Responding to Enterprise Challenges in Real-Time with AI and Machine Learning

It doesn’t matter whether you’re an enterprise on the cusp of digital transformation, or well into implementing the disruptive power of artificial intelligence (AI) and machine learning (ML). The ML market will be worth an estimated $96.7 billion by 2025, while the AI market is projected to reach $202.57 billion by 2026, both markets driven by the rapid increase in technology adoption as organizations respond to the new urgency of being able to effectively analyze their growing volumes of data and act on intelligent insights that can help them innovate and solve business-critical challenges.

As your organization grows it has multiple solutions and processes that accumulate over time, all of which are impacted by changing business needs. These growth spurts inevitably lead organizations to experience challenges as they navigate new changes and opportunities for efficiency, innovation, and success.

All of an organization’s processes and solutions might be using workflows, documents, and integrations that today are typically handled by IT departments. Some of the processes are partially or manually automated, and front-end systems and processes might be more optimized than back-end processes, which is often the case in many organizations.

The disconnect between front-end and back-end processes, coupled with other enterprise challenges, means that key people don’t have the insights they need in context. Decision making becomes a crawl instead of a sprint, and attempts in providing a fantastic customer experience are hindered.

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With so many components in the working cogs of an enterprise, information can become disparate and non-contextual, leading to even more issues with data security, compliance, or a lack of central governance.

Ronald van Loon is a Newgen partner, and as a fixture within the industry for over 20 years he has witnessed first-hand the unique obstacles enterprises come across as they grow on their journey towards innovation.

Now, just facilitating an enterprise to listen to all of these challenges isn’t enough, especially when there’s multiple processes, communication channels, and customer touch points in the mix. The organization has to empower relationships between communication and responses, and then take action. If the organization’s system can’t easily and efficiently support this process, then they will face difficulties providing real-time, contextual experiences for their customers.

Enterprise challenges are a sum of their parts – different teams, departments, user groups, and geographies. It’s the whole of the organization that’s impacted, and a complete solution is required. One that supports real-time decision making, removes process bottlenecks and incumbrances, and ensures talent has the right insights in the right moment to create a better, brighter customer experience.

Enterprises not only need to overcome these challenges, but also use technologies like AI and Machine Learning to simplify, automate, and bring intelligence into the customer journey, while supporting your employees with the right insights.

Acting on Customer Communication: A Banking Use Case

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Although AI and ML is a solution that applies to all industries, in my recent discussion with Newgen experts, they described an interesting example in the banking industry. Let’s say you’re at one of your bank’s local ATMs and it’s defunct. Unable to make a deposit, you jump onto Twitter and complain to your bank. Does your bank disregard you, or does it respond and take appropriate measures to resolve the problem? As a customer, you would lose faith in your bank if it failed to promptly resolve the issue.

Similarly, a bank may face some roadblocks while communicating with a customer. An interaction scenario between a customer and a bank might go something like this:

  • Communication is received from a customer.
  • The bank tries to identify the nature of the communication; is it a compliment, complaint, or request?
  • Then the information is passed to the back-end systems to take action.

When dealing with 200 or 300 requests a day, this system might work.

But once a bank is opened up to a multitude of different communication channels, the amount of information entering the bank’s system becomes magnified. Think hundreds of thousands, or even millions, of requests comprised of unstructured content per day. Moreover, the same customer may be reaching out to the bank in multiple ways. The bank must be able to bring it all together and respond in a consistent, incremental way, and automatically process and act upon these requests. Additionally, they need to understand what their customers are talking about, and the sentiment behind it.

AI and ML can be used to analyze customer sentiments and content in real-time, so that the bank can generate an immediate response to a customer, and issues can be prioritized based on urgency. So, in the instance of an ATM not working, like our earlier example, the bank can immediately prioritize and act on the problem, and other less important issues can wait.

There’s a couple of things that are needed for this to work. AI, neural networks, simulation and forecasting, and content classifications could be used to extract and act upon 3 key aspects of information from the customer:

  • Sentiment: The emotion behind the customer’s communication.
  • Entity: For a bank, this could be related to a customer’s checking or savings account.
  • Intent: Why they communicated with the bank in the first place.

Using AI and ML to dissect these 3 components of customer information, a bank can determine the context of the communication, and then map the customer issue, comparing it against any existing cases, or generate new cases if needed. From there, automated responses and actions can quickly resolve customer issues.

Real-time action allows the bank to have a 360° view of their customers so that an excellent customer experience can be provided.

AI and ML in Context

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In light of the impact context can make upon the customer experience, enterprises have to consider how to best use AI and Machine Learning to overcome challenges in order to find the wheat of context from amidst the chaff across organizational content, processes, and communication.

The Story of Your Organization’s Content

There is so much information and documents being generated within an enterprise that it’s too time and resource intensive to manually classify documents and information.

In keeping with our banking example above, during a loan application a bank needs to classify different documents and identify an ID document or application form. In cases where large volumes are involved, classification is inefficient, and crucial information might not be extricated during customer onboarding.

More so, valuable data might be hidden within the content that could be helpful later on in the customer journey. An organization needs the full, rich story of their content in order to see the luster within the pearl of context.

Processes Evolve

As previously mentioned, enterprise processes change over certain time periods. An enterprise who started modeling their processes in a business process management (BPM) system, for example, might discover that the solution doesn’t work a few years down the line. While a BPM system may have been originally implemented to help provide more visibility into business processes and help users work more efficiently, it might not be equipped to help enterprises meet the dynamic requirements of today’s customer expectations.

The back-end processes become increasingly complex with multiple input channels and changes. Whatever the organization had once considered at the time of implementation regarding volume and flow, changes as the company grows. New scenarios and communication are introduced, and when these aren’t validated against real-world scenarios, the processes simply don’t work.

Enterprises need to ask themselves how they can make their processes better, how they can reduce the number of handoffs and resources connected to the process, and evaluate all of the “what if” scenarios to shine their processes to a brilliant, optimal finish.

Communication Channels Need Optimization

Organizations need to effectively manage customer expectations because customers use so many different channels to interact with a company. Companies must have context for the communication so they can respond accordingly.

Something that’s a challenge in and of itself becomes compounded by the fact that communication needs to tie into organizational processes and workflows. An organization needs to be able to identify sentiments, categorize the information, take swift action… and all of this should be done by their system.

Automating this process and using the intelligence of ML and AI to classify and understand complex communication, enables organizations to deliver upon customer expectations with immediate responses and corresponding action.

Automation, Simplification, Intelligence

Organizations undergo a plethora of growth challenges and must improve their decision-making capabilities to successfully continue on their digital odyssey. Barriers to decision making need to be ruthlessly eliminated, and automation must occur whenever possible within the customer journey so that enterprises can pave a path that’s ripe for customer experience innovation.

Newgen helps enterprises confront disruptive growth challenges head-on through its low code digital platform with AI/ML capabilities that rejuvenate the core of their business.

 

Original post: https://www.linkedin.com/pulse/responding-enterprise-challenges-real-time-ai-machine-ronald-van-loon

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