Generative AI The New Frontier of Automation

In the past, drivers took months and even years to learn the streets well enough to pass the city’s notoriously difficult taxi driver exam, known as “the Knowledge.” Then came Google Maps and Waze. These apps did not eliminate the differential between the veterans and the newcomers, but they certainly reduced it. This leveling-up effect on employee performance seems likely to become a general consequence of the advent of powerful AI digital assistants in many parts of the economy. The first was that productivity for the group with the AI assistants was on average 14 percent higher. The second, and even more significant, was that, although everyone in the group with the AI assistant had productivity gains, the effect was much higher for relatively inexperienced agents. In other words, the AI assistant was able to markedly close the gap in performance between new and seasoned agents, suggesting generative AI’s potential to accelerate on-the-job training.

This approach significantly reduces the barriers to entry for creating complex AI algorithms, making it more accessible to a broader range of users, from small businesses to educational institutions. Artificial Intelligence and Machine Learning technologies to elevate the efficiency and scope of automated processes across various industries. This advanced automation spectrum, ranging from robotic process automation (RPA) to intelligent business process management, is redefining the landscape of organizational operations. Hyperautomation, by extending and enriching these capabilities, plays a pivotal role in scaling automation possibilities for businesses far and wide. Each of these approaches contributes uniquely to the AGI landscape, blending theory, experimentation, and technological innovation. The journey towards AGI is characterized by both immense potential and significant challenges, promising a future where artificial intelligence might one-day parallel human intellect in its breadth and depth.

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For instance, a provider of market intelligence to business clients that delivers a natural language alternative to keyword searches, correlating summarized data on customer sentiment from the vector store with heterogeneous data from a document database such as MongoDB. There is a lot of to-do about the defensibility or lack of defensibility for AI companies. But when the economic benefits are as compelling as they are with generative AI, there is ample velocity to build a company around more traditional defensive moats such as scale, the network, the long tail of enterprise distribution, brand, etc. In fact, we’re already seeing seemingly defensible business models arise in the generative AI space around two-sided marketplaces between model creators and model users, and communities around creative content.

A logical extension to these auto-documentation capabilities would extract data from business glossaries and correlate them with table metadata, or vice-versa. The automatic summarization capabilities of gen AI could be pointed at written policies, rules and incidents to document compliance with risk management guardrails. The reading of table metadata and SQL transformations could enrich or generate reference data that reconciles data between databases and applications, and identifying gaps or omissions. For instance, it’s well-documented that the reliability of facial recognition systems gets easily skewed by over- or under-sampling of different races and nationalities. The same goes with analyzing demand for products or social services when different census tracts or demographic cohorts are sampled at different rates.

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With that said, the invention, development, and scale-up of disruptive technologies has also been recognized as geographically skewed. By providing key financial services that enable the transaction, marketplaces can drastically improve the incentive for both sides of the market to keep their transactions both online and exclusive. Building incentives for both sides of the marketplace to stay in-network is a top priority for marketplaces.

But it was what gen AI could do and make from those fluid chats that soon became the bigger story. As distinct from “traditional AI” systems, which react to inputs by following pre-set rules, gen AI models can create information. If traditional AI in a streaming service can recommend a movie you might like, generative AI can, in seconds, write an original movie script precisely tailored to your individual tastes and requests. Despite the promise of AI, much of the public debate about it has focused on its controversial aspects and its potential to do harm. Their outputs can sometimes reflect the bias of their training sets, produce erroneous material, or include so-called hallucinations—assertions that sound plausible but do not reflect the reality of the physical world. Researchers are trying hard to address these issues, including by using human feedback and other means to guide the generated outputs, but more work is needed.

This strategic approach and the vision to develop Singapore as a ‘smart nation’ have significantly contributed to its status as a major tech hub. Given that over 80% of current breaches can be traced back to leaked credentials or passwords, this issue remains a significant concern in security. GitHub believes that its secret scanning capabilities will make a substantial impact, especially with the recent advancements in detecting generic secrets. It is vital to address prevention and detection to effectively tackle this ongoing security challenge. For instance, this year alone, GitHub has blocked over 30,000 secrets through push protection before reaching the repository. It’s already embedded in the code, requiring time and resources to invalidate the token, generate a new one, and correctly reposition it.

For example, an LLM given a prompt to write an article on inflation not only produced the article but concluded with a list of additional reading that included five articles and books that do not exist. Obviously, in applications that require factual accuracy, made-up answers pose a major concern. Even when not hallucinating, LLMs can produce bad, seriously biased, silly, or obnoxious predictions that require human review. Thus, the careless or overly expansive implementation of generative AI could lead to the perpetuation of flawed information or even to malpractice.

Risks

The latter, which helps generate code by using simple instructions written in plain text, is transformative. While natural language processing (NLP), a subset of AI, was already adding plenty of capabilities to startups creating no-code development platforms, generative AI is what will lend scale to no-code service providers. It will also rope in more clients, who will begin experimenting with the kind of application development that may in the long run democratise the entire realm of developer activities. For coders, no-code platforms expedite code writing and make the overall process very simple. A number of companies have started to actively analyze the roles within their organization to see how this new technology will impact jobs. Deduce how AI can help with skills intelligence and strategic workforce management to meet emerging, evolving business needs.

What is readily clear is that businesses of all walks have an unprecedented opportunity to realize the transformative value of becoming a knowledge-driven enterprise. To seize a competitive advantage and drive growth, companies need to start turning a strategic plan into action today. Throughout the information revolution era, enterprise knowledge systems have grown rapidly—progressing from vast repositories of codified data to information, insights, and intelligence. All the while, enterprise tacit knowledge—that uniquely human ability to intuitively understand something—has also been growing. Organizations rely heavily on tacit knowledge and manual processing activities to draw insightful connections across their enterprise for generating new ideas, making countless business decisions, and translating those decisions into actions.

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These waves have brought both the diffusion of new technologies into new regions (with local economic benefits), but also intense clustering of jobs and businesses related to them, which has contributed to vast inequality among regional economies. The effective universal deployment of generative AI may well widen the geography of the overall AI industry—but then again, it might instead harden the dominance of the industry’s core hubs, especially when it comes to research and development. High and persistent levels of industry concentration could have negative implications for economic opportunity, regional growth, and the development of an AI sector that serves a broad consumer base with varied products and services. By integrating the third party and bringing financial services on-platform, fintech-enabled marketplaces will be well-poised to provide a superior user experience, allowing them to break into major industries with historically low tech penetration. On the flip side, by adding a marketplace network effect to financial services, fintech-enabled marketplace companies will enjoy a level of defensibility not traditionally seen in financial service companies.

• Further, the LLMs really do understand natural language, and therefore are being pushed into service as a new consumption layer for programs.
• In 2024, global regulations on climate change will enforce stricter rules on emissions and the impact on the operations of various industries.
• For example, concerns over how people’s jobs may change, whether they will be automated, and opportunities for reskilling should be addressed with care.
• The geography of research hubs, major technology companies, and talent continues to concentrate on the coasts.
• After its G20 presidency and the Chandrayaan-3 lunar mission, India is taking on a more ambitious global role, including in science and technology.

Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with work automation could add 0.2 to 3.3 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations.

William Blair Thinking Presents—Generative AI: The New Frontier of Automation

Tuned to local tech sector specializations, such campaigns can and should target both the current federal push to democratize investment as well as national and local philanthropy. Georgia Tech’s acquisition of significant R&D funding for its AI Manufacturing Pilot Facility shows how the new federal programs can be leveraged. In light of that, there is an urgent need for Congress to scale-up such place-based investments in emerging AI communities.

One such example is the CogPrime architecture, which integrates symbolic and sub-symbolic knowledge within a unified framework known as AtomSpace. This approach underpins the development of sophisticated social humanoid robots like Sophia, created by Hanson Robotics and OpenCog, blending neural architecture with cognitive and symbolic AI. According to the organization’s experts, realizing the full potential of generative AI will take time, primarily to optimize the risk management system, improve the skills of employees and rethink work processes. Is it a wiser bet for businesses to take an incremental use case-driven approach, or will they lose the opportunity for competitive advantage by not placing a bigger bet on GenAI? Because GenAI is in its infancy, businesses are challenged to understand the overall investment cost and business impact. While the potential uses for GenAI continue to explode, we believe the truly transformative business value will be achieved as organizations advance their use of GenAI for personalized augmentation.

For starters, AI could play a formidable role in accelerating content creation on social media platforms. Moreover, advertisers and influencers use AI to spur creative experiments, such as identify high-engagement posts, explore new audiences, improve retention rates, and manage spending better. By the end of 2023, cloud infrastructure-as-a-service is anticipated to be a $150 billion market, expected to grow approximately 30% year-over-year.5 We believe AI will accelerate the trajectory of growth. Hyperscalers, as well as the large infrastructure service providers, could leverage the AI wave to capture a new breed of consumers and cross sell more cloud products to them. The most important question will be how we will live in a new technological era, when a human-like intelligence, perhaps one day an artificial general intelligence, is a part of our world.

China’s crackdown on technology companies, dating back to at least 2020, and dramatic shifts like the cancellation of a leading Chinese technology company’s IPO, increases uncertainty in the non-state sector, decreasing the dynamism of China’s technology ecosystem. Let us now close the model and examine the incentive to become an AI-based entrepreneur and the way in which this incentive depends on the increasing importance of the operational data possessed by the incumbent. To this end, we assume that the entrepreneur faces a fixed R &D cost or investment cost, F, to become an entrepreneur.

Employing a cluster analysis of metro area data covering seven measures of AI research and commercialization across 384 metro areas, we found that AI activity in the U.S. is highly concentrated in a short list of “superstar” hubs and “early adopter” centers. The report begins by reviewing the intensely concentrated nature of the overall AI industry (as opposed to the recent boom in generative AI) and suggesting the need to widen the sector to ensure broader participation. Ultimately, the report suggests that policymakers now have an opportunity to bring about more geographically inclusive development for one of the most important innovations of our time. At the same time, the relative scarcity of AI research centers and companies—and Big Tech’s domination of those that do exist—is raising questions about the opportunity structure and geography of the AI revolution, if left to its own devices. Dramatic advances in generative artificial intelligence (AI) have touched off intense debates about who will “win” the rush to deploy these technologies. Much has been written about AI’s potential to displace millions of workers, but leaders across the country also see the emerging technology as an opportunity to boost the productivity and dynamism of their regional economies.

The same applies for higher education, with heavily misaligned incentives between student loan companies, students, and universities. There’s an enormous white space for innovation and opportunity within major sectors which are plagued by misaligned incentives. You’ve probably seen the chart below, which shows price changes of US goods plotted against wages. In red are those industries which have seen prices increases that have outpaced wage inflation. Returning to the evolution of 2-sided marketplaces, we have seen the trend illustrated above unfold across a number of market categories. For example, in hospitality, we see trends towards verticalization and toward bringing more of the listing, booking, and transaction processes online.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.