Generative artificial intelligence has rapidly captured the imagination of millions, thanks to the way it uses a broad range of inputs to create new stories, code, images, music, video and more. For organizations seeking business value from this dynamic, it is the underlying process, not the eye-popping results, that raises the critical strategic consideration of all: what AI will mean as a pervasive computing platform.

What do I mean by the AI platform? Simply put, it is where business requirements and specifications for software converge at a rate higher than could be achieved in the workflows of traditional, more deterministic software. Using generative AI, anyone in an organization can envision a new customer experience and leverage an AI platform to jump start the process of building. Even in its early days, this new platform has implications for teams, organizational needs and customer experiences.

Rapid results in new ways

Recently the CEO of a gaming company told me his vision for a virtual concierge that would be the basis for persistent, personalized journeys across a range of different gaming environments. Traditionally, testing this idea would require engaging and fusing the talents and work habits of a number of industry silos and business models, including the different creative, data gathering and financial processes of arcade games, terminals,and mobile apps. Since this kind of testing could take lots of time and millions of dollars, the normal momentum and demands of the business shelved this potentially breakthrough idea.

Using generative AI, within a few hours we had the beginnings of an engaging animated avatar with a realistic voice, using off-the-shelf services from Google and its partners, along with someone from an entirely different industry who was skilled in the new art of “prompting,” or training the AI model to produce optimal results. The CEO’s leadership team could jump into the meatier aspects of building a new customer experience, accelerating the creative and business process by months. An actual prototype is driving the next steps, not theoretical arguments, hopeful slides or received dogma.

The games industry requires hundreds, if not thousands, of projects every year, so the prospect of radically cutting the time and cost of creative brainstorming and prototyping is, ahem, game-changing. Add to that the increasing ability to use generative AI in software development, and the time from concept to code goes from months to days.

This revolutionary dynamic generalizes to almost any industry, and it’s happening now. Online travel companies like Priceline are improving trip planning capabilities, retailers such as Carrefour are creating full marketing campaigns in a matter of minutes, and consulting organizations such as Capgemini are building hundreds of industry-specific use cases to streamline time-consuming business processes.

Collectively this shows a larger pattern of increasingly easy and powerful human-computer interaction, management of complexity at unprecedented scale, and greater corporate awareness through improved data collection and management. All three are critical aspects of the AI-based technology platform.

Platforms and human-computer interaction

Technology platforms are transformational on a micro and a macro scale for the ways in which they can reduce friction to existing processes and spur innovations that create entirely new industries. Good examples of big new technology platforms include the mainframe computer, the personal computer, the World Wide Web, mobile devices with apps, and public cloud computing. In every case, they supported and extended the value propositions of a diverse range of companies, while others were able to harness the platform to do new things, such as desktop publishing or social media.

A notable feature of these platforms is their progression toward closer and easier connection between humans and computers. Starting with punch cards, we’ve seen transitions to command lines, drag-and-drop icons and chatbots, along with new frameworks and languages that exploded the role of software in the world economy. AI-enabled computers will do increasingly sophisticated computation while we talk.

New platform, new values

In all its forms, AI is powerful because it spots and leverages patterns. This makes it a tool aiding one of humankind’s greatest cognitive skills. Pattern insight is the basis of the scientific method and the servicing of markets — our society’s twin cornerstones of innovation. For example, pattern-spotting AI is core to understanding how proteins fold, and it’s how a generative AI service trains on an LLM, deciding what to write next.

Whether it’s humans or machines searching for patterns, and increasingly it will be both, the quality of the outcome depends on the quality of the data, to a point with rich, diverse and above all accurate data may be the single greatest driver of success. Serving this need will be a big business in the growth of the AI platform.

Like its predecessors, the capabilities of the AI platform will improve, to a point where both employees and customers will expect accurate and timely information, more efficient use of resources, and personalization that changes depending on the context of the moment. Thus, it is a business not just of one pattern, but an intersection of several, at new levels of complexity and risk management.

To accomplish this, traditional software developers will be able to build more sophisticated programs, increasing the system’s capabilities to interconnect, improve data quality and increase system efficiency. They will become greater participants in the creative process, finding numerous niches to enrich.

Who wins, and how

I’m confident this will happen, because I see the new platform being built today. Developers at some of the world’s largest companies are looking at future environments where efficiencies such as coding prompts and model tuning increase the speed and power of existing implementations and architecture. New AI features are finding rapid uptake among business and consumers, accelerating virality and redefining the rate at which a process can be improved. They will build inside established rules, building best practices, rather than risk the cost of stopping.

The knowledge they are gaining will not remain siloed, as entrepreneurism rushes to fill out the needs of a new platform. New teams and companies address new platform needs like specialized chips and data management. Hyperscale clouds are provisioning backend systems and creating new ways to meet a demand for AI processing, which can rise overnight 10-fold or 100-fold at a single customer. New data frameworks and multicloud technologies already unify data from disparate sources, for better analysis and action.

Established enterprises historically have difficulty adapting to new platforms. The challenge to leadership is inertia, along with managing the cash flows from existing businesses. This time, companies have some platform advantages, however, such as rich data sets, established brands and good customer understanding. They can form alliances, make acquisitions or create wholly owned subsidiaries that bypass institutional resistance.

Besides products, the generative AI buzz has injected concerns, particularly around distorting the uses of these tools. This will become even more important as the AI platform matures. Our recently published best practices in generative AI are already design features, and along with other AI companies we are incorporating new elements in areas like security and watermarking. We are committed to working with a broad range of interests on building sustainable standards for the common good. The opportunity to bring these powerful tools to more of humanity, safely and transparently, is too good to pass up.

Will Grannis is vice president and chief technology officer of Google Cloud. He wrote this commentary for SiliconANGLE.

Image: geralt/Pixabay