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The Future Of AI Is Finally Here – And A Lot Of People Are Going To Be Out Of A Job

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By Bryce Hoffman

For years, folks have been talking about the coming AI revolution, about how it’s going to change everything, and about how it is going to cost a lot of people their jobs.

Well, the future is now – and if you are a business leader, you need to start dealing with it today. But don’t take my word for it:

The AI revolution is transforming industries and organizations around the world. As a business leader, it’s important to understand the potential of AI and how it can be used to drive business growth and success. This means staying up to date on the latest developments in the field and learning about the potential applications of AI in your industry. It also means being proactive in incorporating AI into your business, whether through the adoption of new technologies or the development of internal AI capabilities. Finally, it’s important to consider the ethical implications of AI and to ensure that your use of the technology is responsible and aligned with your company’s values.

That passage was written by Open AI’s ChatGPT, a free research AI model that was opened to public last week. It was a response to my question, “What do business leaders need to know about AI?” and it took less than 10 seconds to generate.

It is also completely accurate.

So was its response to my request to write a blog post on the state of Agile in 2022. In less than 20 seconds, the AI generated a 453-word article that was 100% grammatically correct, cogent, and offered valuable insights – explaining that agile has moved beyond the domain of software development where it was born to drive broader business transformations while at the same time warning that those efforts often fail when not coupled to a culture that fosters collaboration and open communications between departments and functions.

That could put a lot of content creators out of a job, but bloggers are not the only ones who need to worry.

Last week, marketeer Zain Kahn asked the AI to perform the same series of tasks that an employee at a marketing firm might be asked to undertake for a client: create an SEO strategy for a website, develop a list of target keywords, write a content strategy for the website, develop 10 blog ideas, then write one of those blogs itself. He even asked the AI to create metadata and simple code for the website to optimize it for bilingual search.

Then he rated its performance.

“I’d rank it as a 5/10. The equivalent of an SEO marketer with 1-2 years of experience earning around $50k/year. Not excellent, but definitely more competent than an SEO intern,” Kahn wrote on Twitter. “All of this took me 5 minutes. In the real world, all of this would probably take at least 5-10 hours.”

In response, one witty marketer posted a revised version of his resume listing his only skill as “chatbot query writer.”

What else is this going to disrupt?

  • Search engines: Why go to the trouble of googling a topic, then clicking on a website to find the answer you’re looking for when you can just pose your question directly to the AI and get a comprehensive answer? I asked it who the greatest American general of World War II was and was told that Eisenhower, Patton, and MacArthur were all compelling candidates for that title, explaining the relative merits each commander.
  • Education: I am sure some fast-thinking (but otherwise lazy) students have already used ChatGPT to write their homework papers for them. I asked it to compose an 800-word paper on the key themes in the novel Of Mice and Men and it promptly produced an original essay discussing the American Dream, loneliness, and the bond between George and Lennie. How would a teacher or professor prove I hadn’t written it myself?
  • Consulting: Why hire Bain when you can type in “develop a business strategy to grow retail sales of diapers in India” to an AI and, in less than 30 seconds, get back a pretty comprehensive plan to “focus on increasing brand awareness, building customer loyalty, expanding the company’s distribution network, and developing a range of products that are tailored to the specific needs of the Indian market” while at the same time pursuing partnerships with local charities to provide free diapers to poor families with a portion of the revenue generated from sales to middle-class consumers. Obviously, there is a lot more to a good business strategy than that, but not as much as consultants would like you to think.

Is it any wonder then that more than a million people signed up to use this experimental AI last week?

Every leader should be asking themselves what this means for the future of their business.

So should songwriters. Before logging off, I asked ChatGPT to compose a rap song in the style of Eminem about how hard it is to write a good Forbes column:

Verse 1:

I sit down at my desk, ready to write

But the words just won’t come, no matter how hard I try

I’m trying to make this Forbes column the best it can be

But my mind is blank, and I’m starting to see

Chorus:

The struggle, the struggle to write a good Forbes column

It’s real, it’s tough, and it’s taking its toll on me

But I won’t give up, I’ll keep on fighting

Until I finally get this column right

Verse 2:

I stare at the screen, feeling the pressure

I need to deliver, or my readers will desert me

I know I have the talent, I just need to find the flow

But it’s like my mind is a blank slate, and I don’t know where to go

Chorus:

The struggle, the struggle to write a good Forbes column

It’s real, it’s tough, and it’s taking its toll on me

But I won’t give up, I’ll keep on fighting

Until I finally get this column right

Verse 3:

I keep typing, trying different approaches

But nothing seems to work, and I’m starting to lose hope

But then, out of nowhere, I finally hit my stride

The words start flowing, and I know I’m on the right side

Chorus:

The struggle, the struggle to write a good Forbes column

It’s real, it’s tough, but I finally overcame

I kept on pushing, and now my column is a hit

The struggle was worth it, and I’m feeling like a king

Outro:

The struggle is real, but I never gave up

Now my Forbes column is the talk of the town

I kept pushing, and now I’m on top

The struggle was worth it, and I’m feeling like a king.

Feature Image Credit: getty

By Bryce Hoffman

Follow me on Twitter or LinkedIn. Check out my website or some of my other work here.

I am a bestselling author, speaker and president of Red Team Thinking LLC. I teach organizations and individuals around the world how to see the truth, tell the truth and act on the truth – because I believe it is the lies we tell ourselves that hold us back. My books include Red Teaming: How Your Business Can Conquer the Competition by Challenging Everything and American Icon: Alan Mulally and the Fight to Save Ford Motor Company. I also lecture at U.C. Berkeley’s Haas School of Business.

Soured from Forbes

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Omneky uses AI to generate social media ads

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By Romain Dillet

Meet Omneky, a startup that leverages OpenAI’s DALLE-2 and GPT-3 models to generate visuals and text that can be used in ads for social platforms.

The company wants to make online ads both cheaper and more effective thanks to recent innovations in artificial intelligence and computer vision. Omneky is participating in Startup Battlefield at TechCrunch Disrupt 2022.

While many fields have been automated in one way or another, creating ads is still mostly a manual process. It takes a lot of back and forth between a creative team and the person in charge of running online ad campaigns.

Even when you manage to reach a final design, the new ads might not perform as well as expected. You often have to go back to the drawing board to iterate and create more ads.

Omneky aims to simplify all those steps. It starts with a nice software-as-a-service platform that centralizes all things related to your online advertising strategy.

After connecting Omneky with your accounts on Facebook, Google, LinkedIn and Snapchat, the platform pulls performance data from your past advertising campaigns. From this analytics dashboard, you can see how much you’re spending, how many clicks you’re getting, the average cost per click and more.

But it gets more interesting once you start diving a bit deeper. Omneky lists your top-performing and worst-performing images and text used in your ads. Customers can click on individual ads to see more details.

Omneky automatically adds tags to each ad using computer vision and text analysis. The result is a dashboard with useful insights, such as the dominant color you should use, the optimal number of people in the ad and some keywords that work well in the tagline.

This data will be used to generate new ads. Customers write a prompt and generate new visuals using DALLE-2. Omneky also helps you with those prompts as it also uses GPT-3 to generate prompts based on top-performing keywords from past campaigns.

Customers then get dozens of different AI-generated images that can be used in online ads. Similarly, Omneky can generate ad copy for the text portion of your ads.

If you have a strong brand identity, Omneky can take this into account. On the platform, customers can upload digital assets and historical ads so that the platform acts as the central repository.

“Customers can upload the brand guidelines, the font, the logo. All of this is integrated into our AI to generate content that is on brand,” Omneky founder and CEO Hikari Senju told me in a call before TechCrunch Disrupt.

Image Credits: Omneky

Of course, some images and text don’t work well for one reason or another. That’s why Omneky doesn’t run any ad campaign without the customer’s approval. Team members can add comments, provide feedback and request approval from the platform directly.

As soon as customers approve a new ad, it is automatically uploaded and displayed on social platforms — Facebook, Google, LinkedIn and Snapchat.

After that, you are back to square one. You can track the performance of your new ads from the analytics dashboard, iterate and improve your ad performance.

The company charges a subscription fee that varies depending on the number of integrations with social platforms that you want to use. Omneky’s long-term vision expands beyond advertising.

There’s a lot of data involved with online ads, that’s why it’s easy to automate some of the steps needed to run an online ad campaign. But the startup thinks it could apply the same methodology to other products, such as AI-generated landing pages.

If you extrapolate even more, it’s clear that AI-generated content will cause a revolution in the martech and adtech industries — and Omneky plans to participate in that revolution.

Feature Image Credit: Omneky

By Romain Dillet

@romaindillet

Sourced from TechCrunch

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Land your dream job with this AI-assisted resumé writing tool

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If you’re currently looking for a job, there’s a lot you need to get in place, from your video interview space to the tools you need to do the job. But perhaps the most useful document you need to prepare to attract recruiters and hiring managers is a resumé. Take a task off your list with a lifetime subscription to the Complete Resoume AI Assistant Resumé Writer.

Let AI write your resumé

Enjoying 4.7 out of five stars on AppSumo and a No. 1 Product of the Day award on Product Hunt, Resoume ensures you’re writing an industry-standard CV every time. It starts by importing and collating the data from your LinkedIn profile, which helps you keep your message consistent and save you from copying and pasting.

Then, use one of 20 templates, and get pointers from the resumé feedback page before you submit. Resoume can be used from anywhere, so if you meet a contact in person, you can write, customize and send a CV straight from your iPhone.

From there, the AI designs your resumé to be friendly to applicant-tracking systems and your personal brand, with customizable themes, fonts and more. It also uses the resumé to write an effective cover letter. (All you need to do is tweak the letter with personal touches.) All documents are easily shared online and can be downloaded as PDFs, so you’ll never have to worry about having enough copies.

In addition to a strong resumé, Resoume offers a suite of tools to support your achievements. A portfolio-building tool helps you quickly assemble your best work for sharing both in documentation and on job sites. Resoume also offers website creation features to help you create a crisp, focused applicant profile. Actionable analytics show you how many views you’re getting and how to refine your presentation and targeted industry for better results.

Save on a Complete Resoume AI Assistant Resumé Writer: Lifetime Subscription

An effective job search starts with an effective resumé. A lifetime subscription to the Complete Resoume AI Assistant Resumé Writer gives you AI-powered job search tools for $39.99 (regularly $600).

Prices subject to change.

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Sourced from Cult of Mac

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One of the World’s Biggest Booze Companies Wants to Use AI to Recommend Your Next Whisky

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By Jonah Flicker

Or how I learned to stop worrying and love the algorithm.

You know how your Instagram feed starts sending you ads for khakis the minute you think about how you need a new pair of pants? Well, spirits giant Diageo is further immersing itself in the world of tech that knows what you want before you know what you want with the acquisition of flavour matching company Vivanda.

While not quite as nefarious sounding as the real life blocking or memory recall of a Black Mirror episode, this is indeed a look at what the future may hold for whisky consumers. Diageo has actually been using Vivanda’s technology since 2019 in several markets, including the “Journey of Flavour” experience at Johnnie Walker Princes Street in Edinburgh, as well as stores, ecommerce channels and the website Malts.com. It’s also the foundation of the “What’s Your Whisky” website, which works like this: Vivanda’s “FlavorPrint” system is powered by artificial intelligence, and by asking you a series of questions it’s able to map out your individual flavour preferences and suggest which whisky you should try based on your specific “Flavour Print.” Once you get your results, you are able to click to purchase a bottle of Talisker or Lagavulin or Oban, depending on your results.

Diageo plans to expand the use of Vivanda’s technology to other categories within its sizable portfolio, as well as using it to support “the continued development of our advanced analytics and digital marketing capabilities” to provide better understanding of just exactly what it is you like to drink, according to a press release. “We know consumers are looking for more personalized, interactive experiences and that they are increasingly engaging with our brands digitally as well as in person,” said Diageo chief marketing officer Cristina Diezhandino in a prepared statement. “We’re delighted to welcome Vivanda to Diageo and we are looking forward to working together to connect with consumers in more innovative ways that help shape the future of how we socialize in person and virtually.” So far the whisky has not become sentient and experienced its first sensation of love, but we are still in early days.

Feature Image Credit: Charl Folscher/Unsplash

By Jonah Flicker

Sourced from Robb Report

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Turning point for artificial intelligence: Will the large cloud providers dominate?

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Could the tech giants take control of the AI narrative and reduce choices for enterprises? Experts weighed the pros and cons in a recent online conference.

Artificial intelligence and machine learning requires huge amounts of processing capacity and data storage, making the cloud the preferred option. That raises the specter of a few cloud giants dominating AI applications and platforms. Could the tech giants take control of the AI narrative and reduce choices for enterprises?

Not necessarily, but with some caveats, AI experts emphasize. But the large cloud providers are definitely in a position to control the AI narrative from several perspectives.

That’s part of the consensus raised at a recent webcast hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School.

There’s more to AI than cloud. The complexity and diversity of AI applications go well beyond the cloud environments where they are run — and therefore reduce the dominance of a few cloud giants.

Certainly, “AI will require more capacity in storage, of the information flow,” says Bughin. At the same time, “cloud is only one part of the total pie of the platform. It’s part of infrastructure, but the platform layer is what you develop in house and through a third party. This integration is going to be hybrid, even more important than the cloud itself. Let’s be very clear, it’s not about operation, it’s a lot of algorithms, it’s a lot of different data, that integration piece, that will require system integration, architecture and design. That means that different types of firms will be involved in that work.”

What Bughin worries about more is the innovation potential from AI startups that may be squashed by larger players gobbling up smaller companies and startups through mergers and acquisitions. “Companies like the big internet or AI guys are going and buying a lot of very small and very clever AI firms.”

At the same time, Sadun points out that smaller companies may be in a better position to leverage AI innovations — but need help with training and education to prepare them. “This issue of who benefits from AI is really important,” she says. “On the one hand, we might think the smaller firms may be able to use these technologies more effectively, because they are more nimble, more agile. Companies that have already digital can exploit and scale AI.”

Where the large cloud providers may also make their dominance felt is in the monopolization of the data that feeds AI systems, says Acemoglu. Cloud architecture itself can be based on price-sensitive and competitive cloud services, he explains. “But the cloud architecture will not enable you to exploit data. The area, where I worry about the future of AI technologies are those that enable firms to monopolize data. That’s where firms have an oversized effect on the future direction of technology. That means a few people in a boardroom are going to determine where a technology’s going to go. We want more people focused and people-centric AI. That’s not going to be possible if a few firms that have a different business model dominate the future of technology. ”

The value of an AI-driven enterprise “does not reside in the cloud that enables it,” Bughin believes. “I think there’s enough of competition for the price point not to destroy the value. The value will come from the fact that you have integrated these technologies where you work, and the way your company works, in your own back end. The back end is not going to be the battlefield. The value is from generating productivity and revenue, at a rate faster than what we’ve seen in traditional digital transformations.”

And, for the first time, we see the terms traditional and digital transformation used together in the same sentence. As these thought leaders relate, such transformations are moving to the next phase, enabling autonomous, software-driven operations and innovation through AI. It’s a question of whether large tech vendors control the momentum, or if it remains a market and practice with a diversity of choices. Stay tuned.

Feature Image Credit: Joe McKendrick

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Sourced from ZDNet

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How AI adds value to crisis communications systems

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By Paul Kirvan

AI provides key enhancements to existing emergency notifications systems that can reduce the amount of time a business needs to effectively prepare for and respond to a crisis.

Crisis communications have come a long way from call trees and text chains. Today’s emergency notification systems and cloud-based notification services are far more effective than relying on employees to call each other.

However, these developments have not made crisis communications foolproof. For example, if emergency messages never reach their intended recipients, the sender might not get a notification of the message delivery failure. If a reply message is not generated, an organization’s emergency teams could be facing an incident that escalates into a full-blown crisis due to the lack of clear communication.

Artificial intelligence (AI) and machine learning (ML) are highly proficient in capturing a wide variety of data inputs and then making predictions and emergency recommendations. Organizations can use these technologies for identification and classification of emergency tasks, as well as to provide communications and intelligence at the right time and to the right people. AI has a role to play in the future of crisis communications, and it’s only just getting started.

What does AI bring to the table?

AI and ML can provide additional value to emergency notification system (ENS) technology. Today, ENSes are generally programmed to disseminate a variety of message types, such as email, text and SMS, to preset lists of individuals. While some more traditional systems can request replies from message recipients, AI-enhanced systems can do that and more.

AI crisis communications systems can use multiple channels of information to provide value to emergency message delivery. These channels can include weather forecast data or drone-generated video, among others. An AI-enabled ENS, for example, can take weather data generated by the National Oceanic and Atmospheric Administration and translate it into forecast data that can then be formatted into a series of alert messages helping people to prepare for an impending hurricane or other severe weather.

Another example of AI-enhanced crisis communications is using the system to ask specific questions about a situation, such as the likelihood of tornadoes or other natural disasters forming. The system can examine multiple resources to provide message recommendations and other analyses.

Inclusion of AI and ML technology is increasingly found in ENS offerings from traditional vendors as well as messaging system vendors. It is up to the user to determine which AI-enabled capabilities will be best suited to the organization and how it will add value to corporate ENS requirements. Non-AI systems will still provide rapid dissemination of emergency messages, and many can support reply messages, so at that point the added edge — and expense — of AI becomes a business decision.

AI-enhanced vs. traditional ENS

Earlier ENS technology was largely on site, with a server designated to provide ENS functions connected to either landlines from the local telephone company or via the internet to deliver messages. Figure 1 depicts how a traditional premises-based ENS uses the internet to deliver messages.

Diagram of a non-hosted ENS
Figure 1. A non-hosted emergency notification system.

By contrast, today’s systems are often hosted by a specialized ENS vendor, with the technology in the cloud. All resources are located with the vendor, and access is as simple as using a laptop or smartphone. Figure 2 depicts a hosted ENS configuration. Users are completely dependent on the ENS vendor to deliver emergency messages when the system launches.

Diagram of a hosted ENS
Figure 2. A hosted emergency notification system.

When AI and ML are in the mix, the configuration is largely unchanged except for the added capabilities of the ENS when AI and ML are implemented. Figure 3 shows a possible configuration of an AI-enabled, cloud-based ENS.

Diagram of an AI- and ML-enabled ENS
Figure 3. An AI- and ML-enabled emergency notification system.

Traditional ENS message delivery and reply features are enabled, and AI capabilities add value by using a variety of other resources.

Market options and pricing

Prices for standalone crisis communications systems can range from under $5,000 to well over $200,000.

Managed ENS offerings usually require payment of a monthly fee for the service. This is typically based on the number of contacts in the database, the features being used and the network transport services that deliver the messages. There can also be activation fees when the system is used in a disaster, and some systems will have setup fees. Monthly fees can range from under $500 to over $25,000, depending on the system configuration.

Hosted ENS tools require no physical space for equipment, there are minimal or no upfront installation fees, and customers can discontinue service with minimal technical effect on the organization. The inclusion of AI features will vary by vendor, and organizations should carefully research all options before making a buying decision.

Organizations that already use emergency notification systems will need to evaluate the added value versus the cost to upgrade their existing tool to an AI-enabled one. For example, an existing system might not be upgradeable to one with AI, and a replacement would be needed.

There are several crisis communications vendors that offer AI- or ML-enabled platforms and products. Vendor options in this market include the following:

  • Omnilert started as the developer of a campus emergency communications system. Current hosted products use AI to detect, analyze and visualize emergency situations through intelligent data capture and analysis, and the products offer an easy-to-use interface. Omnilert offers a free trial; check with the vendor for more pricing information.
  • Quiq provides an AI-enabled messaging platform that businesses can adapt to different situations, such as customer order placement and customer service inquiries. Although ENS is not specifically listed as an application, the Quiq platform is easily adaptable to crisis communications applications. Pricing begins at $12,000 per year.
  • OnSolve offers a variety of hosted ENS tools. It also has an AI engine to provide emergency intelligence that businesses can use for decision-making. Pricing ranges from a basic system for under $2,000 to more complex systems with a variety of pricing plans.
  • Everbridge offers numerous ENS options for many different applications and uses AI functionality to analyse data from multiple sources to provide intelligence for emergency management. The company offers on-site as well as managed emergency notification services, with fixed and monthly pricing plans.

By Paul Kirvan

Sourced from TechTarget

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AI can predict risk of a heart attack in the next year from a routine eye scan

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By Jeanna Vazquez

An artificial intelligence system is capable of spotting whether someone will have a heart attack within the next year — through a routine eye scan.

A team from the University of Leeds believes this AI tool opens the door to a cheap and simple screening program for the world’s No. 1 killer. Their tests find the computer can predict patients at risk of a heart attack in the next 12 months with up to 80% accuracy. The breakthrough adds to evidence that our eyes are not just “windows to the soul,” but windows to overall health as well.

“Cardiovascular diseases, including heart attacks, are the leading cause of early death worldwide and the second-largest killer in the UK. This causes chronic ill-health and misery worldwide,” project supervisor Professor Alex Frangi says in a university release.

“This technique opens-up the possibility of revolutionizing the screening of cardiac disease. Retinal scans are comparatively cheap and routinely used in many optician practices. As a result of automated screening, patients who are at high risk of becoming ill could be referred for specialist cardiac services,” Frangi adds.

Looking at the retina to discover red flags in the heart

The retina is a small membrane at the back of the eye containing light sensitive cells. Doctors have found that changes to the tiny blood vessels can hint at vascular disease, including heart problems.

Study authors used an advanced type of AI, known as deep learning, to teach the machine to automatically read more than 5,000 eye scans. The scans come from routine eye tests during visits to opticians or eye clinics. All of the participants are part of the UK Biobank, which tracks the health of half a million adults.

“The system could also be used to track early signs of heart disease.”

Deep learning is a complex series of algorithms that enable machines to make forecasts based on patterns in data. The technique, described in the journal Nature Machine Intelligence, could revolutionize heart therapy, according to the researchers.

“The AI system has the potential to identify individuals attending routine eye screening who are at higher future risk of cardiovascular disease, whereby preventative treatments could be started earlier to prevent premature cardiovascular disease,” says co-author Professor Chris Gale, a consultant cardiologist at Leeds Teaching Hospitals NHS Trust.

The study identified associations between pathology in the retina and changes in the patient’s heart. Once the system learned each image pattern, the AI could estimate the size and pumping efficiency of the left ventricle from retinal scans alone.

This is one of the heart’s four chambers. An enlarged ventricle can increase the risk of heart disease. The computer combined the estimated size of the left ventricle and its pumping efficiency with data like age and sex.

The eyes are revealing a lot about disease and death

Currently, doctors determine this information using an MRI (magnetic resonance imaging) or echocardiography scans of the heart. The diagnostic tests are expensive and are often only available in a hospital. The tests can be inaccessible for many people in countries with lesser health care systems. They also increase health care costs and waiting times in wealthy nations.

“The AI system is an excellent tool for unravelling the complex patterns that exist in nature, and that is what we have found – the intricate pattern of changes in the retina linked to changes in the heart,” adds co-author Sven Plein of the British Heart Foundation.

A recent study discovered a similar link between biological aging of the retina and mortality. Those with a retina “older” than their actual age were up to 67% more likely to die over the next decade.

Feature Image: Their tests find the computer can predict patients at risk of a heart attack in the next 12 months with up to 80% accuracy. (CREDIT: Getty Images)

By Jeanna Vazquez

Sourced from Brighter Side of News

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How AI Is Transforming The Future Of Digital Marketing

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By Bernard Marr

When people think about artificial intelligence (AI) today, they might think of computers that can speak to us like Alexa or Siri, or grand projects like self-driving cars. These are very exciting and attention-grabbing, but the reality of AI is actually thousands of tools and apps running quietly behind the scenes, making our lives more straightforward by automating simple tasks or making predictions.

This is true across every industry and business function, and particularly true in marketing, where leveraging AI to put products and services in front of potential customers has been standard practice for some time, even though we may not always realize it!

In business today, the term AI is used to describe software that is capable of learning and getting better at doing its job without input from humans. This means that while we’ve become used to using machines to help us with the heavy lifting, now they can start to help us with jobs that require thinking and decision-making, too.

A huge number of questions that would previously have needed human intervention to answer – such as “will this person be interested in my products?” or “what results will I get from this advertising campaign?” can now be answered by machines – if they are given the right data. And because machines can answer questions far more quickly than humans, they can easily chain together complex strings of queries to come up with predictions, such as who is most likely to buy your products and where the best places to advertise might be.

That’s the basic principle behind all business AI today – automating the processes of learning and decision-making in order to create knowledge (usually referred to as “insight”) that helps to improve performance. And marketing is one area where it’s certainly been put to good use!

Targeted marketing

The high-level use case for AI in marketing is that it improves ROI by making your marketing – often one of a company’s biggest expenses – more efficient. In the old days, before online advertising, businesses would pay huge amounts of money for TV, radio, or newspaper adverts, in the full knowledge that only a small number of the people who saw their ads would ever become customers. This was tremendously inefficient, but companies didn’t have any choice if they wanted to position themselves as market leaders.

In the online age, we’ve developed the ability to learn a great deal about who is or isn’t interested in our products and services. The first breakthroughs came thanks to the likes of Amazon with their recommendation engine technology and Google and Facebook with their targeted advertising platforms. Today, each of those platforms has been augmented with machine learning technology that allows them to become increasingly effective as they are fed more data on customers and their buying habits.

AI-driven content marketing

The rise in social media marketing and our growing appetite for online content has made content-based marketing the dominant form of marketing in many industries. AI lends a hand here by helping us work out what type of content our customers and potential customers are interested in and what the most efficient ways are to distribute our content to them. Advertising creatives have always strived to find formulas for creating adverts that will get people talking and sharing the message with their friends. Now, this can be done automatically using any number of AI-powered tools. For example, headline generation algorithms that monitor how successful they are and tweak their output to achieve better metrics, such as the open rate of emails, or the share rate of social media posts.

Taking this a step further, AI is developing the ability to take care of the entire content generation process itself, creating copy and images that it knows are likely to be well-received by its audience. A huge buzzword in this space will be personalization – where individual customers are served content that’s specifically tweaked to them, perhaps using information and reference points that the AI knows are relevant to them, intertwined with the overall marketing messages.

AI will also increasingly be useful for identifying what stage of the buying process a customer is at. If it detects that they are “shopping around” – comparing products and services that are available – it can serve content designed to differentiate your product or service from those of competitors. If it detects that they are ready to make a purchase, it can target them with promotions urging them to “act now” to take advantage of a limited-time offer.

A digital marketing agency called 123 Internet has embraced the ongoing industry developments by utilizing various AI-based technologies to improve service delivery. Scott Jones, CEO said:

“We’ve been using AI tools for a while now, in particular automatically checking website designs in hundreds of screen and browser types, this speeds up our design and development process”.

Their team also use an AI generated website audit which can be downloaded from their website and runs without human interaction.

Identifying micro-influencers

Influencers are another huge trend in marketing right now, and AI algorithms are already in use to make sure the personalities that are most likely to appeal to you are appearing in your search results and social feeds.

Increasingly, advertisers will also use AI to identify smaller influencers that are most likely to gel with their brands and audiences. This has led to the emergence of “micro-influencers” – typically everyday people, rather than celebrities, who have a specialist knowledge they’ve used to build a niche audience that cares about their opinion. AI enables companies to find the micro-influencers with the right audiences for them, across a large number of niches and audience segments. AI helps establish when it makes sense to pay 100 people $1,000 each to talk about their product, rather than pay $100,000 to Justin Bieber or a Kardashian. Once again, here it is about creating efficiency by following the data, rather than simply doing what a marketer thinks or feels is the best plan.

AI in CRM

Customer relationship management is an essential function for any marketer to master, as existing customers are often the most important source of a company’s revenue. Here, AI can be used to reduce the risk of customer “churn” – by identifying patterns of behaviour that are likely to lead to customers heading elsewhere. These customers can then be automatically targeted with personalized promotions or incentives to hopefully restore their loyalty. AI-augmented marketers are also increasingly turning to chatbot technology – powered by natural language processing. This can segment incoming customer inquiries, meaning those who require a quick response can be urgently catered to, to minimize dissatisfaction. AI-driven CRM will also allow businesses to more accurately forecast sales across all the markets where a company operates, meaning stock and resources can be more efficiently distributed. Additionally, it can be used to maintain the quality of data in the CRM system, identifying customer records where errors or duplicates are likely to exist.

The future of the marketer

If you work in marketing, you would be forgiven for worrying that we’re heading for a future where humans in your role will be redundant. You can take heart, though, from current predictions that state AI will end up creating more jobs than it destroys. It’s inevitable that your job will change, though. Marketers will spend less time on technical tasks such as forecasting or segmenting customers and more time on creative and strategic tasks. Those who are competent at working with technology, and identifying new technological solutions as they become available, will be hugely valuable to their companies and are likely to have a bright future!

Feature Image Credit: Adobe Stock

By Bernard Marr

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. Why don’t you connect with Bernard on Twitter (@bernardmarr), LinkedIn (https://uk.linkedin.com/in/bernardmarr) or instagram (bernard.marr)?

Sourced from Forbes

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7 Tips to Use AI to Acquire and Retain Customers

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By Kevin Payne

Artificial Intelligence (AI) mimics the cognitive functions of the human mind, particularly in learning and problem-solving. Many of the apps that we use today are powered by AI. From voice-activated virtual assistants to e-commerce, AI applications are everywhere.

With the advancements in AI technology and access to big data, companies across different industries are integrating AI into their processes to find solutions to complex business problems.

The application of AI is most noticeable within the retail and e-commerce space. Websites and apps can interact intelligently with customers, creating a personalized approach that enhances the customer experience.

No matter what industry your business operates in, these seven tips can help you acquire and retain customers more efficiently at a fraction of the time it takes to do things manually.

How to Use AI to Get and Keep Customers

1. Identify Gaps in Your Content Marketing Strategy

If you’re just starting with content marketing, you’ll need to know what type of content to create.

By using AI, you can identify the gaps, find fixes, and evaluate the performance of your content marketing campaign.

Take Packlane, a company that specializes in custom package designs, for example. They came up with high-quality content like helpful blog posts that provide valuable information. At the same time, the content they publish makes it easier for their target market to understand their brand and services.

If you’re in the retail or e-commerce space, you can use AI to identify the gaps in your content marketing. Your content may be focused on your products and their features, but through AI, you can determine the relevant content that addresses your audience’s needs and pain points.

2. Pre-Qualify Prospects and Leads

Not every visitor to your site will become a paying customer. If you’re not getting sales despite the massive traffic, it means you’re generating low-quality leads.

Some reasons why this happens includes:

  • Targeting the wrong audience
  • Poor content marketing strategy
  • Using the wrong type of signup form
  • Promoting in the wrong social media platforms
  • Ineffective calls to action

These explain why 80% of new leads never convert into sales. The mistakes can be rectified with the help of artificial intelligence.

AI tools can extract relevant data to help you learn more about your target audience. These tools also provide predictive analytics on your customers’ behaviour. They, in turn, help improve your lead generation strategy because you’ll know which leads to pursue, where to find them, and how to effectively engage them.

3. Provide Personal Recommendations

According to a report by the Harvard Business Review, even though there are privacy concerns when consumers’ personal information changes hands, people still value personalized marketing experiences.

Brands that tailor their recommendations based on consumer data boost their sales by 10% over brands that don’t.

Recommendation systems’ algorithms typically rely on data on browsing history, pages visited, and previous purchases. But AI is so advanced that it can analyse customers’ interactions with the site content and find relevant products that will interest the individual customer. This way, AI makes it easier to target potential customers and effectively puts the best products in front of the site visitors.

Because of AI, recommendation engines are able to filter and customize the product recommendations based on each customer’s preferences. It’s a cycle of collecting, storing, analysing, and filtering the available data until it matches the customers’ preferences.

This is an effective way of acquiring and retaining customers because there’s an element of personalization.

4. Reduce Cart Abandonment

A high cart abandonment rate is the bane of e-commerce business owners. According to a study by the Baymard Institute, online shopping cart abandonment rate is close to 70%.

Users abandon their online carts for various reasons:

  • high extra costs
  • complicated checkout process
  • privacy concerns
  • not enough payment methods, or
  • they’re not ready to buy yet.

Using AI-powered chatbots is one way to reduce cart abandonment. AI chatbots can guide the customers through their shopping journey.

AI chatbots can have a conversational approach and give the customer a nudge to prompt them to complete the purchase. These chatbots can also act as a virtual shopping assistant or concierge that can let a customer know about an on-the-spot discount, a time-sensitive deal, a free shipping coupon, or any other incentives that will encourage them to complete the checkout.

With AI, lost orders due to cart abandonment are recoverable and can lead to an increase in conversion rate for e-commerce businesses.

5. Increase Repurchases With Predictive Analytics

Predictive analytics is the process of making predictions based on historical data using data mining, statistical modelling, artificial intelligence, machine learning, and other techniques. It can generate insights, forecast trends, and predict behaviours based on past and current data.

In marketing, predictive analytics can be used to predict customers’ propensity to repurchase products as well as its frequency. When used to optimize marketing campaigns, AI-powered predictive analytics can generate customer response, increase repurchase, and promote cross-selling of relevant products.

It’s all part of the hyper personalized marketing approach, where brands interact and engage with customers and improve their experience by anticipating their needs and exceeding their expectations.

With predictive analytics, you can focus your marketing resources on customer retention and targeting a highly motivated segment of your market that are more than happy to return and repurchase your products. This approach is less expensive than advertising or implementing pay-per-click campaigns.

6. Improve Your Website User Experience

Every business—big or small—knows the importance of having a website, where visitors can interact with the brand, respond to a call to action, or purchase products. But it’s not enough to just have an online presence; it’s important that visitors to the site have a great experience while navigating through your site.

What makes for a great user experience? Users have different expectations. Some of them want faster loading time, while others want a simple and intuitive interface. But most important of all, they want to find what they’re looking for. It could be a product, content, or a solution to a problem. Whatever they may be, it’s up to you to meet their expectations.

With artificial intelligence, you can improve your website user experience tenfold. Here are some of the ways AI can be used to improve user experience.

Search relevance

This pertains to how accurate the search results are in relation to the search query.  The more relevant the results are, the better search experience the users will have. This means they are likely to find relevant content answering their queries or finding products that solve their problems.

Personalized recommendations

Content that is tailor-made for the user tends to have greater engagement which increases the likelihood of conversation. Amazon has perfected the product recommendation system using advanced AI and machine learning. AI gets data from customers and uses it to gain insights and apply predictive analysis to recommend relevant products for cross-selling opportunities.

AI chatbots

The presence of chatbots contributes to a great user experience because they provide 24/7 assistance and support in the absence of human customer service.  Users can get accurate answers to their inquiries quickly and efficiently, compared to scrolling through a text-based FAQs.

7. Social Listening for Potential Customers

Social listening is the process of analysing the conversations, trends, and buzz surrounding your brand across different social media platforms. It’s the next step to monitoring and tracking the social media mentions of your brand and products, hashtags, industry trends, as well as your competitors.

Social listening analyses what’s behind the metrics and the numbers. It determines the social media sentiment about your brand and everything that relates to it. It helps you understand how people feel about your brand. All the data and information you get through social listening can be used to guide you in your strategy to gain new customers.

Social media monitoring and listening can be done much more efficiently with the help of artificial intelligence. It’s an enormous task for a team of human beings to monitor and analyse data, but with AI-powered social media tools, all the tedious tasks can be automated. They can be trained to leverage data to provide valuable insights about your brand with high accuracy.

With AI and machine learning, your social listening can easily determine your audience, brand sentiments, shopping behaviour, and other important insights. By having this information within reach, you’ll know how you can connect with them more effectively and turn them from prospects to paying customers.

Key Takeaways/Conclusion

More companies across different industries are using the power of artificial intelligence and machine learning to significantly increase brand awareness, enhance customer engagement, improve user experience, and meet customer expectations.

  • AI can identify gaps in your content marketing strategy so that you can create content that’s relevant to your target audience.
  • AI can help you generate high-quality leads that are likely to buy your products.
  • With AI, you can personalize and tailor-fit your product recommendations based on your customers’ preferences, increasing repeat purchases.
  • AI can be integrated into your e-commerce site to reduce shopping cart abandonment.
  • AI significantly improves website user experience by making it intuitive, accessible, and easy to navigate.
  • AI-powered social media tools can help you monitor and gain valuable insights about your brand. You can then use this to develop a social media marketing strategy to gain new customers.

Achieve these milestones, and you’ll be sure to acquire new customers and retain existing ones.

Feature Image Credit: iStock/monsitj

By Kevin Payne

Sourced from Black Enterprise

 

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Scientists develop AI to predict the success of start-up companies

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Sourced from Brighter Side of News

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool (www.venhound.com) that has the potential to help investors identify the next unicorn.

It is well known that around 90% of startups are unsuccessful: between 10% and 22% fail within their first year, and this presents a significant risk to Venture Capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi’s The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy. This means that potentially 9 out of 10 companies are correctly assessed.

“This research shows how ensembles of non-linear machine-learning models applied to big data have huge potential to map large feature sets to business outcomes, something that is unachievable with traditional linear regression models,” explains co-author Sanjiv Das, Professor of Finance and Data Science at Santa Clara University’s Leavey School of Business in the US.

The authors developed a novel ensemble of models in which the combined contribution of the models outweighs the predictive potential of each one alone. Each model classifies a company, placing it in one of several success categories or a failure category with a specific probability. For example, a company might be very likely to succeed if the ensemble says it has a 75% probability of being in the IPO (listed on the stock exchange) or ‘acquired by another company’ category, while only 25% of its prediction would fall into the failed category.

 
Credit must be given to the creator. Only noncommercial uses of the work are permitted. No derivatives or adaptations of the work are permitted. (CREDIT: Greg Ross)

The researchers trained the models on data sourced from Crunchbase, a crowd-sourced platform containing detailed information on many companies. They married the Crunchbase observations with patent data from the USPTO (United States Patent and Trademark Office). Given the crowd-sourced nature of Crunchbase, it was no surprise to learn that some companies’ entries miss information. This observation inspired the authors to measure the amount of information missing for each company and use this value as an input to the model. This observation turned out to be one of the most critical features in determining whether a company would be acquired or otherwise fail.

Lead author Greg Ross of Venhound Inc. notes that the ensemble of models, along with novel data features, “generates a level of accuracy, precision and recall that exceeds other similar studies. Investors can use this to quickly evaluate prospects, raise potential red flags and make more informed decisions on the composition of their portfolios.”

Feature Image Credit: Creative Commons

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