Jun. 17, 2024
Get inspired by these profitable digital products and learn what marketplaces you can sell them on.
The company is the world’s best digital board rate supplier. We are your one-stop shop for all needs. Our staff are highly-specialized and will help you find the product you need.
Selling digital products is a low-cost, low-hassle way to open a new revenue stream. Whether that stream is a trickle or a torrent will depend on how much time you invest, the quality of your products, and how well you promote them.
You may have hopes of making it big, or maybe you just want a passive income stream that can grow to eventually replace your job.
Whatever your reasons are, diversifying your revenue&#;and finding new ways to earn from home&#;is a smart move. Best of all, you get a chance to put your knowledge and talent to work helping others.
This article should give you an idea of the best digital products that you can sell online and where you can sell them.
Skip ahead:
A digital product is any virtual or intangible item that you can buy and sell online.
Ebooks and webinars are part of this, yes. But it also includes paid subscriptions to your favorite creator&#;s social media page, community memberships, newsletters, and so much more &#; which we&#;ll cover shortly.
As long as you&#;re selling or buying something that doesn&#;t have a physical form you can hold and feel, it passes for a digital product.
Why bother selling something that isn&#;t tangible? Here are a few reasons.
By , the digital product industry will be worth $74 billion, per a JP Morgan study. This translates into a large potential market for digital creators, whether you&#;re new or have been at it for a long time.
The truth is that many people want access to your knowledge and expertise and are willing to pay for it. All you need to do is package it into an easily accessible format that delivers value to them &#; and you&#;ll earn your share of the market.
Creating and selling digital products requires minimal technical skills and resources, making it easy for anyone to get started. You don&#;t need to have a physical storefront, technical skills, or even a large team to create and sell digital products.
As long as you have access to the internet and are willing to share your knowledge, you can create a digital product from any part of the world. Plus, you&#;re not restricted to a physical location when it comes to sales.
Digital products can be sold globally without any geographic limitations. This means that you can reach customers from all over the world, expanding your customer base and potential revenue.
There&#;s more than one way to monetize your knowledge. Not a fan of writing? You can create a webinar, sell consulting packages, sell membership packages for your social accounts, templates, and so much more.
No matter what your bandwidth, skills, and interests are, you&#;ll find a digital product format that works for you.
Digital products have lower overhead costs than physical ones.
You can create a digital product using your knowledge (which is free) and some free and low-cost online tools. Let&#;s say you want to write and sell an ebook; here&#;s a 3-step process that costs little or nothing:
Steps
Tools
Process
1. Create
1. ChatGPT (Free)
2. Google Docs (Free)
Once you come up with an outline, you can input it into an LLM chatbot like ChatGPT to generate a first draft. Then edit and host the final copy in Google Docs.
2. Host
1. Carrd (Free)
2. Thinkific (Free)
Create a free ebook landing page on Carrd.
Alternatively, use Thinkific to host, sell, and market your ebook from a single platform.
3. Market
Social media (Free)
Promote your course for free on your social media channels.
It&#;s different when you&#;re creating paperbacks. For one, you&#;ll need to pay for physical book production, which can cost thousands of dollars depending on how many you print. On top of that, you&#;ll also incur third-party costs like shipping fees.
If you&#;ve already got a day job keeping you busy, the digital products you sell online can keep money coming in even if most of your week is spent at your full-time gig.
You can market and sell your digital product on auto-pilot with a sales funnel. A sales funnel is an automated sequence that captures potential customers and nurtures them until they make a purchase. It sounds complex, but it&#;s a series of simple steps that pretty much looks like this:
The link to download lead generation asset your social media profile &#;&#;Landing page with capture &#;&#; Digital product promotional emails &#;&#; Landing page for product purchase and access
Once you&#;ve set this up, you can make money from your digital product all year round with minimal effort.
Sometimes, it helps to be good at something obscure. Honing in on a specific audience with specific needs is easier than competing broadly.
Lots of people want shredded abs in 30 days. A much smaller minority wants the perfect physique for water polo. That&#;s called serving a niche.
The challenge with selling niches locally is there often isn&#;t a big enough market to sell to. But even the smallest niches are big enough to serve globally with no geographic boundaries limiting who you can sell to.
With more businesses shifting online, the demand for digital products continues to grow, with the demand for online courses estimated to reach $319+ bn by .
And adoption is accelerating&#;our report on the impact COVID-19 had on the online course industry found a 217% increase in student enrollment in online courses in the second half of March , as COVID social distancing measures came into play. Now that we&#;re coming out of COVID, the demand for online courses is here to stay!
Now that you know what digital products are and what their benefits are, let&#;s look at 13 of the most popular digital products you can create and sell this year.
Ebooks
Many creators are making bank from selling ebooks. Take Carol Tice, for example. She has made over $45,000 selling ebooks with minimal effort.
You don&#;t need to be the world&#;s greatest novelist to write a book thousands will buy. Think about what you do best and how you can teach it to others in writing.
For instance, Tiffany &#;The Budgetnista&#; Aliche is a financial educator with the goal of empowering women worldwide. She runs a popular online school, the Live Richer Academy. On top of that, Aliche has published a number of successful ebooks for students and non-students alike.
Before you create an ebook, make sure there&#;s a market for it first. You&#;ll need to do two things here:
Ask your audience what challenges they are facing and pick one you can solve with your knowledge
Ask your audience if they like to read ebooks or prefer some other content format
Once you&#;ve established demand for it, you can create your ebook using the steps outlined in this article. On a high level, you&#;ll need to come up with a topic and outline, develop the outline, proofread the content, and create a simple book cover using a tool like Canva or AI art generators.
You can sell your ebook on a self-hosted platform created with Thinkific. You can also sell it on ecommerce marketplaces like Amazon and Smashwords.
Courses
Teaching online lets you turn something you already have&#;your expertise&#;into a new source of revenue. If you have the expertise, and a passion for sharing it with the world, then an online course is the perfect digital product for you to sell.
People are eager to learn from home, whether they want to learn a new hobby or advance themselves professionally&#;you can serve them by launching a digital course.
The appeal of creating online courses isn&#;t just limited to traditional classroom environments like high school and university, or corporate trainers adapting with remote onboarding&#; And you don&#;t need to be formally trained as an educator, either.
You can create an online course to teach people just about anything &#; from tai chi and drone piloting online to yoga and guitar.
Here are some other areas of expertise that we&#;ve seen translate into profitable online course businesses to give some ideas of what you can teach:
Sewing
Yoga classes
Laughter yoga
Guitar lessons
Meditation
Dance classes
Juggling
Resumes and job search
Professional development
Arts and Crafts
Photography
Copywriting
Graphic Design
If you don&#;t want to deal with the pressure of self-hosting and marketing your online course, you can sell it on a global marketplace like Udemy and Coursera. Once your course meets their standards, these platforms will add it to their library and market it to platform users on your behalf.
Alternatively, you can create and sell online courses on your own website using Thinkific. This gives you total control over your course material and students.
Memberships and paid communities
If your goal is to build recurring revenue by building and serving a community on an ongoing basis, then a membership is the perfect digital product for you to sell.
Whether you plan on selling a series of online courses, or simply charge members for the ability to pick your brain, a membership site can help you build up a monthly revenue stream.
Membership sites aren&#;t just geared towards one specific type of business. As you can see from these membership site examples, anyone with niche expertise and a passion for sharing it with the world can create one.
With a paid community membership, students get the chance to participate in exclusive discussion groups, workshops, and Q&A sessions. And when they subscribe, they add to a steady, predictable stream of income for you, their teacher.
In addition to offering standalone yoga classes geared towards a range of skill levels, Lasater Yoga has bridged the gap between online courses and membership sites by offering personalized mentorship through monthly office hours calls. You can purchase any of their classes, but for $19 per month, you get the full experience and the opportunity to engage the experts.
If you already have a strong social media following, you can sell your membership program on your social account. Your audience already knows and trusts you and is likely to sign up for paid membership.
Graphic design
If you&#;re a graphic designer, you already have the tools you need to create digital items that sell. That&#;s because other designers, some of them on tight deadlines, are looking for the elements they need to make their work shine.
Vector icons, textures, objects, typefaces&#;there&#;s a whole range of digital products you can create and sell on the side while you work day-to-day with clients. If you have a passion for design, it can be a chance to let your creativity shine outside of a client&#;s project.
For instance, Iuliia Mazur is a professional graphic designer based in Ukraine who sells popular clip art packs on the side. By selling her designs on Creativemarket.com, she can generate passive income and leads for her freelance consulting business.
There are several platforms where you can sell your art and design work online. Some popular options include Etsy, Society6, Redbubble, Fine Art America, and Artfinder.
Additionally, you can always sell your work directly through your own website or social media channels.
Templates
There&#;s a world of people who want a head start on whatever it is that they&#;re working on&#;from WordPress bloggers to newlyweds.
Why reinvent the wheel? A template can save someone the effort of designing a website from scratch. It can also help them get their wedding invitations sent out on time, create personalized business cards, plan their marketing strategy, crunch some numbers on Excel, or polish their resume in a pinch.
For instance, designer Janna Hagan made over $5,000 just selling resume templates during a period between jobs. Obviously, not everyone is going to have the same level of success&#;but reading Jana&#;s story may inspire you to start your own venture.
Other templates you can sell include:
Business planning templates
Marketing strategy templates
Excel templates
Google Data Studio Analytics templates
Business contracts
Canva Templates
WordPress themes
DIY home project blueprints
PowerPoint presentations
Look for niche marketplaces where you can sell your template. Say you&#;re selling WordPress themes, Theme Forest is a great choice. Meanwhile, Notion Utopia is the top marketplace for &#; you guessed it &#; Notion templates.
You can also sell templates on the social platform where your target audience hangs out. For example, if you&#;re selling a social media calendar template, you can market it on LinkedIn, X, and Instagram.
Craft patterns and downloadable prints
If you decide to sell on Etsy, you&#;ll be marketing your digital products to a captive audience of 46 million, many of whom joined the site because they love crafting and DIY projects. There&#;s a huge market for patterns&#;for sewing, knitting, macrame, papercraft, and just about any project you can imagine.
On top of that, there&#;s the printable market: whether you offer coloring pages, kids&#; workbooks, posters, or birthday cards, someone out there is looking for them. For instance, Lena Miramar&#;s summery printable posters are a huge hit, and she&#;s built a business out of selling digital files.
Etsy is obviously the first choice. But, if for some reason you don&#;t want to sell on Etsy, you can explore platforms like Shopify and Makerist.
You can also set up a store on your preferred social platform &#; like a Facebook or Instagram store &#; to sell your prints.
Music and audio
The hills are alive with the sound of music. Or the internet is, at least. Podcasters, YouTubers, bedroom pop stars, film producers, marketing teams&#;they all use audio in one form or another, and they&#;re all in the market for that special element to make their work stand out.
Examples of audio products include:
Beats and instrument samples
Plugins for music software
Stock music
Sound effects
For instance, Mattia Cellotto is a digital sound producer who sells recordings of everything from trained animals to vintage lab equipment.
You can independently publish and distribute your music on digital music stores like CD Baby and Tunecore. These platforms will help you publish your music on streaming platforms and online stores for a fee.
If you want to sell stock or royalty-free music, you can use platforms like Audio Jungle and Pond5.
Stock photography
Stock photos get a bad rep. We&#;re all familiar with the cliché: a model, awkwardly posed, performing some indecipherable activity against a stark white background. Or better yet&#;viral sensation, Hide the Pain Harold.
However, beautiful stock photography does exist&#;just check Unsplash for inspiration. If you have an eye for light, color, and framing, selling your photos online can supply you with a steady side income.
Another option is to give away stock photos for free in order to promote other products. For instance, self-described &#;hobbyist photographer&#; Annie Spratt shares free photos on Unsplash. But she also sells photo rights online. Customers can buy full commercial licenses on a per-photo basis, which allows them to legally create and sell prints of the work.
Stock photography marketplaces like Alamy and Shutterstock pay percentage commissions for photo sales.
SmugMug, on the other hand, is a paid membership platform for stock photos. Once you sign up, the platform takes care of sourcing buyers for your photos. On top of that, it will let you keep up to 85% of the profit on the sales.
Software and games
It&#;s no secret there is a large market for both software and digital games. While both of these digital products require coding knowledge to create, for those who do have a development background, they can be immensely lucrative. If you&#;re interested in getting into the software or gaming market and don&#;t have the technical skills to build a product yourself, consider working with someone who does.
Software can be any solution or software that is powered by code. This includes mobile apps, web apps, desktop software, and many other options. The difference in effort between a basic mobile game, for example, vs. complex accounting software, is something to take into account when scoping out what kind of software you would like to sell. Smaller niche software offerings may be a faster route to making money.
The indie gaming market has also exploded in the last few years, with platforms like Steam allowing developers to sell directly to gamers without large studio interference. Games like Hollow Knight and Among Us both have indie roots and have exploded in popularity. While creating a digital game is undoubtedly a large time commitment, the payoffs can be major.
Shopify is one of the most popular platforms for selling software. It lets you upload digital copies of your software. Customers can download these after making verified payments.
On the other hand, many creators like to sell games on Ecwid. You can create a free online game store or set up an Ecwid store on your website to make sales directly.
Webinars
If you don&#;t have the time to write an ebook, consider hosting a paid webinar to share your knowledge.
It&#;s an easy way to build authority in your industry, plus the resale value is great too. You can record and sell it to people who couldn&#;t attend the event live.
Freelance writing business coach, Paulette Perhach, hosts a paid webinar at the beginning of the year to help writers set their goals for the year. She&#;s built up credibility in her niche, so it&#;s easy to convince people to sign up to learn from her.
If you&#;re just starting out, a webinar might not be the ideal digital product for you. Build credibility by sharing your knowledge for free first. When you&#;ve gained a loyal audience, you can ask them to pay to learn from you in real-time.
The first place to promote your webinar is your social platform. After all, people who are already familiar with your knowledge and expertise are more likely to pay to learn from you than those who aren&#;t.
You can create a landing page webinar using Thinkific for registrations. You can also host the webinar recording on Thinkific for post-event sales.
Related: How to create a webinar
Coaching and consulting sessions
Another way to monetize your knowledge is to provide paid coaching and consulting services. On average, consultants make more than $8,000 a month, so it&#;s a juicy financial reward for your expertise.
We&#;ll recommend this digital product for busy 9-5ers who want to make some extra cash on the side. On top of that, it helps you build credibility within your industry and become an authority in your niche.
You can organize one-on-one coaching sessions, or group calls with a small community. These sessions can be offered as one-time purchases or as part of a package that includes multiple sessions.
You can even offer tiered coaching packages where people pay more for a one-on-one session or pair your consulting offering with paid membership communities &#; it&#;s up to you!
Where to sell your consulting and coaching packages
Use Thinkific to create a landing page or website for your coaching and consulting services. It&#;s free and saves the time you would have otherwise spent on building a website from scratch. It also gives you full control over your branding, messaging, pricing, and overall website aesthetics.
Alternatively, list your coaching services for free on coaching directories like Noomii and Life Coach Hub to connect with potential clients. These directories are often free to join but may charge a fee for premium features.
Learn more: How to set up an online coaching business
Social media paid subscriptions
Social platforms like X and Instagram have rolled out subscription services, providing another path for digital creators to earn from their audience. Creators like Bianca Araduta and Dan Pulzello diversify their Instagram earnings via paid subscriptions.
On Instagram, for example, verified creators can ask their audience to pay a monthly fee to access exclusive content. The same goes for X &#; if you&#;re a verified creator with 500 followers, you can receive payments from your followers via paid subscriptions.
Subscriptions are a low-hanging fruit for creators who already have an active audience on social media. If you&#;ve built a loyal following, then a handful or more of them are definitely willing to pay for exclusive access to your content.
Where to sell paid subscriptions
You&#;ll sell subscriptions on the social platform you want people to subscribe to. Say you want people to subscribe to your Instagram account; you can record a reel telling them that you now offer subscriptions plus the type of content they&#;ll get if they sign up.
Learn more: How to set up Instagram subscriptions
Newsletter subscriptions
If you have an active social following but don&#;t want to offer paid subscriptions, you can sell newsletter subscriptions instead. In other words, people pay to receive exclusive newsletters from you at regular intervals.
You can sell newsletter subscriptions as a standalone digital product or as part of a community offering. For example, you can pair community memberships or coaching services with the newsletter offering. Creators like Jujureel offer a closed paid community and newsletter.
Where to sell newsletter subscriptions
You can start subscription-based paid newsletters on Substack and Patreon. These platforms charge a percentage of your profits and handle all of the admin work for setting up your newsletter and collecting payments.
You can also host your newsletter on platforms like Beehiiv and Convertkit. Beehiiv offers a referral program that rewards subscribers for sharing your newsletter with their audience &#; helping you reach more people organically and hopefully convert them into subscribers.
Learn more: The best paid newsletter platforms for creators.
Marketplaces like Amazon, Skillshare, and Udemy will take a big cut of your profits in exchange for the consumers they bring you. You don&#;t have any control over the branding, and it&#;s their logo on the site, not yours. When someone views your listings, they&#;ll also see your competitor&#;s products.
Marketplaces should be viewed as a marketing channel to complement your storefront because they are a great way to generate leads that you can drive to your own online store for larger purchases and subscriptions.
Related: How to Sell Digital Products on Amazon
Platform BasicsUseful FeaturesEtsyBest for: Downloadable prints, craft designs, and how-to guides.
Cost: $0.20 for a 4-month listing, plus 5% of each sale
Large community of crafters and vintage sellers you can target
Small percentage taken from each sale
Option for an unbranded online store using Pattern
Best for: eBooks, music, or art.
Cost: The individual plan costs $0.99 per unit sold, and the professional plan costs $39.99 per month no matter how many units you sell.
Kindle Direct Publishing (KDP) is a massive platform for ebooks sales
Super easy word doc to ebook conversion
Massive user traffic
Best for: Online courses
Cost: You are paid a relatively small royalty for the number of minutes students watch your courses (about $200/ month for first-time producers).
Good for lead generation / promotion
Shorter class formats
Mobile app
Best for: Online courses
Cost: Revenue share (50% of revenue goes to Udemy for being listed in their marketplace).
If you are looking for more details, kindly visit interactive panel manufacturer.
Featured content:Lead generation / promotion
Longer class formats
Mobile app
Read More:
New platforms for selling digital products seem to pop up every day. Below, you&#;ll find six tried-and-true industry standards, and how they&#;re used.
Selling digital products on Amazon
As one of the Big Five tech companies, Amazon seems like a natural first choice for getting your product out into the world. But how well does it work with digital products?
While Amazon lets you sell music and videos, the service was originally designed for physical products. For digital items, you can find more robust, easier to use solutions tailored to what you&#;re selling for a lower price.
Where Amazon shines, though, is ebook sales. In , 83% of independently published ebooks (books without ISBNs) were sold through Amazon, and they claimed 47% of ebook sales overall.
More than market share, Amazon boasts an easy-to-use publishing platform, Kindle Direct Publishing (KDP). If you&#;re just testing the waters, KDP could be a great intro to online publishing. As writer Biron Clark explains, formatting your Kindle ebook is as simple as uploading a Word document.
If you&#;re selling other products besides books&#;such as classes or workshops&#;Amazon isn&#;t a one-stop solution. But Amazon&#;s suggestion algorithms make this marketplace a great source of awareness for other products you may be selling
Selling digital products on Etsy
With more than 46 million active users, and 15 years dominating the market, Etsy is the go-to online service for craftspeople, artists, and vintage sellers.
It can also be an excellent place to sell digital products. For example, a search of &#;sewing pattern pdf&#; brings up over 400,000 results. Not only does Etsy make it easy to upload and sell craft guides, it exposes you to a huge market of crafters and DIYers&#;some of whom are bound to fit your niche. Instead of selling someone a poster, you can sell them a downloadable print.
Etsy is more suited to digital publications whose value lies in their visuals. Amazon&#;s Kindle has graphics support, but you won&#;t find many coloring books or clipart collections in that format. For visually rich items, and the people who want to buy them, Etsy is your best choice.
On the other hand, Etsy&#;s features for selling audio or video items aren&#;t robust. If those are going to be the bread and butter of your business, you&#;re better off going a different route.
Etsy is sort of like a marketplace, that gives you the option to open your own e-commerce storefront. If you&#;d like to have your own site on top of Etsy&#;s marketplace, Pattern lets you design and launch a separate, non-Etsy-branded website. Your Etsy shop will still exist, and automatically sync with your Pattern site. But you&#;ll have the chance to do your own branding, with your own domain.
Selling digital products on Shopify
Shopify has rapidly become one of the most popular e-commerce sites in the world, but people aren&#;t just selling physical products on Shopify.
Their Digital Downloads plugin allows you to offer digital products in your store. When someone purchases a digital product from you on Shopify, they can download the product immediately, and receive a link to download in their .
You can even add online courses built on Thinkific to your Shopify store, which means that you can sell other products alongside your course, like a physical copy of your book, an ebook, templates, merchandise, bonus study guides, and whatever else you want.
Because Udemy and Skillshare are online course marketplaces for students looking for online courses, these platforms are a great way to generate awareness.
But there&#;s a catch&#;
You&#;d have to sell a lot of courses on these platforms to make a profit because of their profit sharing and royalty schemes, as well as limits on the price you can charge.
There&#;s no fee to host a course on Udemy, and you can publish as many courses as you like. The catch is, Udemy will keep up between 50% &#; 75% of your revenue in exchange for driving visitors to your courses.
Skillshare operates under a subscription model, which means students pay a recurring fee for unlimited access to the entire catalog of premium classes. Teachers are paid out from a royalty pool based on the number of minutes their courses were watched by premium members.
As you can see, it&#;s best to view these marketplaces as a lead generation source that drives people back to your own online course platform for larger purchases and recurring revenue.
Selling with an online course and membership platform &#; Thinkific
(Hey&#;that&#;s us!)
If you&#;re ready to create and sell online courses, and you want to keep all of the revenue for yourself, Thinkific should be the first place you look. Thinkific is designed for independent instructors who want to teach what they know, rather than spend time learning a new software platform.
Since Thinkific provides the tools to create your digital storefront, you have complete control over how much you charge, and the design of your site.
It&#;s a great way to take one step beyond ebooks. If you&#;ve already had some success selling your book online, Thinkific lets you take that information and turn it into a rich, feature-filled online learning experience. Get started with our guide on how to transform your book into an online course.
You can take a full tour of Thinkific&#;s core features by signing up for free. Since the trial has no expiry, you can create and sell 1 course with students on Thinkific for free.
There are a number of different ways to promote your digital products, and what works the best will depend greatly on the platform you are selling on and what you&#;re actually selling. However, there are some principles to keep in mind across the board.
Don&#;t rely on your chosen platform to market your products. For example, if you decide to sell on Etsy or Amazon, you can&#;t expect to gain large amounts of traffic just for existing on these marketplaces. Build audiences for your products through social media platforms like Instagram or TikTok. If you&#;re stuck for ideas, pay attention to the most popular content and emulate those posts.
Once you start to see some growth try networking with other digital product creators in your space. This will help you build connections, get tips, and open up opportunities to cross-promote. If budget allows, try out a few paid advertisements on social media or on your selling platform if the platform supports ads. Test some different ideas and pay attention to what converts the most people to buy your product.
Find answers to common questions about buying and selling digital products.
Can I sell digital products without a website?
Yes. You don&#;t always need a website to sell digital products. You can host your digital products on third-party websites and marketplaces to make them available to potential customers. You can also sell your digital product exclusively on social media using in-app marketplaces and organic promotions on your page.
If you decide to create a website, you can do so easily with Thinkific. Thinkific has drag-and-drop templates that you can use to create a professional landing page for your digital product quickly.
Can I make money from selling digital products?
Yes. Digital products can provide both passive and full-time income for you. Many creators earn exclusively from selling different types of digital products like ebooks, paid subscriptions, and membership communities.
What is the best digital product to sell?
There&#;s no consensus on the best digital product to sell. It all depends on your skills, time, and audience. If you&#;re a photographer, you can sell stock photos and paintings. If you have an engaged social media following, you can sell membership subscriptions and newsletters.
How do I start making digital products?
First, you need to figure out what topic you&#;ll cover in your digital product. This should ideally be an area of knowledge your audience is struggling with. Next, choose the type of digital product and create content for it.
You can read our article on how to create digital products for the full scoop.
With The Leap, creators can build digital products and an online storefront in no time and for zero dollars. If that&#;s not music to a creator&#;s ears, we don&#;t know what is. Ready to create and sell your own digital products? Try The Leap for free today!
This post was originally published in and refreshed in January to be even more useful.
Amid the excitement surrounding generative AI since the release of ChatGPT, Bard, Claude, Midjourney, and other content-creating tools, CEOs are understandably wondering: Is this tech hype, or a game-changing opportunity? And if it is the latter, what is the value to my business?
The public-facing version of ChatGPT reached 100 million users in just two months. It democratized AI in a manner not previously seen while becoming by far the fastest-growing app ever. Its out-of-the-box accessibility makes generative AI different from all AI that came before it. Users don&#;t need a degree in machine learning to interact with or derive value from it; nearly anyone who can ask questions can use it. And, as with other breakthrough technologies such as the personal computer or iPhone, one generative AI platform can give rise to many applications for audiences of any age or education level and in any location with internet access.
All of this is possible because generative AI chatbots are powered by foundation models, which contain expansive neural networks trained on vast quantities of unstructured, unlabeled data in a variety of formats, such as text and audio. Foundation models can be used for a wide range of tasks. In contrast, previous generations of AI models were often &#;narrow,&#; meaning they could perform just one task, such as predicting customer churn. One foundation model, for example, can create an executive summary for a 20,000-word technical report on quantum computing, draft a go-to-market strategy for a tree-trimming business, and provide five different recipes for the ten ingredients in someone&#;s refrigerator. The downside to such versatility is that, for now, generative AI can sometimes provide less accurate results, placing renewed attention on AI risk management.
With proper guardrails in place, generative AI can not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones. Imagine a customer sales call, for example. A specially trained AI model could suggest upselling opportunities to a salesperson, but until now those were usually based only on static customer data obtained before the start of the call, such as demographics and purchasing patterns. A generative AI tool might suggest upselling opportunities to the salesperson in real time based on the actual content of the conversation, drawing from internal customer data, external market trends, and social media influencer data. At the same time, generative AI could offer a first draft of a sales pitch for the salesperson to adapt and personalize.
The preceding example demonstrates the implications of the technology on one job role. But nearly every knowledge worker can likely benefit from teaming up with generative AI. In fact, while generative AI may eventually be used to automate some tasks, much of its value could derive from how software vendors embed the technology into everyday tools (for example, or word-processing software) used by knowledge workers. Such upgraded tools could substantially increase productivity.
CEOs want to know if they should act now&#;and, if so, how to start. Some may see an opportunity to leapfrog the competition by reimagining how humans get work done with generative AI applications at their side. Others may want to exercise caution, experimenting with a few use cases and learning more before making any large investments. Companies will also have to assess whether they have the necessary technical expertise, technology and data architecture, operating model, and risk management processes that some of the more transformative implementations of generative AI will require.
The goal of this article is to help CEOs and their teams reflect on the value creation case for generative AI and how to start their journey. First, we offer a generative AI primer to help executives better understand the fast-evolving state of AI and the technical options available. The next section looks at how companies can participate in generative AI through four example cases targeted toward improving organizational effectiveness. These cases reflect what we are seeing among early adopters and shed light on the array of options across the technology, cost, and operating model requirements. Finally, we address the CEO&#;s vital role in positioning an organization for success with generative AI.
Creating value beyond the hype
Let&#;s deliver on the promise of technology from strategy to scale.
Excitement around generative AI is palpable, and C-suite executives rightfully want to move ahead with thoughtful and intentional speed. We hope this article offers business leaders a balanced introduction into the promising world of generative AI.
A generative AI primer
Generative AI technology is advancing quickly (Exhibit 1). The release cycle, number of start-ups, and rapid integration into existing software applications are remarkable. In this section, we will discuss the breadth of generative AI applications and provide a brief explanation of the technology, including how it differs from traditional AI.
Generative AI can be used to automate, augment, and accelerate work. For the purposes of this article, we focus on ways generative AI can enhance work rather than on how it can replace the role of humans.
While text-generating chatbots such as ChatGPT have been receiving outsize attention, generative AI can enable capabilities across a broad range of content, including images, video, audio, and computer code. And it can perform several functions in organizations, including classifying, editing, summarizing, answering questions, and drafting new content. Each of these actions has the potential to create value by changing how work gets done at the activity level across business functions and workflows. Following are some examples.
As the technology evolves and matures, these kinds of generative AI can be increasingly integrated into enterprise workflows to automate tasks and directly perform specific actions (for example, automatically sending summary notes at the end of meetings). We already see tools emerging in this area.
Glossary
Application programming interface (API) is a way to programmatically access (usually external) models, data sets, or other pieces of software.
Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence.
Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected &#;neurons&#; whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio.
Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.
Foundation models (FM) are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for a wide range of tasks out of the box or adapted to specific tasks through fine-tuning. Examples of these models are GPT-4, PaLM, DALL·E 2, and Stable Diffusion.
Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to generate content. Foundation models can also be used for non-generative purposes (for example, classifying user sentiment as negative or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to generative AI in this article, we include all foundation model use cases.
Graphics processing units (GPUs) are computer chips that were originally developed for producing computer graphics (such as for video games) and are also useful for deep learning applications. In contrast, traditional machine learning and other analyses usually run on central processing units (CPUs), normally referred to as a computer&#;s &#;processor.&#;
Large language models (LLMs) make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural language text, performing tasks such as summarization or knowledge extraction. GPT-4 (which underlies ChatGPT) and LaMDA (the model behind Bard) are examples of LLMs.
Machine learning (ML) is a subset of AI in which a model gains capabilities after it is trained on, or shown, many example data points. Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt and can become more effective in response to new data and experiences.
MLOps refers to the engineering patterns and practices to scale and sustain AI and ML. It encompasses a set of practices that span the full ML life cycle (data management, development, deployment, and live operations). Many of these practices are now enabled or optimized by supporting software (tools that help to standardize, streamline, or automate tasks).
Prompt engineering refers to the process of designing, refining, and optimizing input prompts to guide a generative AI model toward producing desired (that is, accurate) outputs.
Structured data are tabular data (for example, organized in tables, databases, or spreadsheets) that can be used to train some machine learning models effectively.
Transformers are key components of foundation models. They are artificial neural networks that use special mechanisms called &#;attention heads&#; to understand context in sequential data, such as how a word is used in a sentence.
Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced techniques to extract insights.
As the name suggests, the primary way in which generative AI differs from previous forms of AI or analytics is that it can generate new content efficiently, often in &#;unstructured&#; forms (for example, written text or images) that aren&#;t naturally represented in tables with rows and columns (see sidebar &#;Glossary&#; for a list of terms associated with generative AI).
The underlying model that enables generative AI to work is called a foundation model. Transformers are key components of foundation models&#;GPT actually stands for generative pre-trained transformer. A transformer is a type of artificial neural network that is trained using deep learning, a term that alludes to the many (deep) layers within neural networks. Deep learning has powered many of the recent advances in AI.
However, some characteristics set foundation models apart from previous generations of deep learning models. To start, they can be trained on extremely large and varied sets of unstructured data. For example, a type of foundation model called a large language model can be trained on vast amounts of text that is publicly available on the internet and covers many different topics. While other deep learning models can operate on sizable amounts of unstructured data, they are usually trained on a more specific data set. For example, a model might be trained on a specific set of images to enable it to recognize certain objects in photographs.
In fact, other deep learning models often can perform only one such task. They can, for example, either classify objects in a photo or perform another function such as making a prediction. In contrast, one foundation model can perform both of these functions and generate content as well. Foundation models amass these capabilities by learning patterns and relationships from the broad training data they ingest, which, for example, enables them to predict the next word in a sentence. That&#;s how ChatGPT can answer questions about varied topics and how DALL·E 2 and Stable Diffusion can produce images based on a description.
Given the versatility of a foundation model, companies can use the same one to implement multiple business use cases, something rarely achieved using earlier deep learning models. A foundation model that has incorporated information about a company&#;s products could potentially be used both for answering customers&#; questions and for supporting engineers in developing updated versions of the products. As a result, companies can stand up applications and realize their benefits much faster.
However, because of the way current foundation models work, they aren&#;t naturally suited to all applications. For example, large language models can be prone to &#;hallucination,&#; or answering questions with plausible but untrue assertions (see sidebar &#;Using generative AI responsibly&#;). Additionally, the underlying reasoning or sources for a response are not always provided. This means companies should be careful of integrating generative AI without human oversight in applications where errors can cause harm or where explainability is needed. Generative AI is also currently unsuited for directly analyzing large amounts of tabular data or solving advanced numerical-optimization problems. Researchers are working hard to address these limitations.
Using generative AI responsibly
Generative AI poses a variety of risks. CEOs will want to design their teams and processes to mitigate those risks from the start&#;not only to meet fast-evolving regulatory requirements but also to protect their business and earn consumers&#; digital trust (we offer recommendations on how to do so later in this article).
Fairness: Models may generate algorithmic bias due to imperfect training data or decisions made by the engineers developing the models.
Intellectual property (IP): Training data and model outputs can generate significant IP risks, including infringing on copyrighted, trademarked, patented, or otherwise legally protected materials. Even when using a provider&#;s generative AI tool, organizations will need to understand what data went into training and how it&#;s used in tool outputs.
Privacy: Privacy concerns could arise if users input information that later ends up in model outputs in a form that makes individuals identifiable. Generative AI could also be used to create and disseminate malicious content such as disinformation, deepfakes, and hate speech.
Security: Generative AI may be used by bad actors to accelerate the sophistication and speed of cyberattacks. It also can be manipulated to provide malicious outputs. For example, through a technique called prompt injection, a third party gives a model new instructions that trick the model into delivering an output unintended by the model producer and end user.
Explainability: Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given answer is produced.
Reliability: Models can produce different answers to the same prompts, impeding the user&#;s ability to assess the accuracy and reliability of outputs.
Organizational impact: Generative AI may significantly affect the workforce, and the impact on specific groups and local communities could be disproportionately negative.
Social and environmental impact: The development and training of foundation models may lead to detrimental social and environmental consequences, including an increase in carbon emissions (for example, training one large language model can emit about 315 tons of carbon dioxide).
While foundation models serve as the &#;brain&#; of generative AI, an entire value chain is emerging to support the training and use of this technology (Exhibit 2). Specialized hardware provides the extensive compute power needed to train the models. Cloud platforms offer the ability to tap this hardware. MLOps and model hub providers offer the tools, technologies, and practices an organization needs to adapt a foundation model and deploy it within its end-user applications. Many companies are entering the market to offer applications built on top of foundation models that enable them to perform a specific task, such as helping a company&#;s customers with service issues.
2
The first foundation models required high levels of investment to develop, given the substantial computational resources required to train them and the human effort required to refine them. As a result, they were developed primarily by a few tech giants, start-ups backed by significant investment, and some open-source research collectives (for example, BigScience). However, work is under way on both smaller models that can deliver effective results for some tasks and training that&#;s more efficient. This could eventually open the market to more entrants. Some start-ups have already succeeded in developing their own models&#;for example, Cohere, Anthropic, and AI21 Labs build and train their own large language models.
Putting generative AI to work
CEOs should consider exploration of generative AI a must, not a maybe. Generative AI can create value in a wide range of use cases. The economics and technical requirements to start are not prohibitive, while the downside of inaction could be quickly falling behind competitors. Each CEO should work with the executive team to reflect on where and how to play. Some CEOs may decide that generative AI presents a transformative opportunity for their companies, offering a chance to reimagine everything from research and development to marketing and sales to customer operations. Others may choose to start small and scale later. Once the decision is made, there are technical pathways that AI experts can follow to execute the strategy, depending on the use case.
Much of the use (although not necessarily all of the value) from generative AI in an organization will come from workers employing features embedded in the software they already have. systems will provide an option to write the first drafts of messages. Productivity applications will create the first draft of a presentation based on a description. Financial software will generate a prose description of the notable features in a financial report. Customer-relationship-management systems will suggest ways to interact with customers. These features could accelerate the productivity of every knowledge worker.
But generative AI can also be more transformative in certain use cases. Following, we look at four examples of how companies in different industries are using generative AI today to reshape how work is done within their organization. The examples range from those requiring minimal resources to resource-intensive undertakings. (For a quick comparison of these examples and more technical detail, see Exhibit 3.)
Changing the work of software engineering
The first example is a relatively low-complexity case with immediate productivity benefits because it uses an off-the-shelf generative AI solution and doesn&#;t require in-house customization.
The biggest part of a software engineer&#;s job is writing code. It&#;s a labor-intensive process that requires extensive trial and error and research into private and public documentation. At this company, a shortage of skilled software engineers has led to a large backlog of requests for features and bug fixes.
To improve engineers&#; productivity, the company is implementing an AI-based code-completion product that integrates with the software the engineers use to code. This allows engineers to write code descriptions in natural language, while the AI suggests several variants of code blocks that will satisfy the description. Engineers can select one of the AI&#;s proposals, make needed refinements, and click on it to insert the code.
Our research has shown that such tools can speed up a developer&#;s code generation by as much as 50 percent. It can also help in debugging, which may improve the quality of the developed product. But today, generative AI cannot replace skilled software engineers. In fact, more-experienced engineers appear to reap the greatest productivity benefits from the tools, with inexperienced developers seeing less impressive&#;and sometimes negative&#;results. A known risk is that the AI-generated code may contain vulnerabilities or other bugs, so software engineers must be involved to ensure the quality and security of the code (see the final section in this article for ways to mitigate risks).
The cost of this off-the-shelf generative AI coding tool is relatively low, and the time to market is short because the product is available and does not require significant in-house development. Cost varies by software provider, but fixed-fee subscriptions range from $10 to $30 per user per month. When choosing a tool, it&#;s important to discuss licensing and intellectual property issues with the provider to ensure the generated code doesn&#;t result in violations.
Supporting the new tool is a small cross-functional team focused on selecting the software provider and monitoring performance, which should include checking for intellectual property and security issues. Implementation requires only workflow and policy changes. Because the tool is purely off-the-shelf software as a service (SaaS), additional computing and storage costs are minimal or nonexistent.
Helping relationship managers keep up with the pace of public information and data
Companies may decide to build their own generative AI applications, leveraging foundation models (via APIs or open models), instead of using an off-the-shelf tool. This requires a step up in investment from the previous example but facilitates a more customized approach to meet the company&#;s specific context and needs.
In this example, a large corporate bank wants to use generative AI to improve the productivity of relationship managers (RMs). RMs spend considerable time reviewing large documents, such as annual reports and transcripts of earnings calls, to stay informed about a client&#;s situation and priorities. This enables the RM to offer services suited to the client&#;s particular needs.
The bank decided to build a solution that accesses a foundation model through an API. The solution scans documents and can quickly provide synthesized answers to questions posed by RMs. Additional layers around the foundation model are built to streamline the user experience, integrate the tool with company systems, and apply risk and compliance controls. In particular, model outputs must be verified, much as an organization would check the outputs of a junior analyst, because some large language models have been known to hallucinate. RMs are also trained to ask questions in a way that will provide the most accurate answers from the solution (called prompt engineering), and processes are put in place to streamline validation of the tool&#;s outputs and information sources.
In this instance, generative AI can speed up an RM&#;s analysis process (from days to hours), improve job satisfaction, and potentially capture insights the RM might have otherwise overlooked.
The development cost comes mostly from the user interface build and integrations, which require time from a data scientist, a machine learning engineer or data engineer, a designer, and a front-end developer. Ongoing expenses include software maintenance and the cost of using APIs. Costs depend on the model choice and third-party vendor fees, team size, and time to minimum viable product.
Freeing up customer support representatives for higher-value activities
The next level of sophistication is fine-tuning a foundation model. In this example, a company uses a foundation model optimized for conversations and fine-tunes it on its own high-quality customer chats and sector-specific questions and answers. The company operates in a sector with specialized terminology (for example, law, medicine, real estate, and finance). Fast customer service is a competitive differentiator.
This company&#;s customer support representatives handle hundreds of inbound inquiries a day. Response times were sometimes too high, causing user dissatisfaction. The company decided to introduce a generative AI customer-service bot to handle most customer requests. The goal was a swift response in a tone that matched the company brand and customer preferences. Part of the process of fine-tuning and testing the foundation model includes ensuring that responses are aligned with the domain-specific language, brand promise, and tone set for the company; ongoing monitoring is required to verify the performance of the system across multiple dimensions, including customer satisfaction.
The company created a product road map consisting of several waves to minimize potential model errors. In the first wave, the chatbot was piloted internally. Employees were able to give &#;thumbs up&#; or &#;thumbs down&#; answers to the model&#;s suggestions, and the model was able to learn from these inputs. As a next step, the model &#;listened&#; to customer support conversations and offered suggestions. Once the technology was tested sufficiently, the second wave began, and the model was shifted toward customer-facing use cases with a human in the loop. Eventually, when leaders are completely confident in the technology, it can be largely automated.
In this case, generative AI freed up service representatives to focus on higher-value and complex customer inquiries, improved representatives&#; efficiency and job satisfaction, and increased service standards and customer satisfaction. The bot has access to all internal data on the customer and can &#;remember&#; earlier conversations (including calls), representing a step change over current customer chatbots.
To capture the benefits, this use case required material investments in software, cloud infrastructure, and tech talent, as well as higher degrees of internal coordination in risk and operations. In general, fine-tuning foundation models costs two to three times as much as building one or more software layers on top of an API. Talent and third-party costs for cloud computing (if fine-tuning a self-hosted model) or for the API (if fine-tuning via a third-party API) account for the increased costs. To implement the solution, the company needed help from DataOps and MLOps experts as well as input from other functions such as product management, design, legal, and customer service specialists.
Accelerating drug discovery
The most complex and customized generative AI use cases emerge when no suitable foundation models are available and the company needs to build one from scratch. This situation may arise in specialized sectors or in working with unique data sets that are significantly different from the data used to train existing foundation models, as this pharmaceutical example demonstrates. Training a foundation model from scratch presents substantial technical, engineering, and resource challenges. The additional return on investment from using a higher-performing model should outweigh the financial and human capital costs.
In this example, research scientists in drug discovery at a pharmaceutical company had to decide which experiments to run next, based on microscopy images. They had a data set of millions of these images, containing a wealth of visual information on cell features that are relevant to drug discovery but difficult for a human to interpret. The images were used to evaluate potential therapeutic candidates.
The company decided to create a tool that would help scientists understand the relationship between drug chemistry and the recorded microscopy outcomes to accelerate R&D efforts. Since such multimodal models are still in infancy, the company decided to train its own instead. To build the model, team members employed both real-world images that are used to train image-based foundational models and their large internal microscopy image data set.
The trained model added value by predicting which drug candidates might lead to favorable outcomes and by improving the ability to accurately identify relevant cell features for drug discovery. This can lead to more efficient and effective drug discovery processes, not only improving time to value but also reducing the number of inaccurate, misleading, or failed analyses.
In general, training a model from scratch costs ten to 20 times more than building software around a model API. Larger teams (including, for example, PhD-level machine learning experts) and higher compute and storage spending account for the differences in cost. The projected cost of training a foundation model varies widely based on the desired model performance level and modeling complexity. Those factors influence the required size of the data set, team composition, and compute resources. In this use case, the engineering team and the ongoing cloud expenses accounted for the majority of costs.
The company found that major updates to its tech infrastructure and processes would be needed, including access to many GPU instances to train the model, tools to distribute the training across many systems, and best-practice MLOps to limit cost and project duration. Also, substantial data-processing work was required for collection, integration (ensuring files of different data sets are in the same format and resolution), and cleaning (filtering low-quality data, removing duplicates, and ensuring distribution is in line with the intended use). Since the foundation model was trained from scratch, rigorous testing of the final model was needed to ensure that output was accurate and safe to use.
Exploring opportunities in the generative AI value chain
The use cases outlined here offer powerful takeaways for CEOs as they embark on the generative AI journey:
Considerations for getting started
The CEO has a crucial role to play in catalyzing a company&#;s focus on generative AI. In this closing section, we discuss strategies that CEOs will want to keep in mind as they begin their journey. Many of them echo the responses of senior executives to previous waves of new technology. However, generative AI presents its own challenges, including managing a technology moving at a speed not seen in previous technology transitions.
Many organizations began exploring the possibilities for traditional AI through siloed experiments. Generative AI requires a more deliberate and coordinated approach given its unique risk considerations and the ability of foundation models to underpin multiple use cases across an organization. For example, a model fine-tuned using proprietary material to reflect the enterprise&#;s brand identity could be deployed across several use cases (for example, generating personalized marketing campaigns and product descriptions) and business functions, such as product development and marketing.
To that end, we recommend convening a cross-functional group of the company&#;s leaders (for example, representing data science, engineering, legal, cybersecurity, marketing, design, and other business functions). Such a group can not only help identify and prioritize the highest-value use cases but also enable coordinated and safe implementation across the organization.
Generative AI is a powerful tool that can transform how organizations operate, with particular impact in certain business domains within the value chain (for example, marketing for a retailer or operations for a manufacturer). The ease of deploying generative AI can tempt organizations to apply it to sporadic use cases across the business. It is important to have a perspective on the family of use cases by domain that will have the most transformative potential across business functions. Organizations are reimagining the target state enabled by generative AI working in sync with other traditional AI applications, along with new ways of working that may not have been possible before.
A modern data and tech stack is key to nearly any successful approach to generative AI. CEOs should look to their chief technology officers to determine whether the company has the required technical capabilities in terms of computing resources, data systems, tools, and access to models (open source via model hubs or commercial via APIs).
For example, the lifeblood of generative AI is fluid access to data honed for a specific business context or problem. Companies that have not yet found ways to effectively harmonize and provide ready access to their data will be unable to fine-tune generative AI to unlock more of its potentially transformative uses. Equally important is to design a scalable data architecture that includes data governance and security procedures. Depending on the use case, the existing computing and tooling infrastructure (which can be sourced via a cloud provider or set up in-house) might also need upgrading. A clear data and infrastructure strategy anchored on the business value and competitive advantage derived from generative AI will be critical.
CEOs will want to avoid getting stuck in the planning stages. New models and applications are being developed and released rapidly. GPT-4, for example, was released in March , following the release of ChatGPT (GPT-3.5) in November and GPT-3 in . In the world of business, time is of the essence, and the fast-paced nature of generative AI technology demands that companies move quickly to take advantage of it. There are a few ways executives can keep moving at a steady clip.
Although generative AI is still in the early days, it&#;s important to showcase internally how it can affect a company&#;s operating model, perhaps through a &#;lighthouse approach.&#; For example, one way forward is building a &#;virtual expert&#; that enables frontline workers to tap proprietary sources of knowledge and offer the most relevant content to customers. This has the potential to increase productivity, create enthusiasm, and enable an organization to test generative AI internally before scaling to customer-facing applications.
As with other waves of technical innovation, there will be proof-of-concept fatigue and many examples of companies stuck in &#;pilot purgatory.&#; But encouraging a proof of concept is still the best way to quickly test and refine a valuable business case before scaling to adjacent use cases. By focusing on early wins that deliver meaningful results, companies can build momentum and then scale out and up, leveraging the multipurpose nature of generative AI. This approach could enable companies to promote broader AI adoption and create the culture of innovation that is essential to maintaining a competitive edge. As outlined above, the cross-functional leadership team will want to make sure such proofs of concept are deliberate and coordinated.
As our four detailed use cases demonstrate, business leaders must balance value creation opportunities with the risks involved in generative AI. According to our recent Global AI Survey, most organizations don&#;t mitigate most of the risks associated with traditional AI, even though more than half of organizations have already adopted the technology. Generative AI brings renewed attention to many of these same risks, such as the potential to perpetuate bias hidden in training data, while presenting new ones, such as its propensity to hallucinate.
As a result, the cross-functional leadership team will want to not only establish overarching ethical principles and guidelines for generative AI use but also develop a thorough understanding of the risks presented by each potential use case. It will be important to look for initial use cases that both align with the organization&#;s overall risk tolerance and have structures in place to mitigate consequential risk. For example, a retail organization might prioritize a use case that has slightly lower value but also lower risk&#;such as creating initial drafts of marketing content and other tasks that keep a human in the loop. At the same time, the company might set aside a higher-value, high-risk use case such as a tool that automatically drafts and sends hyperpersonalized marketing emails. Such risk-forward practices can enable organizations to establish the controls necessary to properly manage generative AI and maintain compliance.
CEOs and their teams will also want to stay current with the latest developments in generative AI regulation, including rules related to consumer data protection and intellectual property rights, to protect the company from liability issues. Countries may take varying approaches to regulation, as they often already do with AI and data. Organizations may need to adapt their working approach to calibrate process management, culture, and talent management in a way that ensures they can handle the rapidly evolving regulatory environment and risks of generative AI at scale.
Business leaders should focus on building and maintaining a balanced set of alliances. A company&#;s acquisitions and alliances strategy should continue to concentrate on building an ecosystem of partners tuned to different contexts and addressing what generative AI requires at all levels of the tech stack, while being careful to prevent vendor lock-in.
Partnering with the right companies can help accelerate execution. Organizations do not have to build out all applications or foundation models themselves. Instead, they can partner with generative AI vendors and experts to move more quickly. For instance, they can team up with model providers to customize models for a specific sector, or partner with infrastructure providers that offer support capabilities such as scalable cloud computing.
Companies can use the expertise of others and move quickly to take advantage of the latest generative AI technology. But generative AI models are just the tip of the spear: multiple additional elements are required for value creation.
To effectively apply generative AI for business value, companies need to build their technical capabilities and upskill their current workforce. This requires a concerted effort by leadership to identify the required capabilities based on the company&#;s prioritized use cases, which will likely extend beyond technical roles to include a talent mix across engineering, data, design, risk, product, and other business functions.
As demonstrated in the use cases highlighted above, technical and talent needs vary widely depending on the nature of a given implementation&#;from using off-the-shelf solutions to building a foundation model from scratch. For example, to build a generative model, a company may need PhD-level machine learning experts; on the other hand, to develop generative AI tools using existing models and SaaS offerings, a data engineer and a software engineer may be sufficient to lead the effort.
In addition to hiring the right talent, companies will want to train and educate their existing workforces. Prompt-based conversational user interfaces can make generative AI applications easy to use. But users still need to optimize their prompts, understand the technology&#;s limitations, and know where and when they can acceptably integrate the application into their workflows. Leadership should provide clear guidelines on the use of generative AI tools and offer ongoing education and training to keep employees apprised of their risks. Fostering a culture of self-driven research and experimentation can also encourage employees to innovate processes and products that effectively incorporate these tools.
Businesses have been pursuing AI ambitions for years, and many have realized new revenue streams, product improvements, and operational efficiencies. Much of the successes in these areas have stemmed from AI technologies that remain the best tool for a particular job, and businesses should continue scaling such efforts. However, generative AI represents another promising leap forward and a world of new possibilities. While the technology&#;s operational and risk scaffolding is still being built, business leaders know they should embark on the generative AI journey. But where and how should they start? The answer will vary from company to company as well as within an organization. Some will start big; others may undertake smaller experiments. The best approach will depend on a company&#;s aspiration and risk appetite. Whatever the ambition, the key is to get under way and learn by doing.
Are you interested in learning more about 86 inch digital board price? Contact us today to secure an expert consultation!
Previous: How to Choose interactive flat panel manufacturer?
Next: How About The Industrial Touch Screen? What Are Its ...
Related Articles
If you are interested in sending in a Guest Blogger Submission,welcome to write for us!
All Comments ( 0 )