Business Automation – Why Businesses Need To Think Bigger When It Comes To Automation
In the pandemic-transformed economy, digital transformation, data-driven decisioning, and automated workflows have evolved into the holy trinity of business strategy.
Every business is forced to do more with less as we continue dealing with rising inflation and a potential recession. Technology has the potential to automate laborious, prone-to-error, manual processes while also providing customers with quicker, more individualized experiences.
However, despite their promise, many of these projects fall short. 30% to 50% of process automation projects, according to EY, are unsuccessful. Common reasons for these failures range from unclear project goals to poorly executed technology designs, but tunnel vision is the main culprit.
Silos: The Enemy Of Automation
Automation projects are often treated as isolated, one-off opportunities to optimise a business function or process. Maybe it’s the development of a new payment processing or credit decisioning solution to expand a digital payments infrastructure or introducing a conversational AI tool to enhance front-line customer support functions.
Regardless of the specific use case, automation projects that aim to accelerate or increase the efficiency of a single aspect of the business frequently fail because they aren’t sufficiently large.
Those that do not will spend lots of money on software that won’t solve their problems. To put it in the words of Gartner Inc. Research Vice President Fabrizio Biscotti: “Hyperautomation has shifted from an option to a condition of survival.”
Putting The Pieces Together
Consider the introduction of conversational AI as a first-line customer support solution to put this into perspective. This is one of the most prevalent types of automation projects. To act as a first line of customer engagement, many businesses have implemented a variety of chatbots, more sophisticated AI-powered natural language processing technologies, and other automation tools. The majority of them fall short of their promises.
These failures can have many causes, but the most frequent is a lack of integration with the rest of the company. Your business won’t change just by adding a chatbot to the front-end customer engagement funnel.
Businesses must integrate customer engagement, data collection and analytics, and digital operations to transform the entire process from the initial customer inquiry to sales, marketing, product development, and all other aspects of the business to fully realise this technology’s benefits.
As an illustration, consider a project that my company recently finished with a sizable international insurance brokerage firm. Initially intended to help it organize the petabytes of unstructured data coming into the company from the call centre, customer emails, and even snail mail, the initiative quickly evolved into a chance to hyper automate.
As soon as the business began collecting and organizing this data, it became obvious that it could be used to inform and enhance other business processes. For example, it could provide account managers with the knowledge they need to provide more individualized solutions, accelerate cycle times and new customer onboarding, and surface crucial intelligence on at-risk customers needing further intervention.
We could hyper automate various connected business processes by combining conversational AI, comprehensive, fully integrated customer data, advanced analytics, and digital workflows. Importantly, by using this all-encompassing strategy, we enhanced the final customer experience, cut costs, and deferred customer contact.
Where To Start
Before implementing the first hyper-automation solution, enterprises must take three critical actions.
1. Enterprise-wide support. The first step is securing enterprise-wide buy-in and project governance. The ability to take a holistic enterprise view of end-to-end processes and results and break down legacy silos are necessary for the various interconnected systems and processes that will ultimately be involved in a hyper-automation process. The right teams must be given the authority to support that effort across the organization.
2. Data-led discovery approach. A strict, data-led approach that relies on process mining techniques to transform conventional processes into a coherent data flow is perhaps the most important technical requirement for any hyper-automation project.
Instead of waiting years for a company-wide data integration project before the build can begin, project developers can now concentrate on quickly identifying and integrating the most important functional data into the build.
3. Set realistic expectations. Major automation projects are measured in years, not weeks and their scope and duration are determined iteratively as they progress. Projects that are started hoping to get quick, unexpected results always fall short of producing lasting change.
A Cycle Of Continuous Improvement
Enterprise automation projects frequently receive a bad rap for putting cost-cutting above the needs of the customer. However, that’s only the case when the automation projects in question are too narrow in scope to provide a more strategic outcome. Businesses can improve how they connect and communicate with customers while delivering faster, more accurate insights and minimizing the number of manual processes and data handoffs along the way by shifting the focus away from automation and toward fully connected hyperautomation initiatives.
Hyperautomation is a continuous process. Organizations can put themselves on a road to continuous improvement, better profitability, and improved customer experiences by using data to build connected processes enhanced at each step through the appropriate digital solution.
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