Science & Technology

Today, most people take their checkout experience at the supermarket for granted. They push up their carts, unload their groceries and watch as the cashier scans item after item. It just makes sense, so much that older generations hardly remember the days when products were stamped with prices to be rung up manually, and younger ones rarely consider a world where that was the norm.

That’s because the Universal Product Code (UPC) — the barcode used on every product in grocery and retail stores all over the globe — changed everything 50 years ago. Barcodes are scanned billions of times each day, and award-winning engineer Paul McEnroe, who spent more than two decades in leadership roles at IBM, assembled and led the team that transformed the technology from an idea into the reality that endures.

But McEnroe’s role in developing the barcode hasn’t been fully acknowledged. The UPC’s unpatented technology is in the public domain, so McEnroe earns no royalties from the invention, and a quick Google search for “who invented the barcode” turns up a Wikipedia page lauding someone named Norman Joseph Woodland.

So, what happened?

It’s a big question that McEnroe is eager to answer in his memoir The Barcode: How a Team Created One of the World’s Most Ubiquitous Technologies, forthcoming from Silicon Valley Press on September 19.

Image credit: Courtesy of Smith Publicity

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Entrepreneur sat down with McEnroe ahead of the memoir’s publication to learn more about how the UPC came to be, where the confusion over its inventor stems from and what advice he’d give young leaders who want to change the world like he and his team did so many years ago.

“We could come up with some kind of a symbol to be read optically or magnetically.”

It all started in 1969. IBM wanted to explore growth opportunities and buy startups, but the company was told it wouldn’t succeed. “Everybody important, all the inventors and so on, are going to quit the next morning because they don’t want the white shirts, blue coats, red ties and black wingtip shoes that was the culture of [IBM at] the time,” McEnroe says.

Determined to innovate despite that reputation, IBM “decided to paint an imaginary red line” around part of the company and charge McEnroe with leading it — so he did. The Dayton, Ohio native grew up “in the shadow of the National Cash Register Company,” which had a monopoly on checkstands worldwide, and he knew there was a better way to do the “big, old, cast-iron cash register’s job.”

People in the supermarket and retail sectors agreed. There was a common refrain: We need to do a better job of inventory control. We’re spending a tremendous amount of money. Marking the price on every item in the store takes a lot of time, and as soon as we mark the prices, they’re wrong.

McEnroe was well aware of technologies that could address the problem. “We could come up with some kind of a symbol to be read optically or magnetically,” McEnroe says. He and his team would do both — though the optical one “has taken off the most.”

The recently invented laser provided a directional and consistently distributed light source, and McEnroe was familiar with the technology, having used it in previous projects. The low-cost, low-power, safe option offered an opportunity to make progress on the UPC.

Related: IBM Says 7,800 of Its Roles Could Be Replaced By AI | Entrepreneur

“So, I went to [IBM], just like you would go to a venture capitalist today in Silicon Valley or Boston or wherever, and I proposed that we go after this business,” McEnroe recalls, “that we build equipment that would fit in the stores, inventory equipment that would sit in a warehouse and control equipment that would sit at the headquarters. And it would all work together.”

The technology would use item identification for automatic reordering and listing on the display and cash register slip, featuring the name of the product purchased, how much it cost and any other necessary information.

McEnroe requested three years of funding from IBM: $300,000 for the first year, $1,000,000 for the second, and $3,000,000 for the third. The company agreed and asked McEnroe to do the job in North Carolina, where it had just built a plant that wasn’t yet filled with production equipment. So McEnroe made the move and hired six people for his team.

“I think it’s been very good for society because nobody has had to pay anything for the use of the code.”

The following year, in 1970, the National Association of Food Chains (NAFC) formed a committee to examine the problem of item identification, hiring consulting company McKinsey to help it do so. By 1971, McEnroe and his team had developed their code, and in 1972, the supermarket committee asked all interested companies to present proposals for how they would automate supermarkets, including item identification.

Those who submitted codes had to agree to donate them to the public domain before they could be accepted, which meant no patents or royalties for their inventors. As a result, McEnroe and his team didn’t expect to see any money from the UPC. “That’s the way it’s worked out,” McEnroe says. “I think it’s been very good for society because nobody has had to pay anything for the use of the code.”

McEnroe and another team member named Jack Jones share the patent on the pistol-grip handheld barcode scanner, though they don’t earn any royalties from it either. “As far as I know and at least during my time, when engineers signed on with IBM they signed all the rights to any patent they may do while working for the company to the company itself, so IBM owns the patent not the inventor,” McEnroe explains.

In 1973, the committee chose McEnroe’s team’s code and proposal, and they shipped the first products out in 1974, though only to five supermarkets. It took several years to “achieve a reasonable volume,” McEnroe recalls. At that point, he left the program to work in a different part of the company. By the early 1980s, the UPC had exploded.

Image credit: Courtesy of Smith Publicity

“[It] couldn’t have misreads and charge people the wrong price. That was the biggest single problem.”

Naturally, McEnroe and his team came up against some significant challenges during the UPC’s development and launch. They had to create a code that was “robust” enough to be read through plastic, which could be compromised by the way light shone, frost or any number of other factors. It had to work when it was pulled across the checkstand quickly and not necessarily held flat.

“You didn’t want the operator to even have to see the code,” McEnroe explains. “If she knows the codes are typically on the bottom of packages, pulls the package across, doesn’t even look at the bottom of the package. So it had to be very reliable. [It] couldn’t have misreads and charge people the wrong price. That was the biggest single problem.”

It took McEnroe and his team two years to build the self-correcting code that could fix itself automatically in real-time.

Other “extraneous technical problems” arose too — How to send the signal from the front of the store to the back efficiently? — but McEnroe says one of the biggest, unexpected problems was actually the technology’s public reception. “[There was] the social problem of people and organizations coming to grips with the fact that the price [was] no longer on the merchandise,” McEnroe explains.

“Labor unions were afraid that they were going to lose checkstand operator positions.”

The backlash was so extreme that the UPC’s public unveiling in 1974 didn’t go as planned. McEnroe’s chief engineer called him to say the store couldn’t open. McEnroe was shocked; they’d checked the technology so often, and it was so reliable. But it wasn’t a technical failure: It was a picket line.

Labor unions were afraid that they were going to lose checkstand operator positions,” McEnroe says. “So they picketed the store, and then 18 states passed laws against taking the price off of the merchandise or laws that made it so if you had a scanner in the store, you couldn’t take the price off your merchandise.”

Related: Report: AI Will Take More Jobs Away from Women Than Men

McEnroe had to travel around the country to explain the technology’s benefits and safety, as operators might have to stand over the lasers for years. McEnroe knew the technology was safe; he’d brought monkeys from Africa and tested them at Stanford Research Institute.

“And of course it was safe because A, [the laser] didn’t come out the window, except rarely, and B, we had proved that if it did come out the window and they looked at it for several years, it still wouldn’t hurt them,” McEnroe says.

“I had known his name because I had studied the history of the codes. And his name was Joe Woodland.”

McEnroe built and led the team that developed the UPC, which became widely used by the 1980s and still is. But if you Google “who invented the barcode,” McEnroe’s name is scarcely seen. Instead, a Wikipedia page cites Norman Joseph Woodland and Bernard Silver as the technology’s co-creators.

Why? It started with the ring of a telephone.

After he and his team came up with their code, McEnroe got a call from another IBM employee based in New York. “I had known his name because I had studied the history of the codes,” McEnroe says. “And his name was Joe Woodland.”

On the call, Woodland reiterated that he’d invented a bullseye code for supermarkets in 1948. The code was patented in 1952, per the Smithsonian. By 1973, the Radio Corporation of America (RCA) owned the patent and was one of 14 companies that submitted a code to the committee for consideration, McEnroe recalls.

But there was one major issue with Woodland’s code, McEnroe says: It didn’t work well enough for supermarket and retail adoption.

According to McEnroe, Woodland knew it, too. “I’ve studied the code that your team has come up with, and I know my code, and your code is so much better that there’s no comparison,” McEnroe remembers Woodland telling him over the phone. “I would like to join your team. I would love to live in North Carolina and work for you and be your interface and your marketing person to market your code to the world.”

Gee, what could be better than having the guy who invented the first supermarket code — but one that didn’t work and had never been implemented — on your team? McEnroe thought.

McEnroe did hire Woodland, who moved down to Raleigh and spent the rest of his career working on the UPC project. McEnroe acknowledges that Woodland invented the first supermarket code but maintains that “Wikipedia just got it wrong.” “[Woodland’s code] was never used in any volume at all,” he explains. “Just in the test store that Kroger did in Cincinnati. I went and visited that store and saw it didn’t work either. And we tested it six ways from Sunday.”

Related: Can Robot Shoppers Tell If the Bananas Are Ripe? | Entrepreneur

“I don’t know what he told them. Whatever he told them, they actually gave him the Medal of Technology.”

So why do most inquiries into the barcode’s creator lead back to Woodland? McEnroe says George H.W. Bush’s presidential run against Bill Clinton played a significant role.

At the time, about 20 years after the UPC’s development, Bush was “not seen as coupled with the reality of the American housewife,” McEnroe says. Most people had been using the technology for more than a decade; to present a relatable front, Bush partook in a demo run by the Super Market Institute in Florida, “where a supermarket had been artificially set up inside of a convention center.”

Bush had a chance to use a scanner on the code for the first time. “So he got his picture taken, scanning items, and he said, ‘This is amazing. It’s the greatest technology I’ve ever seen — who invented this thing? And they said, ‘Well, we don’t know. Somebody from IBM,'” McEnroe says.

Bush told them to find out who it was because he wanted to give them the National Medal of Technology. When his people called the supermarket committee, they were given the original proposal put together by Woodland. “That’s the first thing I’d asked Woodland to do as a marketing guy — not an inventor of our code, but a marketing guy — to write the proposal for the barcode,” McEnroe explains.

Woodland wrote the proposal, and his contact information was on the back in case anyone had questions. The engineering group had already been disbanded; Woodland, nearing retirement, was the last one working on the barcode. “I don’t know what he told them,” McEnroe says. “Whatever he told them, they actually gave him the Medal of Technology.”

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The Bush-Clinton race would further solidify the narrative of Woodland as the barcode inventor. As the tide turned against the incumbent, McEnroe recalls that the supermarket demo was written up in the news quite a bit, and he began to recognize a pattern in the telling of the technology’s development.

Typically, one paragraph would say Woodland invented the first supermarket code (“That’s quite true,” McEnroe admits). Another would describe Woodland’s role in marketing and selling the code McEnroe and his team created later.

“What he doesn’t say is that in between the two, his code wasn’t used for years and years,” McEnroe says. “And the other code was taken as the national standard. And it’s the one that everybody’s using. So the one that he promoted and the one that he was the marketing guy for was successful. And that is the universal product code; that’s the vertical bar code we have today. His original code didn’t go anywhere.”

Woodland’s patent had already expired by the time the supermarket committee convened. “The IBM lawyers said it didn’t even read,” McEnroe adds. “The whole method of deciphering how wide a black bar is in a white space is dramatically different. It’s a technical thing, but it’s dramatically different in our code [than] it was in [Woodland’s] circular code.”

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“This is a story that…gives you ideas about how you go about things to this day.”

McEnroe recognizes there’s “a lot of misunderstanding about who did what with the code” — and says ‘”it was time to get that corrected.” That’s why, 50 years after the UPC’s development, the engineer is publishing his memoir to help set the record straight. The book includes an appendix with documents that refute certain claims, like the misconception that IBM came up with the UPC in just a few weeks.

“There are a couple books that have been written by the Super Market Institute about incorporating [the UPC] into the supermarket itself and how it affected that,” McEnroe says. “But this is a story that shows not only that, but also how it was created — it gives you ideas about how you go about things to this day.”

McEnroe says he and his team thought the UPC might last 20 or 30 years, not as long as it has. But the UPC solved a problem that society was struggling with for some time, made checkouts work more efficiently, and was reliable and easy to implement — all things he believes contribute to a technology’s longevity. Even QR codes, which allow scanning in two directions for more data, are a variation of McEnroe’s code.

Related: How Menu QR Codes Became an Essential Tool for Every Restaurant and Bar

Not surprisingly, McEnroe is often asked for his advice to young professionals who want to change the world. And perhaps the most important thing, alongside a certain amount of luck and patience? Keeping an open mind, McEnroe says — but being “disciplined in your curiosity.”

“Fifty years ago, it was a lot harder to understand what was available in the world,” McEnroe explains. “And nowadays, with the internet, that’s a lot easier. Before, you had almost no choice but to be a degreed engineer, go to school, study at the school, go to the libraries. Now that would be fine, but you also can do a great deal of this work by using the internet and just searching things up. But you have to be careful about what you look up. Is it right, or just something somebody else wrote down?”

Being part of a team and recognizing that you can accomplish more together than you can on your own is also vital, as is maintaining those relationships along the way.

“Don’t burn your bridges as you go through life,” McEnroe says. “When you have to go back and call upon people, they’re going to be happy to come and help you again. I had to do that so many times, and everybody that I called upon to help me was extremely helpful. As you’re working with people when you’re younger, work with them well and leave a good taste in their mouth, and then they’ll help you later on.”

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The sky appears to be the limit for the ways entrepreneurs and CEOs can incorporate generative AI tools into their workflows. The one thing you can’t afford to do with generative AI is ignore it.

I recently hosted a workshop for 30 early-stage CEOs to discuss ways to fuse generative AI into their business strategies. Here is some of the intel I shared, plus how you can give yourself an edge by responsibly and effectively incorporating this technology into your startup or business.

Entrepreneurs wisely using generative AI can strategically implement it into their narrative and general storytelling of their companies to in turn receive higher valuations from investors as well — here’s how.

Related: How AI Is Becoming a Game-Changer in Startup Fundraising

1. Expand your product offerings and stay competitive

I suggest reading up on Microsoft’s collaboration with OpenAI. Microsoft jumped to incorporate ChatGPT’s technology into Bing and its other products. Now, its users can work more efficiently in PowerPoint and its suite of Office products. Google responded by exploring the use of generative AI to expand its search capabilities.

Software development is another key area getting gen AI attention. Gen AI is helping developers code more efficiently, predicting the next lines of code based on code already written and responding to prompts. There’s a spotlight on generative AI algorithm models like Large Language Models (LLMs) that can craft text based on the user’s input data.

Every entrepreneur who wants to edge out the competition must find ways to apply generative AI to improve products and develop new offerings.

My company, Verbit, hosted an internal hackathon to gamify identifying ways to incorporate generative AI. It helped to get greater buy-in and inspire ideas. Our hackathon uncovered 13 ways to employ more AI, including two that we’re commercializing.

Consider replicating this hackathon idea or encouraging brainstorms. Run them company-wide. Instead of just involving your more obvious teams, acknowledge that generative AI has the ability to impact the roles of nearly everyone. By involving less obvious stakeholders, you’ll identify use cases for generative AI to disrupt processes you weren’t even aware of.

Engaging your team in these ways won’t just boost morale; it will release apprehension around the “negative” human impact of greater generative AI use. Instead, your team will be inspired by how they can apply it to expand your offerings to deliver better.

Related: The Secret to How Businesses Can Fully Harness the Power of AI

2. Drive employee productivity

AI should be seen as a gateway to make work more meaningful and efficient, not replace jobs. Using generative AI to eliminate dreaded, time-consuming tasks will keep your employees engaged. It will grant them the ability to focus on more creative tasks they’re passionate about. Employee engagement is a metric entrepreneurs can’t overlook because it translates to 23% higher profitability.

Since newer forms of AI are learning to be intuitive and interact naturally with humans, start by using AI that communicates with your teams and learns from their feedback to boost productivity. For example, generative AI has advanced the possibilities of working with chatbots. Teams can now summarize and pull data from chatbot-powered customer surveys and much more.

3. Predict market trends more accurately

For entrepreneurs to make informed decisions about investments, strategies and products, they must understand market trends. Generative AI is helping entrepreneurs gather more quality data than earlier AI forms.

AI is excellent at analyzing large sets of data, but generative AI can gather insights from unstructured data, like social media posts, audio files, text and other content. To be successful, entrepreneurs must pull in this additional information accessible to them through generative AI.

Generative AI can also create simulations to determine the impact of hypothetical “what-if” situations. Researchers at the University of Pennsylvania used generative AI to simulate the spread of COVID-19 and the efficacy of different responses. Audi used simulations to model manufacturing strategies and reduce its assembly line cycle time by 30%.

As an entrepreneur, you can benefit greatly by using generative AI for market simulation. If you don’t use these tools, you’ll be operating with less complete, lower-quality information than your competition.

Related: How to Protect and Improve Your Business with AI During Challenging Times

Know where to draw the line

There are dangers in relying too heavily on gen AI. For example, AI uses data inputs for results. If the data is flawed, it can have consequences. This issue is already appearing in recruitment and hiring practices. Amazon canceled an AI-powered recruitment program after it proved to be biased against women. If you lean too much on AI alone, you could find yourself violating employment laws.

You’ll need to be aware of ethical concerns to avoid instances of sharing sensitive information or violating data privacy laws as well. Generative AI can also hallucinate, meaning that it might give entirely wrong information, but package it in convincing language and reassuring confidence. Turning over too much responsibility to a chatbot could cause more harm than good.

For example, experts are warning against relying too much on tools like ChatGPT for search engine optimization (SEO). Google may decide to penalize companies that publish automated content, undermining their past SEO work. Make sure that your team has a process in place to check the outputs of the AI it’s using.

There was the case of the “ChatGPT lawyer,” who used the tool to draft a motion and ended up citing fake cases in court. The firm faced a fine and public humiliation, but in fields like health care, the consequences of faulty information could be worse and more dangerous.

Smart entrepreneurs will understand how to intelligently and strategically use generative AI, but they’ll know where to draw the line. My advice is to be as savvy about the technology you employ as you are about the people you hire.

However, don’t delay. Challenge your teams to use generative AI to work productively. Decide on a few areas of focus to implement it now, whether it’s personalized content creation, marketing efforts, software development, customer operations or data analysis. Trust me, your competitors are already doing so.

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A few years ago, I was attending a business conference and had an interesting conversation with a fellow business leader. We were discussing what we do and the challenges we faced. What stuck with me was her comment on artificial intelligence (AI): “You either embrace it or get left behind.”

Her words stayed with me, and as I delved deeper into the topic, I realized how crucial it was for every company to incorporate AI into their strategies. No, really. Embracing AI is no longer an option but a necessity for companies looking to thrive and succeed in the digital age.

And as CEOs and business leaders, we have the responsibility to stay ahead of the curve and identify opportunities that can propel our companies to new heights. Today, I want to urge you to join the AI revolution and explore the untapped potential that AI has to offer your business.

Related: 3 Ways to Drive Business Growth Using AI

AI as a catalyst for explosive business growth

The potential for AI to catalyze business growth is unprecedented. Imagine unlocking an entirely new level of operational efficiency, freeing up valuable time for your employees to focus on higher-value activities that can drive innovation.

AI technologies allow us to streamline operations and automate processes, significantly reducing human error while providing unparalleled speed and accuracy. With AI powering our businesses, we can gain a substantial competitive advantage in the data-driven business landscape.

The need for a brave and comprehensive AI strategy

AI solutions are unlikely to be effective if they’re not implemented wisely. CEOs and leaders need to embrace a brave and comprehensive AI strategy that aligns with our business objectives. It means that we must identify relevant use cases in advance, assess the technology infrastructure, address ethical and privacy concerns and implement the strategy from the bottom up, regardless of what naysayers in your company might say.

We must spend time building a robust AI strategy, ensuring our organizations are set up to take data-driven and informed decisions, optimize operations and articulate exceptional customer experiences.

AI’s impact on real-world applications

The impact of AI is evident across various industries. Technologies such as machine learning and natural language processing have advanced the art of simplifying life for humans. In the healthcare sector, AI enables us to analyze patients’ data from various outlets, making diagnosis times faster and more precise.

In finance, AI algorithms are automating processes, optimizing investment strategies and detecting fraudulent activities. For manufacturing, AI is enhancing efficiency and productivity. By taking advantage of the transformative power of AI, businesses can innovate and create disruptive business models. Real-world success stories cement the potential for AI, whether it’s improving processes, delivering personalized customer experiences or making data-driven decisions.

Related: 4 Ways to Use AI to Enhance the Customer Experience

Driving progress through data-driven decision-making

It’s a fact that technology has shaped the way businesses operate. However, AI analytics enables CEOs to make data-driven decisions across various departments and functions. These decisions can range from predicting market trends to optimizing supply chains and enhancing customer experiences.

AI algorithms continuously learn and improve over time, offering endless possibilities to refine decision-making processes and increase operational efficiency. As a result, driving progress through data-driven decision-making is now a necessity to prove to your customers that you are genuinely putting them at the center of your business.

Empowering personalized experiences and customer engagement

AI demonstrates tremendous potential for delivering personalized experiences at scale. With AI, companies can gather customer data from various touchpoints, drawing deeper insights into individual preferences, behaviors and needs.

This knowledge helps organizations tailor their products, services and marketing efforts to cater to each customer segment, driving higher engagement, loyalty and customer lifetime value. The benefits for ecommerce are tremendous as AI-powered recommendations increase purchasing intent, which in turn, works in the favor of your business profitability.

We can’t shy away from AI anymore because the old ways of conducting business are not going to be enough. We must change and embrace brave, transformative technologies such as AI to build better business futures.

Related: Ashton Kutcher Warns Companies to Embrace AI or ‘You’re Probably Going to Be Out of Business’

As CEOs and business leaders, we have the responsibility to lead our companies to embrace AI technologies, unlocking their potential to drive explosive business growth, optimize operations, make data-driven decisions and deliver exceptional customer experiences.

We must have the courage to take that first step of embracing AI to stay ahead of the competition and unlock new possibilities for our organizations. So, let’s seize the AI revolution, driving our companies to new heights of success.

Let us embrace AI, leverage its power and together create a future where our companies thrive, innovate and shape industries like never before.

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No, digital twins aren’t anything as nefarious as a Dopplegänger impersonating you online. However, it does refer to a copy of sorts. And with all the technologically advanced practical applications digital twins offer, it won’t be long before you find them positively impacting your lives in any number of ways.

A digital twin is an interactive virtual replica of a physical asset, process, system, or environment that bears an indistinguishable resemblance to its corresponding entity in the real world. This counterpart assimilates data and mirrors functionalities, enabling the anticipation of potential performance outcomes and issues that the actual product may encounter. It offers real-time monitoring and data collection, simulation and analysis, predictive maintenance, remote control and collaboration, and much more.

Almost like a digital carbon copy, it works like this: The real-world asset is equipped with special sensors that collect data and specific information to describe the asset’s situation. That data is then used to create a digital copy of that asset. It bridges the physical and digital worlds, allowing users to monitor an asset in real time through the digital twin (or copy) and even predict its future state.

Here’s an example of how the city of Orlando improved its urban planning sector, not just in the present but possibilities for going forward as well. In efforts to demonstrate the region and gather up-to-date information for informing decisions in infrastructure, utilities, and business development, Orlando constructed the largest digital twin in the world (there have been larger since). During a recent hurricane, the monitoring of lake levels through the digital twin aided in identifying areas where storm drains were becoming obstructed, enabling prompt assistance to address the problem. Looking ahead, the integration of sensors within the stormwater system has the potential to connect with the digital twin, transforming it into a central command center.

For JTC in Singapore, which is the country’s government infrastructure agency, digital infrastructure plays a pivotal role in the development of its highly anticipated smart region known as the Punggol Digital District. JTC’s digital twin technology also serves as a crucial foundation for driving operational functionality, offering a comprehensive perspective on infrastructure throughout the country, and promoting sustainability. By leveraging advanced capabilities, Singapore aims to optimize operations, promote holistic infrastructure management, and enhance its commitment to sustainability.

With vast applications across various industries, digital twins are truly unlocking the full power of complex data. From the automotive and manufacturing industry, urban planning, infrastructure, and more, digital twins are helping test and solve problems before they appear in the real world. During the COVID pandemic, Vancouver International Airport leveraged reduced traffic to create a comprehensive digital twin of every operational element within their airport. This initiative resulted in enhanced operational efficiency, streamlined passenger flow, and various other benefits.

Digital twins have also found intriguing applications in the healthcare domain. Cincinnati Children’s Hospital Medical Center is taking digital twins to an incredibly intricate level by developing a pediatric cardiac surgical planning application. This innovative tool allows surgeons to examine the hearts of young patients in great detail, facilitating the creation of personalized and precise step-by-step surgical plans. Furthermore, the immersive experience can be shared with families, helping to ease their concerns and reduce anxiety related to upcoming surgeries.

It’s an exciting new world in which the real and virtual worlds meet, and it’s just the beginning. Find out more about digital twins, their uses and applications — and how it’s changing the landscape across industries for the better.

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Just like revolutionary technologies such as the internet, computers/laptops, iPads, iPhones and many more, generative AI is the new technology in the era of 2023, changing everything around us at an exponentially fast pace. Generative artificial intelligence has enormous potential to revolutionize various aspects of our lives even faster than the internet or the iPhone did. It impacts our lives in numerous ways: the way we work online, study and learn, conduct business, communicate, write or express ourselves, create art or videos, analyze data, make informed decisions, discover drugs or conduct research and manage patients in hospitals and clinics, to name a few.

Similarly, the expectations are changing in terms of how entrepreneurs enhance their businesses and their ability to engage customers using generative AI. This includes using useful and meaningful ChatGPT prompts or getting trained to incorporate ChatGPT into their workflows or business enterprise in the form of a new feature called a ChatGPT plugin.

While large enterprises like Microsoft, Salesforce, Bain and many more are adopting ChatGPT within their workflows to improve the performance of their workforce, the enterprises that have taken the lead in developing plugins to attract customers include but are not limited to: Expedia, FiscalNote, Instacart, Kayak, Klarna, Milo, OpenTable, Shopify, Slack, Speak, Wolfram and Zapier.

Related: 2023 Is the Era of Generative AI Like ChatGPT. So What’s in it for Entrepreneurs?

What are ChatGPT plugins?

Let’s take a step back and understand what a ChatGPT plugin is and how it impacts the organization, app or product that gets plugged into ChatGPT.

ChatGPT plugins are new add-ons to an individual’s ChatGPT interactions. These are essentially beta features within ChatGPT developed by OpenAI and third-party vendors (startups/entrepreneurs).

Designed as an add-on, ChatGPT plugins can be seamlessly integrated into the interactions with the AI chatbot. Its primary purpose is to furnish users with supplementary information on topics that pique their interest during chatbot sessions. For instance, if someone finds themselves in need of a last-minute flight, the inclusion of either Expedia or Kayak plugins within their ChatGPT browser enables them to solicit the chatbot’s assistance in locating a flight tailored precisely to their individual requirements.

These plugins utilize data from an organization, product or app to generate texts based on customers’ specific needs or inquiries. While ChatGPT itself is trained on a dataset dating back to the year 2021, using data from these plugins allows it to generate responses that are recent and up-to-date.

Thus, with these recent ChatGPT plugins, data can be fetched from the internet in real-time, allowing users to inquire about the weather in their area on the same day. There are currently around eighty ChatGPT plugins available for users within their own ChatGPT accounts through the plugin store. However, there is a limitation of using only three plugins at a time, as the plugins are still in their early testing beta stage.

Related: The Top 3 Do’s and Don’ts of Integrating ChatGPT into Your Business

3 potential ways ChatGPT plugins can be beneficial for business enterprises

In the generative AI intelligent chatbot and ChatGPT market, the latest trend for entrepreneurs and business enterprises is to develop ChatGPT plugins (add-on features) for their business products.

  1. Enhance business operations and boost productivity. An illustrative example is the implementation of a ChatGPT plugin in the domain of grocery shopping by Instacart. By incorporating a ChatGPT plugin, the process of ordering groceries online has been markedly expedited and simplified. Customers can now enjoy personalized meal recommendations and the add-on automatically generates a new order for the customers. As a result, it ultimately lead to improved business operations and heightened productivity.

  2. Enterprises can effectively engage a larger customer base. Take, for instance, Khan Academy, established in 2006 with the noble purpose of delivering free, world-class education to all students. With the passage of time, their user base skyrocketed to nearly 20 million per month, primarily because of their provision of concise online videos and an engaging and effective teaching approach. Recently, they introduced ChatGPT as an add-on to their platform — a customized AI tutor called Khanmigo. This innovative AI tool not only assists students in efficiently navigating online videos and practicing exams but also actively engages teachers by aiding them in designing new, innovative teaching lesson plans. Consequently, the customer base expanded from students to include teachers.

  3. Give enterprises a competitive advantage in the market. These chatGPT plugins offer significant benefits to business owners or enterprises that encounter fierce competition in the market, particularly in domains like online shopping assistants, virtual assistants, etc. As shopping apps such as Klarna and Shop’s ChatGPT plugins were among the first to be included in the ChatGPT plugin list, there is a higher likelihood that customers will use these plugins, providing these apps with a competitive edge over their market rivals.

Related: What Does ChatGPT Really Mean For Businesses?

However, it’s important to note that with benefits, there are also associated risks that can potentially lead to harm. Here are a few to keep in mind, acknowledging that there can be many more that can be added to this list:

  1. If any misinformation, hallucination or glitches occur in ChatGPT, that could give customers a negative perception of the plugins.
  2. There are concerns regarding the safety, privacy and potential bias in the responses generated by ChatGPT plugins as well as the lack of regulation.

All of the above concerns are particularly relevant considering that ChatGPT plugins are still in their beta version, and there is room for improvement.

At the end of the day, amid the prevailing stress and uncertainty in life, we all yearn for business enterprises or startups that can simplify and enrich our lives through engagement with generative AI applications. These ChatGPT plugins, even in their beta phase, possess the potential to bring about positive transformations in human lives and thus could lead customers toward a future that is more efficient, convenient and productive.

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Since generative AI tools became available to us all late last year, many people have asked me for my thoughts on the technology — particularly ChatGPT. If you’ve been following me for any length of time, you can probably predict that my approach to these innovations is similar to the way I approach any new challenge or development: I love to learn and am always open to exploring new opportunities.

I’ve spent the last several months immersing myself in all the resources I could find about generative AI. I also set up a ChatGPT account to experiment and test its capabilities, even though I had some reservations at first. As this technology grows in power and potential, it will require regulations and guardrails as many have pointed out — including the CEO of OpenAI himself. As always with something new, I’m approaching it with optimism and thoughtful curiosity.

It wasn’t until I shared ChatGPT with my 75-year-old mother that I started to become genuinely excited about its potential. My mother lives in India and had only just heard about ChatGPT. Despite her initial hesitation, she gave it a try. She prompted it to write a poem in Shakespearian style about her home back in the village and started enjoying her new assistant! She was able to see how this technology could be useful in so many ways and that it could also be fun.

It’s worth reiterating that I’m well aware of the risks that come with it — ethics, copyright, security and many other critical issues that need to be resolved. I’m keeping a close eye on these conversations. But the reality is, this tech is here to stay, and it will result in a cognitive revolution akin to the Industrial Revolution.

And that’s why my personal approach to generative AI has been to activate my growth mindset. While I recognize there are legitimate concerns and questions around these tools, I’m energized by their possibilities and potential. For business leaders and entrepreneurs who are also new to generative AI, here’s how embracing a growth mindset has helped me navigate this new technological frontier.

Related: 5 Ways Artificial Intelligence Is Radically Transforming Creativity in Business

Learn from the experts

I’m a big fan of learning as much as you can from people who know more than you do — I believe this is essential to continue growing one’s skills and knowledge. I’ve spent a lot of time reading and listening to podcasts about a wide variety of topics, including generative AI, and have found many experts whose intent to harness GPT-4 for the greater good has resonated with me.

Reid Hoffman, for example, the venture capitalist and founding CEO of LinkedIn, who was also a founding investor in OpenAI (creators of ChatGPT), recently co-authored a book with GPT-4 (yes, the generative AI wrote the book with him!) entitled Impromptu: Amplifying Our Humanity Through AI. In it, he asks a key question: How does this technology make things better for human beings?

I appreciate that there are many business leaders and entrepreneurs who are looking at positive social use cases. I was similarly inspired by a recent podcast featuring Salman Khan, founder of the Khan Academy, on his collaboration with OpenAI to develop a tool that has the potential to truly democratize education. Its new GPT-4-powered learning guide, Khanmigo, acts as a tutor using highly nuanced conversation. Unlike ChatGPT, it will not generate work, but instead help to guide students through a learning process.

And this is just the beginning. As companies like Google and Microsoft accelerate the development of their own generative AI tools, I’m excited to keep applying a growth mindset to learning about emerging opportunities to make the world a better — and smarter — place.

Start experimenting with the tools

If my mother can take a growth mindset approach to generative AI by creating poetry, think about what business leaders and entrepreneurs can do with a little experimentation and creative thinking. A recent Gartner poll showed that 70% of organizations are already exploring how they can leverage generative AI, and 68% of executives believe the benefits outweigh the risks.

With an open mind and curiosity, anyone can find ways to put this technology to good use. And many entrepreneurs are already doing so, using it to inspire ideas, conduct research, develop content and even improve their work processes. If you can save time by delegating menial tasks to the bots, you get more time to engage in work you’re truly passionate about.

I’ve personally experimented with ChatGPT for content generation (and for the record, it did not co-author this article). I initially asked it to write a piece about applying a growth mindset to exploring generative AI, and I fed it some of my previous LinkedIn articles so it could write in my style. The results weren’t bad!

As fun as it is to play around, I think the real value in tools like ChatGPT is their ability to amplify our collective brain power. Just imagine the possibilities for people all over the world and what they’ll be able to accomplish with the help of this technology. It all starts with a willingness to learn, grow and explore.

Related: 3 Forward-Looking Mindsets Entrepreneurs Need to Have About AI

Reframe fear as opportunity

While the last few years have sent us a lot of curveballs, I don’t think many of us had a generative AI revolution in our predictions for 2023. But now that it’s happening, we need to approach it constructively. For me, that means embracing it as an opportunity. And I’m not the only one. Only two months after its launch, ChatGPT had already set a record for the fastest-growing user base, reaching 100 million active users. Many people are setting their fears aside and opening themselves up to the untapped possibilities this technology can offer them.

Whether we have mild reservations or feel full-on panic about the future of AI, it’s clearly here to stay and it will undoubtedly change the way we live and do business. In many ways, it already is. Those who don’t learn how to use it will inevitably risk not keeping pace with the future. On the other hand, taking a growth mindset approach by being curious, open-minded and willing to learn may just inspire you to find ways to use it to improve your own life. At its core, large language models and generative AI have the potential to magnify human intellect and be our buddy or co-pilot, or in some cases, even our guru! There’s never been a better time to imagine — and create — a better future.

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A 2023 Verta survey found that 66% of businesses plan to either increase or maintain their artificial intelligence/machine learning spending over 2023. Pair this with the oft-cited 2018 Gartner survey that predicted 85% of AI projects would fail to deliver on their initial promises through 2022, and you have a world where the majority of businesses are investing in AI projects that have or are likely to fail.

The question becomes, then, is the problem with the AI landscape or the ways in which businesses approach this technology?

As someone whose job is to help build custom AI applications for startups and other businesses, I can say with some confidence that the answer is the latter. Often, businesses rush headfirst into building AI for themselves because of their need to keep up with the competition, but they fail to truly consider what they actually need that AI for.

The feeling of lagging behind the competition is a strong force. It can lead to a lot of anxiety and fear, driving leaders to take action and try to build something completely new — even if it means pushing the boundaries of their own innovation capabilities. When it comes to AI, however, it’s crucial not to succumb to this technological FOMO. Because if you do, you’ll end up investing a lot of time and money in a solution that doesn’t work for your business.

Related: Artificial Intelligence Strategies Startups Should Use to Grow

AI doesn’t need to be revolutionary

Let’s look at a real-world example. Recently, a customer came to us with big, AI-inspired ideas. This client envisioned a whole new world for their startup and had even secured significant money from investors. We spent several days on it, building on strategy session after strategy session. Finally, the company’s leaders asked us why it seemed like we were trying to develop less of an AI solution than they thought their company needed.

The reason was simple: We were thinking practically, not idealistically. We were committed to building a meaningful AI product from concept to version one that would be ready for public consumption in 90 calendar days. Through this approach, we were able to build the organization a successful AI solution quickly and at a low cost.

AI is an exciting technology, but to make use of it, you have to take it step by step, building something you can actually use straight out of the gate and iterating on that.

But how do you know if you’re starting off on the right foot? By making sure you avoid these four mistakes:

1. No clear strategy

In my experience, there are two ways to make use of AI that nearly always lead to success: to help bring your company into the modern age and to add new value just slightly ahead of the competition. These areas are rarely what people talk about when discussing how AI can help small businesses, however.

Instead, people assume AI’s best use case is to help companies create completely unique solutions that are way ahead of their time. Though this isn’t out of the realm of possibility, the probability of success is significantly lower. In the context of AI for startups, your goal shouldn’t be to transform the world but to improve your bottom line.

What do you want your AI to accomplish? How do you measure success? If you can’t answer these questions, any solution you try to build will be rudderless, most likely leading you nowhere.

2. A lack of quality resources

Falling in love with the next big thing is human nature, but once the honeymoon is over, investors only care about the return on their investment. You can’t pivot to find product-market fits if you run out of cash before getting proper feedback. If your pivots are too far off the mark, you’ll burn through cash faster than any financial model you could imagine, much like what happened with AI startup Mythic.

Just because people have tech experience doesn’t mean they can make a productive AI solution. If success matters to your business, don’t cobble together a ragamuffin team with little-to-no experience. Get an expert to help.

Related: How to Avoid Wasting Millions on AI

3. Poor data quality

A report by Gartner found that bad data costs organizations almost $13 million per year on average. So, perform an assessment of your data ahead of time. If you don’t have enough, or it’s in bad shape, you’ll need to either purchase the information you need or hire a professional to help you bring your ideas to life.

Years ago, we worked with an enterprise that was in love with the idea that it had been collecting quality data for 15 years and was ready to deploy unique models. When our team investigated the situation, we realized that the IT group was overwriting the previous day’s data with the current day, erasing the history of the company’s customer base.

It was a crushing blow to the business’s AI ambitions. Luckily, we were able to get creative and still help achieve its goals. But if it hadn’t gotten the help it needed, the company would have been completely stuck.

Related: Bad Data: The $3 Trillion-Per-Year Problem That’s Actually Solvable

4. Underestimating complexity

Startups and established companies alike can easily fall into the trap of believing in data magic instead of data science. Data science applies scientific methods, processes, algorithms and systems to pull knowledge and insights from all kinds of data. Data magic is, well, magic. No one really knows how it works.

If something sounds too good to be true, it probably is. Data science might be more complex, but you’ll actually be able to understand how it works. Understanding the complexity of AI will help you better prepare for the challenges you face along the way. It will also help you create something reproducible and consistent — both vital factors for long-term success.

AI really can be the key to your startup’s success. It can provide the competitive edge you need and help you adapt more quickly to whatever comes next. But embracing AI for the sake of AI is not the way to get there. By taking a methodical, planned approach and taking advantage of the help of AI experts, you can make the most out of AI and truly gain the edge your startup needs to succeed.

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There’s no doubt that the age of technology has transformed various sectors of society, but its impact on education is particularly profound. We’re now at a point where we must reassess our traditional notions of education and begin to reimagine it in the light of technological advancements.

In the traditional classroom, education has long been a one-size-fits-all affair. With a single teacher facing a room full of students, the pace of teaching is often dictated by the average student’s ability. This approach leaves little room for individual attention, which can lead to students at both ends of the spectrum — the struggling and the gifted — feeling underserved.

Related: How Will Technology Transform Global Education In 2023?

The benefits of technology in education

The advent of technology, however, opens up a world of possibilities for personalized, adaptive learning. Educational platforms are now harnessing artificial intelligence (AI) to create learning environments that adapt to the needs of each student. Lessons can be presented in an array of formats, from text and graphics to videos and interactive simulations, catering to different learning styles. With real-time feedback, these platforms can adjust the level of difficulty, the pace of lessons and the types of exercises to fit each student’s unique learning curve. This individualized approach could address the challenges of the traditional classroom, offering a more efficient and inclusive education.

Further, the connectivity offered by the internet has made knowledge more accessible than ever. It’s not just about connecting to a vast amount of information, it’s also about connecting to people. Platforms like Coursera and edX have democratized education, enabling anyone with an internet connection to access courses from prestigious universities worldwide. Online communities and discussion forums have turned learning into a collaborative, interactive experience, not confined by geographical boundaries.

But as we embrace the benefits of technology in education, it’s equally important to remain aware of the challenges that lie ahead.

The challenges ahead

First, there’s the issue of digital divide. Not every student has access to the technology required for digital learning. Even when the devices are available, reliable internet connections may not be, especially in rural and low-income areas. It’s crucial that we address this disparity and ensure that the benefits of technology-aided education are equitably distributed.

Second, while technology offers personalized learning, there’s a risk of isolating students. Traditional classrooms foster social interaction and teamwork — vital skills for the real world. Therefore, it’s essential that the design of digital learning environments incorporates features that promote collaboration and interaction.

Third, the privacy and security of students’ data is a significant concern. As more of our children’s education takes place online, it’s paramount that platforms adhere to strict data privacy standards to protect students’ sensitive information.

Finally, there’s a concern about the readiness of our educators. Teachers need to be equipped with the skills and knowledge to use these technologies effectively. They need to transition from being knowledge dispensers to learning facilitators, a shift that requires significant training and support.

Related: How This Startup Is Infusing Technology with Education in Rural Schools

What to keep in mind going forward

In conclusion, there’s no denying that technology has immense potential to revolutionize education. It promises personalized, accessible and collaborative learning that could address many of the flaws of our current system. However, as we chart the path for this new era of education, it’s essential that we do so thoughtfully.

We need to ensure that the benefits of technology in education reach every student, regardless of their socioeconomic status. We must incorporate social interactions and collaborations in the digital learning environment to prepare students for the real world. We need to prioritize the security and privacy of students’ data. And, most importantly, we must equip our teachers with the skills and support they need to navigate this new terrain.

The journey to reimagine education in the age of technology is complex and fraught with challenges. However, if we approach it thoughtfully and inclusively, we have the opportunity to create an education system that truly serves every student’s unique needs and prepares them for the future. We have the opportunity to democratize knowledge, ensuring that learning is not a privilege for the few but a right for all.

Moreover, the successful integration of technology into education has broader implications for society. It could foster a culture of lifelong learning, where individuals continuously upgrade their skills to stay relevant in the fast-paced world. In a future where AI and automation are set to disrupt job markets, such a culture is not just desirable but necessary.

Furthermore, a more educated populace could drive innovation, economic growth and social progress. Imagine the solutions we could create if more minds had access to quality education and the tools to apply that knowledge. Imagine the societal problems we could solve if critical thinking and problem-solving were ingrained in our education system.

Related: 3 Challenges of Education that Ed-tech is Addressing

So, let’s not shy away from the challenges of integrating technology into education. Let’s see them as opportunities to refine and improve the system. Let’s learn from the successes and failures of early adopters and strive to create a digital learning environment that is inclusive, engaging, secure and effective.

At the end of the day, education is not just about imparting knowledge; it’s about empowering individuals. It’s about fostering curiosity, creativity and empathy. It’s about equipping our youth with the skills and mindset they need to navigate the future. Technology can aid in this endeavor, but only if we use it thoughtfully, responsibly and inclusively.

In this age of technology, let’s not merely digitize education. Let’s reimagine it. For the potential rewards — a more educated, innovative and inclusive society — are well worth the effort.

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People are scared of generative AI, but the future is safe and bright if you prepare now.

I recently published an expert roundup on the benefits of generative AI. Some people worried about bias and political agendas, while others thought jobs would disappear and technocrats would hoard all wealth. Fortunately, we can mitigate risks through transparency, corporate governance and educational transformation.

Below, I’ll discuss the fears and dangers of generative AI and potential solutions for each:

Biased algorithms can shape public opinion

Bias is inherent in every system. Editors have always selected stories to publish or ignore. With the advent of the internet, search engines rewarded publishers for optimized content and advertising, empowering a class of search engine marketers. Then, social media platforms developed subjective quality standards and terms of service. Additionally, bias can arise from algorithm training with disproportionate demographic representation. As such, we’ll face the same problems, solutions and debates over safety and privacy with generative AI that we already face in other systems.

Some people believe in legislative solutions, but those are influenced by lobbyists and ideologues. Instead, consider competition among ChatGPT, Bard, Llama and other generative AIs. Competition sparks innovation, where profits and market share drive unique approaches. As demand increases, the job market will explode with demand for algorithm bias auditors, similar to the growth of diversity training in human resources.

It’s challenging to find the source of bias in a black-box algorithm, where users only see the inputs and outputs of the system. However, open-source code bases and training sets will enable users to test for bias in the public space. Coders may develop transparent white-box models, and the market will decide a winner.

Related: The 3 Principals of Building Anti-Bias AI

Generative AI could destroy jobs and concentrate wealth

Many people fear that elite technocrats will replace workers with robots and accumulate wealth while society suffers. Consider how technology replaced jobs for decades. The cotton gin replaced field workers who toiled in the hot sun. Movable type replaced scribes who hand-wrote books, and ecommerce websites displaced many physical stores.

Some workers and businesses suffered from these transformations. But people learned new skills, and employers hired them to fill talent gaps. We will need radically different education and training to survive. Some people won’t upskill in time, and we have an existing social safety net for them.

Historically, we valued execution over ideas. Today, ideation may set humans apart from machines, where “ideators” replace knowledge workers. Our post-AI world will require critical thinkers, creatives and others to innovate and define ideas for AIs to execute. Quality assurance professionals, algorithm trainers and “prompt engineers” will have a vibrant future, too.

There will also be a market for “human-made” products and services. People will hunger for a uniquely human touch informed by emotional intelligence, especially in the medical and hospitality industries. An episode of 60 Minutes ended with “100% human-generated content,” and others will follow.

Generative AI may create an influx of spam

Many marketers saw ChatGPT as a shortcut to content creation, publishing articles verbatim. The risky technique is just a cheap, fast, low-quality form of ghostwriting.

In contrast, generated content may make digital marketing more equitable by reducing ghostwriting costs for bootstrapped entrepreneurs. The key is understanding Google E-E-A-T, which stands for Experience, Expertise, Authoritativeness and Trustworthiness. Your Google reputation and ranking hinge on your published work. So, people who improve and customize generated content will prosper, while Google flags purveyors of “copy-paste” as spammers.

Rogue AI could pose cybersecurity risks

A rogue coder could create harmful directives for an AI to damage individuals, software, hardware and organizations. Threats include malware, phishing schemes and other cybersecurity threats. But that’s already happening. Before the internet, we battled computer viruses targeting people, organizations and equipment. For-profit antivirus providers have served this market need to keep us safer.

Zero-trust platforms like blockchain may detect anomalies and mitigate cybersecurity risks. In addition, companies will create standard operating procedures (SOPs) to protect their systems — and profits. Therefore, new jobs will materialize to develop new processes, governance, ethics and software.

Related: Why Are So Many Companies Afraid of Generative AI?

Stolen identities and reputation attacks could be imminent

People already create deepfake videos of celebrities and politicians. Many are parodies, but some are malicious. Soon, humans will be unable to detect them. Historically, we’ve had this capability since PhotoShop was released, and teams are already in place to address misinformation and fake images at social media companies and news outlets.

Regulations and policing will never prevent the creation of fake content. Nefarious characters will find tools on the black market and the dark web. Fortunately, there are solutions in the private sector already.

Social media platforms will continue to block presumably fake content and stolen identities. And more solutions will come to fruition. Tools can already detect generated content and continue to improve. Some may become integrated with internet browsers that start issuing fake content warnings. Or celebrities may wear timestamped, dynamic QR codes for authentication when filming.

The singularity may finally arrive

The thought of a conscious AI megalomaniac crosses sci-fi geek minds everywhere. Find comfort knowing that it may already exist. After all, we can’t detect biological or technological consciousness. Yet, consciousness may emerge from complex systems like generative AI. Indeed, the simulation hypothesis suggests we’re in a simulation that an AI controls already.

Related: Addressing the Undercurrent of Fear Towards AI in the Workforce

History is full of dangerous technology. Warren Buffet compared AI to the atom bomb. If he’s right, then we’re as safe as we have been since 1945, when the U.S. government dropped a nuclear bomb for the first and last time. Systems are in place to mitigate that risk, and new systems will arise to keep AI safe, too. Our future will remain bright if enough people pursue cybersecurity and related fields. With that in mind, learn to use this technology and prepare for the shift towards AGI.

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ChatGPT’s prototype launched less than half a year ago, shocking though it may seem. In the short time since, the platform has transformed how marketers, creators and inventive students view content creation. Depending on who you ask, and depending on how they are embraced, generative AI platforms like ChatGPT and Google’s newly released Bard will either improve the way we do business or put us out of business.

One year ago, generative AI art approximating colleagues’ facial characteristics littered our LinkedIn feeds. Now we’re discussing the future of work as we know it … and it will likely be determined by those same behind-the-scenes algorithms.

I encourage you to consider not what generative AI can take away but what it can provide. While it’s only natural for a technology that solves the unsolvable to cause a certain amount of trepidation, it’s not a reason to try to ignore or suppress its capabilities. In fact, consider for a moment the major technological advancements of past centuries and the significant impact they had on GDP (Gross Domestic Product) over the years.

For example, the industrial revolution in the late 18th and early 19th centuries led to a massive increase in productivity and output in manufacturing industries, which contributed to the growth of GDP. Similarly, the advent of the internet and digital technologies in the late 20th century has revolutionized communication and information sharing, which has led to the growth of many new industries and businesses, contributing to an increase in GDP. As we once again face a revolutionary technology, I contend that we should spend less time on what it will take away, and more time on how we will leverage it to create and accomplish more.

Related: Why Are So Many Companies Afraid of Generative AI?

AI has already enabled remarkable innovations

Enterprises handle a massive amount of data. From CRMs to APIs to consumer-facing technologies and beyond — everything in the enterprise tech stack generates an enormous volume of insight-rich data. Globally, The World Economic Forum predicts we will generate about 463 exabytes of data daily as early as 2025. For context, that’s 1,000 bytes multiplied by a factor of six. In other words, it’s more data than we as humans know what to do with or how to maximize the value of.

While our species is incapable of fully understanding data at this scale, much less extracting value from it, non-generative AI applications have been heavily leveraged to categorize, analyze, correlate and draw conclusions from such data at a phenomenal rate. Generally speaking, this level of efficiency-driving data science and machine learning was quickly embraced and adopted across numerous industries from business to healthcare. Because such non-generative AI was not seen as a threat to human competencies, but rather as a tool to help us achieve more, we have made tremendous progress by leveraging such technologies.

Such is not the case with this new form of generative AI that has now emerged, the capabilities of which are deemed to overlap a lot more with those of humans. In fact, generative AI is causing citizens and business leaders alike to be seemingly much more cautious about its applications and the threat that it may pose to jobs, industries and our current way of life than they were about its non-generative predecessor. While some of these concerns are certainly founded, it is imperative that we look not only at what we may lose but what we stand to gain by embracing this new technology — and even what we stand to lose by not embracing it.

Related: How ChatGPT and Generative AI Can Transform the Way You Run Your Business

AI has the potential to transform workplace tech

One example of how generative AI can change our lives, or at least the course of our workday, is its ability to transform our relationship with the everyday software around us.

Today, humans use software to accomplish or be more efficient at certain tasks. In most cases, we are required to physically interact with the software, digest information it gives us, make decisions on the tasks and strategies we will implement using said software, then, of course, use the software itself to execute those tasks. While ultimately the software is helpful, there is no ignoring that to reap its full benefit, we must invest time and effort into it which is taken away from other core parts of our day.

But consider for a moment a world where such a relationship is antiquated, and that software is no longer a tool that we have to spend time using, but rather a partner that will give us time back by doing things for us. Generative AI is one of the keys to realizing this new relationship with software. Time-consuming decisions and tasks across organizations and society are now automatically completed on our behalf, giving us time back to do more of what we are passionate about and great at. The efficiencies gained, not to mention the optimizations leveraged, will not only transform our outputs as a society, but the learnings and further innovations that will result will transform our economies, technologies and ways of life. This is where we at SOCi see software going and where we are investing our time and leveraging new AI technologies.

Related: The Perfect Blend: How to Successfully Combine AI and Human Approaches to Business

How do we move toward an AI-based future?

My answer to this question is simple: optimistically, but cautiously. Although I’ve discussed the positives of AI maturity at length, I must also emphasize what AI can’t accomplish.

AI, generative or otherwise, is a powerful tool that can be leveraged, but it is seldom the end product. It is our responsibility to train the tool to be effective, to integrate it into workflows and processes that we need to achieve our goals and to continue to consider the needs of our customers. While the AI models flooding the market today are powerful, they still need direction, application and that “human touch” to be rendered into specific solutions suitable for our businesses.

It is also important to note that while AI may be leveraged to provide insights and complete certain tasks, it does not (yet) “think” as humans do. AI models are built to process data and deliver outputs but not to produce original thoughts and complex solutions. For the time being, humans will still be at the helm of crafting such strategies and solutions to larger societal or organizational challenges.

In the end, it will be the innovators amongst us that accept these challenges and embrace the benefits of AI that will dictate the advancements that we make and the transformations that our way of life will undergo. Our creators at SOCi are deeply passionate about being at the forefront of this movement and specifically about authoring the transition of the relationship our customers have with marketing software — from a tool that they use to accomplish meaningful tasks to a co-marketer who can execute on thousands of data-driven decisions and tasks — for them to deliver real-world results and give them time back to do what they are passionate about.

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