Have we entered an era of Artificial Intelligence industrialization for enterprises? What is needed to embark on AI industrialization journey?

We sure have entered an era of AI Industrialization! I can say it emphatically because I am helping a Fortune 15 company execute its AI Industrialization Strategy. I  believe that most of the Fortune 500 companies will follow suit sooner rather than later.

So, what does AI Industrialization really mean? What are the business and technical drivers that lead any company to embark on this journey?  Also, why are they calling it Industrialization of AI? What are the prerequisites needed for companies to start on this journey, to set a solid foundation and reap all the benefits?

Let’s revisit the concept of industrialization by looking at history to understand the different phases of industrialization.  If we just look at first phase of US Industrialization from (1820 -1910), it went through phases of industrialization: cotton plantations, canals and boats, railroads, immigration, banking, steel mills, automobiles, and telephones.  If we analyze it further, we realize that the key characteristics of this industrialization process were:

1.          Inventions and Innovations

2.          Entrepreneurship

3.          Funding

4.          Improved Productivity

5.          Mass Production

6.          Cheaper Production

7.          Repeatability of Processes

8.          Critical Mass of people producing goods

9.          Critical Mass of people consuming goods

10.      Improved Standards of Living

11.      Better Experiences

12.      Cheaper to Buy

13.      Increase in Real Incomes and Return on Investment

 

Now let’s try to draw parallels with AI timeline:

1956:    John McCarthy coined the term AI

1997:    IBM's Deep Blue beat Gary Kasparov

1980s:   Neural network is popularized

2010s:   Amazing breakthroughs with Deep Learning technology

2011:    IBM’s Watson beats the two best human performers on Jeopardy

2015:    Google DeepMind’s AlphaGo beats human champion

2017:    Google announces AI First strategy

Last 3-4 years: Hundreds of new AI startups have been funded from Silicon Valley to Boston to Tel Aviv to Bangalore.

What have we really achieved in AI if we compare it against all 13 characteristics of industrialization listed above? The reality is that from an enterprise perspective, we have only achieved only the first 3 out of the 13. The majority of AI accomplishments in the enterprise context have been limited thus far to inventions, entrepreneurship, and funding. Of course, I am not including what pure play Web companies like Google, Amazon, Netflix, Uber etc. have accomplished. When it comes to mass production, faster production, cheaper production, as well as achieving the ROI, AI in enterprises falls far short of true industrialization. There are always few use cases to boast about, but it doesn’t mean that AI industrialization has truly happened. It means there is tremendous opportunity to build the right AI strategy for AI industrialization.

Traditionally, AI has been a hand-crafted, POC-driven and expensive initiative but the hype and the promise must now deliver at scale. It is high time; we have to start scaling AI initiatives across the enterprise in a cost effective and consumable manner so that it is truly democratized and is accessible to everyone. The operational efficiencies by AI across the board should never be questioned. The core value proposition of AI Industrialization is to take thousands of models in lab to enable and empower LOBs by embedding AI in business processes and in digital transformation journey.

What is missing today is an enterprise enablement view of AI. Let’s take a snapshot of sample of AI opportunities in a one of largest Fortune 15 company today.

SAMPLE OF AI USE CASES FOR A LARGE ENTEPRISE

 

These use cases are typically led by different lines of business who face many challenges in scaling AI. Typical challenges in implementing and deploying AI use cases include the use of different technologies or versions, difficulty governing the process, lack of repeatability and automation, and complications with collaboration and transfer of knowledge between AI engineers.

Another challenge is that you don’t want every LOB to worry about building their own AI COE, AI infrastructure, end-to-end AI platform, building data pipelines, machine learning pipelines, machine learning operations and catalogue, experimentation platform, design their own feature stores, data ops strategy, data quality, model decay, develop their AI cloud strategy, worry about bias, compliance, and AI ethics. You want them to focus on building AI use cases only and rest of the AI infrastructure is provided by centralized AI COE team chartered to develop the AI industrialization strategy as well as governance process to enable LOBs.

The reference architecture for AI Industrialization looks something like this:

Best practices around intelligent enrichment of data and methodology to deploy data pipelines need to be developed? What is the best way to build collaboration ecosystem between engineering, data analysts and data scientists? How do you figure out where manual intervention is needed versus complete automation? How do you create a map of where data starts, how it changes and where it is viewed from cloud as well on-premise perspective? How do you measure error rate decline in production? How to build features at scale from raw data for training? How do you combine features into training data? Calculating and serving features in production? How do you know which sources are biased? What are the best practices for model deployment, retraining and monitoring? How do you design a model workflow from approval perspective in a large enterprise? How do you decouple your data pipelines from your model development and governance? How do you democratize model building so that even data analysts and power business users can start contributing? How do you shorten the life cycle of model creation from deployment to deployment for hundreds of models? What AI components should you leverage from each cloud provider? How do you create a mix of on-premise and hybrid AI cloud strategy? How do you build standardization and repeatability across the board? Who is accountable when things go wrong?

There are hundreds of questions and answers that have to be figured out and best practices needs to be developed and communicated. AI Industrialization is a necessary journey for this era. But it is extremely important that it is setup for success for the long run by team of experts who understand what it takes to build this end-to-end.