essentially trial-and-error -- which makes me think of optimization by evolutionary algorithm, which is just fancy trial and error.

Even more limiting, the size and breadth of the models and the processing required to provide the most valuable answers to the most complex questions are still not there, at least  in a way that doesn't produce an error or nonsense at a certain level of complexity. 

The next wave of generative AI will be able to provide specific answers to unique and complex questions. The magic will be found in automating the creation of those questions.

BY JOE PROCOPIO, FOUNDER, TEACHINGSTARTUP.COM@JPROCO

WHAT ARE.........DIGITAL TWINS?

BMW Group has started work on one of the most ambitious car factories in its nearly 100-year history, set to open for production in 2025. But this factory isn’t filled with assembly lines and car parts — its contents are entirely virtual. The German automaker is unveiling a completely digital facsimile of a real, carbon-neutral plant that is expected to produce 150,000 vehicles per year and employ 500 people.

Known as a “digital twin,” the world’s first entirely virtual factory will use technology from Nvidia to test and eliminate kinks in the designs, machinery, and processes that are expected to be deployed in the future Debrecen, Hungary plant — saving time, money, and thousands of headaches.

It is a classic example of how digital twin technology is allowing its human counterparts to gain all sorts of insights into physical objects to radically improve business and operational efficiency.

How it works

Think of digital twins as models that duplicate physical objects to help you visualize things not yet built, track the performance of existing items, and train workers on how to use equipment safely and efficiently. 

For existing parts or products, digital twins work by integrating connected sensors into the physical component and collecting real-time data on its performance. The data collected by some sensors is analyzed by AI and machine learning algorithms to identify patterns and make predictions about the item’s behavior. For example, in manufacturing, an engineer or operator based in California might remotely collect data about the temperature, pressure, and vibration of machinery located in Pittsburg. She then could run a variety of simulations on its digital twin to identify ways of optimizing performance, minimizing equipment downtime, and making real-time adjustments to keep everything running smoothly.

Digital twins can also simulate the performance of product prototypes. By integrating data from various sources, such as CAD (computer-aided design) files, you would create a working model for testing various product attributes. Automakers, for example, might simulate the aerodynamics of a car and optimize its design for maximum fuel efficiency. Similarly, an aerospace company might digitally replicate the behavior of a new plane in various weather conditions.

The a-ha moment

The history of digital twins can be traced back to the US space program of the 1960s when NASA used something called pairing technology for remotely improving the operations and maintenance of its space vehicles and systems. In fact, NASA used this technology to simulate solutions for returning its crippled Apollo 13 module and crew to Earth in 1970.

The specific concept of digital twins was later articulated by David Gelernter, a Yale computer science professor, in his 1991 book Mirror Worlds: or the Day Software Puts the Universe in a Shoebox. But Dr. Michael Grieves, formerly of the University of Michigan, is widely credited with applying the concept of digital twins to product lifecycle management (PLM) in 2022. He theorized it was possible to have all information related to physical objects residing “within a digital representation.”

What it means for everyday life

Digital twins are already improving design, maintenance, and training in just about every industry. Engineers are using it to improve jet engines. Doctors are using it to map activity in the human heart and simulate how patients might react to various treatments. The technology has been employed to optimize production in oil and gas fields and has even improved the performance of Formula 1 race cars.

How it might change the world

As Industry 5.0 technologies continue to evolve, it’s likely most — if not all — parts and products will have a computer doppelganger telling people how to make improvements or use them more efficiently. Digital twins are also seen as invaluable tools for urban planning (for example, smart cities). Heck, some people think human beings will have digital twins connecting with technology we wear to help doctors, clinicians, and even physical trainers remotely model and track what’s going on in our bodies at any given time.

5 TOP BLOGGING PLATFORMS FOR EVERY KIND OF BUSINESS

Posting about your brand on social media has become an essential part of any business marketing plan. But if you've let your blog lapse because longer-form content seems like too much of a commitment, you're missing out on a major opportunity to connect with your audience -- and convert them into customers. 

At the content marketing agency I co-founded, I've seen just how incredibly powerful (and successful) blogging has been for clients like OXO and Nutanix that incorporate it into their business strategies. In fact, 68 percent of marketers find blogging more effective than it ever has been, according to data tracking tool Databox; brands that post content on blogs produce about 67 percent more leads than those that don't. That's because blogging can be one of the best ways to drive visitors to your site, whether it's through organic search or the call to action you include in your posts on another platform -- I like to think of both of those as free digital foot traffic. 

If the idea of having to write blog posts regularly feels overwhelming, keep in mind that one of the best parts about blog content is it can be repurposed in so many ways. You can rework blog articles into social media posts, LinkedIn thought-leadership pieces, and editorial-style newsletters, helping to fill several channels at once and reach different audiences. Even if you don't have the time to post very often, as long as you do it with some regularity, you'll build a body of work that serves to tell your brand's story and lets customers feel more connected to you. 

There are lots of excellent blogging platforms out there, and they serve different needs. These are five of my favorites.

1. WordPress

This might be the first site you think of when it comes to blogging. That's because WordPress made a name for itself in the early days of self-published websites and blogs. Now, 43 percent of all websites are built using the platform.

WordPress has ready-made themes and layouts but also has a treasure trove of customization options. It's easy to manage and maintain, thanks to the number of tutorials, and also has plug-ins that can help you drive sales, create newsletters, and more. The platform supports various types of media, so if you want to spice up your blog posts with images and videos, WordPress can handle it. Another plus is that most creators already know how to use the platform, so if you're thinking of hiring someone to help write your blog posts, they'll most likely be able to jump right in--no training required.

WordPress is best for those who want heavy customization, greater control over the function of the blog, and search engine optimization features. You can set up a site for free if you don't mind the ".wordpress.org" tacked on to your URL. If you'd prefer your own domain name, you can do that starting at $4 a month.

2. Wix

If you're not too concerned with customization, Wix is the platform for you. The drag-and-drop builder plus the ready-made layouts mean you'll soon be able to get down to writing. The platform is optimized for mobile, so once you get your feet wet, if an idea for a post strikes you, you can write and publish even when you're on the go.

Though Wix wasn't always known for good SEO tools, a recent update means you can now optimize your blog posts. The paid plan is free for the first year and $22 per month thereafter. So you can play around and get up to speed at no cost, and once the paid plan actually kicks in, you may already be seeing the ROI.

3. Squarespace

Squarespace is the place for e-commerce businesses that want to leverage content to help them reach potential new customers and boost sales. And Squarespace is one of the best platforms for e-commerce functionality. With its easy-to-use platform (like Wix, it is drag and drop) and e-commerce features (including integrated shopping carts and product pages), Squarespace is ideal for that combination of selling products while sharing your brand story. Prices start at $16 a month, but the platform does have a free trial, so you can give it a test drive before committing. 

4. LinkedIn

You may be surprised to see a career platform on a list about blogging, but you can easily create "article" pages from your own personal account or business page. It's as simple as typing up your article, choosing a header image to go with it (always a best practice to include an image!), and clicking publish. 

I personally use LinkedIn as my blogging platform because it has the best engagement with our client base at Masthead Media and has allowed me to build a stronger connection between the LinkedIn community and my company. I highly recommend it if you already have a large following and if SEO isn't your top priority.

5. Medium

If writing is something of a passion for you, and you like to share insights and opinions about your industry, Medium is your platform. Unlike WordPress and Wix, Medium won't give your company a homepage with a unique URL, but it comes with an already-engaged audience who receive a daily email promoting the best new stories posted to the site. You simply write your piece and publish it, and it has the potential to be shared with millions of readers. 

The platform has also rolled out a new payment model whereby popular pieces can earn you money. So if your blogging objective is to share your thought leadership with a broad audience, check out Medium.

BY AMANDA PRESSNER KREUSER, CO-FOUNDER AND MANAGING PARTNER, MASTHEAD MEDIA@MASTHEADMEDIA

HOW TO MAKE THE SHIFT FROM KNOWLEDGE WORKERS TO KNOWLEDGEABLE WORKERS.

I think we've reached peak fear-mongering for A.I. as it relates to taking jobs from hard-working folks. Maybe they can still paint a little bit more dystopian hellscape to squeeze the last of the clicks out of us, but if you haven't already found yourself in a dark moment wondering how to safeguard your position against the machines, well, you might just be a machine yourself.

That's a joke. I like breaking the ice with a joke.

But what I really want to talk about is the one skill you need to A.I.-proof what your company gets paid to do.

And the good news is, a lot of you are already doing it.

If You Can't Beat Them, Please Don't Join Them

Here's what I don't want you to do. 

I don't want you to run off with a half-cocked understanding of how Generative A.I. is hitting the mainstream and then come up with some expensive and useless use case of someone else's tech that is only going to make you more disposable and someone else a little more rich.

Yeah kids, the GPT gold rush is over. It happened quick, like when a lot of folks were still buying NFTs. But Google, Microsoft, and Amazon are involved now, they'll take it from here, like the FBI commandeering jurisdiction from the local sheriff. So unless you plan on getting deep into the deep tech, don't try to start a dot-a-i copycat.  

I've been involved with Natural Language Generation (NLG) and Generative A.I. since 2010, and to be totally transparent, I still need a bunch of refreshers on the tech to get me back up to speed on the low-level code. 

But to be even more honest, I'm not burning a lot of midnight oil trying to figure out the syntax that's all of a sudden making people with money throw that money at people who are getting machines to do what they've always done, only faster. 

I'm more concerned with coming up with the right possibilities to maximize those advances. 

GPTs Are Hitting Us Like Bricks

That's happening and it's not going to change. 

Generative A.I. has pretty much ruined the quest for unique human insight. It's threatening the entire creator economy. And it's got its sights set on knowledge-workers, supporters, salespeople, and even coders. 

In one of those articles (I honestly can't remember which), I referenced the need for suboptimal humans like you and me to change our skillset from "knowledge worker" to "knowledgeable worker."

Again, rooted in one of those articles, is the difference between the two terms. When my previous NLG company started automating quarterly earnings reports for the Associated Press, allowing it to increase its output from 100 articles per quarter to 4,400, not one AP journalist lost their job. This was because the AP, in advance, understood and promoted the shift of their journalists' skillset from knowledge worker to knowledgeable worker.

A shift, by the way, their journalists were not only ready for but rejoiced in.

A journalist who regurgitates insights from the data drop of a public company's quarterly earnings release is a knowledge worker. Generative A.I. is very good at regurgitating insights from data at blinding speed. Even more blinding today. 

A journalist who understands those insights, can reflect on them and put them in context, talk to the people behind them and the people they impact, and do all that in a way that captures the attention of the people who need that information the most -- those are knowledgeable workers.

The former got taught what to do by some editor or manager and never looked beyond that. The latter developed the skills that turned into the talent that is the reason for journalism in the first place.

You Can't Be Great at One Thing Anymore

Here's the Generative A.I. tectonic shift in a nutshell. 

Conventional wisdom and advice has always taught the innovator to focus on being great at one thing. Get really good at it, spend 10,000 hours on it, and then build on that greatness. That's the path to success.

That's no longer true.

Generative A.I. -- in fact all A.I. -- is really good at doing one thing quickly, accurately, and unapologetically. 

The era of the knowledge worker is not coming to an end. The era of mastering the niche skill is. 

But this has been true for years, maybe decades. Anyone can create content. Anyone can analyze data. Anyone can answer questions. Anyone can sell product. 

Now machines can too.

The skill you need to have is understanding the connection between all those simple, repeatable, automatable tasks and figuring out how they become the talent that the "artificial intelligence" is trying to mimic.

BY JOE PROCOPIO, FOUNDER, TEACHINGSTARTUP.COM@JPROCO