AI Success Issues

Many underlying issues to consider as intelligence applications are built.

AI success is being limited by poor digital transformation

Balakrishna DR,  Infosys,   Podcast , January 31, 2023 11:07 AM

Digital transformation concept. Binary code. AI (Artificial Intelligence).

Digital transformation has multiple dimensions and complexities, which are sometimes lost on organizations undertaking it. The recipe for success lies in rethinking the processes and the organizational structure to generate maximum value from the technology framework — something many enterprises continue to struggle with.

A 2020 study by Boston Consulting Group found that about 70% of digital transformation projects fall short of their goals even when the priorities are clearly mapped and leadership is aligned.  Compounding the challenge is the need to bring AI into the organization that is transforming. AI is everywhere today and promises great returns from customer experience and organizational efficiency. Not investing in AI is a non-starter now when a digital transformation effort is begun, but the investment can feel like an insurmountable task. Why is this? 

Gaps in the digital transformation roadmap can hinder success of AI initiatives

The factors that lead to failed digital transformation initiatives also act as roadblocks to the success of AI initiatives. These include:

Learn the critical role of AI & ML in cybersecurity and industry specific case studies. Watch on-demand sessions today. ... 

Identifying the right problems to solve: Without proper project design and outside intervention, identifying the right problem and the right approach to solve it is unbelievably difficult. This is where a poorly executed digital strategy or a faulty transformation roadmap will act as a bottleneck for AI success: The underlying data strategy was not aligned to the organization’s unique needs in the first place.

Lack of an overarching data strategy: Companies must have a clear idea of what kind of data they need for digital transformation. Otherwise, they risk investing in improper tech stacks. A proper data infrastructure and strategy form the foundation upon which emerging technologies are built, and formulation of an AI strategy is created on top of it. ... '

Linkedin Leverages Data for Success Prediction

The Linkedin data begs for such studies to be done, should they be? 

LinkedIn ran undisclosed social experiments on 20 million users for years to study job success

By USA Today, September 28, 2022

A new study analyzing the data of over 20 million LinkedIn users over the timespan of five years reveals that our acquaintances may be more helpful in finding a new job than close friends.

Researchers behind the study say the findings will improve job mobility on the platform, but since users were unaware of their data being studied, some may find the lack of transparency concerning.  

Published this month in Science, the study was conducted by researchers from LinkedIn, Harvard Business School and the Massachusetts Institute of Technology between 2015 and 2019. Researchers ran "multiple large-scale randomized experiments" on the platform's "People You May Know" algorithm, which suggests new connections to users. 

In a practice known as A/B testing, the experiments included giving certain users an algorithm that offered different (like close or not-so-close) contact recommendations and then analyzing the new jobs that came out of those two billion new connections....

Privacy advocates said some of the 20 million LinkedIn users may not be happy that their data was used without consent. .... 

Coke Rewards Failure

But not failure to learn.  We did not do enough to understand and document precisely why we failed.

Coke CEO: Why we have an award for projects that fail   By Catherine Clifford

Coca-Cola was invented in 1886 by Dr. John S. Pemberton in Atlanta, Georgia. That year, sales of the drink totaled $50. In fiscal year 2018, The Coca-Cola Company reported $31.9 billion in sales.

Today, the company has more than 700,000 employees and over 500 beverage brands — from Fanta and Minute Maid to Honest Tea and Odwalla to SmartWater and Dasani — sold in 200 countries.

When a company becomes to be so massive, it can be hard to keep the creativity and innovation that helped it grow in the first place. And that’s largely because with so much success people begin to fear failure, according to Coca-Cola Co. CEO James Quincey.

“Fear of failure is often the biggest hurdle for innovation in large organizations,” Quincey said in a recent Harvard Business Review Analytic Services (HBRAS) report commissioned by Mastercard.  ... .... "

Bob Herbold on Amazon's Success

From Bob Herbold's Blog:

Amazon’s Secrets to Success
Posted on November 12, 2019  

The article on Amazon’s master plan for success that appeared recently in the Atlantic magazine is a very interesting read. I was most impressed by the clear description of the core principles instilled by Jeff Bezos right in the beginning on how the company would operate. It outlines how the organization protects and effectively executes these principles on an ongoing basis. As I detail below, I also found it interesting how these principles are very similar to what I experienced at Procter and Gamble and Microsoft, the two companies where I spent 26 and 9 years respectively. Here are the principles:  .... " 

How Businesses Succeed with AI: Survey of Execs.

In the HBR via O'Reilly ... Good thoughts here.  Much more at the link.

A Survey of 3,000 Executives Reveals How Businesses Succeed with AI
Jacques Bughin, Brian McCarthy, Michael Chui

The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.

While it’s clear that CEOs need to consider AI’s business implications, the technology’s nascence in business settings makes it less clear how to profitably employ it. Through a study of AI that included a survey of 3,073 executives and 160 case studies across 14 sectors and 10 countries, and through a separate digital research program, we have identified 10 key insights CEOs need to know to embark on a successful AI journey.

Don’t believe the hype: Not every business is using AI… yet. While investment in AI is heating up, corporate adoption of AI technologies is still lagging. Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013. Despite this level of investment, however, AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more. (Our results are weighted to reflect the relative economic importance of firms of different sizes. We include five categories of AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.)

For the moment, this is good news for those companies still experimenting or piloting AI (41%). Our results suggest there’s still time to climb the learning curve and compete using AI.

However, we are likely at a key inflection point of AI adoption. AI technologies like neural-based machine learning and natural language processing are beginning to mature and prove their value, quickly becoming centerpieces of AI technology suites among adopters. And we expect at least a portion of current AI piloters to fully integrate AI in the near term. Finally, adoption appears poised to spread, albeit at different rates, across sectors and domains. Telecom and financial services are poised to lead the way, with respondents in these sectors planning to increase their AI tech spend by more than 15% a year — seven percentage points higher than the cross-industry average — in the next three years. .... " 

Set Diversity Optimization

A topic not often distinctly mentioned, but it is an element of many problems.  Should get more attention.  In Open Data Science.   Contains a number of good business examples.   A broad definition of decision success.  Might be a good idea to always think of problems this way so that measures used and solutions are thought of in the same way.  Technical:

" ... Consider the following problem: for a number of items U = {x_1, …x_n} pick a small set of them X = {x_i1, x_i2, ..., x_ik} such that there is a high probability one of the x in X is a “success.” By success I mean some standard business outcome such as making a sale (in the sense of any of: propensity, appetency, up selling, and uplift modeling), clicking an advertisement, adding an account, finding a new medicine, or learning something useful.   .... "