Surf’s Up: Three Waves of Generative AI Innovation
Generative AI business use cases for enterprise value generation
Over 150 meetings we’ve had with senior leaders and developers in the past four months reveal a remarkable maturation with generative AI. While much of the media coverage of AI is stuck treating it as a threat to editors and artists, this misses and reduces generative AI’s potential. More and more leaders get it, though: they’ve gone from asking, “How do I use AI to reduce cost?” to “How do I use AI to rethink my business?”
We’ve seen other hype curves flatten: machine learning, deep learning, and NLP failed to deliver on the promise of change. What’s different this time? What are the business value-adding applications of generative AI that will help rethink business models instead of optimizing yesterdays?
Generative AI is the Connective Tissue of Innovation
Enterprise AI value comes from applications that stitch enterprise data and generative AI into one workflow. The intersection of structured and unstructured data is where the magic lies, where predictions in context reach a singularity.
For example, using generic generative AI to answer a question like “Why did my portfolio decline in value?” yields a pretty banal response (at left, below), citing obvious factors like market volatility, currency fluctuations, and interest rate changes. But when AI is used with enterprise data, we get something special, a vivid answer that matters: Why my portfolio declined, and why my portfolio, compared to its index, actually did pretty well.
Soon, AI applications will combine unstructured and structured data into secure, hyper-personalized responses like the one above, rich with context and meaning.
Put another way, when generative AI is used as connective tissue by connecting prior innovations and by empowering domain experts to compress the end-to-end journey to insights, we are, in essence, standing on the shoulders of giants and designing something new.
A Shift Toward Contextual Thinking
The rise of generative AI marks a significant shift in how we perceive AI’s capabilities and defines future applications and business models that use them. That future leverages knowledge and information through language as an interrogation or prompt language. As in the example above, the language isn’t what’s new; it’s that generated language has context that breaks new ground.
For example, this use of AI can build a natural language bridge from complex stochastic calculus or machine learning to make it useful to the masses. Just as few of us are expert painters, anyone can generate a pretty good digital image with Midjourney with a few meaningful keywords.
Similarly, generative AI is used in the enterprise to build a bridge between prior innovations and natural language interfaces with one efficient workflow. For example, in Singapore AI provides context to patients’ diagnosis as they arrive at the hospital, and conversational intelligence pioneer Talkmap applied generative AI to provide context to millions of call center calls, chats, and emails.
Wave One: Non-Core Business Use of Generative AI
Generative AI’s transformative power will come in waves. The first two are evolutionary, and the third is revolutionary.
The first wave of generative AI enhances efficiency and complements the most basic creative human for research, image generation, or writing support. These applications are well known today, including text, code, and image generation.
These tools aid the creative and communication process. They provide a headstart in creative endeavors and serve as an effective bridge for all employees. While writing, creating presentations, and generating unit testing code are essential jobs in the enterprise, they don’t strike at the core of an enterprise business model. Those applications ride another wave of innovation.
In The Joy of Generative AI Cocreation, we explained the importance of this first wave of generative AI applications in the enterprise because “Lawyers that use AI will replace lawyers that don’t; marketers that use AI will replace marketers that don’t; salespeople that use AI will replace salespeople that don’t. AI is a crafty, essential cocreator. It makes humans better.”
Wave Two: Use AI to Enhance Existing Products or Services
The second AI wave is shifting from efficiency gains to innovation. Here, AI is not just a tool but an integral part of the heart of a business. This wave isn’t about efficiency but new ways of thinking and reimagining what’s possible.
For example, Syneos Health provides analytical insights across patient journeys in clinical trials. Instead of relying on experts to guess the best way to plan clinical trials based on experience, trial, and error, Syneos leverages a database of 300 billion HIPAA-compliant, deidentified patient-level data points about the medical & pharmacy claims from almost 300 million patients. That’s over a petabyte of uncompressed Parquet1 data.
That data is used to plan new trials, use context from history and predict the best conditions for new trials to minimize risk and avoid errors in the context of geographic, cultural, or regulatory factors.
For a complete presentation about how Syneos uses this data is used to rank and optimize trial outcomes, watch AI Superheros in Healthcare. Data and algorithms like this differentiate your service from your competitors.
Wave Three: Reimagining “Domain Expertise as a Service”
Wave Three applications allow companies to monetize their institutional expertise and wisdom embedded in proprietary data assets that will help consumers (businesses or individuals) ascend the ladder from data and information up through knowledge and wisdom.
For example, what if your bank could provide you with a “BankGPT” interface that empowered you to ask, explore, and understand your investments independently, with natural language, on your own? Would this fundamentally change your relationship with the bank? Just as ChatGPT and Midjourney make text and image generation easy for anyone to use, so will innovations like BankGPT change the banking business.
In the third wave, companies will offer their special sauce, content, and services as an AI-fueled service while preserving underlying data privacy. This will result in highly personalized user experiences, similar to current streaming subscriptions, and offering verified, explainable information at their fingertips.
For example, in the future, a company like Syneos Health could provide healthcare trial-as-a-service to help pharmaceutical companies interact with the wisdom, experience, and context Syneos has directly. HealthGPT, if you will.
The right approach and the right equipment for the job
To surf big waves, you need the right equipment. Today’s technology doesn’t allow you to ask about your portfolio, as we described above, and get a contextual response without some work. For generative AI, that means orchestrating a series of tools to handle prompt-based questions, vector embeddings for similarity search, and plugins to combine the structured and unstructured data needed to provide that context.
That isn’t easy–AI-driven apps require vast computational power and in-built privacy and security, all while keeping data models up to date and using the latest governed data.
But to establish a stronghold in the era of AI, companies are employing these new tools and re-aligning AI strategies with business goals. Winners will reimagine their goals, not merely optimize yesterday’s.
To seize generative AI-age leadership, look beyond efficiency and towards hybrid applications that transform the heart of your business: your products, services, and their very essence.
Surf’s up.
https://en.wikipedia.org/wiki/Apache_Parquet