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Using AI-powered intelligent automation for digital transformation success

Robotic process automation is a hot topic these days, and understandably, a point of interest in many digital transformation initiatives. In fact, the battle over which vendor’s RPA tool has the most gadgets and widgets has become somewhat of a proverbial “street fight.”

But those focused at this tactical a level can lose sight of the bigger picture: To truly get ahead with digital transformation initiatives and solve a wider set of business problems with automation, organizations are increasingly looking to a platform automation capability that allows them to optimize end-to-end operations.

This is a primary shift from exploring stand-alone technologies for use within pockets of the business. For example, the primary reason organizations are finding it slow and difficult to scale the benefits of RPA isn’t because the technology itself is inherently limited, but more so in how it’s being applied. Organizations are trying to digitize entire processes.

Although this does translate into some use of task-based automation, many times complementary technologies also become necessary to accommodate the ingest of data from multiple input channels, process unstructured data, manage complex exceptions, and facilitate interaction between people and automation.

Organizations are able to bridge back-office automation efficiency gains to front-office automation customer experience enhancements with a platform of complementary, AI-powered technologies built to work together so that they can automate the entire process journey.

Analyst firm HFS recently called out the shortcomings of a ‘single technology’ approach to digital transformation and specifically used RPA as an example. They expressed support for an integrated approach: “RPA provides a terrific band-aid to fix current solutions; it helps to extend the life of legacy. But does not provide long-term answers... Integrated Automation is how you transform your business and achieve an end-to-end Digital OneOffice.”

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Lights illuminate flash memory cartridges the back of a server unit inside the data center of FinTech Group AG's campus offices in Frankfurt, Germany, on Tuesday, April 12, 2016. The volume of investment in Germany's fintech industry will probably quadruple to 2 billion euros ($2.2 billion) in the next five years, said Hubertus Vaeth, managing director of Frankfurt Main Finance. Photographer: Krisztian Bocsi/Bloomberg

An integrated platform hopefully reduces cost, time to value and complexity, while maximizing customer and employee satisfaction.

It may seem initially daunting to deploy a platform against end-to-end business operations. However, organizations can look to a five-step framework below to help them bring structure to their corporate initiatives for digital transformation:

Step 1: Understand where you are today with a maturity model assessment

A maturity model assessment begins with evaluating automation readiness from a technology and process perspective. IT should be involved in the discussion early on because they understand how automation technologies will fit within the larger IT framework. They’re also responsible for managing the environments that these technologies operate in and for ensuring proper security protocols are followed throughout the deployment process.

From a business process and operations standpoint, organizations should assess how well-documented current processes are during this stage. If there’s room to improve prior to automation, this presents an opportunity to make upfront investments in this respect. Automation is most powerful when deployed against processes that are already running properly; it isn’t intended to ‘fix’ or alleviate the pain points around broken processes. In other words, optimize first and then automate for the best results.

Step 2: Establish a business case to quantify the benefits, along with a plan for implementing automation

After conducting ‘process readiness’ assessments above, organizations can move into establishing a business case for automation. This allows the business to clearly make connections between automation and corporate strategic, workforce, operational and financial objectives. It also includes ranking of use case candidates based on their potential to yield the greatest return.

Lastly, via the rigor of this exercise, an organization is able to come to a clearer conclusion of which technologies are required to develop its unique the platform-powered solution given characteristics of the business problem(s) that automation will be deployed against. Typically, problems range from improving the way information is captured and extracted, to taking action on that information in downstream systems/applications, and then facilitating check-points with people along the way such that they can collaborate with automation. This spectrum of problems translates into and cross-walks with core Intelligent Automation technologies below.

These technologies drive significant value for an organization when bundled and well integrated into a platform:

  • Cognitive capture: Ingest/understand any document and extract relevant information via any channel.
  • Process orchestration: Integrate people, automation technologies, systems, applications, etc. along a broader workflow in a business-user intuitive manner.
  • Mobility & engagement: Engage customers efficiently and effectively via web or mobile devices.
  • Advanced analytics: Provide data-driven insights with respect to outcomes as driven by automation.
  • Artificial Intelligence: Automate complex decision making and personalize service to end users.

After taking steps to align with respect to use cases that drive ROI, along with platform technologies required to automate them, an organization can transition into designing automation solutions within the platform. This entails:

  • Initial documentation: Capture what exactly an automation solution is intended to do, which functions as the ‘blueprint’ for its design below.
  • Solution design: Building components of the platform solution across the various technologies. A platform is powerful because given the interoperability of its technologies, the business is alleviated time that would have been spent integrating a string of individual, un-integrated software products.
  • User testing: Confirm that the solution designed above follows protocol and operates in alignment with what was specified in initial documentation.
  • Deploy automation: Run automation in a live/production environment and begin to realize benefits.

These four steps represent a broad, yet systematic approach for ensuring that all automation solutions are designed and deployed to a certain standard.

Step 3: Create an operating model for an automation program

It’s important to create an operating model around automation that fits your organization. This can also be referred to as the Center of Excellence for Intelligent Automation. It represents a group of core resources and people that guide all things related to automation, including maintaining and overseeing standards across the business; training; management of vendors; establishing best practice and so much more. Organizations also have flexibility in terms of how such a program can be structured.

There are three main models, each differing in how responsibilities are shared across the enterprise.

  • Centralized operating model: A single team is responsible for running and controlling all aspects of the program.
  • Decentralized operating model: The responsibilities for running the automation program are replicated across separate business units within the organization.
  • Hybrid operating model: Some aspects of the automation program are run by a single, centralized team, while others are replicated across business units.

Of course, there is no ‘right’ or ‘wrong’ answer. Organizations should start with the model that makes the most sense given where they’re today, and adjust down the road as needed.

For example, if an organization’s just getting started with automation, it may be best to begin with a centralized approach. As adoption spreads and scaling takes place, certain responsibilities will likely need to be transferred and/or replicated across the business.

Step 4: Scale

Once you have conducted your maturity model assessment, established a roadmap for implementation, deployed automation, and launched a program office to govern it, you’re ready to focus on scaling. This is a function of people, processes and technology.

Your automation program will be a lever for securing buy-in and adoption of automation by people across the organization, while also championing the exercise of re-thinking processes and business culture to further facilitate its spread.

From a technology perspective, the platform of choice can have a real impact on ability to scale as its architecture, user-intuitiveness, breadth of automation capabilities, and the governance mechanisms it has in place to control ‘bad behavior’ will impact adoption rates.

Step 5: Innovate

The last step in true digital transformation is to always keep an eye on innovation. Successful organizations continually look for new technologies that can benefit the automation program, and in fact, the Intelligent Automation Center of Excellence can be empowered to keep track of such developments as they arise. This includes staying on top of software releases from the IA platform vendor.

A ‘single solution’ technology approach can limit an organization’s ability to fully realize the benefits of digital transformation. With an integrated platform, organizations can more consistently realize outcomes from automation such as improved productivity, faster decision-making, personalized service to customers, and a more empowered workforce.

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