IBM Exec: 7 Places to Start Using Intelligent Automation

TECHNOLOGY STRATEGY
IBM Exec: 7 Places to Start Using Intelligent Automation
Both within IT and throughout the business, AI-driven tools can be a significant boon for enterprises—and their workers, according to IBM data, AI, and automation executive Dinesh Nirmal
Automation and AI may be increasingly commonplace in the business world, but frequently perpetuated fears about the technology—specifically, that it will “steal” workers’ jobs—often persist. In fact, the reality is quite the opposite, contends Dinesh Nirmal, general manager at IBM for data, AI, and automation.
Dinesh Nirmal
“When machines start handling routine work, people can reclaim their time, find new roles, and carve out new innovative work that really matters,” Nirmal says.
Intelligent automation can bring myriad benefits to the enterprise, both within IT and across the business. In a recent conversation with Deloitte Consulting LLP Managing Director Brijesh Singh, Nirmal explains.
Singh: What are some of the top operational challenges businesses face today?
Nirmal: There are many. On a macro level, they include inflation and the need to acquire the right skills for competitive success. At the same time, however, the complexity arising from decades’ worth of legacy infrastructure presents another set. The hybrid cloud, application management, and siloed data all pose a significant challenge to numerous enterprises today as a result.
What technologies are businesses embracing in response?
In the typical enterprise, there are some technologies being deployed within lines of business and others that are especially advantageous within IT. Intelligent automation, which is the use of automation technologies including AI, business process management, robotic process automation (RPA), and others to streamline and scale decision-making across organizations, can simplify processes, free up resources, and improve operational efficiencies. Among the many potential benefits are reduced costs, increased productivity, more resilient supply chains, and an improved customer experience.
I see five technologies that can be powerful within lines of business:
Process mining. This is a critical capability that helps enable enterprises to uncover bottlenecks and determine which processes throughout the organization can be automated with the greatest impact. It provides a complete, analytical picture of the enterprise from end to end.
RPA. Although RPA is often equated with automation in general, it’s actually just one type of automation, and can be applied both to lines of business and to IT. It’s focused more on task automation than on processes, but it offers a lot of opportunity.
Workflow. A good workflow tool can help organizations manage the automation journey. Often these tools are low-code or no-code.
Rules engines. Some areas don’t need AI but can still benefit from a rules engine based on a simple rules model. Decisions can then be made with or without human involvement using preset criteria.
Document automation. Last but not least, a document processing tool can enable organizations to extract fields out of documents using optical character recognition and other technologies, further advancing their automation capabilities.
Within IT, meanwhile, other technologies may provide benefits in specific areas:
Observability. Particularly as companies embrace the public cloud, they need observability, including application performance assurance as well as visibility into costs and resource management.
AIOps. Just as important are AI-driven predictive capabilities that monitor logs for anomalies, enabling companies to avoid problems before they happen—and to resolve those that do occur. These are areas where a shifting mindset with innovative methodologies such as Touchless IT , enabled by AI for IT operations (AIOps), can deliver real benefits by helping to optimize the enterprise.
“Lack of skills remains one of the biggest barriers to AI adoption. ”
— Dinesh Nirmal, general manager for data, AI, and automation, IBM
In what use cases have you observed intelligent automation making the most impact?
Enterprise verticals are seeing rapid progress. In hiring, for instance, companies can use automation to scan resumes for desired skills, dramatically shortening the time needed for that process. Having a human in the loop on hiring processes and decisions is critical, but intelligent automation that is explainable and trustworthy can help ensure hiring managers can then spend more of their time on the more nuanced and complex mission-critical tasks.
Another example is claims processing in the auto insurance industry. When a customer has an accident, many insurance companies are using intelligent automation to request a picture of the damaged car. AI can use that uploaded image to verify the make and model along with estimated costs, and then find the closest body shop to the customer—all within minutes or hours rather than days. The result is a better customer experience. 
What role does data play in all this, and what guardrails are important?
Digitization starts with data—there’s no AI without data. Whether it’s structured, unstructured, transactional, or analytical, data plays a huge role. There’s a constant stream of data coming into most enterprises, but is it the right data? Traditionally, data is siloed in many organizations. The challenge is to securely deliver the right, unbiased data to the right people with the right data and AI governance processes in place.
Are there talent implications? What new skills do organizations need to support intelligent automation, and how do they need to think about the target-state operating model?
There is no shortage of workers in the workforce today—just a shortage of workers with the right set of skills. Data scientists are a notable example. Lack of skills remains one of the biggest barriers to AI adoption within the organization, so it’s imperative that we cultivate that pipeline. With automation, there is immense opportunity to upskill and train workers so they have the right set of skills needed to work with these newer technologies. Many tasks, such as labeling the data needed for AI, can be augmented with intelligent automation, freeing up staff to focus more on innovation.
How would you recommend CIOs and CTOs get started putting these new capabilities to work?
The tools are already out there, but a holistic view of the enterprise is key for revealing what percentage of the enterprise is digitized, what percentage ought to be automated, which tools could help, and what cultural changes are needed along the way. Hiring a chief automation officer, for example, can help provide that big-picture perspective. Automation is a journey, but it can be well worth the effort. An automated enterprise is an intelligent enterprise.
Katherine Noyes , senior writer, Deloitte Insights for CIOs
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PUBLISHED ON:
Nov. 7, 2022 3:00 pm ET
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