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Three Reasons AI-Powered Platforms Fail Posted on : Sep 14 - 2020

If you’ve found yourself thinking, "There's an AI-powered solution for everything these days," you’re not far off.

Today, more than 37% of organizations have implemented artificial intelligence (AI) in some form, and it is estimated that by 2021, 80% of emerging technologies will have AI foundations.

When successfully executed, AI is a powerful tool for businesses and consumers alike. It can help businesses scale faster by automating time-consuming processes, create deeper connections with customers through hyper-personalized interactions, make smarter business decisions with access to real-time data from across an organization, plus so much more.

But time and time again, we read about promising AI-powered startups coming up short, shutting down their businesses because they've failed to build meaningful AI solutions for the problems they hoped to solve.

After 10-plus years of leveraging AI, machine learning and natural language processing to build and grow successful AI-powered platforms, I've identified three key areas where most startups and businesses go wrong when building AI-powered platforms.

Automating The Wrong Functions

First and foremost, businesses must have a clear idea of exactly what they want to replace with machines. If you shoot for the moon before understanding gravity, you're not going to get very far.

When it comes to building AI-powered platforms, you have to build up to solving the big-picture problem by first automating lots of small functions and tasks. Often, businesses automate the wrong things and end up creating technology that is unable to deliver on its promise.

Start by studying the industry to understand the most mundane, time-consuming, human-intensive or manual processes of a task or function; focus on areas like repetitive tasks, data entry, common requests, etc. This is where your automation work should begin.

It is paramount that the foundational elements of an AI-powered platform are consistently operating with 100% accuracy before moving on to building the next layer of automation.

Overlooking Critical Early Hires

It's a given you need to hire strong data scientists and technologists experienced in AI, machine learning and natural language processing, and many businesses are following this protocol: Job postings for AI-related roles grew 14% year over year prior to the Covid-19 outbreak in early March 2020.

Where they often go wrong, however, is not prioritizing hiring experts in the field(s) they are automating to work alongside the technologists. To build a successful AI-powered platform, engineers must work closely with industry experts to incorporate human intelligence into their automation.

Successful automation of tasks requires a clear understanding of the nuances related to completing each task; applying this filter will make or break the effectiveness of an AI solution. View More