Nothing is worse than AI project failure. 20/20 hindsight hurts
Everyone has AI on their 2017/2018 roadmap these days. Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain (anyone else we should throw under the bus?) are even starting to talk about it. Everyone wants to hire a data scientist. Fortune 1500 companies are throwing out multi-million dollar data leadership positions to lead their data teams to success. Despite the hype and excitement, the majority of companies that commit to tackling AI projects will fail. Even that $1M+ hire won’t save you from failure.
Based on my own personal failures and companies I have interacted with here are some of the main reasons why your AI project failed or will fail. These have seemed to resonate well with others so I figured I would share.
(1) Science Project Sharks:
Many of the companies we consult with are surrounded by science project sharks. “Wouldn’t it be cool if we could do <geek-fetish>!”, “We want all user uploaded images to align with our brand using AI!”, “This data set has value, let’s extract it!”. The question we ask to cut through the crap is:
“Which projects have the largest impact on your BHAGs/KPIs/revenue?”
Wow-factor won’t feed your family, revenue will. You can’t afford for your first AI project to be a failure, that will set you back behind your competition. It would be better for you to postpone jumping into AI than to fail on your first attempt. Failure will also burn political capital and excitement to pursue the next project. I have even seen senior people fired for AI failures. Don’t be that guy/girl. Once you align your projects with BHAGs (Big Hairy Audacious Goals), KPIs (Key Performance Indicators), or Revenue you are talking a familiar language, the language of business. Your first AI project must be business focused. If you are successful your executives/board will love you, they will back you with more resources for your next win. For many, this may fall into hiring, customer churn, and marketing email messaging. If the majority of your revenue comes from an unoptimized email marketing campaign you would be an idiot to start anywhere else.
(2) Communication Breakdown:
The majority of data scientists can’t speak $$. For being amazing data geniuses they are as dumb as they come when it comes to running a business. They want to bring their jargon and personal geek passions to the conversation where they don’t belong.
“What the hell am I looking at?!!”
“We know you are smart, that is why we hired you, you don’t have to remind us”
Inspiring words from Mark Newman 4 years ago when I sent him a gorgeous bootstrap plot at HireVue. Your job is not to educate your executives on data science methods or jargon. They are literally drowning with priorities focused on keeping the company growing, hitting revenue quarter goals, appeasing investors, and making sure you have a damn job. Their plate is overflowing, so simplify the discussion. Make whatever you say to the point and actionable, they don’t have time for anything else. Give them a number with a dollar sign attached, if you need assumptions to get there do it. Speak their language, avoid all possible jargon, align your AI projects with the highest priorities for your company.
(3) Fail Before You Start:
Following lean startup principals what is the least amount of time/effort you can spend to find out if you are going to fail? Is there any way to fail before you start!? Wouldn’t that be great! I was involved with a fantastic AI project using deep siamese nets only to find out two months later the customer wasn’t willing to pay for it. The AI project was dead before it started. Imagine instead pitching your best customers with a slide deck showing a successful AI project you haven’t done yet. Fake the results. Sample a user focus group and get feedback, whoever is paying your $$ make sure you get their engagement before you pull the trigger.
(4) You Need A Data Warrior:
Newbies are kind of like Russian roulette. I have seen companies hire new college grads for compensation discounts or out of necessity (nobody good wants to work for us, sorry join the club). I don’t care where they are coming from (Stanford, MIT, Oxford, etc…), without real-world experience you could be hiring a loose cannon or worse an academic! An academic will waste your time and resources and have an unlimited number of excuses why their AI project never crystallizes into anything useful. You want someone who has worked for someone else, made mistakes and shipped AI product. You want a Scarface.
[New applicants in the job market might cringe with my recommendation that all companies hire data scientists with real work experience. How am I supposed to get started?!? If you have to ask that question you have an uphill battle ahead of you.]
(5) Home Grown Talent/Software:
We have internal data science talent, homegrown baby! Most of the time that means you have soft/distracted talent. So you are telling me you have internal data science talent who have been sheltered from the upside-down world outside your organization?
If they haven’t mixed with the surrounding data science community/job market [data battlefield] how are you ensuring you are using the latest and greatest? Are they 3 years behind? Probably. Can a scarface data scientist eat them for dinner? Absolutely. Even worse than homegrown talent are companies that think homegrown software is a good idea. If a third party vendor/partner can do something better/faster (i.e. DataRobot, AWS) use them! I have found that most companies that attempt to create homegrown software end up doing it for more time/money and get lower quality. Leveraging third party software I can outpace your entire data science team singlehandedly, no comparison. This gets back to the business objectives, a true business-minded leader doesn’t mind paying for value. I prefer to have my data talent focused on the hard/custom problems that aren’t turnkey yet.
(6) Start Simple:
Right now you are getting 0% value for your AI project, you haven’t implemented it yet. The moment you roll out a dead simple Bayesian method you are realizing 80% of the value. Sure a gradient boosted regression or deep network can take you to 97% but why not bag 80% value today!? Some AI projects are overcomplicated with time horizons that are too long. I always recommend focusing on a 30-60 day proof of concept. Executives also like this because it usually means you are derisking the problem by reducing the cost. I would much rather have you fail on a 30 day project than a 1 year Hail Mary pass that ends up being incomplete.