Enter AI And The Real Fourth Industrial Revolution

Running out of ideas to inspire, a false fourth industrial revolution was coined by the World Economic Forum. The move to digitization and digital transformation was a necessary half-step to the move towards AI.  While some will now profess that the Fifth Industrial revolution is AI, AI is more exponential than the internet or the fake fourth industrial revolution.  The dawn of the fourth industrial revolution begins with AI. This Cognitive Era will be with us for decades to come.

Post AI World Requires Lots Of Precision Data

Digital transformation has made more data available by digitizing the physical world.  Enterprises who led the way in digital transformation built a foundation to take advantage of AI.  In 2023, enterprises rushed to AI for an exponential advantage.  Unfortunately, many organizations realized they did not have enough data to achieve precision decisions.  Even more concerning, most enterprises do not have enough data, compute power, or energy to solve their problems.  In fact, 91% of companies will determine they do not have enough data to achieve a level of precision their stakeholders will trust.  Some will find internal data good enough but not good enough for many use cases.

For example, would 91% accuracy be good for customer service and support? Probably yes.  Would 91% accuracy cut it for procurement? No.  Would 91% accuracy make a CFO feel confident In finance? Definitely no.  Would 91% accuracy be okay for patient visits in healthcare? Absolutely not!

Sadly, hundreds of millions, even billions will be wasted because organizations did not develop a data strategy. Without enough precision data, AI cannot be trusted.  Hallucinations in generative AI lend well for creativity but become dangerous when users expect precision and trust.  As skepticism grows of AI, mistrust will increase.  The timeline over the next five years will look like this (see Figure 1).

Figure 1. Business Expectations For AI

 

Source: Constellation Research, Inc.

 

Data Collectives Emerge To Fuel Data Inc. Companies

Trust and transparency requires constant training and a voracious appetite for increasing amounts of data.  Yet, data half-life makes much of your data worthless in seconds. The battle for more data and increasing number of signals requires data mastery.  Data mastery results in not only better AI use cases, but also a new class of organizations known as Data, Inc. companies.  

Deep data resident knowledge of the flat sides of data provides rich understanding.  Unique data sets and partner models will augment synthetic data approaches to feed the beast.  Despite best efforts, no organization will have enough data. 

For this reason, hybrid models for data persist as a shift back to on-premises will accelerate because of security concerns and efficiency reasons for mission critical data sets.  Data collectives will bring richer and higher precisions inputs.  Small language models become as valuable as large language models as data collective orchestrate market places of insight.  Data collectives will provide insight in the “dark ages” of post AI where publicly available information is rare and useless.  Winners will emerge to reap exponential advantages.

Data Inc. Companies Will Deliver Trusted AI

A new class of company known as Data Inc. will emerge.  These organizations will be valued by their data in addition to their revenues.  Moreover,  these companies will emerge as their own asset class valued by the market for their ability to create flywheels of monetization.  Five types of Data Inc companies will emerge: (see Figure 2)

1. Unique data sets
2. Data driven digital networks - network + data
3. Longitudinal data sets
4. Derived data advantage
5. New classes of data

 

Figure 2. Five Types Of Data Inc. Companies Emerge

 

Source:  Constellation Research, Inc.

Recommendations: Seven Rules To Create Trusted AI

In discussions and workshops with over 50 leading enterprises, seven rules have emerged to crate trusted AI entities and Data Inc companies:

Rule 1: Achieve data mastery

Rule 2: Understand how to partner for data sources and signals

Rule 3: Generate new derivatives

Rule 4: Monetize outcomes

Rule 5: Engage stakeholders

Rule 6: Nourish networks

Rule 7: Trust but verify

The Bottom Line: Trusted AI Is Not Only Just About Ethics, But Also About How We design AI for Human Scale In A World Built For Machine Scale

In the Post AI world, organizations no longer compete with humans for labor.  Humans will compete with bots and AI to get work done.  The quest for more data sources will result in the “dark ages” of publicly available data.  Most entities will not want their data to be publicly available for ingestion by other AI models.  Hence, high quality public data will be scarce in the future.

In order to source high quality data, data collectives will emerge to create trusted AI models in private networks. Data collective will reward small language models for providing the last mile in precision.  Solid data strategy will drive the valuation of companies in the future and provide stakeholders with trusted AI. 

As organizations build AI for machine scale, humanizing AI will require different design aesthetics.  Organizations will have to determine when they will insert a human in the process and at what cost – benefit to the organization.

Trust in an age of AI will emerge as the north star to guide Data Inc companies.