Generative AI: Will the Bubble Burst Like the Metaverse?

How to Make Smart, Unbiased AI Decisions + 90 AI Tools You Need to Try

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Philip Mandelbaum
05/25/2023

When OpenAI released its first iteration of the large language model (LLM) that powers ChatGPT, venture capital investment in generative AI companies totaled $408 million. Five years later, analysts are predicting AI investments will reach “several times” last year’s level of $4.5 billion. “It is undeniably a major inflection point, and great products and companies are going to be built,” said Matt Turck, an investor specializing in AI at FirstMark. However, “as in prior hype cycles, a lot of this will not end well,” he continued. “The market cannot sustain, all of a sudden, a million different companies with half-baked ideas. It feels like the gold rush.” Indeed, investors are “jumping into AI startups even when it isn’t clear how they will make a profit,” reminiscent of the last (minor) tech bubble to pop, the metaverse, which caused lots of big brands to waste big budgets. The question, then, is: Will AI go the way of the metaverse, or withstand the overhype and streamline everything from content creation to “movie production to customer service to grocery delivery?” Only time will tell, but I believe there’s ample evidence AI is here to stay — and well worth the investment, when there’s a sound business case behind it (hint: using ChatGPT to write your meta descriptions is not one of them).

A conference room full of office workers wearing VR goggles

What Happened to the Metaverse?

After years of success running Facebook as a social media company, founder and CEO Mark “Zuckerberg waxed poetic about the Metaverse as ‘a vision that spans many companies’ and ‘the successor to the mobile internet,’ but he failed to articulate the basic business problems that the Metaverse would address.” As a result, and despite all the media fanfare and glowing analyst projections, the metaverse flopped, taking Meta (partially) down with it.

Despite spending more than $100 billion on metaverse research and development, Meta no longer even pitches the metaverse to potential advertisers. In its February 1, 2023, earnings report, the company reported a single-year $13.72-billion loss by Reality Labs, the brand’s virtual reality division. Not surprising, considering even Meta employees refused to engage in the virtual world.

“Every single business idea or rosy market projection was built on the vague promises of a single CEO. And when people were actually offered the opportunity to try it out, nobody actually used the Metaverse,” remarked Ed Zitron, an award-winning tech culture author and the CEO of EZPR, in Business Insider.

Decentraland, “the most well-funded, decentralized, crypto-based Metaverse product,” only had around 38 daily active users in its $1.3-billion ecosystem, as of late 2022. All the failed metaverse investments by the likes of Microsoft, Disney and Walmart “led to thousands — if not tens of thousands — of people losing their jobs.” And then Zuckerberg himself put the final nail in the coffin, declaring in March 2023 that Meta’s new “single largest investment is advancing AI and building it into every one of our products.”

In the end, the lesson was simple. As Zitron explains, “a functional business proposition requires a few things to thrive and grow: a clear use case, a target audience, and the willingness of customers to adopt the product.”

In other words, Meta failed in its mission by:

Losing focus on what it had always done best
Refusing to listen to — and empathize with — its customers and prospects, investing everything in a product concept that wouldn’t appeal to its target audiences

3D rendering of AI-powered assembly line with robotic arms picking up or putting down pairs of pink sneakers

Why Generative AI Isn’t Going Anywhere

Last year, CNBC host Jim Cramer “nodded approvingly” when Zuckerberg claimed one-billion people would use the metaverse, even though Zuckerberg couldn’t explain where users would spend their money (creating income for metaverse brands) or “why anyone would want to strap a clunky headset to their face to attend a low-quality cartoon concert.”

Unlike the metaverse, which was almost entirely conceptual (but might’ve worked had the pandemic-era lockdowns not been lifted), AI is not meant to be a distraction from everyday life or the workplace — it’s meant to improve everyday life and the workplace.

Facts are facts: AI tech can enhance business productivity by 40% — and businesses that employ AI are expected to double their cash flow by 2030 while brands that don’t are expected to see a 20% reduction.

Already, more than three quarters of businesses are using or exploring AI and nearly three quarters of executives believe AI will be their greatest future business advantage. Fortunately for consumers and workers:

The increase in the number of AI startups (1,400% since 2000) should increase the likelihood of thoughtfulness, empathy, diversity and inclusion in machine learning
AI will automate only 16% of American jobs
Only about one third of the C-suite sees AI primarily as its tool for optimizing internal operations
52% of experts believe that, overall, AI will increase the total number of employment opportunities
77% of the tools we’re already using leverage AI in one form or another

So, even if more than half the AI startups never turn a profit or even advance beyond funding, and even with all the poorly built AI products flooding the market, the advancements enabled by the best AI tech will change everything. In fact, from revolutionizing Photoshop design to automating task management, and from touch-free accounts payable and receivable to chatbots that listen, learn and empathize, it already is.

The Top 5 AI Tools for Every Important Function (90 in All!)

AI Audio Editing Software

AI Chatbots

AI Content Generator Tools

AI Content Repurposing Tools

AI CRM Tools

AI Customer Data Platforms

AI Cybersecurity Tools

AI Data Visualization Tools

AI DXP Software

AI Email Marketing Tools

AI Image Generators

AI Productivity Tools

AI Project Management Tools

Asana
Basecamp
Monday
Trello by Atlassian
Wrike

AI Recruiting Tools (and HR Management Systems)

AI Sales Assistants

AI Social Media Tools

Content Studio
Emplifi (formerly Socialbakers)
Lately
Meltwater (formerly Linkfluence)
Sprout Social

AI Text to Speech Software (and Voice Generators)

AI Video Generators (and Video Editors)

With countless AI tools released every week, the responsibility of your tech decisionmakers is to determine which use(s) of artificial intelligence make the most sense for your organization, as well as which tools would best meet each of your needs.

A man using a keyboard, with a phone to his ear, doing robotics

How AI Can Advance Your Digital Marketing, CX and Sales Efforts

When you incorporate the right AI (and the applicable process updates) — not as replacements for employees but as tools to make employees’ lives easier — you improve:

Employee experience, employee engagement and company culture
Efficiency of processes and communication
Organization of projects
Quality of content marketing output
Effectiveness of digital marketing efforts
Return on investment

By using artificial intelligence to automate employees’ more mundane responsibilities, you can free them up to focus on the message, the big picture, and the most effective tactics for delivering a consistently personalized, empathic and authentic customer experience (and user experience).

AI can help you with:

Lead generation, including targeting, personalizing and capturing customer data from content marketing, digital ads, organic social posts and landing pages. Companies can save more than six hours per week simply by automating social media.
Lead nurturing, including A/B tests, targeted drip campaigns and triggered and scheduled emails. Automated emails have a 70.5% higher open rate and a 152% higher click-through rate than generic email newsletters.
Upselling, like sending automated post-purchase emails to customers recommending additional products based on purchase history, interests and/or how they’ve used your website.
Productivity, by enabling campaign autopilot, which allows employees to measure, iterate and optimize in real time.
Campaign measurability, by providing deeper insights on what inspires different user personas to take various actions.
Customer engagement/success, by providing auto responses when applicable, guiding agents through conversations, and maintaining comprehensive customer databases. Using chatbots alone can save 30% of customer support costs and speed up response times by up to 80% for routine questions.
Sales, including lead scoring, pipeline customization, trigger emails and scheduling.

By reducing human intervention and streamlining repetitive processes and tasks, AI provides the following benefits to businesses:

Time savings and improved productivity. When you automate your marketing, sales and customer service processes, you free yourself to focus on strategic tasks and boost productivity. With the right automation solution, you can put your strategy to work and let it run, collecting data to improve future campaigns while you focus on other initiatives.
Cost savings and higher ROI. Automation can enable staffing cuts, which can lower business expenses. More importantly, though, automation reduces human error and the costs associated with correcting mistakes by limiting human intervention in repetitive, mundane technical tasks, while allowing employees to focus on delivering the human connection customers have come to expect from their favorite brands.
Better internal relationships. 97% of employees and executives believe that a lack of alignment impacts the outcome of a task or project. By simplifying processes and integrations, automation improves the working relationships among marketing, sales and customer service employees. Instead of staying in silos, these three historically disparate departments can brainstorm cross-departmental strategies, share customer information and integrate funnels, as well as coordinate with IT to implement the right 360-degree automation solution.

Of course — as with any technology that learns from humans, with all our biases, misinformation and preconceived notions — safety, security, diversity and inclusion are all key considerations.

An older Black woman wearing a cap and gown, with a huge smile as two young Black girls (presumably her grandkids) kiss each cheek

How to Prevent Bias in AI

Ray Kurzweil, the renowned futurist and technologist, predicted that AI “will achieve human levels of intelligence” within six years. Mo Gawdat, a former Google X exec, predicted that AI will be a billion times smarter than the smartest human by 2049. “With that kind of raw power and intelligence,” writes Peter H. Diamandis, an international pioneer in the field of innovation, “AI could come up with ingenious solutions and potentially permanently solve problems like famine, poverty, and cancer.” But, as Gawdat has pointed out, “solving such problems doesn’t only rely on intelligence — it’s also a question of morality and values.”

In fact, even Sam Altman, the CEO of OpenAI, has called for government regulation of artificial intelligence — and his company has offered 10 $100,000 grants “to fund experiments in setting up a democratic process for deciding what rules AI systems should follow.”

For Vera CEO Liz O’Sullivan, a member of the US Department of Commerce’s national AI advisory committee and an expert on AI, “fair algorithms” and digital privacy, “battling bias in AI is a little bit like fighting it in the real world — there’s no one-size-fits-all solution, technical or otherwise; it’s highly dependent on the use case, the goal and the team, and it’s something that takes continual work over time.”

According to Diversity Cyber Council president Odie Martez Gray:

The function of AI is to logically draw relationships to data and formulate or compile an aggregated result. Knowing this, a simplified overview of the challenge concerning AI and racism is first determining ‘ethical’ data sources that consistently produce unbiased data — because we all know, ‘bad data in, bad data out.’ The second challenge is in implementing compensating controls that moderate data and autocorrect an AI’s logic to a predetermined mean.

Jacques Bastien, founder of boogie and a UI/UX professor at the University of Albany, is more hopeful, telling me he’s “optimistic… because, unlike human beings, [AI] can be improved.”

DEI thought leader Samantha Karlin agrees, adding that “it’s just like being a DEI trainer — the number one thing is that you have to constantly be sensitive to all different groups of people and think about how they perceive things differently based upon their differing lived experiences.”

The best way to do that is to ensure that “whoever is creating, training or using the AI has actually been trained on DEI principles and/or comes from a directly impacted group.”

Plus, added data scientist Noah Giansiracusa:

As more organizations think about developing their own AI systems, they should think very seriously about not just the data they feed it and the computational resources to train it, but also the importance of having a secondary training process of direct human feedback, which really seems to be the way to significantly improve a lot of the problematic aspects of AI we saw initially.

My advice is to subscribe to Mindful AI, a series developed by Kieran Snyder, co-founder and CEO of Textio, a “platform for inclusive and equitable communications.” In the first article, Snyder tackles “understanding training data and bias,” providing insights on “how to use generative AI in ways that don’t perpetuate harm.”

A conference room with a diverse group of employees comprising the AI working group

5 Steps to Making Smart, Unbiased AI Decisions for Your Brand

Step 1: Create a diverse and inclusive artificial intelligence working group

Once your organization is prepared to truly investigate the potential uses of artificial intelligence while adhering to the principles of diversity, equity and inclusion, it’s time to build your AI working group.

Assign your CIO, CTO and/or IT director to the manager or coach role, and your DEI director as team staffing and inclusion consultant, and instruct them to work together to build out your working group with:

Enterprise, IT systems, network, technical, software, platform and data architects and engineers
Business intelligence and systems analysts
Cybersecurity and security systems specialists
Database administrator, data privacy officer and data scientist or data intelligence specialist
UX designer
UI designer
Back-end developer
Release manager

There should also be mid- to senior-level representatives of each of the impacted business areas, including:

Marketing
Sales
CX
HR
Ops
Finance

The goal of the AI working group is to complete the remainder of the steps without sacrificing your commitment to DEI.

Step 2: Determine if artificial intelligence is a worthwhile investment

The first mission of your AI working group must be to determine whether this exploration is even worth the organization’s time and resources. Kick off your introductory meeting by discussing and soliciting detailed responses to the following:

What would we hope to achieve by introducing AI? What are the biggest weaknesses, gaps and opportunities we could address using AI? Would we create better products or services, expedite our time to market, mitigate risk and increase compliance, overcome our inefficiencies, or improve our customer or employee experience?
Which types of AI platforms and AI tools would best meet our needs?
Which tech improvements would be required to enable AI implementation?
What new roles, if any, would we need to create to train, monitor, manage and report on our AI investments? And what methodologies would we adopt to prevent biased advancement decisions?
What new processes and procedures would we need to develop to ensure the proper, unbiased use of AI?
What types of training would be required to ensure all new, reassigned or role-enhanced employees are equipped to perform their AI-related job functions?
What obstacles would we face in implementing AI that doesn’t leverage or produce data that proliferates bias? How would we overcome them?
What is our max budget for the initial investment?
What other tech and people costs would we incur to enable full AI adoption?
Which types of benchmarks and metrics would we use to track performance and ROI?

If, after reviewing the group’s written responses to all of the above questions, you and your working group leader believe you should continue exploring AI at your organization, task your tech experts with the next steps.

Step 3: Assess your IT infrastructure for AI capabilities

Even with outdated legacy systems and complicated tech stacks, you can still implement artificial intelligence, intelligently. Of course, before you identify practical use cases or develop an AI strategy, you need to determine:

What your IT infrastructure can handle
What can (and cannot) be updated or upgraded to enable not only the adoption but continued use and further development of AI
What resources you’ll require to compensate for any gaps or weaknesses
What infrastructure costs you’ll endure

For smaller and/or older organizations transitioning from AI experimentation to implementation, overhead costs may skyrocket as AI tech becomes increasingly more complex, better positioning more strategic, meticulous, innovative and cash-flush organizations that heavily research and identify cost-effective systems and methodologies to run their AI software.

When considering the bandwidth, strength and integration capabilities of your current systems and tech stack, as well as what you’ll need to take advantage of all the benefits of AI and automation, prioritize the following:

High-performance computing capacity. GPUs, for instance, can accelerate deep learning by 10,000%.
Tailored, high-capacity, scalable storage. Ensure you have a database/storage system appropriate for the amount of data your AI tools will take in and put out, as well as the ways you’ll use that data in real time and over time.
High-bandwidth, low-latency, scalable networking infrastructure. AI puts strain on your networks, so invest in a seamless, secure global infrastructure provider that can prevent disruptions and outages and expand with your AI/data requirements.
State-of-the-art security technology. Whether for the privacy and protection of your customers or that of your intellectual property, it is critically important to prevent invasion or data leakage of any kind.
Affordability. The price of AI won’t shrink any time soon, as it becomes increasingly more complex and expansive. Don’t get left behind, but be mindful of accruing costs and scale intelligently, increasing investment size and implementation speed only with confirmed results.

Step 4: Identify AI use cases, and test and select AI solutions

There’s an automated option for everything, but all artificial intelligence isn’t created equal. Before demoing the most buzzed-about or high-powered AI tools or hiring an AI developer to create your own, instruct your AI working group to first identify the types of AI tools and platforms that would most significantly impact the work and lives of their fellow employees.

Start with the list in this article of the 90 best AI tools for all critical business functions.

Step 5: Implement, test, monitor, report, iterate and optimize

As with any new tool, tactic or strategy, artificial intelligence isn’t a set it and forget it solution.

The final role of your AI working group should be to confirm with you, the C-suite and all impacted managers all the new AI-related assignments. Your working group leader should then continue to oversee reporting on/from all campaigns, business units, processes and employees leveraging and/or responsible for artificial intelligence.

This reporting will determine whether you need to: