Facing skepticism about the ROI of intelligent automation? Here’s expert advice on escaping the IA doom loop

During my interactions with customers, many IT executives and senior leaders have been inquiring about intelligent automation (IA)’s transformation value. Of course, every enterprise investment, whether technology such as cloud computing or a practice like the four-day week, needs to predict strong ROI before leadership approves it.  

Quantifying the value-add of IA for IT or business operations is particularly tricky, for various reasons. These include the still-common idea that IT services are merely a cost center, so there aren’t universally accepted metrics for the benefits they bring.  

Another is the mind-fogging power of the term “AI,” which provides the “intelligence” in “intelligent automation.” Introducing AI in an enterprise context would often make executives nervous when they considered it uniquely risky or hard to understand. Now “generative AI” is becoming the new hot tech trend, even though many Gen AI projects aren’t yet ready for prime time. Neither extreme – avoidance or hype – is accurate. And both can hamper sober attempts to sum up AI’s value-add in the practical world of IT operations.  

In fact, most IT executives and leaders who are advocating for IA projects at their organizations understand the value of AI-driven intelligent automation perfectly well. But they struggle to build a business case that financial and operational leaders will find convincing, as well as to define a methodology that will achieve it.  

In this blog I will not only define value guidelines but also suggest an approach to achieve the business case for IA projects in the IT sphere. These suggestions derive from my experience (more than 25 years) of being an IT leader and executive for successful enterprises.  

Defining IA value

In most companies, the value provided by IT falls into two major categories: 

1. Improving service and security – including availability, reliability, issue resolution time (MTTR), and expediting the adoption of new technology, while guaranteeing the security of company data. 

1. Reducing operating costs – IT services are often perceived as merely a cost center. Therefore, they are under constant reduction pressure. 

To both improve service and reduce costs, any Intelligent Automation transformation needs to create a set of KPIs that are defined by value generation (while also driving program progress). I’m comfortable suggesting these KPIs: 

• Total number of tickets resolved by an IA solution per year  
• Total number of alerts managed by the IA solution per year 
• Total amount of non-ticket support activities resolved by the IA solution per year (including system health monitoring, IT operation reporting, performance tuning, and batch management). 

A well-designed IA solution will boost these value drivers: 

• Customer satisfaction increases. With an effective IA platform supporting the whole IT production environment 24×7, MTTR shrinks, incidents happen less often, and uptime improves. 
• Operating cost decreases. ITOps staff resources are used more efficiently, requiring less overtime pay. 
• Attrition and turnover for IT staff decreases. IT staff will have greater control of operations, fewer trivial, manual tasks, less unplanned overtime, and greater job satisfaction – leading to less turnover. 
• Negotiating power with system integrators and consultants increases. Tribal knowledge becomes encoded in a software platform the user owns, which leads to better control of system integrators and consultants. 

• Ability to create value via IT operations increases. As I explained in my August 2022 column, IT operations are typically considered merely a cost center, under constant pressure to ensure continuous data flow while both reducing expenses and upgrading service quality. IA offers a way out of this dilemma. Because it’s powered by machine learning, intelligent automation can detect patterns of behavior in IT landscapes, spot unhealthy patterns, diagnose them, offer fixes, and even perform those fixes autonomously. In fact, really effective IA can identify ways to optimize operations beyond simply solving problems.  

Designing transformation projects for success

The fact that I keep fielding questions about transformation value indicates a major issue affecting IT projects: Chronic skepticism due to their poor track record of value realization. Most IT projects do not achieve the benefits promised in their business case. (In fact, the Boston Consulting Group estimates that up to 70 percent of digital transformation projects don’t hit their ROI targets.)  

Therefore a “black box” approach to swift, efficient, value-focused IA transformation will not work. Very few IT executives or senior leaders will be willing to commit all the resources up front that are needed for the transformation project. 

Below is a model I use, drawing on Agile methodology of Sagas, Epics, and Sprints (which I discussed in more depth in an earlier blog). It has two fundamental parts: 

1. Immediate action (the cascade diagram on the left) 

1. Governance direction (the circular diagram on the right, illustrating an Agile approach based on repetitive review). 

This model is framed as an overall Saga (two-year program goals), containing six-month Epics in which resources and goals are committed. Each Epic contains month-long Sprints. After each Sprint, the IA project’s transformational KPIs are measured to assess progress, and can be adjusted as needed. At the end of each Epic, value drivers are measured. (For more details, please refer to my blog post “How to make an elephant fly.”) 

Deriving value with ignio’s IA approach

While this model can be used in any IA transformation, it is especially effective with Digitate’s ignio platform, because ignio is designed to be an AI-based, autonomous solution for IT operations. ignio was designed as a machine that draws on powerful, proprietary AI algorithms to control other machines.  

Its unique “blueprint” function lets it create an accurate representation of an IT production environment. That means it can predict incidents, offer ways to prevent them, and even suggest improvements to the technical design/architecture to optimize operations. 

And unlike classic digital twins, ignio doesn’t just create a “model train” representation whose findings need to be manually transmitted back to the real-world original; it’s a digital model of digital operations, so it can directly and seamlessly modify them when you want it to. This frees up humans to focus on technology adoption and innovation.  

From IA to AI assistants

Of course, we haven’t rested on our laurels since launching ignio; we’re constantly researching the frontiers of AI and machine learning and developing solutions that harness these innovations for practical benefit.  

So now we’re proud to add the unique powers of generative AI and large language models to the ignio platform with two new tools, AI Assist and Knowledge Accelerator. AI Assist is an intelligent conversation engine designed to offer simple, intuitive explanations about ignio’s diagnoses, fixes, and recommendations for continuous improvements. Meanwhile, the Knowledge Accelerator captures enterprise context and technology models to manage last-mile automation such as resource provisioning, service configurations, and patch management. This lets ignio adapt on-the-fly to technological changes and automate lifecycle-specific service operations more quickly and nimbly. 

For more details on how we’re using generative AI, watch the new on-demand webinar with our Chief Product Officer, Rahul Kelkar , and Chief Data Scientist, Maitreya Natu , on generative AI scenarios for IT and business operations. They offer a primer on the power and creativity of this new technology (don’t miss the “Game of Thrones” and Women’s World Cup cameos) and explain more about Digitate’s AI Assist and Knowledge Accelerator.  

Written by Ugo Orsi