In the crowded digital marketplace, standing out is challenging. Enter AI-powered marketing, a revolutionary upgrade transforming brands into digital powerhouses.

Hyper-Personalized Campaigns: Beyond Basic Personalization

Gone are the days of generic marketing. Today’s gold standard is AI-driven hyper-personalization. This approach uses customer data analysis to create deeply resonant, individualized marketing campaigns. With AI’s ability to segment audiences based on intricate criteria, including purchasing history and browsing behavior, your messages can hit the mark every time.

Enhanced Customer Journey Mapping

AI’s capabilities extend to mapping the entire customer journey. By predicting needs and preferences at each stage, AI aids in crafting narratives that guide customers from discovery to purchase, integrating your brand into their personal stories.

SEO Wizardry: Mastering Search Engine Dynamics

With ever-changing algorithms, SEO is a complex puzzle. AI serves as a sophisticated navigator, deciphering these changes through machine learning. It aids in keyword optimization, understanding search intent, and aligning content with search trends.

Predictive SEO

AI tools offer predictive SEO, anticipating search engine and user behavior changes. This proactive stance ensures your brand’s prominent visibility in search results, capturing the right audience at the right time.

Social Media Mastery: Crafting a Digital Narrative

AI transforms social media strategies from uncertain to precise. By analyzing vast social data, AI provides insights into resonating content.

Content Optimization

AI analyzes performance data to recommend effective content types. This data-driven approach refines your social media content strategy.

Engagement Analysis

AI examines user interaction nuances, understanding engagement patterns. It helps tailor interactions for maximum impact, including adjusting posting schedules and messaging for increased relevance.

Conclusion: Navigating the AI-Driven Marketing Landscape

AI-powered marketing is essential for thriving in the digital age, offering precision and personalization beyond traditional methods. For small businesses, it’s a chance to leverage AI for impactful, data-driven strategies.

As we embrace the AI revolution, the future of marketing is not just bright but intelligently radiant. With AI as your digital ally, your brand is equipped for a successful journey, making every marketing effort and customer interaction count.

In the ever-evolving landscape of modern commerce, small business owners find themselves at a crossroads of opportunity and obsolescence. Enter Artificial Intelligence (AI) – once the exclusive domain of tech behemoths, it now stands as the great equalizer, offering small businesses a competitive edge previously unthinkable. The emergence of AI as a wingman for small businesses is not just a fleeting trend but a fundamental shift in how entrepreneurs can leverage technology to revolutionize their operations.

The 24/7 Customer Service Hero: Chatbots

In the digital storefront, customer service is the heartbeat of business survival and success. Chatbots emerge as the indefatigable heroes of this domain. Envision a customer service agent that never clocks out an entity that requires no sleep or sustenance yet delivers consistently and instantaneously. These AI-driven chat interfaces embody the essence of your brand’s voice, capable of handling a barrage of customer queries with a speed that outpaces the swiftest of typists. They are the embodiment of efficiency – ensuring that customer satisfaction is not just met but exceeded around the clock.

Unearthing Market Treasures: Data Dive

AI’s prowess in pattern recognition has catapulted data analytics into a realm once considered the stuff of science fiction. Small business owners armed with AI tools can sift through vast swathes of data to extract actionable insights. These algorithms act as modern-day oracles, predicting market trends, discerning customer behaviors, and offering sales forecasts with remarkable accuracy. Equipped with: this knowledge, small businesses, can navigate the market with the foresight and precision of an experienced captain steering through foggy seas.

Personalization at Scale: Customize Like a Boss

The age-old business mantra of the customer is king is given new potency with AI’s personalization capabilities. Tailoring the customer experience is no longer a luxury but a necessity. AI enables small businesses to offer bespoke experiences to consumers, making them feel like the sole focus of their attention. It’s personalization executed with such finesse that customers are left marveling at the thoughtfulness and individual attention, fostering loyalty and establishing deep-rooted brand connections.

Offloading the Mundane: Task Slayers

Repetitive tasks are the bane of creativity and innovation. AI steps in as the ultimate task slayer, automating routine chores that once consumed disproportionate amounts of time. From scheduling appointments to managing inventory, AI liberates entrepreneurs from the drudgery of administrative duties, freeing them to refocus on the creative and strategic endeavors that propel business growth.

Mastering Social Media: Social Savants

Social media – the pulsing vein of modern marketing – demands astuteness and agility. AI emerges as the savant of social media, capable of demystifying platform algorithms to optimize content delivery. It knows the optimal times to post, the types of content that resonate with audiences, and the strategies that convert passive scrollers into engaged customers. By automating your social media presence, AI transforms your brand into an online sensation, cultivating a digital community of brand ambassadors.

The Verdict: Embracing AI

For a small business owner, AI is not about an overnight overhaul but strategic integration. The goal is to start small, allowing AI to shoulder incremental aspects of your business, learning and scaling as you witness tangible benefits. The transition to AI-enablement does not necessitate a background in technology; it requires a willingness to embrace change and a vision for the future.

In summary, as the digital revolution marches forward, AI stands ready to partner with small businesses, providing them with tools once deemed the province of giants. This partnership promises to elevate the small business landscape, ushering in an era of democratized technology where every entrepreneur can harness the power of AI to write their own David vs. Goliath success story. AI, the once-distant dream, is now the most loyal wingman a small business can enlist in its quest for growth and innovation.

As our resident Apple expert Brian Heater wrote, a new 24-inch iMac and a MacBook Pro refresh would be the most likely new announcements to expect from the October event, and as it turns out, he was spot-on. Apple’s new M3 chip lineup was the focal point of the event, powering each of the devices Apple showcased in their half-hour prerecorded event that had some fog, some bats and ominous choir music…but no big surprises for those following the rumor mill.

Since the event kicked off off at the uncharacteristically late time of 8pm ET / 5pm PT, so you might have missed out out on the reveals while putting the finishing touches on your Halloween decorating, or watching Monday Night Football. No judgement, we’re here to recap everything the October Apple event showcased in one spot.

New M3 chips

The “scary fast” part of the Apple event, as expected, are the new M3 chips. Apple has announce a M3, M3 Pro and M3 Max, which will be included in Apple’s new 24-inch iMac, MacBook Pros.

This time around, Apple has placed an emphasis on graphical horsepower, with hardware-accelerated ray tracing, mesh shading and Dynamic Caching, which Apple claims “dramatically increases the average utilization of the GPU” by allotting exact amount of local memory to given tasks. These new chips were frequently benchmarked against their M1 predecessor, with Apple claiming the M3 renders at 2.5x the speed of the M1 and its CPU is 30% faster than the M1.

Check out the full rundown of the three M3 chips right here.

New MacBook Pro models

Credit: Apple

Yes, the new 14-inch and 16-inch MacBook Pros come with upgraded internals, but the first thing you might notice is the new color: Space Black. Beneath that color, you’ll find that new line of M3 chips. The 14-inch MacBook Pro can contain any of the trio, while the 16-inch model will only come with the M3 Pro or M3 Max chips.

As we’ve noted, the M3 chips packed into both models are putting an emphasis on getting the most out of the new GPU, though Apple also boasts that both form factors’ battery can last 22 hours on a single charge.

Both are available for preorder tonight, with the 14-inch MacBook Pro starting at $1,599 and going to $1,999 with the M3 Pro. The baseline 16-inch MacBook Pro goes for $2,499 and the pricing for the M3 Max chip upgrade for both models has yet to be disclosed.

And that space black color is exciting news for any Mac fan still pining for the 2006 MacBook, whose dark tone hadn’t been replicated in the MacBook iterations that followed, even those Midnight MacBook Airs.

Check out the full rundown on the new MacBook Pros here.

New M3 iMac

Credit: Apple

Apple’s iMac line is getting a colorful refresh, with an added M3 chip to add horsepower to the palette change. Apple is sticking with the 24-inch form factor, and upgrading the screen with a 4.5K retina display, 1080p FaceTime camera and a six-speaker system supporting Dolby Atmos and Spatial Audio. The new iMac will be available for preorder with green, yellow, orange, pink, purple, blue and silver options starting tonight.

The $1,299 baseline comes with a 8-core GPU and 8-core CPU, with a $1,499 version upgrading you to a 256 SSD.

For more info about what else is new in the M3 iMac, head here.

An sneaky iPhone showcase

You may not have noticed it, but at the very end of the event, Apple dropped a quick note on the stream: “This event was shot on iPhone and edited on Mac.” It’s a bit of a victory lap, but as our other Apple expert Darrell Etherington notes, it’s a pretty impressive flex for Apple to shoot its half-hour hardware showcase entirely on a phone.

Recap the full Scary Fast event

If you want to just dive right in and experience the October event all over again or for the first time, you can catch the entire archive via the YouTube embed below right on Apple’s website.

Many companies are experimenting with ChatGPT and other large language or image models. They have generally found them to be astounding in terms of their ability to express complex ideas in articulate language. However, most users realize that these systems are primarily trained on internet-based information and can’t respond to prompts or questions regarding proprietary content or knowledge.

Leveraging a company’s propriety knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational Innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how. However, knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings. As such, a company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.

Emerging technologies in the form of large language and image generative AI models offer new opportunities for knowledge management, thereby enhancing company performance, learning, and innovation capabilities. For example, in a study conducted in a Fortune 500 provider of business process software, a generative AI-based system for customer support led to increased productivity of customer support agents and improved retention, while leading to higher positive feedback on the part of customers. The system also expedited the learning and skill development of novice agents.

Like that company, a growing number of organizations are attempting to leverage the language processing skills and general reasoning abilities of large language models (LLMs) to capture and provide broad internal (or customer) access to their own intellectual capital. They are using it for such purposes as informing their customer-facing employees on company policy and product/service recommendations, solving customer service problems, or capturing employees’ knowledge before they depart the organization.

These objectives were also present during the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task. Today, however, generative AI is rekindling the possibility of capturing and disseminating important knowledge throughout an organization and beyond its walls. As one manager using generative AI for this purpose put it, “I feel like a jetpack just came into my life.” Despite current advances, some of the same factors that made knowledge management difficult in the past are still present.

The Technology for Generative AI-Based Knowledge Management

The technology to incorporate an organization’s specific domain knowledge into an LLM is evolving rapidly. At the moment there are three primary approaches to incorporating proprietary content into a generative model.

Training an LLM from Scratch

One approach is to create and train one’s own domain-specific model from scratch. That’s not a common approach, since it requires a massive amount of high-quality data to train a large language model, and most companies simply don’t have it. It also requires access to considerable computing power and well-trained data science talent.

One company that has employed this approach is Bloomberg, which recently announced that it had created BloombergGPT for finance-specific content and a natural-language interface with its data terminal. Bloomberg has over 40 years’ worth of financial data, news, and documents, which it combined with a large volume of text from financial filings and internet data. In total, Bloomberg’s data scientists employed 700 tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time. Few companies have those resources available.

Fine-Tuning an Existing LLM

A second approach is to “fine-tune” train an existing LLM to add specific domain content to a system that is already trained on general knowledge and language-based interaction. This approach involves adjusting some parameters of a base model, and typically requires substantially less data — usually only hundreds or thousands of documents, rather than millions or billions — and less computing time than creating a new model from scratch.

Google, for example, used fine-tune training on its Med-PaLM2 (second version) model for medical knowledge. The research project started with Google’s general PaLM2 LLM and retrained it on carefully curated medical knowledge from a variety of public medical datasets. The model was able to answer 85% of U.S. medical licensing exam questions — almost 20% better than the first version of the system. Despite this rapid progress, when tested on such criteria as scientific factuality, precision, medical consensus, reasoning, bias and harm, and evaluated by human experts from multiple countries, the development team felt that the system still needed substantial improvement before being adopted for clinical practice.

The fine-tuning approach has some constraints, however. Although requiring much less computing power and time than training an LLM, it can still be expensive to train, which was not a problem for Google but would be for many other companies. It requires considerable data science expertise; the scientific paper for the Google project, for example, had 31 co-authors. Some data scientists argue that it is best suited not to adding new content, but rather to adding new content formats and styles (such as chat or writing like William Shakespeare). Additionally, some LLM vendors (for example, OpenAI) do not allow fine-tuning on their latest LLMs, such as GPT-4.

Prompt-tuning an Existing LLM

Perhaps the most common approach to customizing the content of an LLM for non-cloud vendor companies is to tune it through prompts. With this approach, the original model is kept frozen, and is modified through prompts in the context window that contain domain-specific knowledge. After prompt tuning, the model can answer questions related to that knowledge. This approach is the most computationally efficient of the three, and it does not require a vast amount of data to be trained on a new content domain.

Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible knowledge on key issues they encounter in their roles advising clients. The prompt-trained system is operated in a private cloud that is only accessible to Morgan Stanley employees.

While this is perhaps the easiest of the three approaches for an organization to adopt, it is not without technical challenges. When using unstructured data like text as input to an LLM, the data is likely to be too large with too many important attributes to enter it directly in the context window for the LLM. The alternative is to create vector embeddings — arrays of numeric values produced from the text by another pre-trained machine learning model (Morgan Stanley uses one from OpenAI called Ada). The vector embeddings are a more compact representation of this data which preserves contextual relationships in the text. When a user enters a prompt into the system, a similarity algorithm determines which vectors should be submitted to the GPT-4 model. Although several vendors are offering tools to make this process of prompt tuning easier, it is still complex enough that most companies adopting the approach would need to have substantial data science talent.

However, this approach does not need to be very time-consuming or expensive if the needed content is already present. The investment research company Morningstar, for example, used prompt tuning and vector embeddings for its Mo research tool built on generative AI. It incorporates more than 10,000 pieces of Morningstar research. After only a month or so of work on its system, Morningstar opened Mo usage to their financial advisors and independent investor customers. It even attached Mo to a digital avatar that could speak out its answers. This technical approach is not expensive; in its first month in use, Mo answered 25,000 questions at an average cost of $.002 per question for a total cost of $3,000.

Content Curation and Governance

As with traditional knowledge management in which documents were loaded into discussion databases like Microsoft Sharepoint, with generative AI, content needs to be high-quality before customizing LLMs in any fashion. In some cases, as with the Google Med-PaLM2 system, there are widely available databases of medical knowledge that have already been curated. Otherwise, a company needs to rely on human curation to ensure that knowledge content is accurate, timely, and not duplicated. Morgan Stanley, for example, has a group of 20 or so knowledge managers in the Philippines who are constantly scoring documents along multiple criteria; these determine the suitability for incorporation into the GPT-4 system. Most companies that do not have well-curated content will find it challenging to do so for just this purpose.

Morgan Stanley has also found that it is much easier to maintain high quality knowledge if content authors are aware of how to create effective documents. They are required to take two courses, one on the document management tool, and a second on how to write and tag these documents. This is a component of the company’s approach to content governance approach — a systematic method for capturing and managing important digital content.

At Morningstar, content creators are being taught what type of content works well with the Mo system and what does not. They submit their content into a content management system and it goes directly into the vector database that supplies the OpenAI model.

Quality Assurance and Evaluation

An important aspect of managing generative AI content is ensuring quality. Generative AI is widely known to “hallucinate” on occasion, confidently stating facts that are incorrect or nonexistent. Errors of this type can be problematic for businesses but could be deadly in healthcare applications. The good news is that companies who have tuned their LLMs on domain-specific information have found that hallucinations are less of a problem than out-of-the-box LLMs, at least if there are no extended dialogues or non-business prompts.

Companies adopting these approaches to generative AI knowledge management should develop an evaluation strategy. For example, for BloombergGPT, which is intended for answering financial and investing questions, the system was evaluated on public dataset financial tasks, named entity recognition, sentiment analysis ability, and a set of reasoning and general natural language processing tasks. The Google Med-PaLM2 system, eventually oriented to answering patient and physician medical questions, had a much more extensive evaluation strategy, reflecting the criticality of accuracy and safety in the medical domain.

Life or death isn’t an issue at Morgan Stanley, but producing highly accurate responses to financial and investing questions is important to the firm, its clients, and its regulators. The answers provided by the system were carefully evaluated by human reviewers before it was released to any users. Then it was piloted for several months by 300 financial advisors. As its primary approach to ongoing evaluation, Morgan Stanley has a set of 400 “golden questions” to which the correct answers are known. Every time any change is made to the system, employees test it with the golden questions to see if there has been any “regression,” or less accurate answers.

Legal and Governance Issues

Legal and governance issues associated with LLM deployments are complex and evolving, leading to risk factors involving intellectual property, data privacy and security, bias and ethics, and false/inaccurate output. Currently, the legal status of LLM outputs is still unclear. Since LLMs don’t produce exact replicas of any of the text used to train the model, many legal observers feel that “fair use” provisions of copyright law will apply to them, although this hasn’t been tested in the courts (and not all countries have such provisions in their copyright laws). In any case, it is a good idea for any company making extensive use of generative AI for managing knowledge (or most other purposes for that matter) to have legal representatives involved in the creation and governance process for tuned LLMs. At Morningstar, for example, the company’s attorneys helped create a series of “pre-prompts” that tell the generative AI system what types of questions it should answer and those it should politely avoid.

User prompts into publicly-available LLMs are used to train future versions of the system, so some companies (Samsung, for example) have feared propagation of confidential and private information and banned LLM use by employees. However, most companies’ efforts to tune LLMs with domain-specific content are performed on private instances of the models that are not accessible to public users, so this should not be a problem. In addition, some generative AI systems such as ChatGPT allow users to turn off the collection of chat histories, which can address confidentiality issues even on public systems.

In order to address confidentiality and privacy concerns, some vendors are providing advanced and improved safety and security features for LLMs including erasing user prompts, restricting certain topics, and preventing source code and propriety data inputs into publicly accessible LLMs. Furthermore, vendors of enterprise software systems are incorporating a “Trust Layer” in their products and services. Salesforce, for example, incorporated its Einstein GPT feature into its AI Cloud suite to address the “AI Trust Gap” between companies who desire to quickly deploy LLM capabilities and the aforementioned risks that these systems pose in business environments.

Shaping User Behavior

Ease of use, broad public availability, and useful answers that span various knowledge domains have led to rapid and somewhat unguided and organic adoption of generative AI-based knowledge management by employees. For example, a recent survey indicated that more than a third of surveyed employees used generative AI in their jobs, but 68% of respondents didn’t inform their supervisors that they were using the tool. To realize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to develop a culture of transparency and accountability that would make generative AI-based knowledge management systems successful.

In addition to implementation of policies and guidelines, users need to understand how to safely and effectively incorporate generative AI capabilities into their tasks to enhance performance and productivity. Generative AI capabilities, including awareness of context and history, generating new content by aggregating or combining knowledge from various sources, and data-driven predictions, can provide powerful support for knowledge work. Generative AI-based knowledge management systems can automate information-intensive search processes (legal case research, for example) as well as high-volume and low-complexity cognitive tasks such as answering routine customer emails. This approach increases efficiency of employees, freeing them to put more effort into the complex decision-making and problem-solving aspects of their jobs.

Some specific behaviors that might be desirable to inculcate — either though training or policies — include:

• Knowledge of what types of content are available through the system;
• How to create effective prompts;
• What types of prompts and dialogues are allowed, and which ones are not;
• How to request additional knowledge content to be added to the system;
• How to use the system’s responses in dealing with customers and partners;
• How to create new content in a useful and effective manner.

Both Morgan Stanley and Morningstar trained content creators in particular on how best to create and tag content, and what types of content are well-suited to generative AI usage.

“Everything Is Moving Very Fast”

One of the executives we interviewed said, “I can tell you what things are like today. But everything is moving very fast in this area.” New LLMs and new approaches to tuning their content are announced daily, as are new products from vendors with specific content or task foci. Any company that commits to embedding its own knowledge into a generative AI system should be prepared to revise its approach to the issue frequently over the next several years.

While there are many challenging issues involved in building and using generative AI systems trained on a company’s own knowledge content, we’re confident that the overall benefit to the company is worth the effort to address these challenges. The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a powerful draw. Generative AI appears to be the technology that is finally making it possible.

In this post, we will look at how technology contributes to the growth and success of small enterprises.

1. Streamlining Operations

Implementing small business technology solutions can automate and streamline various aspects of small business operations. This includes using project management software, customer relationship management (CRM) systems, inventory management tools, and accounting software. Streamlining operations not only saves time and reduces manual errors but also allows small businesses to allocate resources more efficiently.

Tip: Regularly assess your business processes and identify areas that can be automated or improved with technology. This continuous evaluation ensures that your technology solutions remain aligned with your evolving business needs.

2. Enhancing Customer Engagement

Technology enables small businesses to engage and connect with their customers more effectively. Social media platforms, email marketing software, and customer service tools allow businesses to communicate and build relationships with their target audience. Customer relationship management systems help businesses track customer interactions and preferences, providing insights to deliver personalized experiences and improve customer satisfaction.

Tip: Leverage data from customer interactions to create targeted marketing campaigns and personalized offers. Use automation tools to send timely and relevant messages to your customers, enhancing their engagement and loyalty.

3. Expanding Market Reach

The internet and digital marketing platforms provide small businesses with the opportunity to reach a broader audience beyond their local market. Creating a professional website, utilizing search engine optimization (SEO), and leveraging online advertising channels allow small businesses to attract and engage customers from different regions or even globally. E-commerce platforms enable businesses to sell products or services online, further expanding their market reach.

Tip: Continuously monitor and optimize your online presence to ensure your website is discoverable and user-friendly. Leverage analytics tools to track website traffic, visitor behavior, and conversion rates to make data-driven improvements.

4. Improving Decision-Making with Data

Technology provides small businesses with access to valuable data and analytics, enabling informed decision-making. Through data analysis, businesses can gain insights into customer behavior, market trends, and operational performance. This data-driven approach allows small businesses to make strategic decisions, optimize processes, and identify growth opportunities more effectively.

Tip: Invest in data analytics tools and dashboards that can consolidate and visualize your business data. Regularly review and analyze the data to uncover patterns, identify bottlenecks, and make data-backed decisions to drive growth.

5. Facilitating Remote Work and Collaboration

Advancements in technology have made remote work and collaboration more feasible for small businesses. Cloud-based tools, project management software, and communication platforms enable teams to work together efficiently, regardless of geographical location. This flexibility opens up opportunities to access talent from anywhere, increase productivity, and reduce overhead costs.

Tip: Establish clear communication protocols and project management workflows to ensure effective collaboration among remote teams. Use video conferencing tools for virtual meetings and foster a culture of transparency and accountability to maintain productivity and engagement.

6. Embracing Emerging Technologies

Small businesses should stay informed about emerging technologies that have the potential to transform their industries. Technologies such as artificial intelligence, machine learning, blockchain, and the Internet of Things can offer new opportunities for growth and innovation. Being open to adopting and integrating these technologies into your business strategy can give you a competitive advantage.

7. Data Security and Privacy

Data security and privacy are critical considerations when using technology in small businesses. Implement robust cybersecurity measures, such as firewalls, encryption, and secure data storage, to protect sensitive customer information and intellectual property. Regularly update software and educate employees on best practices for data security to minimize the risk of data breaches.

8. Customer Relationship Management (CRM) Systems

A dedicated CRM system can help small businesses manage customer relationships more efficiently. It allows businesses to track customer interactions, store contact information, and monitor sales pipelines. Utilize CRM software to streamline sales and marketing processes, personalize customer interactions, and nurture long-term customer loyalty.

9. Continuous Learning and Skill Development

Encourage continuous learning and skill development among employees to keep up with technological advancements. Provide access to online courses, training resources, and workshops to enhance digital literacy and proficiency. Embrace a culture of learning and innovation to ensure your small business remains adaptable and competitive in the digital age.

10. Scalable and Flexible Technology Solutions

Choose technology solutions that are scalable and flexible to accommodate your growing business needs. Consider cloud-based software and platforms that allow you to easily scale up or down as your business evolves. This scalability enables small businesses to adapt to changing demands and seize new opportunities without significant disruptions.

11. Regular Technology Assessments

Regularly assess your technology infrastructure to ensure it aligns with your business goals and remains up to date. Conduct technology audits to identify areas for improvement, eliminate outdated systems, and explore new technologies that can drive growth. Stay proactive in evaluating and optimizing your technology stack to maximize its impact on your small business.

Conclusion

Technology serves as a catalyst for small business growth. By leveraging technology effectively and staying agile in an ever-evolving digital landscape, small businesses can unlock their full potential, adapt to changing customer expectations, and drive sustainable growth.

After much discussion, the debate over job displacement from artificial intelligence is settling into a consensus. Historically, we’ve never experienced macro-level unemployment from new technologies, so AI is unlikely to make many people jobless in the long term — especially since most advanced countries are now seeing their working-age populations decline. However, because companies are adopting ChatGPT and other generative AI remarkably fast, we may see substantial job displacement in the short term.

Compare AI with the rise of electricity around the turn of the twentieth century. It took factories decades to switch from steam-powered central driveshafts to electric motors for each machine. They had to reorganize their layout in order to take advantage of the new electric technology. The process happened slowly enough that the economy had time to adjust, only new factories adopting the motors at first. As electricity created new jobs, laid-off workers in steam-powered factories could move over. Greater wealth created entirely new industries to engage workers, along with higher expectations.

Something similar happened with the spread of computing in the middle of the twentieth century. It went at a faster pace than electrification, but was still slow enough to prevent mass unemployment.

AI is different because companies are integrating it into their operations so quickly that job losses are likely to mount before the gains arrive. White-collar workers might be especially vulnerable in the short-term. Indeed, commentators are describing an “AI gold rush” rather than a bubble, powered by advanced chipmakers such as Nvidia. Goldman Sachs recently predicted that companies would use it to eliminate a quarter of all current work tasks in the United States and Europe. That probably means tens of millions of people out of work — especially people who thought their specialized knowledge gave them job security.

That leaves two possibilities to mitigate this risk. The first is that governments step in, either to slow down the commercial adoption of AI (highly unlikely), or to offer special welfare programs to support and retrain the newly unemployed.

But there’s another, often neglected possibility that comes without the unintended consequences of governmental intervention. Some companies are rapidly integrating generative AI into their systems, not just to automate tasks, but to empower employees to do more than they could before — i.e., making them more productive. A radical redesign of corporate processes could spark all sorts of new value creation. If many companies do this, then as a society we’ll generate enough new jobs to escape the short-term displacement trap.

But will they? Even the least aggressive company tends to be pretty good about cutting costs. Innovation, however, is another matter. We didn’t worry about this in the past, because we had enough time for a few aggressive companies to gradually change industries. They innovated over time to make up for the slow loss of displaced jobs. That innovation created new jobs and kept unemployment low. But macroeconomically speaking, we don’t have the luxury of time with the AI transition.

So the alternative to relying on the government is to have many companies innovating fast enough to create new jobs at the same pace that the economy as a whole eliminates existing ones. Generative AI is spreading fast in business and society, but that speed also means an opportunity for companies to step up their pace of innovation. If we get enough companies to go on offense in this way, then we won’t have to worry about AI unemployment.

Of course, companies won’t — and shouldn’t — lean into AI in order to solve macroeconomic problems. But fortunately they have good business reasons to do so. The companies that create opportunities from AI will also position themselves to thrive in the long run.

Going on the Offensive with AI

Already we can point to aggressive companies looking to innovate in AI. Having become a trailblazer in reusable rockets and electric cars, Elon Musk is now promising to make Twitter as much of a leader in AI as Microsoft and Google. Musk, however, is a famous outlier and the jury is still out on Twitter. So what does it mean for a company to go on offense with AI?

To answer this question, let’s look at what makes companies adept at navigating the kinds of changes we’re seeing now. One of us (Tabrizi) assembled a team of researchers to study 26 sizable companies with good data from 2006–2022. The team divided the companies into groups of high, medium, and low agility and innovation over time, with comparable data and case studies of each.

What set the agile, innovative companies apart from those who remained neutral or defensive? The team narrowed the differentiators down to eight drivers of agile innovation: existential purpose, obsession with what customers want, a Pygmalion-style influence over colleagues, a startup mindset even after scaling up, a bias for boldness, radical collaboration, the readiness to control tempo, and operating bimodally. Most leaders praise those attributes, but it turns out it’s remarkably hard for big organizations to sustain any of them over time.

Tabrizi has written elsewhere about how Microsoft went on offense to become a corporate leader by overhauling its hierarchy and pursuing partnerships such as with Open AI. But other companies have done something similar with AI as a result of those drivers. Let’s focus on two of the most important drivers here — the bias for boldness and the startup mentality. Getting those drivers in place can take a company far into agile innovation, because these force changes throughout the organization.

A Bias for Boldness

Any company that invests in AI in the near future is likely to make money from it. Yet mere investment is likely to offer only incremental gains. The numbers might look good, especially in cutting costs. But the company will miss the opportunity for big gains by creating substantial value — or a defensible future niche. Cautious investment won’t protect you in the long run from competition, and certainly won’t help us with the macroeconomic challenge we’re facing.

That’s the problem with any new technology: You can proceed cautiously and probably do just fine. Big companies hate risk, which is why they operate as well-oiled machines churning out reliable products at an affordable cost. That’s also why many of them outsource their innovation by acquiring startups — and even that approach often leads to timid improvements. All successful organizations, especially at size, prefer to minimize risk and daring. But as Brené Brown points out, “You can choose courage, or you can choose comfort, but you cannot choose both.”

Boldness has become a corporate cliché, with leaders protesting too much, but with AI we need companies to really mean it — to embrace rather than minimize risk. Take Adobe, whose Photoshop program has long held the largest share of the photographic design market. Adobe could have played it safe as generative AI emerged, adopting it in small areas while waiting to see how the technology worked out. That’s what Kodak did with digital photography, and what Motorola did with digital telephony. But instead, Adobe has pushed generative AI deeply into Photoshop, to the point that ordinary users can create all sorts of videos they couldn’t before. Adobe could have seen AI as a threat or distraction, and it has continued to improve Photoshop without AI. But its leaders had the courage to invest aggressively in AI to elevate what users can do.

Deeper in the technology, Nvidia, the chipmaker, has been getting headlines for offering the best semiconductor chips for AI. To outsiders, the company might just seem lucky, with the right technology at the right time. But Nvidia’s current success is no accident: In the past decade, it aggressively acquired and developed expertise in AI, including creating customized chips and software. We can expect that aggressiveness to continue, enabling not only higher-value offerings for Nvidia, but better uses for AI than simple cost-cutting.

Boldness won’t work every time. But a bias for boldness is essential to overcome the deep-seated risk aversion in corporate hierarchies.

A Startup Mentality

Similar to boldness, and equally important for successful AI, is adopting the mentality of a startup company, no matter your company’s age or size. Startups excel in looking widely at markets and pivoting quickly to what customers are wanting now. Big companies have the resources to apply to those opportunities, but they usually move so slowly, with so many barriers (and lack of boldness), that startups get to markets faster. Open AI, which beat out Google with ChatGPT, had the best of both worlds: a startup mentality free of the hesitations that hampered Google, but with ample resources supplied by Microsoft and other investors.

The startup mindset is not just about courage and flexibility; it also involves a ferocious commitment to big achievement, a kind of hero’s journey to address a great challenge. Instead of predictability churning out good products at scale — though that’s a perfectly worthwhile goal — startups want to create something extraordinary. So they put a premium on looking around, flexibly partnering with others. They dispense with existing structures and biases, no matter how old and respected, in order to get done what needs to be done.

Amazon, the e-commerce giant, demonstrated a startup mentality in its embrace of AI. As the technology developed over a decade ago, the company saw an opportunity in creating a “smart speaker” as a new interface to the web. Amazon had no expertise in AI, but it picked up what it needed through hiring, acquisition, and internal development. The result was the Echo speaker and Alexa digital assistant, which did far more than simply help people order more items for purchase. It opened a new channel for adding value (and jobs) in many areas. Amazon has gone on to invest aggressively in AI beyond Alexa, with CEO Andy Jassy saying the technology promises to “transform and improve virtually every customer experience.”

• • •

Companies can’t adopt these drivers overnight, but they can start moving toward a point of serious commitment to new possibilities. Most of those drivers also work at the level of individuals looking for purpose and achievement in their own careers. They can embrace boldness, adopt a startup mentality, and other imperatives. Like companies, employees can invest aggressively in AI by acquiring the requisite skills and experience — thereby not just protecting their careers, but adding value at a higher level.

Much of corporate life has quite properly been about churning out reliable products at low cost. What we need now, to prevent mass unemployment, is for many firms to break out of this discipline and speed up the AI future. The great danger is that most companies will play it safe, make the easy investments, and do fine in the short term.

Humanity never thrives when it fears innovation. Imagine if the first humans feared fire; yes, they got burned sometimes, but without harnessing the power of it, we might have gone extinct. We think the same applies to AI. Rather than fear it, we need to harness its power. We must put it in the hands of every human being, so we collectively can achieve and live at this higher level.

Offering convenient, secure payment processing is a critical component of any online store, but it can be difficult to evaluate the different options available to small businesses today. In this article, you’ll gain a deeper understanding of what is Stripe, how it works, how to set it up, its key features and benefits and how it compares to other options. After reading, you should have a clear understanding of how Stripe works and whether it’s the right payment processing system for your small business. 

What is Stripe?

Stripe is an online payment processor and payment gateway that lets customers securely pay online for products and services. When customers are ready to make an online purchase, they can submit their payment through Stripe, which processes the payment, communicates the success or failure of the transaction back to the customer, and ensures that the funds are properly transferred to the business.

Stripe has developed integrations with popular ecommerce website builders (such as WooCommerce and Shopify) and also offers a suite of tools and APIs to allow businesses more flexibility in how they integrate its payment functionality into their site. Stripe is a popular payment processing system that is used by businesses of all sizes, from Atlassian and Lyft to small ecommerce stores.

Payment methods accepted by Stripe

Stripe supports a wide range of payment methods, so it’s a very convenient option for your customers. Stripe accepts:

• All major credit and debit cards (e.g., Visa, Mastercard, American Express, Discover, etc.)
• ACH payments (i.e. bank account transfers)
• Digital wallets (e.g., Apple Pay, Google Pay, etc.)

It can also support buy now, pay later style services (e.g., AfterPay). Stripe payment methods work both online and in-person, so it is a great choice for businesses that have online and brick-and-mortar storefronts.

How does Stripe work to process payments?

Because Stripe acts as both a payment processor and a payment gateway, it covers everything you need to process payments online. Here’s a brief overview of how Stripe works:

1. Customer submits payment information.

The customer shares their credit card, debit card, or other payment information details, either on your online store’s checkout page or using a POS terminal (e.g., a card reader) at an in-person retail location.

2. Stripe securely transmits payment information to the acquiring bank.

Once the customer submits their payment information, Stripe encrypts these sensitive details and securely sends them to the bank that will process the transaction (i.e. the acquiring bank). Stripe uses several different acquiring banks, such as Wells Fargo. Merchants don’t need to have a dedicated account with the acquiring bank — you can benefit by using Stripe’s merchant account for these transactions.

3. Acquiring bank connects with the issuing bank.

When the acquirer receives the payment request, it connects with the issuing bank associated with the customer’s payment method (for example, if your customer has a Visa card from Citi, then Citi is the issuing bank).

4. Issuing bank approves payment and transfers funds to the acquiring bank.

If the customer has available funds, the issuing bank approves and authorizes payment, transfers funds, and communicates this back to the acquiring bank.

5. Stripe communicates success to the customer.

The acquiring bank communicates success to Stripe, which passes this message along to the customer (e.g., customer sees an “Order successful!” message on the checkout page). From the customer’s point of view, this entire process takes only a few seconds.

6. Stripe transfers payment to your bank account.

Once the issuing bank finalizes its approval (often the same day), Stripe will payout to your business bank account, minus its payment processing fee. It can take a few days for funds to hit your bank account, and some merchants use payout schedules that transfer payments on a regular basis (e.g., weekly).

How to set up and use Stripe payment processing

Now that you know how Stripe works, let’s cover how to use Stripe as a merchant. We’ll primarily cover how to set up Stripe for an ecommerce website.

Step 1: Sign up for a Stripe account

If you haven’t already done so, you’ll need to start by signing up for a Stripe account. This is a relatively simple process where you provide some basic contact details, business information, and banking details. Once your account is verified, you can continue on to step 2, or spend some time configuring additional settings, such as two-factor authentication.

Step 2: Integrate Stripe with your online store

Integrating Stripe with your online store will vary based on your ecommerce platform. Fortunately, Stripe has pre-built integrations with most major ecommerce platforms, such as WooCommerce, Shopify, BigCommerce, and more. This means that you can start using Stripe on your online store with just a few clicks, without the assistance of a developer.

If you have a completely custom site not built on an ecommerce platform, you’ll need the help of a developer (if you’re not comfortable coding) to build a direct integration with Stripe on your site. Stripe offers extensive developer documentation to support custom builds.

Step 3: Run a test transaction.

Before deploying Stripe to your customers, it’s a good idea to run a test transaction to ensure that everything is working correctly. The steps here can vary based on your ecommerce platform, but you’ll likely be able to enable a test mode to see if Stripe can connect and communicate correctly without actually processing a live transaction. Alternatively, you can test a live transaction and then refund yourself from your store dashboard.

This will give you a chance to experience what it’s like to use Stripe as a customer, as well as see how those payments show up in your Stripe Dashboard.

Step 4: Start selling!

If your test transaction went off without a hitch, you’re ready to start processing payments with Stripe. Disable any test mode you might have turned on for the previous step, and start selling!

Benefits of Stripe

Now that you know how Stripe works and how you can set it up on your online store, let’s explore a few of Stripe’s key benefits for your small business.

Integrations with ecommerce platforms and website builders

As previously mentioned, Stripe has integrations with many popular ecommerce platforms and website builders. This is a huge advantage for small businesses since it means you can get up and running with Stripe on your site without touching a single line of code. In addition to the speed of setup, this also means you can expect great support and continued quality-of-life improvements as those integrations are improved over time.

Endlessly customizable and developer-friendly

While there are many pre-built Stripe apps and plugins, there may come a time when you need to tweak Stripe to meet your site’s specific needs. In those cases, you’ll be pleased to know that Stripe is well-known for being developer-friendly and open to customization. Stripe has great documentation, which makes it easier for a developer to customize Stripe to your specifications.

Seamless payment experience

Your site’s payment experience can have a marked impact on your conversion rates (i.e. the rate at which customers make a purchase on your site) — a seamless experience can improve conversions, while a clunky or slow process can cause customers to abandon their purchases. Stripe provides a streamlined and user-friendly payment experience, ensuring that more customers who start checkout are able to complete it successfully.

Fast onboarding and extensive reporting

As you likely noticed during the setup instructions, it’s easy to onboard as a new Stripe customer. Most notably, Stripe customers don’t have to go through the lengthy and difficult process of setting up a merchant account with an acquiring bank — they’ll automatically use Stripe’s own merchant account for their payment processing. This is a huge time saver for small business owners!

Stripe users also benefit from its extensive reporting capabilities through the Stripe Dashboard. You’ll be able to clearly track your payment activity, transaction fees, and payouts with Stripe’s reporting, giving you greater clarity into the health of your business.

Are Stripe payments safe?

Stripe has extensive security and fraud prevention features, making it one of the safer options for accepting payments on your store. Stripe is a certified Level 1 PCI Service Provider, which means it adheres to very strict security standards set by the PCI Security Standards Council. You can feel confident that your customers’ payment information is safe when processed by Stripe.

Online fraud is a real problem for ecommerce stores, but Stripe offers advanced fraud detection through its Stripe Radar service. This feature, built directly into Stripe, can proactively identify and prevent fraudulent charges, which protects your customers and your business. Additionally, Stripe can support features like:

These bring even more powerful fraud prevention, but please note that utilizing these features may impact your transaction fees.

Is Stripe right for your business?

Not all businesses are created equal! There are a number of factors that will determine if Stripe is the right payment processing solution for your business. Stripe is a great fit for small businesses that:

• Use popular ecommerce platforms. Stripe’s direct integrations with WooCommerce and Shopify make it a great option for businesses built on those platforms. You won’t have to spend any of your valuable time or resources building or maintaining a connection with Stripe.

• Have security or fraud concerns. If you’ve been the victim of fraudulent charges and bad actors before, you know how costly it can be to your business reputation and bottom line. Stripe’s sophisticated fraud prevention and top-notch security make it a great choice for security-conscious merchants.

• Sell internationally. Stripe is supported in over 40 countries, so it is a smart option for businesses with international sales.

• Care about customization. Because Stripe is so developer-friendly, it will be attractive to businesses that want the option to customize Stripe to meet their store’s specific needs.
• Want to give their customers options. Stripe supports a wide variety of payment methods (credit cards, debit cards, ACH payments, digital wallets, buy now / pay later, and more) so it gives customers a lot of flexibility around how to pay for your products and services.

However, Stripe isn’t right for everyone. Stripe cannot be used for selling certain high-risk products (check out their list of restricted businesses to ensure your products and services are supported by Stripe). Additionally, Stripe charges a significant transaction fee of 2.9% + 30¢ per online transaction, so it may not be a cost-effective option for every business.

What are some Stripe alternatives?

While Stripe payment processing is a popular and reliable solution, there are alternatives that might be a better, more affordable fit for your business. These options vary based on their transaction fees, customizability, support for international sales and integrations with other apps used by your online store. Some examples include:

When considering a payment processing system, be sure to consider these transaction fees as well as which features are most important to your business. Ideally, you’ll choose a payment processing system that offers the features you need at a great rate, so that you aren’t overpaying for capabilities that aren’t important to your business.

The frenzy surrounding the launch of Large Language Models (LLMs) and other types of Generative AI (GenAI) isn’t going to fade anytime soon. Users of GenAI are discovering and recommending new and interesting use cases for their business and personal lives. Many recommendations start with the assumption that GenAI requires a human prompt. Indeed, Time magazine recently proclaimed “prompt engineering” to be the next hot job, with salaries reaching $335,000. Tech forums and educational websites are focusing on prompt engineering, with Udemy already offering a course on the topic, and several organizations we work with are now beginning to invest considerable resources in training employees on how best to use ChatGPT.

However, it may be worth pausing to consider other ways of interacting with GPT technologies, which are likely to emerge soon. We present an intuitive way to think about this issue, which is based on our own survey of GenAI developments, combined with conversations with companies that are seeking to develop some versions of these.

A Framework of GPT Interactions

A good starting point is to distinguish between who is involved in the interaction — individuals, groups of people, or another machine — and who starts the interaction, human or machine. This leads to six different types of GenAI uses, shown below. ChatGPT, where one human initiates interaction with the machine is already well-known. We now describe each of the other GPTs and outline their potential.

CoachGPT is a personal assistant that provides you with a set of suggestions on managing your daily life. It would base these suggestions not on explicit prompts from you, but on the basis of observing what you do and your environment. For example, it could observe you as an executive and note that you find it hard to build trust in your team; it could then recommend precise actions to overcome this blind spot. It could also come up with personalized advice on development options or even salary negotiations.

CoachGPT would subsequently see which recommendations you adopted or didn’t adopt, and which benefited you and which ones didn’t to improve its advice over time. With time, you would get a highly personalized AI advisor, coach, or consultant.

Organizations could adopt CoachGPT to advise customers on how to use a product, whether a construction company offering CoachGPT to advise end users on how best to use its equipment, or an accounting firm proffering real-time advice on how best to account for a set of transactions.

To make CoachGPT effective, individuals and organizations would have to allow it to work in the background, monitoring online and offline activities. Clearly, serious privacy considerations need to be addressed before we entrust our innermost thoughts to the system. However, the potential for positive outcomes in both private and professional lives is immense.

GroupGPT would be a bona fide group member that can observe interactions between group members and contribute to the discussion. For example, it could conduct fact checking, supply a summary of the conversation, suggest what to discuss next, play the role of devil’s advocate, provide a competitor perspective, stress-test the ideas, or even propose a creative solution to the problem at hand.

The requests could come from individual group members or from the team’s boss, who need not participate in team interactions, but merely seeks to manage, motivate, and evaluate group members. The contribution could be delivered to the whole group or to specific individuals, with adjustments for that person’s role, skill, or personality.

The privacy concerns mentioned above also apply to GroupGPT, but, if addressed, organizations could take advantage of GroupGPT by using it for project management, especially on long and complicated projects involving relatively large teams across different departments or regions. Since GroupGPT would overcome human limitations on information storage and processing capacity, it would be ideal for supporting complex and dispersed teams.

BossGPT takes an active role in advising a group of people on what they could or should do, without being prompted. It could provide individual recommendations to group members, but its real value emerges when it begins to coordinate the work of group members, telling them as a group who should do what to maximize team output. BossGPT could also step in to offer individual coaching and further recommendations as the project and team dynamics evolve.

The algorithms necessary for BossGPT to work would be much more complicated as they would have to consider somewhat unpredictable individual and group reactions to instructions from a machine, but it could have a wide range of uses. For example: an executive changing job could request a copy of her reactions to her first organization’s BossGPT instructions, which could then be used to assess how she would fit into the new organization — and the new organization-specific BossGPT.

At the organizational level companies could deploy BossGPT to manage people, thereby augmenting — or potentially even replacing — existing managers. Similarly, BossGPT has tremendous applications in coordinating work across organizations and managing complex supply chains or multiple suppliers.

Companies could turn BossGPT into a product, offering their customers AI solutions to help them manage their business. These solutions could be natural extensions of the CoachGPT examples described earlier. For example, a company selling construction equipment could offer BossGPT to coordinate many end users on a construction site, and an accounting firm could provide it to coordinate the work of many employees of its customers to run the accounting function in the most efficient way.

AutoGPT entails a human giving a request or prompt to one machine, which in turn engages other machines to complete the task. In its simplest form, a human might instruct a machine to complete a task, but the machine realizes that it lacks a specific software to execute it, so it would search for the missing software on Google before downloading and installing it, and then using it to finish the request.

In a more complicated version, humans could give AutoGPT a goal (such as creating the best viral YouTube video) and instruct it to interact with another GenAI to iteratively come up with the best ChatGPT prompt to achieve the goal. The machine would then launch the process by proposing a prompt to another machine, then evaluate the outcome, and adjust the prompt to get closer and closer to the final goal.

In the most complicated version, AutoGPT could draw on functionalities of the other GPTs described above. For example, a team leader could task a machine with maximizing both the effectiveness and job satisfaction of her team members. AutoGPT could then switch between coaching individuals through CoachGPT, providing them with suggestions for smoother team interactions through GroupGPT, while at the same time issuing specific instructions on what needs to be done through BossGPT. AutoGPT could subsequently collect feedback from each activity and adjust all the other activities to reach the given goal.

Unlike the above versions, which are still to be created, a version of AutoGPT has been developed and was rolled out in April 2023, and it’s quickly gaining broad acceptance. The technology is still not perfect and requires improvements, but it is already evident that AutoGPT is able to complete a set of jobs that requires the completion of several tasks one after the other.

We see its biggest applications in complex tasks, such as supply chain coordination, but also in fields such as cybersecurity. For example, organizations could prompt AutoGPT to continually address any cybersecurity vulnerabilities, which would entail looking for them — which already happens — but then instead of simply flagging them, AutoGPT would search for solutions to the threats or write its own patches to counter them. A human might still be in the loop, but since the system is self-generative within these limits, we believe that AutoGPT’s response is likely to be faster and more efficient.

ImperialGPT is the most abstract GenAI — and perhaps the most transformational — in which two or more machines would interact with each other, direct each other, and ultimately direct humans to engage in a course of action. This type of GPT worries most AI analysts, who fear losing control of AI and AI “going rogue.” We concur with these concerns, particularly if — as now — there are no strict guardrails on what AI is allowed to do.

At the same time, if ImperialGPT is allowed to come up with ideas and share them with humans, but its ability to act on the ideas is restricted, we believe that this could generate extremely interesting creative solutions especially for “unknown unknowns,” where human knowledge and creativity fall short. They could then easily envision and game out multiple black swan events and worst-case scenarios, complete with potential costs and outcomes, to provide possible solutions.

Given the potential dangers of ImperialGPT, and the need for tight regulation, we believe that ImperialGPT will be slow to take off, at least commercially. We do anticipate, however, that governments, intelligence services, and the military will be interested in deploying ImperialGPT under strictly controlled conditions.

Implications for your Business

So, what does our framework mean for companies and organizations around the world? First and foremost, we encourage you to step back and see the recent advances in ChatGPT as merely the first application of new AI technologies. Second, we urge you to think about the various applications outlined here and use our framework to develop applications for your own company or organization. In the process, we are sure you will discover new types of GPTs that we have not mentioned. Third, we suggest you classify these different GPTs in terms of potential value to your business, and the cost of developing them.

We believe that applications that begin with a single human initiating or participating in the interaction (GroupGPT, CoachGPT) will probably be the easiest to build and should generate substantial business value, making them the perfect initial candidates. In contrast, applications with interactions involving multiple entities or those initiated by machines (AutoGPT, BossGPT, and ImperialGPT) may be harder to implement, with trickier ethical and legal implications.

You might also want to start thinking about the complex ethical, legal, and regulatory concerns that will arise with each GPT type. Failure to do so exposes you and your company to both legal liabilities and — perhaps more importantly — an unintended negative effect on humanity.

Our next set of recommendations depends on your company type. A tech company or startup, or one that has ample resources to invest in these technologies, should start working on developing one or more of the GPTs discussed above. This is clearly a high-risk, high-reward strategy.

In contrast, if your competitive strength is not in GenAI or if you lack resources, you might be better off adopting a “wait and see” approach. This means you will be slow to adopt the current technology, but you will not waste valuable resources on what may turn out to be only an interim version of a product. Instead, you can begin preparing your internal systems to better capture and store data as well as readying your organization to embrace these new GPTs, in terms of both work processes and culture.

The launch and rapid adoption of GenAIs is rightly being considered as the next level in the evolution of AI and a potentially epochal moment for humanity in general. Although GenAIs represent breakthroughs in solving fundamental engineering and computer science problems, they do not automatically guarantee value creation for all organizations. Rather, smart companies will need to invest in modifying and adapting the core technology before figuring out the best way to monetize the innovations. Firms that do this right may indeed strike it rich in the GenAI goldrush.