Debunking Common Myths About Operationalizing AI
October 04, 2021

Alan Young
InRule

Share this

Is your company trying to use artificial intelligence (AI) for business purposes like sales and marketing, finance or customer experience?

If not, why not?

If so, has it struggled to start AI projects and get them to work effectively?

Chances are, you're being held back by one or more operational misperceptions that are causing an overwhelming majority of AI projects to fail. To better understand AI's challenges, InRule Technology tapped Forrester Consulting to explore some common myths about operationalizing AI and suggest ways enterprises can overcome their AI challenges.

The report found that companies believe operationalizing AI can generate real value — helping them gain insights about customers and markets and improve business outcomes. They're just having trouble making it happen; operational silos, data strategy challenges, and a lack of resources are standing in their way.

One commonly held myth suggests that there aren't enough use cases to convince leadership to make AI a priority. Turns out, many companies are overwhelmed by having too many use cases. At least three quarters of AI decision-makers have either a manageable number or too many use cases to manage. This number should grow, since more than two thirds of decision-makers expect their AI and machine learning use cases will increase at least slightly over the next 18 to 24 months.

There's also a wide variety of use cases being exercised across business functions. The most popular involve generating insights into competitors, markets and customer behavior. Others include projects focused on innovation, automation, security, business efficiencies and business automation.

A second myth: AI projects are hard to implement because you can't find enough data scientists with doctorate degrees in statistics. Good data scientists are important, but the truth is, you don't need PhD's to start operationalizing AI. You don't need a PhD to work with most of the machine learning modeling tools in the market today. The real challenge is connecting data scientists to the rest of the ecosystem. Internal silos ranked as one of the top three collaboration challenges firms face, keeping data programmers, gatherers, interpreters and users from communicating with each other. The fact that one in four organizations have cultures that do not encourage data democratization makes the problem worse.

Data is clearly a requisite for AI projects, but the myth that you need lots of data managed by massive data systems is untrue. Regardless of the volume of data available, it's the quality that really matters. Data quality ranked second highest among the top challenges firms encounter when using AI technologies. If your data quality is poor, decisions will suffer, and this likely will impact customer experience and the corporate bottom line.

Another myth: AI learns by itself, so you can set it and forget it. This is where a lot of AI projects fail to live up to expectations. AI models need to be nurtured and continually monitored to make informed predictions and/or recommendations. While 71% of AI decision-makers routinely monitor and retrain models, a surprisingly high 28% build and train models and then leave them alone, creating an incorrect, negative perception about the effectiveness of AI. The most successful AI adopters build models with data feedback loops so they can be continuously retrained. For example, AIOps can enhance IT processes within an enterprise. While AIOps allows for real-time continuous data acquisition, the outcome data is important for model updates and insights as part of an ongoing feedback loop.

What can organizations do to better operationalize their AI?

An important starting point is sharp decision-making. Machine learning algorithms need case-relevant context and decision logic to be successfully operationalized. Decision platforms that incorporate machine learning, human decision logic, and other decisioning technologies and techniques can help scale AI projects, turning them into an integral part of your business strategy. AIOps anchors machine learning, decision automation, digital process and advanced analytics to automate and improve governance of repetitive tasks, freeing teams to focus on new mission critical problems with higher ROI — resulting in faster and more effective completion of projects and higher-impact business outcomes. Forrester data shows that more than two thirds of all enterprises are currently implementing AI and nearly all will be doing so by 2025. Getting up to speed on AI will pay dividends in the future.

Alan Young is Chief Product Officer at InRule
Share this

The Latest

February 29, 2024

Despite the growth in popularity of artificial intelligence (AI) and ML across a number of industries, there is still a huge amount of unrealized potential, with many businesses playing catch-up and still planning how ML solutions can best facilitate processes. Further progression could be limited without investment in specialized technical teams to drive development and integration ...

February 28, 2024

With over 200 streaming services to choose from, including multiple platforms featuring similar types of entertainment, users have little incentive to remain loyal to any given platform if it exhibits performance issues. Big names in streaming like Hulu, Amazon Prime and HBO Max invest thousands of hours into engineering observability and closed-loop monitoring to combat infrastructure and application issues, but smaller platforms struggle to remain competitive without access to the same resources ...

February 27, 2024

Generative AI has recently experienced unprecedented dramatic growth, making it one of the most exciting transformations the tech industry has seen in some time. However, this growth also poses a challenge for tech leaders who will be expected to deliver on the promise of new technology. In 2024, delivering tangible outcomes that meet the potential of AI, and setting up incubator projects for the future will be key tasks ...

February 26, 2024

SAP is a tool for automating business processes. Managing SAP solutions, especially with the shift to the cloud-based S/4HANA platform, can be intricate. To explore the concerns of SAP users during operational transformations and automation, a survey was conducted in mid-2023 by Digitate and Americas' SAP Users' Group ...

February 22, 2024

Some companies are just starting to dip their toes into developing AI capabilities, while (few) others can claim they have built a truly AI-first product. Regardless of where a company is on the AI journey, leaders must understand what it means to build every aspect of their product with AI in mind ...

February 21, 2024

Generative AI will usher in advantages within various industries. However, the technology is still nascent, and according to the recent Dynatrace survey there are many challenges and risks that organizations need to overcome to use this technology effectively ...

February 20, 2024

In today's digital era, monitoring and observability are indispensable in software and application development. Their efficacy lies in empowering developers to swiftly identify and address issues, enhance performance, and deliver flawless user experiences. Achieving these objectives requires meticulous planning, strategic implementation, and consistent ongoing maintenance. In this blog, we're sharing our five best practices to fortify your approach to application performance monitoring (APM) and observability ...

February 16, 2024

In MEAN TIME TO INSIGHT Episode 3, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA) discusses network security with Chris Steffen, VP of Research Covering Information Security, Risk, and Compliance Management at EMA ...

February 15, 2024

In a time where we're constantly bombarded with new buzzwords and technological advancements, it can be challenging for businesses to determine what is real, what is useful, and what they truly need. Over the years, we've witnessed the rise and fall of various tech trends, such as the promises (and fears) of AI becoming sentient and replacing humans to the declaration that data is the new oil. At the end of the day, one fundamental question remains: How can companies navigate through the tech buzz and make informed decisions for their future? ...

February 14, 2024

We increasingly see companies using their observability data to support security use cases. It's not entirely surprising given the challenges that organizations have with legacy SIEMs. We wanted to dig into this evolving intersection of security and observability, so we surveyed 500 security professionals — 40% of whom were either CISOs or CSOs — for our inaugural State of Security Observability report ...