AI Across Markets and Industries
With all the noise around generative AI (GenAI) since the advent of ChatGPT, has the signal gotten lost? Are we missing the one “killer app” that will make GenAI indispensable? What is more likely is that multiple killer apps will surface across many markets and industries.
It’s becoming clear that AI will not be another digital technology revolution—it will be a re-creation of reality as we know it. Speculation about when artificial general intelligence (AGI) will be a reality is rampant. By decade’s end, will we see artificial superintelligence (ASI) that is smarter than any human in all domains of knowledge? A few AI applications within the major horizontal and vertical markets already demonstrate the promise of AI. GenAI is now transforming the most common horizontal market—call center customer service. When customers call with a query, the customer service representative (CSR) can now prompt a large language model populated with the common queries the company has received and get a quick answer to the customer’s question. The first layer of customer service is achieved by AI chatbots commonly deployed today. With the aid of powerful GenAI, CSRs will handle more complicated queries. This makes a company more competitive because it’s offering better customer service, but AI also makes the company more cost-effective because humans become more productive.
THE NATURE OF WORK
AI is already changing the nature of work. It does the drudge work of knowledge work and frees workers to be upskilled for more value-added work. In their 2024 book, Gigatrends: Six Forces That Are Changing the Future for Billions, Thomas Koulopoulos and Nathaniel Palmer predict that digital workers—AI-powered software agents that perform work autonomously in the same capacity as, and in collaboration with, human workers—will increasingly do more of the low-value work that human knowledge workers used to do, freeing up humans to be more creative and innovative.
Digital workers can actually learn and will function as integral members of every team so the human workers can refer to digital workers for accelerated learning and results. Digital workers, explain Koulopoulos and Palmer, excel in systems in which rules or patterns can be defined or learned. Humans then can deal with problems that involve uncertainty, where there’s a need for innovation or new value creation. Digital workers excel at providing outputs, tangible products, or services, while humans excel at providing outcomes, including the long-term impact and value generated by their work, such as increased revenue and customer satisfaction.
On a more macro scale, what are now vertically integrated companies will have to become digital ecosystems. Koulopoulos and Palmer see these as coordinated networks of strategically aligned and intensely collaborative companies or communities. Digital workers will be the collaborative glue that holds digital ecosystems together. These new systems will resemble a utility grid that connects business partners with each other and with their marketplaces. In this new reality, businesses won’t change every so often; they will change constantly and thus must be architected for change.
Digital ecosystems are now viable due to cloud technology that links all partners in the system. The cloud-enabled business partners and marketplaces act in concert—like a nervous system that reacts from stimuli at any point in the system—to kick off automated responses throughout the system for accelerated execution and increased innovation. It’s conceivable that CEOs of each of the partner companies in these systems eventually will be powerful AIs instead of humans. Chinese video game company NetDragon already has an AI-enabled CEO, Tang Yu, whose responsibilities are those of a human CEO, and Yu seems to be doing well at the job. Government is not immune. In September 2025, Albania appointed Diella, an AI chatbot, as Minister of State for Artificial Intelligence, a cabinet-level position.
HEALTHCARE
In healthcare AI is revolutionizing drug discovery. It can analyze large biological datasets to more quickly identify and therapeutically target things like disease-related proteins. GenAI models create molecular structures faster than human trial-and-error approaches to address those that are disease-related. GenAI accelerates virtual screening of chemical libraries for promising combinations from which to create drugs. It’s also improving the speed of genome testing, so it is known soon after birth if the baby has any predispositions to illnesses. This permits early treatment intervention. AI even monitors drug trials to f lag adverse effects in real time to speed up beneficial outcomes. In all of these applications, AI accelerates execution and innovation and drives down cost.
Koulopoulos and Palmer, in their discussion of healthcare, tackle its administrative problems, noting that healthcare data is mostly so siloed, that creating a single continuous history of a patient’s illnesses, treatments, pharmaceuticals, therapies, and outcomes is nearly impossible. U.S. healthcare is episodic, not preventative. This means care is often redundant and too often just wrong, making it unnecessarily expensive.
AI wearables that monitor vital signs and blood sugar, take EKGs, and detect some cancers can provide data in real time so preventative care is possible. Some healthcare providers are using upward of 70 healthcare-tracking devices to populate patients’ medical records. Monthly data traffic from wearables totaled 77 exabytes in 2020. Managing data manually or with technology less powerful than AI will be dramatically underwhelming. Wearables herald an increasing move from patient/doctor to patient/device relationships in healthcare.
AI-driven analytics with algorithms that predict experiences from companies like Prelytics (prelytics.io), now help hospitals determine staffing levels and other resources required at different times, a function hitherto done by guesswork.
Home-based healthcare uses sensors and Internet of Things mobile devices that go where the patient is. The future trend in hospitals is to outsource as much treatment as possible. Many hospitals of the future will offer only ER and big-machine services such as radiology.