3 Surprisingly common ways leaders fail their AI projects👎
3 Surprisingly common ways leaders fail their AI projects👎
Hello,
An exciting announcement - I founded a new data analytics advisory firm, Innovation Titan 🚀
I am continuing my full-time role at Gramener.. excited as ever to work with our leadership & awesome team to help clients win with impactful data analytics solutions🎯
Check out this linked video to discover what Innovation Titan is about, the business challenges we’re tackling, and how you can collaborate with me.
This newsletter will take you about 4 minutes to read.
I. Spotlight: 3 Surprisingly common ways leaders fail their Artificial Intelligence (AI) projects👎
How do most organizations begin their Artificial Intelligence (AI) journey?
Let’s look at how leaders of some large enterprises planned their foray into AI. Here are a couple of recent examples from McKinsey:
The leader of a large organization spent two years and hundreds of millions of dollars on a company-wide data-cleansing initiative. The intent was to have one data meta-model before starting any AI initiative.
The CEO of a large financial services firm hired 1,000 data scientists, each at an average cost of $250K, to unleash AI’s power.
And here’s an example that I witnessed first-hand.
The CEO of a large manufacturer lined up a series of ambitious projects that used unstructured data, since AI techniques are very effective with text, image, and video data.
What do all of these initiatives have in common? They all failed.
In addition to the massive sunk costs suffered by these projects, they led to the organization’s disillusionment with advanced analytics.
McKinsey’s State of AI survey found that only 22 percent of companies using AI reported a sizable bottom-line impact.
This is not uncommon. McKinsey’s State of AI survey found that only 22 percent of companies using AI reported a sizable bottom-line impact. Why do so many projects fail, and how can leaders avoid this?
Most leaders pursuing AI miss out on three areas of ownership. These responsibilities start well before you plan your AI projects, and they extend long after your projects go live.
Here are the three ways to fail your AI initiative:
Mistake 1: Starting AI projects that don't align with the corporate vision
Mistake 2: Waiting to plan for ROI after the project goes live
Mistake 3: Expecting AI-driven transformation without fixing the organizational culture
With industry examples, let’s understand why most leaders do this and how you can avoid them.
II. Industry Roundup:
1. Article: What is ‘dark data’ and how is it adding to our carbon footprints?
04 minutes | Weforum | Tom Jackson and Ian R. Hodgkinson
Have you ever wondered how the data stored by companies drives climate change? Particularly troublesome is single-use data that takes up huge server space. Apart from spiking hidden costs for organizations, this ‘dark data’ has a deep environmental impact. The article shares stunning stats on dark data’s carbon footprint.
2. Article: How homeowners defeated Zillow’s AI, which led to Zillow Offers’ demise
05 minutes | Geekwire | Dr. Oren Etzioni
If you live in North America, you might know “Zestimate” - Zillow’s price-predicting model. This revolutionary AI application recently lost the company over half a billion dollars. The article shares what went wrong with Zestimate. While Zillow is as invested as ever in its AI efforts despite the debacle, this is a teachable moment. Leaders can learn about the perils of automated decisions and how to use AI better.
III. From my Desk:
1. Blog: 3 Data Analytics Challenges and How Decision Intelligence Can Help You Tackle Them
6 min | AtScale
Most executives aspire to build a data-driven organization, but only a few are successful. While helping business leaders build analytics capabilities and a data culture, I’ve encountered three common challenges. I present them here and explain how decision intelligence can help you address them.
2. Interview: Making sense of data with data visualization
13 min | Gramener
How can data visualization transform decision-making? Can it help address user trust & business adoption issues? Check out this interview with Miguel Encarnação, Head of Data Visualization at Regions Bank. He answers these questions and more in this episode of the Gramener Data Leadership interview series.
3. Video: AI is a master content creator, but here's how it can hurt us
7 min | YouTube
Artificial Intelligence is creating a lot of viral content on the internet today. With a few simple prompts, AI can write articles, draw pictures, and create realistic videos. But this may not be great news always. In this video, I share examples of how AI can create deceiving content, deep fake videos, and why we should watch out.
Is generative AI the future of stock images? Check out these stunning pictures shared on LinkedIn
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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My Website | LinkedIn | Twitter | YouTube
I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.
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