Innovation with GenAI falls into two categories.
Productivity use cases reap the immediate benefits of adding off-the-shelf
GenAI apps to existing tasks to make work more efficient.
Real innovation requires more creativity,
tapping into a company's proprietary knowledge to invent
new products, new services, new customer journeys.
The talent disruption will be enormous,
with a host of new roles arising.
Employees will need new skills.
Companies will also need quality controls
that can review and validate GenAI insights.
In both cases, the key difference between conventional AI and GenAI
is that your people don't need the skills of a data scientist or a software engineer.
In a sense, English is the new programming language
that democratizes innovations from within, across your organization.
We've already seen impactful results across a range of industries.
In drug development,
where typical years of trials are now sometimes reduced to days.
In modeling proteins,
where research is accelerating across nearly every field of biology.
In semiconductor design, where every micron of space matters,
and new efficiencies mean leaps in performance.
In the legal field, where the discovery process
is being revolutionized.
In vehicle making,
where parts can be fabricated 20 percent stronger for 40 percent less.
It's two things that lead to this fundamental transformation.
One is the notion of infinite memory,
this entity that knows everything
that humanity has ever developed at your beck and call.
The second is the capacity to reason against the store of knowledge.
Taken together, GenAI has this endless capacity
to analyze, to synthesize, and therefore deduce.
That's the magic.