The Union

Key Points

• Innovation requires change and businesses need abilities to respond quickly
• Most application and automation changes are brittle and require an SDLC every time a change is made
• Allowing businesses to configure rules outside of integration technology increases their responsiveness and velocity

Low-code platforms cause testable events

Low-code platforms create testable events whenever changes are made. This is a problem. These platforms have focused on developing code and app construction instead of maintaining apps and lowering the total cost of ownership. Therefore, changing code or modifying apps built with low-code platforms involves a lot of IT resources like developers, testers, dev environments, and integration environments.

Most platforms are extensible and allow integrations, but the process is brittle since the business logic and integration methods are coupled together. If you have ever seen a step in the process that included "paste your code here", then you understand what we mean.

To make this process smoother, what's needed is something akin to Excel or WordPress that allows building blocks without coding the wiring or integrations and business logic together. This would avoid having a testable event triggered every time you make a change as with certain other platforms. Understanding how these platforms differ from others is an important consideration when it comes to choosing the right way to automate processes across different systems and teams. 

Your automation contributes to your technical debt

Your automation efforts can contribute to technical debt if you are using traditional automation platforms which are essentially a thin veneer over VBScript, JavaScript, or code generators. These changes require coding and testing, however, it is possible to achieve the same velocity with a different architecture pattern. 

For instance, using natural language processing between the configuration of building blocks and the programming logic can alleviate many problems associated with change. This layer allows you to invoke backend systems in much the same way as an Excel function is called - without actually needing to call any programming logic. 

Having this kind of high-velocity change in automation projects allows businesses to keep up with their needs and avoid having to pull down previously created automation that they cannot maintain due to a lack of the ability to test. By distinguishing between the integration technology and the business layer technology, it is possible to avoid triggering a full SDLC to test changes. 

This approach can help reduce technical debt and increase business velocity.

Change without the long-tail

Businesses need to find ways to make changes and automate tasks quickly and cost-effectively without triggering a huge testing cycle. By using modern technology such as natural language processing between the configuration of building blocks and the programming logic it is possible to invoke backend systems in much the same way as an Excel function is called - without needing to call any programming logic. This helps reduce technical debt and increase business velocity by separating automation change management from functional testing.

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• Enterprise IT is increasingly complex 
• Skilled IT staff is scarce and overworked 
• Complex workflows overwhelm your people
• Complexity is the enemy of productivity

Your IT group is increasing complexity, not reducing it. But, it’s not their fault.

Companies contain a myriad of departments and divisions, all with their own specialized teams of workers. Processes are designed to be as efficient as possible, often at the expense of flexibility. And information is collected and analyzed to a level of detail that would have been unimaginable just a few years ago. Amid all this complexity, it’s easy to lose sight of the fact that businesses exist to produce something – whether it’s a product, a service, or simply a profit. In the quest for efficiency and productivity, companies sometimes forget that the goal is to produce results, not just to follow a set of rules. When businesses ask IT to build new apps, it increases complexity and often hard codes rules into the process. Then, tomorrow, you may find yourself bogged down by your own bureaucracy and struggling to achieve your true potential.

App explosion contributes to complexity

Building more apps to support new business needs creates more complexity, not less. Apps, SaaS, low-code apps, and automation have created a more complex landscape than ever before. And while some argue that this complexity is a necessary evil, it introduces more risk and can lead to problems down the road. One of the biggest problems with so many apps is that it makes things more difficult to change. If you want to add a new feature or make a change to a business process running across an app, you have to wade through a lot of code and figure out how everything fits together. Many times, the processes are hard-coded to the app making you choose between innovation and stagnation. If you add an app to avoid the inherent complexity, you add more complexity for tomorrow. If you modify an app or automation to avoid waiting on IT to build another app, it still takes a long time to edit and test your new processes. You have left yourself with two poor choices and both of them from having too many apps for too few developers.

More apps create more work

Enterprises are already struggling to keep up with the sheer volume of apps they have. Therefore, it’s hard to imagine why anyone would want to add more to the mix. CIOs are already stretched thin, and their IT teams are spread even thinner. Can you imagine a CIO stating,

No.

So ask yourself: Are you adding or removing complexity?

Listen to why John Michelsen states building more apps is the wrong way.

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In this episode, Chris Kraus and Scott King discuss the importance of using conversational AI to improve customer and employee interactions. We note that businesses must utilize context in digital conversations just like humans communicate. Remembering context enables digital conversations to be more like conversations we have with friends, family, and coworkers since we understand the context from previous conversations as well as keep up with relevant business rules and conventions.

Utilizing chatbots with natural language processing and entity identification can help ensure your customer’s needs are met quickly and accurately. Chris suggests that businesses should think about using more intelligent chatbots as a way to analyze customer interactions and provide better services, rather than just deflecting inquiries to keep from talking to expensive agents.

Digital leaders should focus on using conversational AI to increase customer satisfaction. This includes utilizing natural language processing to understand customer intent, context, and business rules to ensure customers receive the assistance they need promptly. Ultimately, these strategies should help businesses create more meaningful customer interactions

By following these simple steps, businesses can start to build trust and loyalty with their customers by providing the best services possible. It’s an investment that will pay off in the long run! Whether it’s conversational AI or other strategies, businesses should continue to strive for customer satisfaction.

We hope this episode provided some useful insights into the benefits of using conversational AI in chatbots. Thanks for listening! See you next time!

Links and Resources

• What is conversational AI?
• 5 ways Forrester predicts AI will be “indispensable” in 2023
• Gartner Predicts Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026

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Digital transformation is the process of taking legacy systems and modernizing them to take advantage of current technologies and methods. Companies need to think differently when it comes to creating digital products and services, as well as how they will train users on those products. To ensure successful adoption, companies must focus not only on the functional benefits of their products but also on how they can be deployed easily and intuitively in a conversational manner.

To do this, companies must create a process that makes it easy for users to understand and interact with their products. This means creating an orchestrated process that users are used to or “more of the same” where the product guides them one step at a time in a conversation. Additionally, companies need to ensure that their products provide feedback when something goes wrong or when the user needs assistance. This helps to ensure that users don’t become frustrated and that they can successfully adopt the product with minimal training.

The benefit of taking this approach is that it allows companies to deploy products quicker, with less training required. This means that users are able to get up and running faster, leading to increased adoption and greater value. Furthermore, this approach reduces the need for manual integration allowing companies to quickly add new features and create a better user experience at lower costs.

Ultimately, with the right approach to digital transformation, companies can create better products, faster and with less effort. This leads to improved customer satisfaction, increased adoption rates, and greater returns on investment. By taking the time to think about the process differently, companies can ensure that their digital investments are successful and make the most of their efforts.

Links and Resources

• What is conversational AI?

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Robotic process automation (RPA) is undoubtedly one of the most disruptive technologies in recent years, but what is the future of RPA? Enterprises all over the world have implemented RPA, bots, or digital workers to take stable rule-based processes from workers and hand them over to software. It has created billions of dollars of labor cost savings and value for highly manual, repetitive, and high-volume processes. McKinsey & Company reviewed 16 case studies and calculated anywhere from 30 to 200 percent return on RPA investments inside the first year. That’s a lot of value.

Despite providing high returns on investments, RPA projects frequently fail to meet business expectations. Many achieve early success with simple RPA projects and realize high returns. However, when leaders look into the future of RPA, they recognize that bot maintenance burdens and overhead increase costs over time.

Listen in to Scott King and Chris Kraus explain the future of RPA and how you can adapt your automation strategies.

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