A CIO’s first rule for automation: Have a clear business case
Priorities for CIOs
Automating complex workflows will remain a CIO priority, says Petr Baudis, CTO and chief AI architect at London-based Rossum. The key will be getting such projects to scale beyond departmental silos. A catalyst to make this happen will be the ongoing improvements in AI-enabled data capture.
Fast and accurate data extraction will speed up transactions and automation capabilities, and be the foundational technology within any business intelligence or data analytics platform, enabling better collaboration and B2B communications, he says.
“The types of automation technology we see being vital include RPA along with process and task mining,” says Baudis. “We’re seeing a convergence taking place between all these technologies as enterprises try and scale their automation projects.”
Plus, Adani Electricity this year is continuing with advancements in the areas of distribution management, customer experience, the metering ecosystem, and consumer data analytics, says Tandon.
“We’ve implemented SAS’ AI/ML-based energy forecasting solution to improve our forecasting performance,” he says. “This has helped us achieve a forecast accuracy of around 97%, thereby allowing us to optimize power procurement costs while providing reliable electricity supply to our 2.5 million consumers. We’ll also continue with advancements in distribution management, the metering ecosystem, and consumer data analytics.”
The power company’s flagship automation projects include implementing an advanced distribution management system to create a self-healing grid infrastructure with enhanced visibility and scalability to improve the customer experience. They’re also implementing a cloud-based data lake and analytics solution that will provide what Tandon calls a single source of truth, and drive self-service analytics and data-backed decision-making to help them operate more efficiently.
“Estimated readings for our customers stood at 2.2% three years back, but now we’ve brought them down to 0.3%,” he says. “The whole mechanism was automated so all readings were optically downloaded without any manual intervention. This initiative not only ensured our system accuracy and return of equity (RoE) incentive, but also improved transparency and reduced consumer complaints.”
And at the pharmaceutical segment at Cardinal Health, a main goal is to also boost its efforts in warehouse automation to better serve its customers, Boggs says.
“In IT, we’ll continue to prioritize infrastructure as code, continuous integration and deployment, and AI operations,” he says.
The University of Phoenix has some new automation projects on tap as well. Currently, the institution is developing an enterprise platform that will enable the increased use of ML and automation across a wide range of student and staff journeys, Smith says.
“This engine will be deeply integrated into our data lake to enable truly individualized student support at the right time, through the best channel,” he adds.
The university also plans to continue improving student support by continuing to automate increasingly complex tasks in matriculation, transcript processing, and student financial aid.
“Recent advances in the ability to consume unstructured documents and natural language processing are enabling a whole new crop of complex tasks to become candidates for automation,” says Smith.
His team is creating platforms and systems by which they can effectively scale and govern automation safely and reliably. After all, he says, there’s nothing less effective than automating a process that shouldn’t exist. Automation combined with AI should significantly help businesses make faster decisions, optimize business processes, and drive higher rates of efficiencies, says Elumalai. “It has the potential to improve business KPIs through auto-detect, auto-heal solutions, and create new channels to improve end-user experience,” he says.
