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How to Streamline Via ‘Pragmatic AI’

The goal of artificial intelligence (AI) for an enterprise is to streamline business processes and improve customer experience to drive positive bottom-line results. It’s about moving enterprises into the on-demand economy by finding that magical balance of increasing customer satisfaction while reducing costs. But artificial intelligence is no silver bullet that works on its own. It’s an integral piece in a broader end-to-end solution that combines the best of technology with the learned behaviors of skilled humans. And the challenge of enterprise AI is finding a solution or approach that provides measurable results.

There’s so much hype around artificial intelligence that it’s easy for enterprises to get lost amid the buzz. Many enterprises acknowledge they need to do something with AI, but they don’t know exactly what. This usually results in businesses deep diving into large-scale AI and automation programs without a thorough strategy for success. This approach is costly and relies on the artificial intelligence to learn, grow and perform the required business processes instantaneously. But, unfortunately, we’re not yet at the stage where AI is advanced enough to perform in such a way.

What’s needed is a totally different approach that doesn’t place all the burden on the technology to work miracles and automate complex business processes upon launch.

Enter the pragmatic approach to AI.

Pragmatic AI

Pragmatic AI leverages a systematic implementation that gradually increases automation and machine learning. In the context of enterprise implementation, there are three phases to Pragmatic AI. At the end of these three phases, the goal is to have an artificial intelligence program that learns and develops on its own. However, before we get to that stage, we must focus on highly repeatable business processes that can easily be automated.

It’s important to note that pragmatic AI fits within a larger digital transformation strategy. AI is by no means a silver bullet that solves all organizational problems on its own. Rather, it augments processes and complements technology that are already in place across the organization. Contrary to the beliefs of many industry pundits, AI is still not advanced enough to work on its own. It needs human management, guiding and careful development.

See also: Strategist’s Guide to Artificial Intelligence  

“Long Tail” and “Fat Tail” Processes

Central to Pragmatic AI is the delineation between “fat tail” and “long tail” processes. Processes that are generic, repetitive and common are classified as fat tail processes. Examples include basic customer service inquiries (technical assistance, account updates, etc.) or bill processing. In contrast, long tail processes are less frequent and require a high-degree of customization to resolve.

The foundation of pragmatic AI is focusing first on solving the fat tail processes before progressing to the complex long tail issues.

The 3 Phases of Pragmatic AI

Phase 1: Automate Repetitive Processes

The first phase of Pragmatic AI is analyzing all the fat tail processes and identifying those that can be easily automated by deploying an AI program. The benefit of starting with these fat tail processes is that there is already a vast amount of data available to leverage for faster engineering. This data means there is less need for the program to “learn” on its own. Instead, it’s programmed, or “taught,” with specific business processes in the development phase so that it’s already functional upon deployment.

Phase 1 includes a combination of Natural Language Processing (NLP) with guided user journeys.

Consider that fat tail accounts for nearly 80% of customer inquiries, meaning the vast majority of inquiries are repetitive and highly common. With this in mind, you can get a sense of the cost reductions and the potential for bottom-line efficiencies when this fat tail of inquiries is automated. All the while, live agents and operators can still be used for the remaining 20% of customer inquiries, the infrequent long tail processes and a safety net for the AI.

With this combination of AI and live agents, enterprises can enjoy the benefits of having always-on customer support without the additional cost of resources, thus delivering a responsive, on-demand and highly effective customer experience that drives satisfaction.

Phase 1 of Pragmatic AI is often sufficient for both small- to medium-sized businesses and large organizations. The significant cost reductions and increases in customer satisfaction can provide significant ROI. Phases 2 and 3 expand the automation and include greater sophistication and more complex machine learning.

Phase 2: Add Advanced Cognitive Services

With AI already implemented to manage the repetitive, high-volume processes, Phase 2 moves further down the tail, addressing more specific processes. More advanced cognitive services are implemented to augment and widen AI’s capacity by analyzing the interactions and identifying patterns to be replicated. These additional services include language translation, image processing and video processing.

Key to success in Phase 2 is the use of analytics to improve the automated processes established earlier. Improvements are made to failure points — i.e. when a bot has to transfer to a live agent for resolution — and fixed to increase the bot’s success rate. Gradually, the AI’s capacity to solve complex problems grows, increasing the utility for the end-user. In addition, NLP is improved to provide more freedom for people to interact with the bot using natural language.

Phase 3: Building the Framework for Machine Learning

The final stage of pragmatic AI is enabling an element of machine learning and growth. Leveraging a combination of cognitive services and pre-programmed business processes, the AI-powered bots learn from their interactions and adjust flows under the supervision of human operators. The machine learning is used to improve existing processes or to train the machine regarding new processes that are currently being managed by humans.

Again, NLP is increased, allowing the bot to comprehend and process a wider degree of words and terms. At this point, the AI should be at a level of sophistication that the need to switch to live agents is less often. Throughout the three phases, the majority of failure points have been addressed and used to train the AI.

The three-phase approach to pragmatic AI allows automation to be developed and deployed using a strategic process that’s unique to an organization. Each phase directly addresses specific challenges within an organization.

See also: Robots and AI—It’s Just the Beginning  

Why Pragmatic AI?

Currently, there’s a gap between what enterprises expect of AI and what the AI can actually do. Instead of letting this gap drive costs higher through ill-fated AI programs, enterprises need to adopt a practical approach. The journey to enterprise AI is about pursuing what’s practical with clear ROI and readily available now and then building upon this foundation with large-scale, sophisticated automation and machine learning programs.

Pragmatic AI’s strength is that it is embedded and grows from current business processes and that it can immediately deliver a positive ROI by increasing customer satisfaction while reducing costs. With these kinds of measurable results, AI programs move from a place of aspiration to an actionable strategy — and finally to a readily deployable solution.

3 Keys to Success for Automation

With the rapid adoption of messaging and artificial intelligence hitting the mainstream, it is “go” time for enterprises to modernize and meet their customers where they want to be met: in mobile chat.

Remember what email did to the fax machine? It won’t take long for email to meet a similar plight with messaging usurping email’s pole position in B2C communications.

In 2016, we saw the rise of chatbots. You couldn’t read a reputable editorial outlet without the term “chatbot” appearing somewhere on the first page. But the hype quickly turned to a sad reality as many bots on Facebook, KiK, WeChat and other platforms failed to deliver on their promise. But, then again, what was their promise? Do consumers really want to “chat” with brands and have relatively meaningless “conversations”?

I say no, and, as a result, pragmatic AI is winning the day.

Pragmatic AI is the key to enterprise transformation in 2017 and beyond. It is the idea that machines can interact with humans through messaging conversations to resolve an issue quickly, efficiently and securely. Consumers are busy people. When they need something from a business, they want it immediately. Pragmatic AI doesn’t put you on hold, it doesn’t give you the wrong answer and it is always available — 24/7/365.

See also: Hate Buying? Chatbots Can Help  

So, with this in mind, here are three ways enterprises can cut through the hype and modernize for the next generation of consumers:

1. Choosing the right AI

There are two flavors of AI: open and pragmatic.

Open AI — like the large-scale cognitive services with high-end AI capabilities — is the kind we’re accustomed to seeing in the headlines. But for the enterprise, this type of AI is often too ambitious to be put to any good use beyond data analytics. It lacks the performance-based capabilities and transactional components that are needed for day-to-day enterprise applications. It is extremely costly and requires a small army of system integrators to install and operate it.

Pragmatic AI, as defined above, works on a functional level. It takes IVR, call center and other scripts to create decision trees, and it  plugs into various backend APIs to execute a myriad of business processes. From changing passwords, to canceling accounts, binding policies and tracking claims, if a human can do it, Pragmatic AI can do it too.

We see the fallacy around deep learning and Open AI catch up with many enterprises that are sometimes six to 12 months in on deployment (after feeling the pressure to adopt AI). These companies see no real solution in sight. Roughly 80% of call center inquiries don’t require cognitive services and deep learning. You have to start small, be practical and use bots that are nimble and functional. If you do this properly, your bots can actively engage consumers and replace email and social media as the primary channel for revenue-driving promotions and marketing initiatives.

2. Increasing loyalty by enabling transactions through automation

Enterprises exist in a world filled with a need to serve and deliver on consumer demands. Consumers are transaction-driven — when they want something, they want it instantaneously. So, when enterprises expand their communication strategies to explore new channels — such as chatbot-powered messaging — they need to ensure the new channels support an even greater level of functionality than all their other existing channels.

A major problem we’re seeing in the industry is enterprises deploying bots on third-party channels that lack basic transactional functionality — whether that be payment processing, scheduling, file transfer and storage or authentication. The resulting experience is usually a negative one for both the customer and the enterprise.

The technology exists to support rich customer interactions over messaging. After all, it is the next frontier for enterprise communication. Enterprise platforms are meant for enterprises. Social platforms are meant for socializing. Let’s keep business with business and pleasure with pleasure; mixing the two can result in major repudiation and fraud issues through identity theft.

3. Protecting customer data through an end-to-end solution

Right up there on the mission critical list of every CIO is data privacy and protection. Mobile messaging is generating newfound challenges for businesses as consumers flock to apps that aren’t secure and can’t support the needs of enterprise communication. This means when businesses add social messaging apps into their communication mix, they can’t provide the functionality for customers to do anything more than merely “chat.” The result is poor customer experiences and lost revenue. The same is true for bots. To avoid potential security risks and wasted investment, businesses need to ensure the platform they intend to use meets the desired requirements so they can adequately serve their customers.

Enterprises in the healthcare, financial services and insurance industries face significant challenges in this respect. Whether it is HIPAA, FISMA, FINRA or other, these enterprises need to meet the various state, federal and international regulatory criteria. A poorly devised automation and bot strategy where one vendor’s bots are bolted onto another vendor’s messaging system almost guarantees compliance failure and legal recourse.

See also: Why 2017 Is the Year of the Bot  

Find an end-to-end solution where the automation, messaging, transactions and consumer experience are all one and the same — built around compliance, privacy, scalability and security.

Driving customer satisfaction and cost savings for the enterprise

There’s been enough hype about chatbots and AI to make a portion of consumers and enterprises a little disillusioned with the technology’s promise. Skeptics begin to question the practicality of bots. But it’s more a case of a tradesman blaming his tools than the tools letting him down. With a strategic and carefully planned approach to bots and automation, the results can transform any enterprise, driving up NPS and dramatically reducing costs. These are just three examples of how enterprises can launch their own thorough and ROI-driven automation strategies to connect with consumers in new and engaging ways.