Process intelligence takes AI to the next level – here’s how

From automation to innovation: AI’s role in IT operations (AIOps)

Artificial intelligence (AI) and machine learning (ML) use cases have multiplied rapidly — from predictive maintenance in manufacturing to enhanced diagnostics in healthcare, from virtual customer service assistants to algorithmic trading and fraud detection in finance.

AI is an accelerator for the enterprise.

As Alex Rinke, Celonis Co-founder and co-CEO, wrote in an open letter to the process mining community:

“It offers us the power to delight customers. Provide seamless experiences. Reduce costs. Improve satisfaction. And enable our teams with the means to make it happen.”

Process intelligence is the enabler for AI.

“It’s the layer that knows how your business flows,” wrote Rinke. “How processes interact and impact each other across every department, every system. With Process Intelligence, processes don’t just run, they work for you.”

When it comes to IT, artificial intelligence affects operations in two ways. First, IT is tasked with enabling the business to take full advantage of the potential benefits, though technologies like process intelligence. Second, IT departments are also among the chief beneficiaries of new AI-powered technologies in the form of AIOps (or artificial intelligence for IT operations).

In this article we take a closer look at AIOps, from implementation and best practices, to the benefits, how to overcome roadblocks and the critical role of process intelligence.

What is artificial intelligence for IT Operations?

Forrester describes AI for IT Operations as a practice that “combines human and technological applications of AI/machine learning, advanced analytics, and operational practices to business and operations data. AIOps enhances human judgment, proactively alerts on known scenarios, predicts likely events, recommends corrective actions, and enables automation. It is fueled by coalescing and transforming sensory data into AI-enriched actionable information.” 

More simply put, AI for IT operations harnesses the power of artificial intelligence to process big data and enhance the speed, intelligence, monitoring and efficiency of IT operations. It is used to streamline repetitive tasks and optimize data infrastructure, IT processes and tech stacks — saving time, money and wasted human resources while enhancing IT operations in terms of both stability and adaptability.

Read more: How LinkedIn IT operations gave 300,000 hours back to employees using process mining and execution management

Implementing artificial intelligence for IT operations

Unsurprisingly, not all AIOps platforms are created equal. Successful implementation of artificial intelligence for IT operations therefore will require a level of due diligence to find the right fit for your organization’s characteristics, data sets, systems and processes.

As Luke Higgins, Accenture AI Operations Lead, told Celonis:

"Based on our experience with dozens/hundreds of client engagements across industries, we’ve seen how AIOps can accelerate value and uncover trapped insights by helping to create a north star for both the business and IT organizations. By collating, managing and actioning instructions across a complex distributed environment spanning infrastructure and applications, AIOps can enable real-time monitoring and impact assessment of key performance indicators."

Here are some core considerations for implementing AIOps.

Feed and train your AIOps solution

The more performance data you can provide from relevant operational applications, the more comprehensive and accurate your AI’s automated solutions will be. Beyond the simple volume of data, it’s important to train the AIOps solution on how different data sets correlate with major IT operational issues — effectively providing background context for indicators of known problems and their resolutions to power automated responses.

Deliver against key business objectives

To win early support for implementing an AIOps solution, select a use case which aligns with and supports key business objectives (such as resource optimization or the delivery of a hyper-personalized customer experience).

Start small and scale up

Making the transition to artificial intelligence in IT operations can seem like a major jump. A pilot use case that targets strategically important ITSM challenges can minimize the culture shock. 

Synchronize and connect event data

It is important to harmonize event data from a variety of sources and monitoring tools, to deliver a unified business perspective. Connecting this consolidated data to your AIOps tooling, provides more comprehensive insights and swifter incident response.

Essential AIOps features

According to IBM, a Celonis Titanium partner, any tools under consideration for a successful AIOps implementation should offer: 

  • Observability — The capacity to absorb, aggregate and analyze all business performance data to provide a holistic view and actionable insights. 

  • Predictive Analytics — The ability to analyze and correlate data, providing insights from which to base automated actions. This enables quicker issue detection, fewer incidents, and optimized resource utilization, ultimately assuring application performance.

  • Proactive response — The functionality to use performance metrics and predictive algorithms to identify and proactively respond to sub-par performance issues across the tech stack. outside identify and respond to use application performance metrics

What are the benefits of artificial intelligence for IT operations?

Cloud technologies and the exponential growth in available operational data have fuelled companies’ appetite to glean actionable insights from that data. AIOps solutions are increasingly effective tools to meet these challenges, delivering big business value from big data on a number of fronts — here are six of the most significant.

1. Faster MTTI and MTTR means lower down time 

According to the 2022 Gartner® Market Guide for SaaS Management Platforms report, organizations maintain an average of 125+ different software-as-a-service (SaaS) applications, generating data sets that are too large or complex to process using traditional methods (the definition of big data). AIOps platforms enable the automation of anomaly detection, root cause analysis, predictive maintenance and solution proposals, enabling IT teams to achieve shorter Mean Time to Identify (MTTI) issues and shorter Mean Time to Repair/Resolve (MTTR) them.

2. Reduced Costs

Accelerating an organization’s MTTR rate helps identify and address potential problems before they become an issue — preventing lengthy, costly service outages. AIOps can also save a lot of time and money by reducing the investigative burden of ‘false positives’ — erroneous reports of systems issues — by automatically analyzing reported problems and remediating where possible.

3. Operational efficiency, innovation and prioritization

A major application of artificial intelligence for IT operations is automating repetitive, manual tasks. Tasks such as error detection, alert analysis, and event reporting, are ideal for automation, reducing human error and buying IT professionals the time and space to focus on strategic tasks and innovation.

Going a step further, AIOps solutions can analyze and act upon usage data to identify critical alerts and prioritize responses — reducing the risk of service interruptions. Not only can the machine learning algorithms optimize IT resource allocation in this way, but also provide detailed, real-time insights into systems’ operational efficiency.

4. Cross-business visibility and collaboration

With IT operations spread across multiple applications in multiple environments (local servers, cloud services and hybrid solutions) it can be difficult to get clear visibility of systems performance. Similarly, this complex landscape can lead to the formation of data silos in business functions, preventing a cross-business view of interoperability. The real-time reporting and dashboards produced by AIOps solutions provide both granular data views but also holistic insights across multiple data sources.This promotes collaboration opportunities, greater cross-functional understanding and the ability to respond to any issues more efficiently.

5. Catalyst for Digital Transformation

Artificial intelligence for IT operations provides both a solid foundation and a springboard for organizations looking to implement digital transformation. Providing an intelligent layer or connective tissue between systems, teams, data sets, and applications, AIOps makes the adoption of new technologies simpler, more transparent and more manageable. The springboard or catalyst comes in the automation of IT processes, optimized resource allocation, and use of data analytics and machine learning to monitor and optimize newly implemented digital technologies. For example, read how Celonis used artificial intelligence, process mining, automation and machine learning to help UK retailer Ocado Group.

This customer story demonstrates a major digital transformation in reinventing their e-commerce operations — fast-tracking and scaling their optimization efforts for its global grocery partners across a rapidly expanding warehouse network.

6. Security

Keeping company networks and company data secure is one of the most potent use cases for artificial intelligence for IT. With systems and apps often spanning microservices, multi-cloud or hybrid cloud architectures and environments, there’s a lot of digital real estate to protect. Which is why traditional ITSM approaches are being augmented or replaced by AIOps solutions. IT systems using AI and ML can protect organizations in many ways, including:

  • Automated threat analysis and detection: AIOps systems continuously monitor networks to identify security threats using machine learning models trained on large volumes of operational data. This threat intelligence enables faster response times.

  • Vulnerability analysis and patching: Some AIOps platforms can proactively test an organization’s data infrastructure and tech stack for vulnerabilities. Areas deemed most critical or susceptible to attack can be prioritized, and patches deployed without human intervention.

  • Fraud and Malware Detection: Using predictive models, data analysis and event processing AIOps platforms can help detect potentially fraudulent activities including ransomware and malware. By automatically analyzing log data across different systems, AIOps can identify relationships between events and spot anomalies that may indicate an attack.

  • User behavior profiling: With machine learning algorithms, these systems can create baseline profiles of typical user activity and scan for outliers (unusual user behaviors). Such outliers can be flagged as potential security breaches, and those users’ credentials flagged or even frozen. 

  • Incident management and security coordination: AIOps can be used to provide standardized, automated, desk-level security protocols and incident management processes — such as systems for individuals to report potential security threats — logging, prioritizing and even addressing responses. 

  • Data compliance monitoring and auditing: artificial intelligence-based systems can be employed to continuously audit infrastructure and activity logs to ensure compliance with security policies, standards, and regulations.

Overcoming AIOps Roadblocks

CIOs and IT leaders looking to implement artificial intelligence for IT operations can sometimes find their progress impeded by one or both of two roadblocks:

  1. Convincing the c-suite (senior leadership) of the ROI benefits of introducing AI into IT operations.

  2. Changing the cultural mindset of the organization to new working practices.

Let’s start with showcasing AIOps for the bosses. Firstly, it’s crucial to provide specific objectives. Rather than adopt an all-or-nothing approach, consider pitching some pilot use cases (likely to most clearly demonstrate the benefits of AIOps) with a view to scaling up later. Reference the problems or operational inefficiencies that introducing artificial intelligence would help overcome. This should include the business areas that would be impacted and the anticipated KPI benefits. When you’re talking ROI, the most persuasive arguments come with dollar signs attached — such as the potential cost of critical systems outages, offline websites or data breaches and the role AIOps could play in saving millions.

Additionally, if you can demonstrate real-world examples of successful implementations it will help solidify the likely benefits within your organization. See for example how Celonis enabled Vodafone to supercharge its procurement systems and process with artificial intelligence, automation and process excellence.

Having secured senior leadership support, it remains to generate a positive cultural mindset around the introduction of AI technology into IT operations. Start with clarity and transparency with your teams when it comes to why AIOps is to be introduced. Provide training to help them understand the benefits and capabilities of AI to them and — importantly — address any misconceptions that the AI is there to replace IT operations personnel.

The likelihood of success here will be enhanced greatly by involving team members early in the decision-making process. Seek their input on the areas AI should be applied, how it aligns with their business goals and identify the areas in which the new technology will provide them with greater opportunities for strategic innovation and cross business collaboration.

Process Intelligence enables AI for IT operations and business

Artificial intelligence for IT operations is both a tool and beneficiary of process intelligence. The rich, comprehensive event data from the Celonis platform, powered by the Object-Centric Data Model and built on object centric process mining (OCPM) technology, is ideal for training AI models. At the same time, AI, ML and predictive analytics enhance Celonis’ ability to deliver process excellence for its customers.

For years these customers have been empowered by tools such as the Machine Learning Workbench (MLWB), its duplicate checker app, or in process simulation analysis. At the kick-off of the Celonis World Tour 2023 we showcased our latest AI tool that uses a large language model (LLM) to translate user enquiries into Process Query Language (the language the Celonis system uses to turn process data into process intelligence). This enables customers to access insights without any prior technical knowledge.

The potential applications for artificial intelligence in IT operations, process mining and beyond seem limitless. As Alexander Rinke, Celonis Co-Founder & Co-CEO put it “...if you can combine the reasoning capabilities of an AI system with the incredibly strong foundation of our platform, you can provide some real value that I think people can't even imagine today.”

For further examples of the future of this symbiotic relationship between AI and process mining, you can also download the Celonis Beyond 2023 journal and the Celonis Datasheet: Make AI work for your enterprise.

Learn more about how Celonis Process Intelligence enables AI

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Bill Detwiler
Senior Communications Strategist and Editor Celonis Blog

Bill Detwiler is Senior Communications Strategist and Editor of the Celonis blog. He is the former Editor in Chief of TechRepublic, where he hosted the Dynamic Developer podcast and Cracking Open, CNET’s popular online show. Bill is an award-winning journalist, who’s covered the tech industry for more than two decades. Prior his career in the software industry and tech media, he was an IT professional in the social research and energy industries.

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