Alan Trefler

Alan Trefler, contributor, is a visionary leader, technology change-agent, innovative philanthropist, and trusted advisor to global business executives. Alan founded Pegasystems in 1983 and has built the company into a $840 million provider of customer engagement solutions with more than 4,200 employees in 37 global offices. His book Build for Change describes a new generation of customers that have unprecedented power to make or break brands and the changes businesses must embrace to succeed in today's digital world. Alan is a member of the IT Steering Committee of the World Economic Forum and a chess Master who was co-champion of the 1975 World Open Chess Championship. A staunch education advocate, Alan and his wife Pam established the Trefler Foundation in 1996 to improve educational outcomes.

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Avoiding Common Pitfalls on the Road to Digital Transformation

Avoiding Common Pitfalls on the Road to Digital Transformation

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By Alan Trefler  |  June 5, 2018
Alan Trefler is Founder and CEO of Pegasystems, and a visionary and expert in the field of customer relations for over 30 years


It’s estimated that nearly 6.5 million smart speakers like Amazon Echo and Google Home were sold in France, Germany, and the U.K. in 2017, and that more than 47 million adults in the U.S. now have access to one. Consumers are spending time – and money – engaging with organizations through smart devices. So, it’s not surprising that, in an effort to improve consumer experiences, organizations are scrambling to capitalize on new mobile and online channels by enhancing their capabilities with rapidly evolving digital technologies like natural language processing, geolocation, artificial intelligence, and decisioning, among others.

But in this rush to quickly stand up a new voice bot, self-service channel, or mobile app, organizations may unknowingly increase their long-term operating expenses and block future agility.

I have the privilege of talking with many of the world's largest and most successful companies about critical digital transformation projects, and I hear some common themes that even the most advanced organizations find challenging -- good ideas aren’t fully realized, “quick win” projects become drawn out – and even abandoned – or technology doesn’t generate the intended results.

Why aren’t companies achieving their digital transformation goals? We’ve found that organizations struggle with some common issues:

1. Custom coding for each channel. Chatbots, voice bots, intelligent assistants, text messaging, web self-service, email… there are so many channels, and the options keep expanding. Organizations that build unique logic into each specific channel end up isolating intelligence and creating disconnected processes. For example, a customer who starts online but wants to finish by talking to a call center representative, will have a bad experience if they need to repeat information or re-identify themselves. When logic is hard-coded into each channel, experiences are inconsistent and incoherent. Worse, the customer gets frustrated because they must constantly restart their journey. When you focus only on creating apps for channels, processes and data become siloed, making it impossible to provide a seamless, consistent customer experience that is truly omni-channel.

2. Focusing on automating tasks instead of outcomes. A customer journey is delivered when you can stitch moments together into an end-to-end experience. When organizations focus only on automating individual tasks, they can lose sight of the larger journey and the desired outcome, and unintentionally impose their own organizational silos on their customers. Discrete robotic implementations, for example, don’t inherently map customer journeys or create end-to-end experiences. All they do is speed the execution of a specific task, like auto-completing a form. Many organizations are discovering that task-centric robotics implementations create fragmentation. Automation technologies like robotics can play an important role in digital transformation—especially when it comes to streamlining employee experiences and pulling data from legacy systems—but they need to operate as part of a larger, connected ecosystem that captures customer data and orchestrates end-to-end processes based on customer intent. 

3. Creating an unintentional digital gap between the front and back office. By isolating their focus onto building digital channel experiences or driving operational cost reductions with task-based robotics, organizations will fail to completely realize the digital transformation advantages they were seeking in the first place. Instead, they build more silos – widening the data gap between dozens of front-end channel apps and back-end processes and data. This fractured architecture becomes wildly expensive – and sometimes impossible – to modify and scale, creating a cycle of “rip and replace” whenever the next new technology is implemented. Worse, they never deliver the desired customer experience and are left unable to pay off the promise of digital engagement.

Organizations need to focus on integrating data from all channels and apps, and then apply AI and machine learning analyses to that data to recommend actions and automate processes based on each customer’s journey. By bringing together data, analytics, and automation, organizations can truly transform operations and create end-to-end journeys that deliver outcomes.

In my own company, we have completely rethought and redesigned the business four times, and we’re always considering how we will need to change our business for the future. What I’ve learned over the years is that real organizational transformation requires a unified vision, a unified architecture, true digital automation, and exceptional customer engagement.


This article originally appeared in Forbes France.


 

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Avoiding the Ultimate Oxymoron -- Ensuring your AI is Intelligent and Not Artificial

Avoiding the Ultimate Oxymoron -- Ensuring your AI is Intelligent and Not Artificial

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By Alan Trefler  |  May 21, 2018
Alan Trefler is Founder and CEO of Pegasystems, and a visionary and expert in the field of customer relations for over 30 years


The industry is abuzz about artificial intelligence, or AI, and specifically the intersection of AI and CRM technologies.


Take the buzzwords away, however, and this is about using smart systems to better engage customers and to make it easier for people to get their work done. That is something I’ve been involved with since the early 80s.  In fact, I began my career in some of the earliest days of AI, teaching computers how to play chess. When my company was first founded, we took that same approach—leveraging business rules and workflow to teach computers to act like skilled humans—to automate processes for banks. Over the years, the technology has evolved. Rather than static business rules, we use machine learning and predictive analytics. We now complement workflows with robotic process automation. And the massive amount of data and connectivity available in the digital world has increased our clients’ ability to build streamlined experiences and engage customers, even proactively. 

After doing this for more than 30 years, we’ve learned some lessons. As you and your organization look to leverage the latest AI technologies, I’d suggest you ask five questions to make sure you get the most business value out of your AI investment:

1.     How do I manage and optimize my AI with rules? AI—especially machine learning—can do incredible things to turn data into optimized customer engagement. But who is making sure the machine isn’t breaking laws? Am I optimizing my customer engagement to ensure retention? Grow share of wallet? Create more advocates? Reduce cost of service? Even the smartest “machine learning” system needs to be pointed at the right business outcomes and properly guided. The right business outcomes aren’t the same in every scenario. There are some things, like regulatory requirements or cultural expectations, you don’t want your machine to learn in real time. Do you have AI technology that provides ways for your business experts to blend predictive and machine learning algorithms with rules to get the right outcomes?
 

2.     Can I build a centralized “brain” that works across all channels? To be most effective, your customer engagement “brain” should provide consistent experiences across marketing, sales, and service, and across all your communication channels. Many of the AI solutions hitting the market today (or promised for tomorrow) are cobbled together from a bunch of acquired technology, much of which was built as point solutions for individual channels. I’ve cautioned before about “Frakenstacks” -- collections of disparate software tools bundled together by mega software vendors. Today I’m seeing signs of a new “FrankEinstack” – a collection of AI tools glued together from multiple software vendors that won’t give you what you need and is likely many months if not several years from being real. It sounds good, but ask yourself: How will your “brain” ensure customers get the same treatment in all channels if each channel is running its own brain?
 

3.     Can I tie insight directly to action? Using AI to make decisions and offer proactive outreach to clients is great, but decisions are made valuable only when you can take action on them. How will you tie AI to the ability to orchestrate outcomes, even those that must cross multiple legacy systems and organizational silos (such as opening a new account or processing a service request), and then use the results of that process to improve future outcomes?  In our world, a brain without the muscle to get things done is a waste. 
 

4.     How do I feed my AI system with distributed data? Data is the lifeblood of AI, but if you are like most organizations, your data doesn’t live in one place, and it definitely doesn’t all live in the cloud. How smart will your AI be if it can’t see your core business systems and use that data or respond to those events? You need an AI platform that can run in the public cloud, but also in your private, on-premise cloud, where it can safely and securely access the legacy and transactional data it needs to make smart decisions.
 

5.     Is it real? Or is it marketing? Focusing AI on solving the real problems of engaging customers to drive revenue and improve experience is hard to do. Lots of companies are talking about the promise of AI but they aren’t necessarily providing tangible, valuable, outcomes. 


 So, I say: “Danger Will Robinson!” The hype cycle machine is at its radioactive peak. Before you jump on board, ask a few fundamental questions. It could save you a whole lot of disappointment and trouble down the road. 


Another version of this article first appeared on LinkedIn


 

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