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New AI Research Shows Myths that Prevent Companies from Operationalizing AI

by | Feb 18, 2022

Artificial intelligence (AI) is transforming every industry. From financial services to manufacturing to media and entertainment, the opportunities for machine learning (ML) and other AI innovations to help businesses grow are tremendous.

However, many executives are unsure of how to evaluate ML, where it can add value, and wary of the challenges to leveraging it. With today’s highly competitive business landscape—made even more unstable by the global pandemic—some may errantly feel that AI represents too much change at the wrong time to incorporate into their operations.

But, this is like a drowning man worried that his life preserver will be too heavy and sink him. 

In fact, businesses that not only use AI—but wholly embrace it—have the best chance to push through these turbulent times and emerge in a better position on the other side.

A recent Forrester Consulting report commissioned by InRule, Why You’re Wrong About Operationalizing AI, dives into this topic, exploring  some of the myths about AI that keep enterprises from implementing AI solutions.

Download the free paper here. Critical insights include:

Proving the efficacy of digital decisions is essential but daunting.

“As digital decisions become critical and repeatable across all parts of the business, decision-makers recognize that defending and proving the efficacy of their digital decisions is increasingly important. However, 58% of decision-makers report it’s challenging to defend and prove the effectiveness of their digital decisions.”

Traditional AI suffers from the “black box” problem; it provides predictions, but it can’t tell you how it arrived at a given prediction and how the various factors weighed on the outcome. You get a prediction, and you get a level of confidence, but that’s where it stops.

InRule Technology’s xAI Workbench enables teams to develop a wide variety of machine-learning models at a massive scale, each with unparalleled explainability. Models don’t just provide predictions but give the reasoning behind each one.  xAI Workbench opens the black box, shedding light on the reasons behind the predictions and allows you to go live with confidence that your logic will work as intended.

Collaboration challenges slow success. 

AI decision-makers face a range of collaboration challenges, but data is the main culprit that slows their organizations’ progress. More than half of decision-makers say their organizations have too much data to make collaboration efficient. Forty-two percent of decision-makers struggle to identify — and access — the right data. Siloed data hinders collaboration with experts and data scientists.

In many cases, issues with incorporating AI into daily operations are less a matter of technology than culture. When asked how confident they are in their organization’s ability to deploy AI successfully, Forester Consulting heard a wide range of answers:

Today’s AI-powered platforms are designed to be business-user friendly. They facilitate seamless collaboration across organizational teams.

For example, InRule Technology’s suite of explainable AI and digital-process automation software connects subject matter experts, data scientists and technical teams, making automation accessible across enterprises. Our decision and process automation tools empower organizations to truly leverage the power of human- and machine-driven AI.

An accessible single source of truth allows enterprises to quickly adapt to competitive threats, changing regulations and shifting market conditions – all without code.

AI success stories are prevalent across business functions.

Forrester Consulting: “At least one-third of decision-makers report identifying too many use cases and application scenarios across various business functions like sales, marketing, and customer experience. Decision-makers explore a range of AI use cases from driving market and customer insights to testing new products, mitigating compliance, and addressing privacy risks.”

A myth regarding AI is that are few available use cases. A common objection of those unfamiliar with AI is that it’s a big investment that won’t get used.

In fact, the opposite is true. Many decision-makers are overwhelmed by too many applications. Three-quarters of decision-makers report either a manageable number or too many AI uses to manage. Fifty-three percent of decision-makers report customer experience as their principal business function for AI, and they have too many application scenarios in this area. This trend will continue as 67% of decision-makers expect their AI/ML uses  to increase at least slightly over the next 18 to 24 months.

If you worry your tech will go unused—think again. Chances are you have more applications for AI than you realize.

Learn more in the Forrester Consulting Report

Find out more about the myths and benefits of artificial intelligence and why you shouldn’t wait for the “perfect time” to embrace it. Download the free report, Why You’re Wrong About Operationalizing AI.

Making automation accessible is the mission of InRule. We seamlessly integrate data science with decision and digital process automation; we empower organizations to truly leverage AI to speed change, raise accuracy and minimize risk.

Check out our platform firsthand. Schedule a demo.




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