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Starting Small: How To Successfully Experiment With Generative AI

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By Alon Goren

At this point, most enterprises are dabbling in generative AI or planning to leverage the technology soon.

According to an October 2023 Gartner, Inc. survey, 45% of organizations are currently piloting generative AI, while 10% have deployed it in full production. Companies are eager to move from pilot to production and start seeing some real business results.

However, enterprises getting started with generative AI often run into a common stumbling block right out of the gate: They suffer analysis paralysis before they can even begin using the technology. There are tons of generative AI tools available today, both broad and highly specialized. Moreover, these tools can be leveraged for all sorts of professions and business purposes—sales, product development, finance, etc.

With so many choices and possibilities, enterprises often get stuck in the planning phase—debating where they should deploy generative AI first. Every business unit (and all of the business’s key stakeholders) wants to own a part of the company’s generative AI initiatives.

Things can get messy. To stay on track, businesses should follow these guidelines when experimenting with generative AI.

Focus On Specific Use Cases With Measurable Goals

Enterprises need to recognize that every part of the organization can benefit from generative AI—eventually. To get there, however, they need to get off the ground with a pilot project.

How do you decide where to get started? Keep it simple and identify a small, specific problem that exists today that can be improved with generative AI. Be practical. Choose an issue that’s been challenging the business for a while, has been difficult to fix in the past and will make a visibly positive impact once resolved. Next, enterprises need to agree upon metrics and goals. The problem can’t be too nebulous or vague; the impact of AI (success or failure) has to be easily measurable.

With that in mind, the pilot project should have a contained scope. The purpose is to demonstrate the real-world value of the technology, build support for it across the organization and then broaden adoption from there.

If organizations try to leverage AI in too many different ways and solve multiple problems, it’ll cause the scope to grow out of control and make it impossible to complete the pilot within a reasonable timeframe. Ambition has to be balanced with practicality. Launching a massive pilot project that requires extensive resources and long timelines is a recipe for failure.

What’s a good timeline for the pilot? It depends on the circumstances, of course. Generally speaking, however, it should only take a few weeks or a couple of months to execute, not multiple quarters or an entire year.

Start small, get something functional quickly and then iterate on it. This iterative approach allows for continuous learning and improvement, which is essential given the nascent state of generative AI technology.

Organizations must also be sure to keep humans in the loop from the very beginning of the experimentation phase. The rise of AI doesn’t render human expertise obsolete; it amplifies it. As productivity and business benefits increase with generative AI, human employees become even more valuable as supervisors and validators of AI output. This is essential for maintaining control and building trust in AI. In addition, the pool of early participants will also help champion the technology throughout the organization once the enterprise is ready to deploy it widely.

Finally, once the project has begun, organizations have to stick with it until it’s complete. Don’t waste time starting over or shifting to other use cases prematurely. Just get going and stay the course. After that’s been completed successfully, companies can expand their use of generative AI more broadly across the organization.

Choosing The Right Technology

The other major component of the experimentation phase is selecting the right vendor. With the generative AI market booming, it can seem impossible to tell the differences between one solution and another. Lots of noisy marketing only makes things more confusing.

The best way to cut through the noise is to identify the requirements that are most important to the organization (e.g., data security, governance, scalability, compatibility with existing infrastructure) and look for the vendor that best meets those needs.

It’s extremely important to understand where vendors stand on each of these things early on to avoid the headache of discovering that they don’t really check those boxes later. The only way to do that is by talking to the vendor (especially its sales engineering team) and seeing these capabilities demoed first-hand.

Get Ahead Of The Competition With A Strong Start

Within the next couple of years, I expect almost every enterprise will employ generative AI in production. Those wielding it effectively will get a leg up on their competition, while those struggling will be at risk of falling behind. Though the road may be uncharted, enterprises can succeed by focusing on contained, valuable projects, leveraging human expertise and selecting strategic technology partners.

Don’t wait. Embrace this unique opportunity to innovate and take that crucial first step now.

Feature Image Credit: GETTY

By Alon Goren

Follow me on LinkedIn. Check out my website.

CEO and Cofounder of AnswerRocket. Read Alon Goren’s full executive profile here.

Sourced from Forbes

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The Amazon app on your phone just got a cool AI feature

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By Nadeem Sarwar

Last year, Amazon CEO Andy Jassy said that every business division at the company was experimenting with AI. Today, Amazon has announced its most ambitious AI product yet: a chatbot named Rufus to assist with your online shopping.

Imagine ChatGPT, but one that knows every detail about all the products in Amazon’s vast catalog. Plus, it is also connected to the web, which means it can pull information from the internet to answer your questions. For example, if you plan to buy a microSD card, Rufus can tell you which speed class is the best for your photography needs.

Amazon says you can type all your questions in the search box, and Rufus will handle the rest. The generative AI chatbot is trained on “product catalogue, customer reviews, community Q&As, and information from across the web.”

In a nutshell, Amazon wants to decouple the hassle of looking up articles on the web before you make up your mind and then arrive on Amazon to put an item in your cart. Another benefit of Rufus is that instead of reading through a product page for a certain tiny detail, you can ask the question directly and get the appropriate responses.

An AI nudge to informed shopping

Amazon app’s Rufus AI.
Amazon

Amazon says Rufus is capable of answering generic queries such as “What to look for before buying a pair of running shoes” or simply telling it, “I need to deck up my workstation,” and it will automatically recommend the relevant products. In a nutshell, it’s a web-crawling recommendation machine that will also answer your questions, product-specific or otherwise.

“Customers can expand the chat dialog box to see answers to their questions, tap on suggested questions, and ask follow-up questions in the chat dialog box,” says the company’s official blog post.

For queries such as “Is this phone case reliable,” the AI bot will summarize an answer based on product reviews, Q&As, and information on the product page. At the end of the day, it’s all about making informed purchasing decisions with some help from an AI chatbot.

Rufus AI answering Amazon product questions.
Amazon

Rufus is currently limited to a small selection of Amazon mobile app users in the U.S. as part of a beta test. However, this is an early version of the product, and Amazon also warns that Rufus “won’t always get it exactly right.” In the coming weeks, the AI chatbot will be made available to a broader set of users in its home market.

Rufus seems to be one of the more thoughtful and practical implementations of generative AI I’ve seen recently, and far away from the hype machinery built around the tech with hidden caveats. Plus, it seems to be free, without any Prime mandates.

Feature Image Credit: Amazon

By Nadeem Sarwar

Sourced from digitaltrends

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Most organizations fear AI failure, but those that implement AI do report benefits

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By Sabrina Ortiz

Implementing AI is only half the battle, but a new report suggests it’s risky not to try. Just make sure you prep your employees first.

When generative artificial intelligence first burst upon the scene, the technology showed potential for making people’s everyday lives easier. Now, AI solutions have been developed to help enterprises optimize their operations, and here’s why you might want to consider using them in your business.

Pluralsight’s AI skills report surveyed 1,200 executives and IT professionals across the US and the UK to better understand how organizations deploy AI and its effects on businesses and their employees.

The report found that implementing AI in organizations had promising results, with 97% of organizations that have already deployed AI technologies benefiting. Moreover, 18% reported experiencing increased productivity and efficiency, 13% reported improved customer service and repetitive task reduction, and 11% said AI reduced business costs.

Pluralsights chart
Pluralsights 

Despite the benefits, 25% of these organizations said they don’t have plans to deploy AI, while 20% already have and 55% plan to. The hesitation stems from inadequate budget or talent required to use the new tools properly.

A majority of the professionals acknowledged that hesitation could be disastrous in the long run, with 94% of executives and 92% of IT professionals sharing that organizations investing in AI in the near future will be better able to compete, according to the report.

However, the lack of talent to properly use the new tools is an obstacle to the successful implementation of AI, and the report finds that the answer may lie in organizations helping upskill employees.

The report cites IDC research that found investments in skills and digital training of employees will be organizations’ most enduring technology investments in 2023 and 2024, even over investments in generative AI solutions.

Feature Image Credit: Getty Images/Andriy Onufriyenko

By Sabrina Ortiz

Sourced from ZDNET

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10 Ways Human Influencers Can Outperform Virtual Influencers

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Human influencers can still thrive amid their AI virtual counterparts. Employ these strategies to stay relevant on social media.

The proliferation of virtual influencers is changing the way brands approach digital marketing. They could make AI-generated personas go viral while simultaneously cutting their ad spend—buying AI tools costs less than hiring social influencers.

You might consider dropping your rates to win back clients, but it’s merely a band-aid solution. Develop more long-term plans instead. Here are simple yet effective strategies to attract brand deals and sponsorships as a human influencer despite the expanding virtual influencer market.

You might consider dropping your rates to win back clients, but it’s merely a band-aid solution. Develop more long-term plans instead. Here are simple yet effective strategies to attract brand deals and sponsorships as a human influencer despite the expanding virtual influencer market.

1. Zero In on Your Target Market

The Lifetime YouTube Studio Insights of Animetorific Channel

Your relevance as an internet personality depends on your impact on market trends and consumer behaviour. Hence, the term “influencer.” Brands will still prioritize your services over AI-generated campaigns and virtual influencers if you have healthy conversion rates.

Go beyond follower counts; study industry data and objectively list the demographics of virtual influencer subscribers. Some markets prefer AI content nowadays, so you might need to overhaul your content strategies if you’re slowly losing subscribers, fans, or engagement.

If market statistics are too generalized, narrow down your research to specific buyer personas. Ensure you understand your target market.

2. Build an Audience Across Various Platforms

TikTok, YouTube, Instagram, and Snapchat Logos on Influencer Girl

Human influencers have an edge over AI-generated personas in executing cross-platform marketing tactics. Virtual influencers perform limited functions made for specific sites. For instance, VTubers gain thousands of views on YouTube and Twitch, but only a few rank on image-based apps like Snapchat and Instagram.

Alternatively, human influencers are versatile enough to maximize various social networks. You could share random activities on Snapchat, post aesthetic shots on Instagram, and upload vlogs on YouTube.

3. Establish Yourself as an Industry Authority

Trendjacking won’t help you beat virtual influencers. Yes, capitalizing on popular topics boosts visibility, but establishing yourself as an industry authority leads to stable long-term growth. Earn the trust and respect of your audience, otherwise, people will quickly forget about you if your content revolves around recent controversies and viral topics.

Let’s say you review Apple products on YouTube. Parroting Apple’s press releases provides zero value to readers—they’ll find the same information on hundreds of other sites. Some AI platforms even scrape and summarize news reports in real-time. The best approach is to provide unique, first-hand insights. Rather than listing new features, walk your readers through them with actual screenshots and demonstrations.

4. Collaborate With Other Industry Experts

Charli and Dixie D'Amelio Talking on Unicef Interview
Image Credit: Priyanka Pruthi/Wikimedia Commons

AI-generated avatars generally publish solo content. Collaboration is almost impossible because they can’t interact as humans do, and this lack of engagement makes them look inauthentic and robotic, which viewers can dislike.

Human influencers can maximize this advantage by regularly collaborating with relevant personalities. Establish yourself as an industry authority among peers and fans alike. Your audience would also appreciate seeing you with their favourite personalities—think of it as fan service.

5. Leverage Your Personal Experiences

Blonde Influencer Wearing Pink Jacket Posing in front of Kia Stinger
Image Credit: Do The Daniel/Wikimedia Commons

 

As an influencer, you can leverage your daily experiences by documenting and sharing them with an interested audience. Virtual personas will never replicate your real-life stories and relatable struggles despite advancements in AI. They’re merely pre-programmed avatars with made-up backstories.

Your viewers would love to see your real side. Talk about your most notable triumphs, share how you overcame your worst challenges, and ensure you thank your loyal supporters.

6. Try to Empathize With Your Audience

AI-driven virtual influencers use natural language processing (NLP) technologies and language models to engage in conversation. While impressive, they only execute patterns. As a result, talking to AI feels inauthentic because it can’t empathize with users or show feelings.

Human influencers can set themselves apart by connecting with viewers on an emotional level. Demonstrate a deeper understanding of your audience by resonating with their struggles and sharing how you overcame them.

Set disclaimers saying that your advice and personal experiences don’t replace professional consultations.

7. Analyse Why Brands Prefer Virtual Influencers

Several Influencers at Party With a Show Host
Image Credit: Juice Krate/Wikimedia Commons

A growing number of companies are offloading their marketing needs to AI. Forbes reports that 61% of businesses use AI for email optimization, while 55% generate user-targeted ads. Going by these trends, some might start replacing their influencers too.

While AI has significantly advanced over the years, it still has shortcomings—understanding them will help you retain projects. Offer what virtual influencers can’t guarantee, like lasting partnerships and collaboration skills.

8. Frequently Engage With Your Audience

We know how nasty some people act online. They use anonymous profiles to leave hurtful comments on various platforms. Even if you understand that these insults are baseless, they could still make you feel bad. You might even stop reading comments sections to avoid haters.

Although your feelings are valid, ignoring your audience will impede your growth and reach as an influencer; people prefer personalities that interact with them. You must answer questions, consider the type of content they want, and work on constructive criticisms.

If you can’t ignore your haters, block them or delete their comments. Just make sure you engage with your audience.

9. Participate in Social Movements

Group of People Wearing Blue Picking Up Trash at the Beach

Joining social movements humanizes social media influencers. Viewers generally see you doing the same things online—participating in new activities emphasizes your individuality. Show that you’re more than your on-screen persona.

However, this isn’t to say you should just take photos of feeding programs and clean up drives. Putting up a façade for attention will only hurt your image. Support social movements that align with your principles and prioritize making a real-world impact over announcing your contributions.

You can also use these social events to expand your network and connect with like-minded individuals.

10. Explore Generative AI Tools Yourself

Influencer Feeding Prompts to ChatGPT for Content Creation

Embrace AI instead of fearing it. AI-driven platforms are here to stay regardless of your opinion—you’d do well to incorporate them into your career. Start with simple, accessible tools. For instance, you could ask ChatGPT to write a short script, generate images on Midjourney, then stitch them together using text-to-video generators.

You can’t claim ownership of your output because copyright laws don’t apply to AI art.

And even if you don’t plan on using AI tools, exploring them helps you understand how virtual influencers work. Remember: you can’t surpass something you barely comprehend. Study the functions and scope of AI before overhauling your content strategies.

Create New Strategies to Beat AI Virtual Influencers

AI platforms and virtual influencers will continue impacting the content industry as they become more accessible. And brands won’t just stop exploring AI suddenly; you must level up your overall marketing strategy as an influencer or risk losing clients to AI.

Also, closely study the most popular virtual influencers to understand how you can beat them. Try looking for issues in their marketing campaigns. You’ll keep attracting new clients if you focus on providing results that virtual influencers and AI tools don’t.

By Jose Luansing Jr.

Jose Luansing Jr. is a staff writer at MUO. He has written thousands of articles on tech, freelance tools, career advancement, business, AI, and finance since 2017

Sourced from MUO Make Use Of

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How to Use AI to Get Your Next Job, According to Career Experts

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By Jaime Stathis

Using AI job search tools can help optimize the hunt—and get you hired. Here’s how to make artificial intelligence work for you.

Sometimes, things seem to escalate quickly, and this was certainly the case with artificial intelligence (AI), which very swiftly transformed from a cool, futuristic idea to a tool that has infiltrated our daily lives. In fact, many of us use AI and don’t even realize it. When you use facial recognition to unlock your iPhone, Google Maps to navigate to your next destination or ask Alexa to play a song, that’s all AI. You might have mixed feelings about robots operating among us, but the future of AI is here—and there’s no going back. The latest technology can even help you level up your career with an AI job search.

Despite concerns about privacy and cybersecurity or whether ChatGPT will eliminate jobs, the benefits are real, and many job seekers are using AI tools to their advantage. Whether you’re considering changing careers or career cushioning, tap into the power of AI to help land your next big job.

How AI can offer opportunity in the job market

“Navigating the job market with AI remains a topic of considerable uncertainty, but also immense opportunity,” says Ozden Onder, chief people officer at Stability AI, a leading open generative AI company. “It’s true that we don’t fully know yet how AI will affect the types of jobs available in the future. But by learning and using the tools we have available now, you can set yourself apart from other candidates and find the right next step for your career.”

There’s no doubt that some jobs will become more automated and potentially disappear, but Ozden says to take advantage of the technology. “If history is our guide, then I believe it’s more accurate to say that in the future, it won’t be AI that replaces people. Instead, people who are enabled by AI will find more ways of doing interesting and creative work. AI is a net positive for the workplace and helping people achieve their fullest potential.” Here’s how to use AI to your advantage, according to our career experts.

Update your LinkedIn profile

Many hiring managers and recruiters look at your LinkedIn profile before they even look at your résumé, so it’s essential that your profile is in tip-top shape—that includes fixing common misspelled words on LinkedIn. “You really want to be sure to make a good first impression,” says Andrew McCaskill, LinkedIn career expert and creator of The Black Guy in Marketing newsletter. Use your profile to highlight every skill that can make you more discoverable to hiring managers, and if you hit mental roadblocks when trying to write about yourself, turn to AI to develop attractive headlines and “About Me” sections that you can then customize to your expertise. “Consider AI to provide a helping hand in helping you stand out,” McCaskill says.

Best AI tool: Get your creativity flowing during your AI job search with LinkedIn’s new AI-powered writing tools. To use the feature, simply open your “About” section and click “Get AI-Powered Suggestions” at the bottom of the text field.

Optimize your résumé for AI filters

Boris SV/Getty Images

Are résumé mistakes the reason why you’re not getting hired? Potentially. “Remember that many résumés are either machine-read or only glanced at for 10 seconds by a recruiter,” says Onder, which means learning how to optimize your résumé for AI is essential.

Bridget Lohrius, founder and CEO of career coaching company Sandwina, says that 98% of Fortune 500 companies use an applicant tracking system (ATS), which means if the key skills aren’t included in your résumé, you don’t make it through the system. “[That means] never getting the chance to dazzle a human being,” she says, adding that it’s estimated these systems reject 75% of résumés before a recruiter ever sees them.

Don’t use intricate Canva templates to build your résumé (they tend to be riddled with unique graphics, icons and fonts), and never include a photo, because ATS programs can’t read them, Lohrius says. “A clean, basic text-only design is best.”

Best AI tool: Lohrius recommends Jobscan for résumé optimization. Their proprietary AI technology analyzes your résumé and compares it with the job listing.

Find the right fit

It’s not just about finding a great job with a terrific compensation package, it’s about finding the right job that suits your skill set and your personality. “Landing a job that makes you miserable is often worse than not getting the job in the first place,” says William Vanderbloemen, founder and CEO of executive staffing company Vanderbloemen Search Group. “If you know a bit about yourself, then you can match yourself with the right job using AI.”

For example, Vanderbloemen knows he’s an Enneagram 7, so he asked an AI assistance bot, such as ChatGPT, “What are ideal jobs for an Enneagram 7?” He learned that career paths that may align well with the traits of an Enneagram 7 include photographer, event planner and sales representative. Your AI job search is only as good as your curiosity about yourself.

Best AI tool: Vanderbloemen recommends ChatGPT to help find the right fit. Simply type in your question or prompt in the message box on the ChatGPT home page. Once you receive a response, you can ask it to elaborate, regenerate a new response or more responses, or take it as it is.

Learn about company culture

There’s finding the right job, and then there’s finding a workplace culture to match your needs. Use AI job search tools to learn more about the company you’re interested in. For example, Vanderbloemen asked ChatGPT about the company culture at Google and which personality types might thrive there. He found out that Google, like many large tech companies, values a diverse workforce with a wide range of skills and personalities.

“There is no single personality type that thrives at Google, as they look for individuals who can contribute to a collaborative and innovative work environment,” Vanderbloemen says. “However, some common traits and characteristics that may be well suited for working at Google include creativity, adaptability and analytical thinking. Google is known for its data-driven approach to decision making, so individuals who are comfortable with analysing data and drawing insights from it can be successful,” he says.

Best AI tool: Vanderbloemen recommends ChatGPT to help with researching company culture.

Personalize cover letters

The job search process can be tedious and repetitive, but personalizing cover letters and creating industry-specific résumés is vital. To help your time management during the job search, tap AI to create an outline for your cover letters. “AI can save you time during that process by creating first drafts or formatting templates, so you don’t have to worry about the busywork,” says Akhila Satish, an award-winning career expert, scientist and CEO of Meseekna. Just make sure you’re personalizing and reviewing any content before submitting an application—not only will hiring managers notice AI-generated cover letters, AI can make some funny mistakes.

Lohrius points out that the cover letter is an opportunity to showcase your personality, your legitimate interest in a role or company, your understanding of their business and above all, how you—and only you—are the best fit. “AI can do the heavy lifting for you, but the most important thing to remember when using AI is that the technology generates a basic letter, so you don’t have to start from scratch, but you still need to dedicate time and energy into the content and voice to make your letter an interview-grabbing magnet,” she says.

Best AI tool: Satish recommends ChatGPT to help organize cover letter structure before you personalize it with your own voice. Lohrius likes the cover letter feature on ResumeGenius. “It takes the user through a series of questions to customize the cover letter, making the process simple and straightforward,” she says. “Once the questionnaire is completed, you add some personal information and a bit about the job you’re applying for, and the cover letter builder gets to work, creating a solid letter that you can then customize to sound like you.”

Identify the best interview questions

Guillaume/getty images

It’s important to remember that when interviewing, you have the best chance to ask questions about the job and the company, and you can use AI to identify the best inverview questions to ask. “After interviewing over 30,000 top candidates at Vanderbloemen, we are convinced that candidates who ask the best questions often gain the winning advantage in landing the job,” Vanderbloemen says.

As an experiment, he asked ChatGPT “What are the best questions to ask when interviewing for an ER nursing job?” A list popped up right away, he says, and included questions about the nurse-to-patient ratio in the ER and how it changes during peak times; how the hospital supports continuing education and professional development for ER nurses; what team dynamics and collaboration among ER staff looks like; and how the hospital manages the influx of patients during its busiest times.

Best AI tool: ChatGPT is a good choice here for your AI job search, but McCaskill also suggests LinkedIn’s Interview Prep tools that help prepare you for commonly asked interview questions with videos of tips and sample responses from real hiring managers and recruiters. “You can also get instant AI-powered feedback to your recorded interview practice session on pacing, how many times you’re using filler words and sensitive phrases to avoid,” McCaskill says.

Stay ahead of the AI curve

No matter your industry, staying ahead of emerging technology can help you stand out. Currently, that’s voice technology. “Voice technology is a rapidly growing field that focuses on natural interactions between humans and machines, requiring employees with highly specialized skills,” says Tobias Dengel, author of The Sound of the Future: The Coming Age of Voice Technology.

“As popularity increases in many industries, such as health care, customer service, e-commerce, financial services, education and automotive, employees with voice technology experience can stand out from other candidates, position themselves as valuable assets and demonstrate the ability to learn and adapt to emerging technologies and industry trends.”

Best AI tool: Ozden recommends Ben’s Bites, which is an excellent daily summary and essential reading on what’s trending in AI.

Create a website

Satish recommends supplementing your résumé and job application with a personal website to boost your AI job search success. “Your résumé is only a snapshot of you as a candidate, so showcasing examples of your best work and sharing more details on your bio can help you stand out in a pool of applications,” she says. Because building a website can take a lot of time and experience, she recommends AI website-building tools to help streamline the process. “That technology will help you navigate the nuances of website building so that you can focus on fine-tuning and polishing before sending it off to a potential job lead.”

Best AI tool: SquareSpace, Wix and web.com are all user-friendly website builders that use AI.

Show that you’ve done your research

Tailor your interview answers based on knowledge about the company. To get that information, use AI for research. “Interviewers love to see that candidates have done their homework,” Vanderbloemen says. He asked ChatGPT, “What are the traits of the best young analysts at Deloitte?” and he learned that strong analytical skills, technical proficiency and problem-solving ability are all traits that Deloitte looks for in its analysts. With this info, alter your answers accordingly.

Interviewing for a job in a new city? “AI can give you a trove of local knowledge you can drop in your interview to show your interest in the job,” says Vanderbloemen. Doing this shows your interest in relocating and that you’re already looking for ways to fit into the local culture. You might even reference local universities, sports teams or museums.

Best AI tool: ChatGPT is a great AI tool for this research. If you’re not sure it works on a local level, use the town you’re in now and see how accurate it is.

Experiment with AI tools

Unless you’re fresh out of college, most of us are just now learning to implement AI job search strategies. But Ozden recommends familiarizing yourself with the tools, even job search. “Experimentation is the best way to get comfortable with AI tools,” he says. Learning by doing is the best way to pick up any new skill, after all.

“We are just getting started with this new world of AI, and you should take time to learn every modality of AI—there is a lot more to AI than language models like ChatGPT,” Ozden says. We now have image, video, audio and so many other areas for experimentation.

Best AI tool: If you want to move beyond ChatGPT, Dengel suggests expanding your skill set to SDXL, which is AI image generation, especially if you’re in the creative fields or you ever need to present.

About the experts

Sources:

  • New York Times: “The New ChatGPT Can ‘See’ and ‘Talk.’ Here’s What It’s Like.”
  • LinkedIn: “LinkedIn’s new AI tool will help you write posts: ChatGPT Impact”
  • Jobscan: “Optimize your resume to get more interviews”
  • Resume Genius: “Make Your Professional Resume in Minutes”
  • Ben’s Bites: “What’s trending in AI”

Feature Image Credit: roberthyrons/getty images

By Jaime Stathis

Jaime Alexis Stathis writes about health, wellness, technology, nutrition, careers and everything related to being a human being on a constantly evolving planet. In addition to Reader’s Digest and The Healthy, her work has been published in Self, Wired, Parade, Bon Appétit, The Independent, Women’s Health, HuffPost and more. She is also a licensed massage therapist. Jaime is working on a novel about a heroine who saves herself and a memoir about caring for her grandmother through the dark stages of dementia.

Sourced from Reader’s Digest

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There Is a Bright Future for AI-Driven Integration

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By Anant Jhingran and Matt Roberts

A look at how an integration layer completes AI applications and how integrations can be done better with the help of AI.

AI is reshaping the enterprise landscape. Already, developer productivity, digital labour, email marketing, website creation, etc., seem ripe for a major transformation. It is also well understood that general AI foundation models like GPT4 and Falcon-40B need to be fine-tuned or prompt-tuned for enterprise-specific tasks, and therefore must be fed some curated data that allows for some subset of the parameters to be “adjusted,” or output changed based on new task information given in prompts.

However, training the models is one thing. Enterprise applications today live and die on access to current enterprise data. For example, an e-commerce website might return the status of the orders of a logged-in customer. Or a chat application might process the return of a product. In neither of these cases can anything useful be done without real connectivity to ( integration with) one or more enterprise applications. First, we’ll speak to how an integration layer completes AI applications.

In addition, these integrations do not magically appear. They have to be coded, and they have to be tested and maintained. Later, we’ll speak to how integrations can be done better with the help of AI.

AI Without Integration is Incomplete

How would an AI application return useful information? AI without integration is like fish without water.

In the above figure, a natural language question, “When will my package arrive?” will need to be parsed by a foundation model, and generate a GraphQL request that then accesses an enterprise data source (and in this case, third-party systems such as FedEx), and then the response needs to be used as the input to generate the output.

The above example, while simple, shows that AI foundation models must be complemented by integration and API technologies. As readers of articles from one of the authors know, we have a particular bias for GraphQL APIs. And in this case, they are especially useful since the AI application can be trained to call one universal GraphQL API, and not have to deal with the subtleties of formats and authorizations and sideways information passing if the application were to learn multiple backends.

Integration Without AI is Incomplete

However, the complement to the above is that the opposite is also true. For each of the personas and task sets in the integration space, there are benefits in the application of AI:

Integration personas in the API management domain

  • Developers are the primary focus of this effort in the industry today. Prior to the rise of AI, domains like API management and application integration have already evolved toward low code/no code tooling for creating integrations, enabling citizen developers with less skill and experience to use them. AI provides the ability to further augment and empower those developers in more advanced or historically specialist scenarios.
  • Administrators, operations folks and site reliability engineers (SREs) of integration deployments will also benefit from the application of AI. Anomaly detection on operational metrics such as API response codes, transaction rate, queue depth and on system logs are all scenarios that machine learning models are well evolved to support – and provide the administrator a sixth sense to observe and maintain the health of a system.
  • Product managers and business owners often being on the less technical end of the spectrum also benefit from the low-code and generative capabilities described above, supporting them to self-serve their needs for query and analysis of data to identify business trends and new revenue streams.

In all cases there are various aspects that require close watching as AI technology matures:

First, the models have to be trustworthy. The art and science of trust in AI is being created rapidly, but of course, the rate and pace of innovation in the core AI algorithms is moving even faster. At some point in time, the trust research will have to catch up with the model research.

Related to this is determinism and repeatability. In scenarios such as generating a mapping between two data objects, it is not desirable that a different mapping be created each time you ask the same question, and yet that is the case today for many foundation models as they balance probability between multiple competing options.

Critical to the effectiveness of AI capabilities is correctness. There are many well-known examples where content generated by AI is plausible at first glance, but flawed in practice. As such, today a skilled expert is often still needed to review, debug and rectify the AI-generated artefact, but as the technology matures, we expect to see growing confidence in the validity of the output that will reduce the need for human oversight.

Next, the cost of inferencing, which is often not talked about, will become the dominant OpEx, and enterprises will have to learn to trade off the size of the model and the size of the prompt (linear and quadratic influences respectively on the cost of inferencing) with the quality of the output (is it worth going from a 8B parameter model to 100B parameter model for a 2% lift in the quality of the output)?

Sensitivity of data ownership is also a key concern for many enterprises. Foundation models work most effectively when they can be trained using the largest corpus of available examples, but if those examples contain sensitive customer information or represent a competitive advantage to the enterprise, then care must be taken in how that data will be further used by the model owner.

Summary

There is a bright future for AI-driven integration, both in the application of integration to provide access to enterprise data for use by AI tools and also for application of AI to benefit the delivery of integration scenarios.

We will be publishing a whole series of articles on the topic of the influence of AI on APIs and integration, and as some of you might know, StepZen was acquired by IBM, so we will be bringing on some additional API and integration experts, such as Matt Roberts, the CTO for IBM’s Integration portfolio.

Feature Image Credit: Shutterstock. 

By Anant Jhingran and Matt Roberts

Anant is the founder and CEO of StepZen, a startup with a new approach for simplifying the way developers access the data they need to power digital experiences. With a career that spans IBM Fellow, CTO of IBM’s Information Management… Read more from Anant Jhingran
Matt Roberts is Distinguished Engineer and CTO for the IBM Integration product portfolio where he has technical responsibility for offerings that cover API management, application integration, security gateways and high-speed data transfer. He has extensive experience delivering software products and…
Read more from Matt Roberts

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3 ways to build A.I. skills even if you don’t work in tech: ‘Suddenly your employability options go through the roof’

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By Jennifer Liu

Artificial intelligence is the hot new skill on the job market, and even those who don’t work in tech could use it to open up a new world of job opportunities.

The U.S. is leading the way in artificial intelligence and generative AI jobs, according to data from the global job search platform Adzuna. Many roles fall squarely in tech, like software engineer, product designer, deep learning architect and data scientist.

But there are plenty of non-technical roles where having the emerging skillset can give you a leg-up, says James Neave, Adzuna’s head of data science. One fast-growing role where there’s “absolutely a shortage” of qualified applicants is tax manager. Accounting and consulting firms are looking for candidates with a mix of financial and AI skills to make their business more efficient using large language models.

It can be a lucrative move, too: The average tax manager job that’ll use AI pays $100,445 a year, according to Adzuna, and the average job using the skill in general pays $146,244.

Experts say there’s also lots of opportunity for AI to be used in customer service, writing, HR, education and health-care jobs, to name a few.

As such, Neave says it would be smart for non-technical workers to consider picking up AI skills and learning how it could apply to their work: “There are brilliant opportunities for people out there who want to get their hands on these tools and get experience,” he says. “Suddenly, your employability options go through the roof.”

Neave says generalist workers can build their AI skills, and boost their employability, in three steps:

  1. First, get to know the most popular AI tools. “Go in and get your hands on the OpenAI website, practice a few prompts and see what comes back.”
  2. Second, seek out online resources to understand how you might apply AI to your own line of work. Neave recommends finding YouTube videos and articles that introduce how ChatGPT, the generative AI tool released in late 2022, is used in different tasks. For example, you might research more about the best way to use ChatGPT to write a blog or create automated responses to customer emails, he says. You could also look into certification and training courses online, from the University of Michigan, Coursera and other e-learning platforms.
  3. Finally, put your new knowledge to work in some of your routine tasks. “Once you feel confident enough using it, seek out and find any way to use it in your day-to-day work,” Neave says. It’s a good idea to check with your manager about your company’s policy on using AI in your work before doing so. And get a clear understanding of what you’re allowed to input into generative AI tools and what you’re not. For example, “there’s a general proviso that workers should not enter sensitive proprietary company data into ChatGPT to get answers, as it’s a public tool,” Neave adds.

Overall, Neave says, “if a future employer is looking at your CV, it’s going to be much more powerful if you can say you’ve gotten hands-on with ChatGPT using it for a certain purpose. That’s going to be the most compelling thing for potential employers.”

Feature Image Credit: Gorodenkoff | Istock | Getty Images

By Jennifer Liu

Sourced from CNBC make it

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Is Your Brand Trusted Significantly More Than ChatGPT?

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By Chad S. White

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favor, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

Feature Image Credit: Andrey Popov on Adobe Stock Photo

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle. Connect with Chad S. White:  

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Diller confirms plans for legal action over AI publishing

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By Miranda Nazzaro

Media titan Barry Diller confirmed Sunday he and a group of “leading publishers” plan to take legal action regarding the use of published works in training artificial intelligence (AI) systems.

Diller, the chairman and senior executive of internet and media conglomerate IAC, said he thinks generative AI is “overhyped, as all revolutions that are in the very beginning,” in an interview Sunday morning with CBS’s Margaret Brennan on “Face the Nation.”

AI systems are trained and improved using large language models, which ingest compilations of written works like books, news stories and social media posts.

Diller said he and others in the publishing industry don’t agree with how AI systems take in publishers’ content.

“It’s not that either Google or Microsoft, who are the two real leaders of this in terms of, certainly Google with having a monopoly on advertising. They, too, want to find a solution for publishers,” Diller told Brennan. “The problem is they also say that the fair use doctrine of copyright law allows them to suck up all this stuff.”

“It is, it will be, long-term catastrophic if there is not a business model that allows people professionally to produce content,” Diller continued. “That would be, I think everybody agrees is catastrophic.”

Diller claimed legislation or litigation is needed to protect the copyright of publishers.

“Of course, say we’re open to commercial agreements. But on the side of those people who are depending upon advertising, Google, for instance, they say, ‘Yes, we’ll give you a revenue share,’” Diller said. “Right now, the revenue share is zero. So, what percent of zero would you like today? I mean that’s rational, but it’s not the point. The only way you get to the point is protect fair use. In other words, protect the copyright.”

Diller would not disclose or confirm who is he planning to launch litigation with, only calling them “leading publishers.”

“It took 15 years to get back paywalls that protected publishers, I don’t think that same thing is going to happen,” Diller said.

When asked if generative AI poses a threat to Hollywood studio workers’ jobs, Diller said, “In this case, I think the one-to-three-year period, not much is going to happen. But post that, there are, of course, all these issues.”

Diller is not the first to consider legal action over AI publishing. Comedian Sarah Silverman and two other authors are currently suing Meta and OpenAI for alleged copyright infringement, claiming the platforms’ AI systems were “knowingly and secretly trained” with unauthorized copies of their books.

The Associated Press announced last week it would license its archive of news stories to ChatGPT maker OpenAI to help train the AI company’s system.

Feature Image Credit: (AP Photo/Kathy Willens)

By Miranda Nazzaro

Sourced from The Hill

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3 Ways Ecommerce Brands Can Use AI for Personalization

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By Shane O’Neill

How AI is revolutionizing ecommerce, from personalized ads to dynamic pricing and enhanced customer support.

The Gist

  • AI powerhouse. AI for personalization enhances individualized ecommerce experiences.
  • Tech advantage. Machine learning dynamically adapts prices, boosting consumer appeal.
  • Customer support. AI-enabled chatbots provide personalized, emotionally intelligent assistance.

Attention ecommerce brands: The days of blanketing consumers with vaguely relevant ads are over.

Seven out of 10 consumers now expect brands to personalize ads and product recommendations, and 76% get frustrated when this doesn’t happen, according to McKinsey research.

In response, nine out of 10 businesses, including Coca-Cola, Netflix and Sephora, are investing in the practice of using artificial intelligence (AI) for personalization to give consumers a one-to-one experience, or something close to it.

In a nutshell, personalization in ecommerce uses data to show customers products and deals tailored just for them. Instead of asking shoppers to sift through a list of products, personalization uses a customer’s purchase history and browsing behaviour with the brand to suggest the most likely item that person would buy.

To return the favour, 78% of consumers are likely to make repeat purchases from companies that personalize, according to the same McKinsey report mentioned above.

Yet personalization will only boost customer satisfaction, brand loyalty and sales if it’s executed precisely. And to do that requires culling insights from droves of customer data that humans simply cannot process and analyse manually.

And this is where artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) come into play for ecommerce brands.

AI for Personalization in Ecommerce

Personalization in ecommerce is still possible without AI, but it relies on grouping customers into “personas” based on shared demographics or interests. While this is an adequate approach, today’s consumer can sniff out when they’re being marketed to as a persona rather than an individual.

AI-based personalization is much more specific, using advanced algorithms to scan volumes of customer data and deliver information to you based on your own specific behaviour.

“AI’s ability to process data in real-time and adapt on the fly to create personalized experiences is a key advantage for ecommerce brands,” said Kristin Smith, managing director and retail commerce lead at Deloitte Digital. “It also helps that AI isn’t prone to human mistakes and can work 24/7.”

With advanced personalization now expected by the majority of consumers, ecommerce brands have a variety of ways to utilize AI to deliver tailored shopping experiences. Here are three of them.

1. Product Recommendations for the Individual

One of the clearest examples of using AI for personalization are the tailored product recommendations we see in emails or when logging on to our favourite ecommerce brand’s web site.

Here, complex machine learning algorithms mine your previous purchases, cart adds, product reviews, and product interactions, and generate personalized product recommendations in real time.

This customer data becomes the basis for training an algorithm that continues to learn and improve on the accuracy of recommendations as it receives new data.

Example to Emulate: Netflix

Netflix is a recommendation trailblazer. The streaming giant’s recommendation engine, called NRE (Netflix Recommendation Engine), uses algorithms to analyse data from each member’s viewing history and generates hyper personalized movie and TV show recommendations.

2. Automated Dynamic Pricing

Constantly adjusting product prices is a necessary but time-consuming task. By incorporating machine learning into pricing, ecommerce brands can automatically adjust prices in real time based on their own manufacturing costs, competitor’s prices, market demand and seasonality.

AI-based dynamic pricing benefits consumers by:

  • Monitoring the competition and adjusting prices to ensure customers get a fair price.
  • Offering real-time personalized discounts based on a customer’s behavior. For instance, if a person continually shows interest in a product, a dynamic pricing algorithm could entice that person with a time-limited discount.

Example to Emulate: Amazon

Amazon is the king of AI-based dynamic pricing. The ecommerce giant uses machine learning to update the prices of millions of products several times every day. Its repricing algorithm factors in product demand, stock availability and customer behavior. This allows Amazon to consistently offer the most competitive prices.

3. Personalized Customer Support via AI-Powered Chatbots

Using NLP and sentiment analysis, today’s chatbots understand not just text but also the emotion behind customer support requests.

When you combine sentiment, access to customer data and speedy responses, it’s easy to see why chatbots are now a personalization tool. Today’s chatbots can greet customers by name, recommend products and discounts based on purchase and browsing data, and even help customers complete online purchases.

Example to Emulate: Sephora

Most ecommerce chatbots can handle rudimentary customer inquiries, but the more innovative chatbots also serve as shopping assistants.

Cosmetics retailer Sephora is a prime example. Sephora’s website chatbot answers questions about returns and exchanges. But it’s also a virtual assistant that asks customers questions about their skin tone and makeup preferences and then gives tailored recommendations.

The Big AI Personalization Challenge: Relevant Data

The benefits of using AI for personalization are clear, but the success of your strategy hinges on your data.

Kristin Smith of Deloitte recommends that ecommerce brands ask themselves the following questions regarding customer data:

  • What is the quality and source of the data your brand is trying to use?
  • Does the brand have permission to collect and use the data they have?
  • How actionable and granular is the data?

“Many organizations have customer data only at a high level,” Smith said. “But high-level, demographic data does not always translate to actionable insights for personalization.”

In addition to having the skilled staff in place to implement and maintain AI tools, the entire marketing and data team should always ensure that the data the AI algorithms are using is unbiased and specific enough to actually help the customer connect with your brand and buy from you consistently.

“There will be a rabbit hole of ideas for data points AI can collect for personalization,” said Derric Haynie, head of demand generation at Pipe17 and co-founder of Ecommerce Tech.

“Maybe you’re going to test new products based on previous purchase history. Or test personalized emails based on when customers last visited the site. There’s a lot to personalize, and the nature of personalization is recognizing each person has a different customer journey, and catering to it.”

Feature Image Credit: Blue Planet Studio

By Shane O’Neill

Shane O’Neill is an award-winning journalist and content marketer with more than 20 years of experience covering digital transformation, content marketing, social media marketing, artificial intelligence, and ecommerce. His work has been recognized nationally, earning an ASBPE Award for Blogging and a Min Editorial & Design Award for Best Online Article. Shane’s experience as both a B2B journalist at CIO.com and InformationWeek and as a content marketing director at tech startups gives him a unique insider/outsider perspective on tech innovation. Connect with Shane O’Neill: https://twitter.com/smoneill 

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