Its difficult for any modern organization to stay relevant without a strong digital transformation strategy in placespecifically when customers and competitors are digitally enabled.

Automation plays a central role in many transformation initiatives today. To support and strengthen their digital transformation objectives, many business and IT leaders are automating processes and workflows to improve the efficiency of operations and create new revenue streams.

According to a 2019 Deloitte survey of executives across 26 countries, 58% have introduced automation into their enterprises. In this group, 38% are conducting pilot projects, 12% are implementing 11-50 automation projects, and 8% have more than 51 automation projects in play.

As some companies begin to scale their automation initiatives, AI technologies are also entering the workplace. AI is by no means a new digital category, but the technologies that fall under AIs umbrella have progressed substantially in recent years with accessible and practical applications in a broad range of industries.

Since no single solution is capable of meeting all process transformation goals, a new approach to automation has emerged, called intelligent automation. This combines multiple technology capabilities into one powerful business optimization toolkit.

This methodology is called intelligent, because it combines readily available AI capabilities with technologies that enable task and process automation. These include:

Deployed alone, these technologies each add immense business value. When they are strategically combined, however, an entirely new level of business optimization is possible.

The concept of an intelligent automation toolkit is fairly new, and many organizations are still coming to terms with the advantages of deploying RPA, DPA, and AI together. The Deloitte survey mentioned earlier revealed that almost half (48%) of survey respondents are not implementing an intelligent automation strategy (often because they are unaware that this is an option available to them).

However, the barriers to entry are relatively low. The technologies used for intelligent automation are market-ready and designed for both ease of use and smooth integration with current IT frameworks.

Working in tandem, process automation and AI solutions allow enterprises to efficiently automate tasks and streamline processes that require multiple resources across various line-of-business systems.

Optimizing and using an intelligent automation-focused strategy can have a positive impact on business, including the following benefits:

Intelligent automation tools can work together to observe and learn what employees are doing and then automate those patterns so that skilled professionals no longer waste time on repetitive, often data-heavy work.

Fewer manual steps and minimized human involvement reduce the risk of human error. This is a boon in any organizationespecially within heavily regulated sectors, where even the smallest error can lead to penalties and reputational damage. For example, a typo in a customers name during onboarding could result in a sanctions screening error, with dire consequences for the organization.

Additionally, with machines handling more process components more quickly, and people being redirected to higher-value workoperational productivity increases across the board. This approach gives companies an opportunity to leverage the skills and knowledge of existing human capital in a more efficient way.

It can also improve the customer experience by accelerating service delivery, increasing accuracy and enabling customer-facing employees to spend less time doing administrative tasks and more time giving customers personal attention.

Machine learning is another AI capability that can be applied during process automation to trigger a new process or reroute running processes according to predictions.

In this type of use case, efficiencies are achieved because machine learning looks at historical data and uses predictive analytics to spot trends and make business decisions based on this data.

With access to deeper process insights and intelligence, organizations can drive continual process optimization projects and scale their digital transformation efforts more efficiently and strategically.

While few companies would disagree that data is an incredibly valuable business resource, many are struggling to manage data efficiently enough to exploit its value.

Integrating an RPA software robot with AI capabilities such as optical character recognition (OCR) and natural language processing can help to address this challenge. This type of intelligent automation solution can quickly and accurately extract relevant information from unstructured datain text, speech, or visual formatand understand meaning, sentiment and intent.

This system can then automate business processes by grabbing actionable data to reduce time and resources required to complete certain tasks. For example, natural language processing can leverage collected data to fill out forms that are often done by hand.

Another potential use case for this type of technology combination is in a scenario where high volumes of customers submit requests for service in a free text format. Rather than dedicating a team of customer service agents to field these requests, the technology can analyze the requests to understand and determine sentiment, respond to routine cases and prioritize the more urgent issues for human action.

These types of approaches allow organizations to achieve higher levels of efficiency and productivity, while skilled professionals have more time to focus on business needs that technology cant meet. These could include handling customer complaints with patience and empathy, developing sales strategies, or supervising the intelligent automation system itself.

Intelligent automation can connect and integrate data systems into process management tools. If data is unstructured (audio files, emails, and even social media posts), AI can transform this into an RPA-friendly format, while also unlocking insights that augment human decision-making capabilities.

With each interaction, the system acquires more data about how decisions are made and statistical analysis is applied to develop rules around decision making. Using this intelligence, organizations can continually improve operational performance, reduce cost structures and gain a competitive advantage, all with extreme efficiency.

As business and IT leaders seek ways to become increasingly nimble and efficient in a fast-paced environment, theyre focusing their transformation strategies on innovative digital technologies. Its critical that these organizations understand the unique value that each technology brings to the table, so they can reengineer processes accordingly and truly optimize the benefits of an intelligent automation approach.

Though it may seem easier and faster to implement new technologies separately, this approach is not well-suited to end-to-end and enterprisewide process transformation. By combining complementary technologies into an intelligent automation toolkit, organizations can rapidly scale up their automation and optimization efforts.

In these ways, intelligent automation toolkits provide organizations many opportunities to gain more value from their personnel and unlock new revenue streams through expanding the scope of positive outcomes that can be delivered.

Jason Trent is group product manager of K2 Cloud at K2 (www.k2.com), which offers software and services for intelligent process automation to help organizations simplify complex workflows to improve the customer experience.

Read the original here:

Creating an intelligent automation toolkit - KMWorld Magazine