Dean DeBiase is a best-selling author and Forbes Contributor reporting on how global leaders and CEOs are rebooting everything from growth, innovation, and technology to talent, culture, competitiveness, and governance across industries and societies.
Brace For Impact-How To Get Past AI’s Painful Frustrating Phase
By Dean DeBiase
August 12th, 2024
With over $35B invested in AI startups this year, and economic projections of $15.7T, look for big impacts and a flurry of investments and M&A.
Throughout history, technological breakthroughs have often come in painful phases—with frustrating waves of hype and exaggerated claims. Amazon’s ‘Just Walk Out’ system, touted as a marvel of artificial intelligence-powered shopping convenience, which was reliant on human reviewers scrutinizing video feeds from stores, is a modern-day example. Similarly, the metaverse was also touted as the next big thing, prompting Facebook to prematurely change its name to Meta. AI too could be distrusted as hype with appropriate skepticism based on the gaps between marketing promises and functional reality.
However, AI has demonstrated great promise across domains and sectors—like the medical industry. Consider the COVID-19 pandemic when pharma companies were racing against time to develop a vaccine. Regardless of your position on vaccines, scientists at Pfizer were able to create the mRNA vaccine with the aid of Machine Learning (ML), which helped them to bend the time curves required for the analysis of patient clinical data.
Since then, we have seen enterprises focusing on implementing task-specific AI solutions where the technology plays a major role in enabling drug discovery, healthcare diagnostics, and supply chain management. Look for AI impacts across sectors, specifically the Banking, Financial Services and Insurance industries, with emerging InsurTech companies like Vertigo embedding AI capabilities into their platforms.
While it ushered in a new era of innovation and practical applications, consumers got a flavor of AI with the introduction of OpenAI’s ChatGPT in 2023. The year marked an inflection point where AI’s capabilities finally caught up with the hype in the eyes of consumers. It also helped create a notable distinction between AI that focuses on analysis, problem-solving, and decision-making and AI-based solutions capable of creating new content as an outcome of conversations which we now call Generative AI (GenAI).
The last year has delivered an unprecedented surge in the development of machine learning models with dozens of s free-to-use GenAI models and platforms entering the market. Too many? Perhaps, but it is all part of a normal hyped-up Hype Cycle either reaching the peak of inflated expectations or the painful trough of disillusionment.
The AI Index reports that the tech industry came up with as many as 51 AI models while academia came up with 15 and industry-academia collaborations with 21 models. In 2024, we have experienced newer state-of-the-art systems, including GPT-4, Gemini, and Claude 3, that can generate text in dozens of languages, process audio, and even engage in games and meme explanations, available free of cost to the public. But what is beyond the early days of text and speech toys?
Q2 ‘24 – Record-Setting Gold Rush
GenAI has witnessed remarkable functional improvements since its debut with access to advanced models, with much of the next wave going behind paywalls. It has sparked a race among enterprises to use these advanced models to build their AI-based products and services and there is a surge in GenAI funding—with billions being invested.
History often repeats itself, as fools rush in to be early adopter winners. But the first in is not always the winner. While private investments in AI declined overall in 2022, the funding for GenAI increased by 8X according to the AI Index report, reaching $25.2 billion in 2023, and 2024 seems on its way to doubling that. Major players who secured funding were OpenAI, Anthropic, Inflection, and collaborative AI community provider, Hugging Face (got to love emoji names), who just acquired yet another company, XetHub.
In spite of a pending trough of disillusionment wave, projections continue for the AI market to hit $1.8T by 2030 with a potential contribution to the global economy of $15.7T. Meanwhile, AI funding for Q2’24 hit an all time high of $23.2B, according to CrunchBase. Look for a flurry of investments and M&A, as companies move to sell and acquire, to scale their platforms beyond first wave capabilities and speeds, like these recent deals:
xAI – $6B Series B, $24B valuation
CoreWeave – $1.1B Series C, $19B valuation
G42 – $1.5B investment, Microsoft
Scale – $1B Series F, $13.8B valuation
Wayve – $1.05B Series C, Softbank/Microsoft/Nvidia
First Waves Of Technology Can Be Painfully Frustrating
While the E-commerce sector, for instance, is using AI to improve customer engagement metrics; others are trying to productize their offerings. However, not everything is perfect. For example, chatbots deployed to improve customer experience can sometimes backfire, as in the case of the delivery firm DPD whose AI-powered online chatbot was rude, swore, and criticized the company while interacting with customers. There have also been failures with products, the wearable personal assistant Humane AI Pin, for instance, struggled with battery life and reliability of AI despite being an interesting concept.
While pushing boundaries deserves praise, one needs to be strategic as well. The technology is still in its early days and there are lessons to be learned from the painful and frustrating days of building and using Web 1.0 services, when you had to ‘dial-up’ the Internet—and the dotcom bubble at the turn of the century. Fueled by hype and media attention stock prices of many internet companies were inflated beyond their actual value back then.
However, despite the crash that followed, when the market eventually corrected itself, the Internet continued to evolve, and the winners, often the 2nd or 3rd companies in, were those who placed their bets on the adoption wave of in-demand services which best optimized the underlying technologies. This wave led to a market condition that some web1 founders regret—oligopolies. Many of us, that helped create Web 1.0 and Web 2.0, would like to see a more democratized wave, away from the control of oligopolies or the Magnificent Seven, which is often proposed in the delayed promises of Web3.
Controlling Or Opening The Promise Of AI
Some believe AI can begin the Web3 shift, but the control and power is often in the models of the leaders—like Google’s proprietary search algorithms. Hugging Face continues to shine a light toward open-source AI and supporting smaller communities. In a statement committing $10M in free shared GPUs to help developers build AI, CEO, Clem Delangue said, “AI should not be held in the hands of the few. With this commitment to open-source developers, we’re excited to see what everyone will cook up next in the spirit of collaboration and transparency.”
Collaboration helps. As the underlying technology of AI matures, its impact on productivity, growth, and employment will become increasingly evident leading to further adoption and more useful applications. The 2024 Global Survey on AI by McKinsey found that 65% of respondents report their organizations regularly using GenAI in 2023, with overall AI adoption jumping to 72% in 2024.
Whether or not democratized Web3 or open-source business models prevail, the next-wave of AI applications will be extremely powerful, granting leaders abilities to transform their companies, industries, and societies. As we move beyond the first wave of chatbots and copilots toward autonomous AI agents, these next-gen LLM-powered bots (agents) will be able to independently reason and execute tasks across the enterprise. CxOs are testing these early agents with AI enabling vendors, each focused on horizontal-swim-lanes to impact key areas of enterprise issues and opportunities like sales, customer support, R&D, productivity, compliance, software development, engineering, storage, and cyber security.
The Power Of Enterprise Partnerships
The adoption of GenAI has the potential to truly turn global, with enterprises across various industries and business functions exploring and making advances in its use cases and testing its limitations. There is a race among enterprises to realize value from this adoption, and I strongly believe the smartest path is through strategic partnerships. It is not enough to just innovate within your own vertical.
Considering the disruptive potential of GenAI to transform workflows and drive competitive advantage, enterprises must look outside their organizations and sectors. This will help them scale and gain the domain expertise required to develop fully integrated solutions. The need of the hour for CXO’s is a GenAI strategy that makes business sense, is easy to integrate, and is secure. Yeah, it’s a big ask—and you shouldn’t do it alone.
In previous articles and broadcasts, I discussed following the smart money being put into the next wave of AI organizations such as Kore.ai that are pushing the capabilities of Gen AI technology and large language models (LLMs). They provide conversational AI solutions, which have the potential to transform how customers, agents, and employees interact with enterprises, improving experiences and driving operational efficiency.
In 2023, Mphasis, an Information Technology (IT) solutions provider, specializing in applied technology and business process services, formed a strategic partnership with Kore. Taking the partnership route helps Mphasis improve its offerings for enterprise clients by transforming customer experience management and employee engagements. Building an effective GenAI strategy entails understanding the advantages of the right partnership. A new Mphasis AI tool, DeepInsights, looks like another good example. Developed to handle documents, it can pull out important information and be customized to meet specific industry and enterprise needs.
I caught up with Nitin Rakesh, the CEO of Mphasis, who like many of us used ChatGPT in his free time to experiment with activities such as playing games, discussing pop culture, and even summarizing the literary classic ‘Romeo and Juliet’ to the pithy two-word description—Love kills. Love that! But in wearing his CEO hat, he also of course uses the tool as a digital analyst to furnish analysis, insights, actionable tips and explore business scenarios. And he has learned a few things on his journey.
Meanwhile, many of you, and your c-suite leaders, continue experimenting like this—but to what end. How do you move beyond the pain and frustration of AI’s Toy Wave toward impacting your businesses, your industries, or even your societies?
Should C-Suite Leaders Go Fast Or Slow?
Rakesh, offers a cautionary note to enterprises on their AI journey. He emphasizes that when leveraging AI tools, clear and focused instructions are crucial for optimal performance. “Enterprises must train their workforce to effectively utilize these tools, ensuring that GenAI’s imagination, brevity, and efficiency are harnessed while minimizing harm. Ultimately the aim of integrating AI into any work process should be to make human lives easier, better, and faster without taking jobs away.”
Ah there it is, the third rail issue, that many of us in corporate and government circles are uncomfortable discussing let alone predicting—cannibalizing jobs. Just like the previous waves of technologies that have automated industries and enabled societies since the early 1900’s, so too AI will shape the future of work and life.
The potential benefits of AI adoption are undeniable, but it’s no magic bullet and enterprises must be mindful. Efforts to ensure that the technology being deployed is ethical and sustainable is a must. Much of the onus is on the senior leadership to separate the hype from reality. They need to provide clear guidance, in the form of a well-defined GenAI governance and strategy, to the boardroom as well as to the people in the organization who will be using the tools.
Technology inflection points are great opportunities for organizations to seize the moment, as the surviving vendors settle in on the best applications and business models—while enterprises deploy diligently to improve their operational efficiencies, and more critically, expand their competitive offerings, global reach and competitiveness. Those that learn the right lessons in the early painfully-frustrating days will be best positioned to lead the way.
