Digital transformation and the rise of Generative AI

The COVID-19 pandemic accelerated digital transformation, leading many enterprises to adopt remote and hybrid workplaces and, with it, a slew of digital collaboration tools. Many observers believe companies are now three to four years ahead in their digital transformation journey. Generative artificial intelligence (AI) tools are just the latest innovation in the digital transformation toolbox.

Tools like ChatGPT have captured the imagination of many, leading to countless articles and podcasts about the end of work as we know it. While it is true that AI tools will fundamentally change many roles and workflows, they won’t be replacing humans just yet. Instead, they will be replacing mainly repetitive and manual tasks, allowing workers to focus on more strategic and creative work that requires human expertise.

For example, AI is set to replace about 80 percent of the tasks currently performed by project managers, including collecting data, keeping track of progress, and generating reports. Likewise, AI can help make research, writing tasks and content development more efficient. But AI currently cannot motivate teams or have a keen eye to ensure quality and factual information.

In fact, AI is prone to “hallucinations” – making up its own set of facts. In our era of AI, it will become even more important to have a human manage the output. Although early discussions around AI have focused on job loss due to the automation of many functions previously performed by humans, the discourse now mainly examines how AI is changing the nature of jobs. AI will both lead to jobs becoming more specialized as routine tasks are automated and to task diversification, where employees carry out a range of tasks that used to require specialist skills.

Beyond automating business workflows and working as assistants, AI is also working behind the scenes, pulling in data from a vast network of sensors and “awareness” applications. This data is fueling a vast ecosystem of invisible business – decisions made by AI behind the scenes that create real-world effects and economic value.

But before we dive into the value AI is creating, we should look briefly at how it all began.

A (Very) Brief History of the AI Industry

In the early 2000s, Marvin Minsky—a cognitive scientist and one of the fathers of artificial intelligence—liked to say that we can make a computer capable of beating the reigning genius of chess, but we can’t make a robot capable of walking across the street as well as any normal two-year-old child.

The real world is not a strictly regulated closed system like a chess game. Sensing moves on a virtual board and responding within agreed-upon rules is one thing. Sensing and physically responding in actual reality—where a huge number and type of unexpected events might occur—is quite another.

In fact, the entire AI industry has been through multiple attempts since the 1950s to grow into a mature market. Each of these efforts collapsed because the technology was unable to meet the unrealistic public and investor expectations generated by non-real-world computing triumphs like those of IBM’s Watson.

COMPLEX ADAPTIVE SYSTEMS RELY ON DATA

As networks continue to invade the “physical” world, traditionally unique components and interfaces between and among electronic as well as electro-mechanical elements are becoming more standardized. Product and service design is increasingly influenced by common components and sub-systems. Vertically defined, stand-alone products and application markets will increasingly become a part of a larger “horizontal” set of standards for hardware, software, communications and data.

As it becomes easier to design and develop smarter more adaptive systems, competitive differentiation will shift away from unique product features towards how the product is actually used, how the product fosters interactions between and among users in a networked context, and, most importantly, how the data from the product will inform these new insights.

Machine data from cyber-physical systems of the real physical world can offer extraordinary business advantages to the companies that understand how to organize that data and model the behavior of the physical world. The ability to develop models from sensor and machine datasets allows not only data patterns but a much higher order of intelligence to emerge.

Applications like computer vision caused the development of systems, based on methods and algorithms that could recognize something and execute an action based on the result. These new capabilities revolve around real-time situational awareness and automated analysis of very large volumes of sensor data. As a result, technology has moved beyond just proposing task solutions — such as executing a work order or a sales order — to sensing what is happening in the world around it, analyzing that new information for patterns, risks and possibilities, presenting alternatives, and automatically taking actions.

These combinatorial innovations are what we call “complex adaptive systems” where multiple technologies converge and reinforce one another.

For example, the development of autonomous vehicles not only depends on advancements in robotics and artificial intelligence to operate vehicles, but also on the maturation of the Internet of Things so an array of sensors can analyze driving conditions and interact with other cars, as well as improvements in lithium and battery technology for cars to be able to efficiently refuel themselves. The interdependence of these technologies has no doubt contributed to their synchronous advancement. For example, many believed a limiting factor in the emergence of driverless cars was the high cost of batteries required to travel long distances. In response, however, battery producers have dramatically increased production to scale down per unit costs.  Over the last ten years, EV battery prices have fallen 90%.

From seemingly disparate innovations powered by self-organizing sensor networks processing voluminous amounts of data, computers are able for the first time to understand and form associations based on statistical methods. Computers can now do what we previously thought only humans could do.

Complex adaptive and autonomous systems have common attributes, including:

• The ability to infer and reason, using substantial amounts of appropriately represented knowledge.
• The ability to learn from their experiences and improve their performance over time.
• The capability of explaining themselves and taking naturally expressed directions.
• The awareness of themselves and ability to reflect on their own behavior and to respond robustly to surprises.

As these systems evolve, we are setting the stage for numerous “invisible” computer-to-computer interactions (a new generation of “machine-to-machine” transactions):

• Autonomous machines making decisions about purchases on our behalf or concluding contracts.
• Multi-agent systems and decentralized autonomous machines enabled to lease themselves out, hire maintenance professionals, and pay for replacement parts.
• Micro-payments between machines, such as a car looking for a specific spare part, or drones and farming systems negotiating directly with each other for services.

Machine data models and analytics allows not only data patterns but a much higher order of intelligence to emerge from large collections of ordinary machine and device data, similar to the neurons of the brain, ants in an anthill, human beings in a society, or information devices connected to each other. The many “nodes” of a network may not be very “smart” in themselves, but if they are networked in a way that allows them to connect effortlessly and interoperate seamlessly, they begin to give rise to complex, system-wide behavior that usually goes by the name “emergence.” That is, an entirely new order of intelligence “emerges” from the system as a whole—an intelligence that could not have been predicted by looking at any of the nodes individually.

IT’S STILL EARLY IN THE GAME, BUT CHANGE IS UPON US

There have been notable achievements in AI and machine learning—autonomous driving, natural language processing, text generation, facial recognition, algorithm design, vaccine discovery and even AI-generated art. Rapid advancements in silicon, computing and networks are clearly forming the foundations for AI and machine learning capabilities to advance.

But these systems, sophisticated as they are, are still in their early stages, and many intended use cases for AI can still be accomplished with more cost-effective traditional tools like basic regressions. It seems clear that real business value from machine learning and AI will be realized unevenly across markets, applications and use cases.

NEW AI TOOLS MUST BE ALIGNED WITH INTENDED APPLICATIONS AND USE CASES

Possibly most important for the growth of AI is that multiple parallel technology developments are now increasingly reinforcing and accelerating one another. Cloud infrastructure resources are providing unprecedented computing scale. Mobile computing devices are extending the reach of computing. Embedded systems and IoT technology are connecting and integrating a broad array of physical and digital applications. And of course the signature achievement of the age of “big data,” the ability to capture and process massive amounts of raw intelligence from the physical world, has the potential to inform many new and diverse capabilities.

Each of those technologies is powerful on its own, but creative combinations of them are what is most exciting. Human-connected devices and machine-connected IoT devices enable exponentially more data at the edge. The scale of core, infrastructural (cloud) computational capabilities enables us to capture and analyze all that information. And this in turn sets the stage for AI and machine learning tools to analyze and capture new insights.

This new chapter is motivating tech developers and users to apply advanced neural nets and deep learning tools to their most intractable problems. Most companies believe that implementing advanced AI solutions will lead to significant efficiencies, growth and competitive differentiation. However, matching new tools to high value applications and use cases will challenge many industry participants.

We all know that AI tools are trained on large data sets, but most people do not grasp that AI applications require thousands or even hundreds of thousands times more data than a human would need to solve an equivalent problem. If you examine applications where machine learning is successful, it quickly becomes apparent that they are in domains where acquiring lots of data is relatively easy—think facial or speech recognition, where technology developers have vast troves of data they can access.

Data models are the core value creation mechanisms that AI will enable within the Smart Systems arena. But the B2B world doesn’t have the same unified sources of data or monolithic usage tracking and analytics that the consumer world utilizes to make money. Based on our consulting work, we estimate that most B2B companies will need to reach beyond their internal company data resources for as much as 50% or more of the data required to inform new models, applications and customer solutions.

An additional challenge is the fact that most machine learning systems today run “narrow purpose” applications that can do only a single type of learning. Current neural networks cannot be trained to run multiple parallel applications, such as identifying images and playing video games, or predictively diagnosing machine failures and listening to and identifying music, all at the same time.

Finally, the impacts of new AI tools will be higher and more straightforward to achieve where the user’s propensity to experiment with new tools and methods is also higher.

WHERE TO LOOK FOR AI/ML ROI

AI and machine learning are being turbo charged. An explosion of AI/ML tools is lowering the barrier to entry to high-end data science. Historically, developing AI/ML applications and use cases involved data teams doing much “heavy lifting” to design and deploy complex custom models. Today, new data tools are gaining wider adoption. Standardized schemas for data ingestion and transformation are setting the stage for many more companies to incorporate AI/ML into their products.

Additionally, domains and applications where customer data is actively collected allows those data sets to be combined with adjacent data to address new opportunities such as customer prediction analytics. New opportunities are emerging all over the economic landscape to fuse very large data sets with new sensor readings and measurements. Logistics companies will use external data to predict disruptions in their retail customers’ supply chains. Smart city emergency response services will get to accidents or disaster sites and on to hospitals as quickly as possible leveraging imaging data, maps, traffic flow data and much more. Combining weather forecasts and legacy weather models with sensors mounted on vehicles that contribute real time road-condition measurements will help large fleet owners optimize the use of their trucks.

In order for developers to provide appropriate tools and services, and for users to effectively justify the significant investment in AI/ML capabilities, identifying and aligning the tools with the intended use cases and applications will be critical. ◆

Stream our latest Future Perfect Tech podcast