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URL:
https://insights.manageengine.com/artificial-intelligence/ai-powered-root-cause-analysis-for-enterprises/?utm_source=Zcampaigns&ut...
Submission: On November 21 via api (November 21st 2023, 1:23:02 pm UTC) from ES — Scanned from SG

Form analysis
3 forms found in the DOM

GET https://insights.manageengine.com/

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Name: ME_Insights — POST https://cdev.manageengine.com/server/send-mail/form/me_insights_subscription/addRecord

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Artificial Intelligence

SHIFTING THE NARRATIVE: AI-POWERED ROOT CAUSE ANALYSIS FOR ENHANCED ENTERPRISE
IT OPERATIONS

By Samudhra Sendhil Enterprise Analyst, ManageEngine

Published on October 19, 2023

In the world of business, there’s a common question that often arises: “Where
did it go wrong?”

Organizations face a variety of challenges that disrupt their operations.
Whether it’s grappling with system failures, navigating complex supply chains,
or safeguarding against security breaches, identifying the core problems is a
must. Failure to do so will affect your organization’s efficiency,
profitability, your workforce’s mental health—you name it.

Fortunately, technology has evolved to a point where we can predict outcomes
before they might happen. It also helps us shift the narrative from “where did
it go wrong?” to “what could go wrong?” and nip any potential issue in the bud.
Cue root cause analysis (RCA).

THE ROLE OF RCA IN ORGANIZATIONS

RCA is a problem-solving methodology designed to identify the underlying causes
of issues within an organization. Rather than merely addressing symptoms or
surface-level problems, RCA delves deep into the core factors that lead to
incidents by constantly analyzing past and current datasets to find patterns and
relationships. With a deeper understanding of these complex issues,
organizations can develop strategies to prevent recurrences.

The significance of RCA for large organizations and enterprises cannot be
overstated. These entities face substantial risks due to the scale and
complexity of their operations. From financial institutions managing vast
portfolios to manufacturing companies handling intricate supply chains,
identifying the causes of problems is crucial for maintaining smooth operations,
safeguarding customer trust, and ensuring compliance with industry regulations.

However, traditional RCA methods are often labor-intensive and time-consuming.
These limitations can be detrimental in our digital world where everything
changes in a matter of hours and time is of the essence. Fortunately for
organizations, the advent of artificial intelligence (AI) has brought about a
transformative change in the RCA landscape. AI, with its capacity for processing
and analyzing vast amounts of data, is poised to revolutionize the way
organizations approach RCA.

UNEARTHING INSIGHTS, STREAMLINING OPERATIONS, AND EMPOWERING DECISION-MAKERS

Large organizations generate an immense volume of data where patterns and
correlations exist. AI-driven RCA, backed by machine learning, outpaces
traditional human analysis, swiftly uncovering irregularities like fraudulent
financial transactions, unusual equipment behavior in a manufacturing plant, or
customer behavior anomalies.

AI-driven RCA automates detection and response processes by offering real-time
solutions. Unlike traditional methods that often require human intervention, AI
systems act swiftly and autonomously, saving critical time and minimizing the
risk of financial losses and reputational damage.

An incident at a leading trading firm in 2012 serves as a stark reminder of how
human error, however subtle, can lead to catastrophic consequences. In this
case, the activation of long-dormant software with erroneous code unleashed a
buying spree of $7 billion in stocks, driving the company to the brink of
financial ruin. Here, AI could have detected the irregular trading behavior and
intervened before extensive losses occurred.

Despite all of this, informed decision-making by human actors remains the
cornerstone of effective problem resolution and prevention. With a deeper
understanding of business operations, AI-driven RCA empowers enterprise leaders
by providing them with comprehensive and well-structured insights. AI systems
produce reports and visualizations that not only highlight the root causes, but
also offer a holistic view of the issue’s impact on various aspects of the
organization.

REAL-WORLD APPLICATIONS OF AI-POWERED RCA

Whether it’s implementing security measures to counter fraudulent activities,
adjusting manufacturing processes to prevent equipment failures, or tailoring
marketing strategies to meet customer behavior trends, AI plays a crucial role
in driving well-informed decisions.

ENHANCING SECURITY: FRAUD DETECTION

Banks and financial institutions are harnessing AI-powered RCA to fortify their
defenses against fraudulent transactions, encompassing credit card fraud and
identity theft. JPMorgan Chase employs AI to scrutinize millions of daily
transactions, instantly flagging suspicious patterns that indicate fraudulent
activities.

Retail giants like Walmart are also using AI-powered RCA to thwart fraudulent
purchases and organized retail crime rings. An internal system referred to as
missed-scan detection technology employs computer vision to oversee
self-checkout sections in each store. This system has the capability to identify
instances where items are not scanned or are scanned incorrectly, promptly
alerting a checkout attendant who can then address and resolve the issue.

PIONEERING MEDICINE: DRUG DISCOVERY

AI-powered RCA is revolutionizing drug discovery by helping pharmaceutical
companies identify the root causes of diseases and develop novel drugs and
treatments. Pfizer employs AI to dissect vast genomic datasets, pinpointing
genetic variations associated with specific diseases, paving the way for
targeted therapeutic interventions.

By analyzing extensive medical data, Pfizer identifies patterns of diseases,
side effects, and drug efficacy, subsequently advancing the development of
diagnostic tests and treatments.

UNDERSTANDING CUSTOMERS: BEHAVIOR ANALYSIS

E-commerce giants like Amazon are leveraging AI-powered RCA to gain deep
insights into customer behavior and enhance marketing and sales strategies. The
primary objective of Amazon’s AI capabilities is to offer personalized
recommendations to its customers without having to look for them. Subsequently,
a report indicates that Amazon’s recommendation engine contributes to a
substantial 35% of its overall sales.

SUSTAINABLE FUTURE: ENERGY AND SUSTAINABILITY

Utility companies are deploying AI-powered RCA to pinpoint the root causes of
energy waste and drive sustainable energy solutions. Intel employs metadata
analytics to optimize energy use in its data centers. This involved analyzing
server utilization, cooling systems, and data storage metadata, leading to the
identification of energy-intensive areas and resulting in significant energy
savings.

Smart cities like Copenhagen use cutting-edge data-driven tools to automate and
streamline energy management in their municipality structures, leveraging
various data sources, including local weather forecasts and building usage
patterns.

ENGINEERING EXCELLENCE: FAILURE ANALYSIS IN COMPLEX SYSTEMS

AI-powered RCA has become an essential tool for aircraft manufacturers, aiding
in the investigation of aircraft accidents and the identification of safety
hazards. Boeing, for instance, employs AI to analyze aircraft data, uncovering
patterns that hint at potential issues, subsequently leading to safety
enhancements and the prevention of future accidents.

CONSTRAINTS, CHALLENGES, AND DANGERS

RCA promises game-changing advantages, but organizations must navigate several
challenges to harness its full potential. One significant hurdle is the
resistance to change. RCA initiatives should be designed to foster a culture of
continuous learning, ensuring that the organization adapts for long-term
benefits.

Skill gaps within the team can also impede the successful adoption of RCA. As
RCA tools and methodologies continue to evolve, investing in ongoing training is
crucial. This ensures that the team remains well equipped to harness the full
potential of RCA, keeping pace with the technology’s rapid advances.

Addressing bias in AI-driven RCA is an ongoing and complex challenge. It demands
vigilant efforts to identify and mitigate biases within the models and data.
This process requires not only advanced technologies but also a deep
understanding of societal and ethical nuances. The goal is to ensure that
AI-driven RCA remains fair and objective, delivering results that are free from
any form of prejudice.

In the landscape of RCA, AI is undeniably the future. It brings speed,
precision, and automation to time-consuming processes. The future holds
countless opportunities for AI to reshape RCA, making it agile and powerful. AI
will prove a necessity for organizations aiming to thrive in a complex,
data-driven world.

* AI
* RCA
* Root cause analysis