Architect for the Future of Automation

Editor’s note: The following is an excerpt from the book, “The Automation Advantage,” by Bhaskar Ghosh, Rajendra Prasad and Gayathri Pallail, published by McGraw Hill, 2021. All rights reserved. 

Constant disruption is now a given for businesses, affecting every part of today’s commercial landscape. Although technology has evolved to address many new business challenges, companies cannot expect to put technology solutions in place and then simply move on. While automation technology has been in place for decades, companies are starting to realize the benefits of applying automation across a large spectrum of business functions and integrating with all kinds of technologies in order to improve performance through the combination of human and machine.

The shift to intelligent, data-driven automation can also generate a steady stream of insights to fuel intelligent technologies. It can make faster, smarter decisions to accelerate innovation. But if an organization has only highly fragmented or low-quality data, little can be done. That kind of data cannot be mobilized. Leaders need to reimagine their organizations’ data supply chains and processes to ensure transparency, trust, and accessibility. If high quality data can be developed with all of these characteristics, the return on technology and AI investments can be maximized.

Businesses entering the next era of automation place much greater concern for data and how to manage it as a crucial enterprise asset. Based on what they have experienced so far, they are excited about the potential for automation to make many aspects of the business more agile and predictable, and acutely aware that good data will be required to yield insights and intelligence. This is the beginning of the intelligent automation phase and lays the foundation for truly AI-driven intelligent automation.

Enterprise automation architecture should be adaptable, based on cloud and AI technologies, and based on quality data, as further detailed below.

Yet most companies today are living with the legacy IT infrastructures and architectures built for a bygone era. Ultimately, the day must come when an organization faces the bigger-picture and longer-term challenge of overhauling the technology supporting the business. As projects proliferate, the cost of all those workarounds becomes unbearable; as the massive potential of intelligent automation starts to become clear, the opportunity for synergy becomes compelling. Surveys of business and IT leaders are beginning to reflect this new sense of priority: across the world, respondents cite architecture inflexibility as one of the biggest barriers to innovating at scale.

Make it Adaptive

When intelligent automation is becoming the core of a business – driving transformations and augmenting business with powerful insights and decisions – it needs to quickly sense and respond to how the business is changing. Systems must be adaptive to enable a business to keep up with dynamic markets. Having a more adaptive architecture for automation enables the business to remove bottlenecks and barriers to change, ensure a smoothly flowing value chain, and continuously stay relevant to its customers. This can be achieved with a plug-and-play architecture that embraces technology changes, seamlessly integrates into a broader ecosystem of partners, and increases agility.

Many business strategies today are predicated on working with alliances and partnerships with other players in the market. Managers have long recognized partnering as a strategy to scale more quickly and to create more seamless end-to-end experiences for customers. If a business chooses to pursue this kind of strategy, however, it should have an automation architecture that can interface smoothly with the partner landscape – while still excelling, from the company’s standpoint, as the central architecture and the hub around which all else revolves. This platform-centric approach can integrate automation efforts and achieve synergies across the enterprise and to support the industrializing and scaling of solutions.

To build out adaptive intelligent automation, system architects can design automation technology to work in a distributed way, with every function that is required by an application set up separately from it and treated as an independent service provider to it.

The benefit of approaching automation in this microservices-oriented way is that it allows those subfunctions – most of which play a significant supporting role and have always shared a great amount of commonality across applications – to be updated independently. Whenever there is some good reason to alter a function, the update process does not affect the rest of an application employing its service. And the same change can instantly benefit any other applications relying on that same function, with no disruption of their own performance.

Add Intelligence, Speed and Security

The increasing adoption of cloud computing services and the emergence of AI and machine-learning technologies are allowing companies to use intelligent automation to make decisions on known problems, predict issues and provide diagnostic information to reduce the operational overheard for engineers. As the power of artificial intelligence is increasingly applied to enterprise automation, systems architects are recognizing that it deserves to be more than just another set of applications to be supported by a long-established foundation. Leading-edge users of AI are rethinking their architectures to put AI at the core. By centering whole architectures on machine-learning and deep-learning technologies, they greatly expand their capacity to deliver solutions with breakthrough economics – and to gain impressive competitive advantages.

Finally, don’t forget about security – embed security into the architecture to maintain the confidentiality, integrity, and availability of sensitive information handled by applications. Every company that handles private, sensitive or proprietary data must have strong protocols for how data is created, processed, shared, stored and destroyed.

Weave the Data Fabric

Anyone would say that decision-making is best done with data, but is it possible for it to be led by data? What would it mean to let data drive the business? Establishing a data fabric for intelligent automation will enable companies to reap the benefits in business intelligence. Keep in mind that the point of the data fabric is not just to centralize enterprise data, but also to enrich it and maximize use.

Consider the software produced by Kensho, which is helping some of the largest trading desks in the financial sector dig through reams of data and market-moving information, searching for correlations between world events and impact on asset prices. Analysts using its data-driven investigative analysis tools and machine learning algorithms are able to discover, visualize, and understand complex relationships hidden in massive amounts of data. And that leads them to investment theories that are more rigorous and defensible [1].

It is possible, then, to be led by data – at least at the individual decision level – but it’s safe to say it must be highly trusted data. With the growing demand by businesses to put faster data access and better analytics at their managers’ fingertips has come the rise of enterprise data fabric: repositories designed to hold vast amounts of raw data in native formats until it is needed by the business. With an enterprise data fabric in place, companies have started to gain various benefits. They have been able to centralize enterprise content silos. They are transforming their insight discovery and analytics processes. And they are able to enrich data in ways that are not possible in the source systems.

Reference

1. Antoine Gara, 2018, “Wall Street Tech Spree: With Kensho Acquisition S&P Global Makes Largest A.I. Deal in History,” Forbes, 2018, https://www.forbes.com/sites/antoinegara/2018/03/06/wall-street-tech-spree-with-kensho-acquisition-sp-global-makes-largest-a-i-deal-in-history/#49d5b86567b8.