Archives For Artificial Intelligence

IBM, with the power of Watson, is a leader in bringing cognitive intelligence to many industries. As IBM’s CEO, Ginni Rometty, said earlier this week, “this year we expect Watson will touch one billion people—through everything from oncology and retail to tax preparation and cars.”  With that kind of market penetration, it is no wonder that companies are lining up to partner with IBM.

At SugarCRM, we’ve had a strategic partnership with IBM since 2010 and continue to work with many of IBM’s lines of business every day. We are also a member of IBM’s Business Partner Advisory Board.

As the world (and CRM industry) figures out how to utilize the power of AI, our team is working closely with IBM to bring the power of Watson into Sugar to help companies offer a better customer experience.

Sugar has been deployed in the IBM Banking Center of Excellence to showcase how Watson and Sugar are helping the financial services industry handle digital disruption and deliver exceptional customer experiences. The IBM team even created a fantastic video to show it off. Because of our longstanding relationship with IBM, this is a solution a customer can view a demo of, and purchase today.

 

Available on SugarExchange, Watson Analytics Expert Storybook for SugarCRM, helps SugarCRM customers identify strengths in their business approach by deal size, campaign effectiveness and company type by evaluating sales wins and losses. We debuted the Storybook at World of Watson last year.

Also, we are very proud that SugarCRM was the recipient of the 2016 IBM Beacon Award for Outstanding Solution for what is now called IBM Watson Customer Engagement (formerly called IBM Commerce).

We look forward to continuing our fantastic relationship with IBM.

steintongueA few weeks ago I wrote a blog post around how artificial intelligence (AI) is more of an arms race than a “killer feature” that tech firms will be making themselves.

I referenced Salesforce, and its supposedly AI-powered Einstein as an example of a risky bet to make. Salesforce’s strengths are not in analytics (one could argue they’re not in CRM anyone either, but that’s a topic for another day), so why invest your own resources to build something that has already been built? And, why invest when something has already been built better than you can build it?

So, long story short – Salesforce today (surprise, surprise) announces that it can not complete its vision for Einstein without a real “arms dealer,” which in this case happens to be IBM’s Watson.

We have been working with integrating Watson into the Sugar platform for a while now, and can agree that Salesforce has chosen a winning tool. But, we wonder how much money and time Benioff and co. wasted by trying to do it themselves first?

Again, in the end, those that leverage the powerful AI tools in Watson, Amazon’s Alexa, etc. in ways that are seamless and delight employees and customers alike will win.

Maybe this was just an “I told you so” kind of post, but it is important to see that we may not be recapitulating as many mistakes as we have in the past with cloud and mobile in the world of AI…

(Editor’s Note, this post originally appeared in the Silicon Valley Business Journal, and was syndicated in 42 Biz Journal publications across the country)

I’m a big Star Wars fan, so when “Rogue One: A Star Wars Story” descended on theaters late last year, I braved the crowds to see it — twice in the first 18 hours. And just like all the other Star Wars movies, “Rogue One” stoked our geeky imaginations with all the technological possibilities of a galaxy far, far away, like holographic displays and all sorts of strange devices.

And did you notice the Imperial server farm? Of course, advanced artificial intelligence (AI) was well-represented too: Like C-3P0, R2-D2 and BB-8 in earlier movies, Rogue One’s K-2SO displayed uncanny humanness.

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The futuristic Star Wars-esque world is still mostly the stuff of Hollywood movies, but technology visionaries are hard at work bringing us ever closer. AI, or “ intelligence exhibited by machines,” is one area that is evolving into reality, and there are some subcategories under AI with practical applications that we use today. Natural language processing (NLP) and machine learning are two of them, and their potential for the future is exciting, especially for B2B technologies like customer relationship management (CRM).

The ultimate destination of AI for the business world is to make people’s jobs easier. Just like K-2SO in Rogue One, AI will become our intelligent personal assistant that saves the day and makes life easier. However, you may have noticed that no one has a droid at the office yet. So, let’s look at what technologies are real right now and what is coming farther down the road.

What’s real: Natural language processing and machine learning

NLP technology is quite adept at decomposing language into parts, understanding the baseline intent of that language, and representing it in both spoken and written word. Perhaps the best-known application of NLP today is Apple’s “Siri.” Ask Siri any number of things — “What time is it in Berlin?” or “When is my next meeting?” — and it can tell you.

NLP has also found a foothold in CRM. Intelligent CRM tools strive to improve customer relationships, and use NLP to mine for topics of interest from customer conversations in email and CRM. What better way to connect than to personalize customer communications? Many agree, and subsequently, NLP has benefited from a lot of investment lately.

Another subcategory of AI that is much more real than ever before is machine learning. Its recent success is due to the availability of huge volumes of discrete data points, and with this deluge of data at the ready — including digital data like social data, data from public records, and IOT data — patterns begin to emerge once analyzed.

Machine learning uses algorithms to understand patterns in data sets, and then applies some logic to the patterns. (“If ‘A’ looks like ‘B,’ and ‘B’ looks like ‘C,’ then ‘A’ also looks like ‘C.’”) Machine learning algorithms are also self-learning and have been designed to take feedback, which means that their “intelligence” grows as they analyze more patterns.

In the last few years, machine learning has made a particularly big splash in image and video recognition. Some visual recognition algorithms can analyze pictures from the Internet and understand the emotional intent behind the picture. This capability is especially valuable in commerce when a brand wants a greater understanding of its levels of customer satisfaction.

For example, one machine-learning technology trawls different social networks, looks for its customers’ brands in photos, and discerns the mood of the people in the picture with the brands. Is the person holding a can of Coke in the picture smiling or frowning? To be clear, the algorithms don’t understand the emotions of sad or happy, but they understand the difference between a mouth that is turned down versus one that is turned up in a smile.

While this latest advance is certainly impressive, machine learning’s greatest strides are yet to be made, and CRM in particular stands to benefit significantly.

What’s not real… yet

In 2013, the world watched as IBM Watson leveled its human opponents on Jeopardy. The idea that a computer powered by NLP and machine learning algorithms could spit out correct answers to so many varied questions was a curiosity. Be fair, though: Watson had a huge data set — the Library of Congress — at its — er — fingertips.

Keep in mind that machine learning’s success practically applied has only come about relatively recently, thanks to the advent and use of SaaS and cloud platforms, which can cost-effectively collect massive amounts of data. It’s also taken awhile to collect, publish and aggregate enough discrete data points in which to find and analyze meaningful patterns.

Organizations in many industries have already found a way to use machine learning to their advantage. In the coming years, CRM, too, will be primed to truly take full advantage of machine learning. Companies have by now collected trillions of rows of customer data to find patterns, and are beginning to train algorithms around how customers act. These algorithms are learning about what customers are most likely to do next based on their behavior patterns.

Tomorrow’s CRM system will be more than just a database. It will capitalize on machine learning to become the ultimate personal assistant. It will not only make a user more efficient and effective at getting the job done, but will also reveal something the user didn’t already know about his customers. This is where machine learning comes in, mining massive amounts of social and other data to uncover unknown details about a customer that will deepen the customer relationship. Tomorrow’s CRM system will also apply AI to supplement declarative rules based on workflow systems that will make predictions as to what a user should be doing next.

Back to Watson. IBM Watson provides a model for CRM to even go beyond the “ultimate personal assistant” with its personality profiler service. The service needs only an email address to scan all content that can be attributed to the person behind the address — every blog article, every tweet, every Facebook post — and then determine the personality characteristics of that person. What would that capability mean for CRM — for the sales engagement process?

The type of personality-rich and amusingly expressive cognitive intelligence displayed by K-2SO is a long way off. But the AI revolution promises new and exciting use cases in the very near future, and holds great possibilities for CRM in particular. With this in mind, in 2017 and beyond, organizations must begin to view their CRM systems as much more than just a database; rather, it must be seen an intelligent tool that has the potential to transform customer relationships.

(Editor’s Note: The following post originally appeared on IBM’s Built With Watson Blog).

In our world of high expectations and ever expanding data on individuals and brands, mastering this data and transforming it into valuable insights to inspire our human connections has become essential for brands.

Take a minute and think about this: How do your customers perceive your brand? Is your brand “shy” online and only speaks when spoken to? Or is your brand overly enthusiastic and always waving its hands in your customers’ inboxes and social feeds? Is your brand a captivating conversationalist that encourages interaction or one that’s a little socially awkward or a little too forced? Are you the tiresome talker that is always extolling your own virtues or one that expresses genuine interest in your customers and what they actually care about? Does your brand need a little coaching in the “delighting customers” department?

Real-time insights can drive meaningful conversations

Although we all seek to be that fascinating brand as we engage with customers, such mastery requires the ability to identify and collect all available and relevant intelligence and distill it down into meaningful insights that can define meaningful conversations.

Meaningful insights that are actually actionable can be attained by a variety of methods. Many data-savvy brands employ big data and predictive analytics to help identify the next-best action based on customer segments and transactional patterns, which only represents about 20% of known information about customers for most brands. Some leading brands take it a step further and use machine learning along with Artificial Intelligence (AI) technology like IBM Watson’s APIs to collect, connect and make sense of the other 80% of the unstructured data such as tweets, Facebook posts, emails, call center audio recordings and other observable customer behaviors, likes and preferences and combine it with their transactional customer data. This approach provides visibility into richer, more comprehensive behavior patterns by customer segment that offers the potential to make your exchanges more interesting, personalized, and if you are really good, even memorable.

Moving from data to insights with AI

If you are truly looking to break away from the herd and into the lead, your CRM and automated marketing platform will need to include AI and cognitive APIs. With AI, the dream can move beyond customer segments and reach down to the individual level by providing the power and speed to make sense of the enormous amount of data out there and surface true insights allowing for individual action, at scale. As the hot new brand coach, AI can lead the way to enhance the human-to-human connection for your brand by understanding the experiences your customers want.

To make your brand truly delight and engage customers, you’ll need to do the following:

  • Stop putting energy into old email marketing practices that don’t deliver and focus only on including the high-value information that is worthy of your customer’s time and attention. If frequent messaging or notifications are important to your core value proposition, only do so based upon explicit customer preference.
  • Be very selective in the type of metrics you choose to run your business. Diligently focus on the ones that are truly your key performance indicators versus trying to distill insight from low value information.
  • Invest in a CRM system with embedded AI that tells you something you don’t already know about your customers by combining behavioral data from both your backend systems and available external and publicly available data sources.
  • Get an automated marketing platform that is synchronized with your CRM system and employs AI so that it allows you to better plan interaction flows, communicate consistently across channels and recommend next best action.

To learn more about how you can truly supercharge your brand’s marketing and customer engagement efforts, download the IBM and SugarCRM white paper: “Becoming a Brilliant Brand Conversationalist.”

robocopRemember when SaaS CRM companies needed to build their own multi-tenant architectures to bring their CRM to market? And how they needed to maintain expensive and unwieldy architectures that took focus away from actual product development? And how the cost and complexity of said proprietary architectures was passed along to the customer to maintain revenue goals?

Oh wait. That’s still going on with companies like Salesforce.

But, even Salesforce has finally admitted that CRM vendors should not also be cloud infrastructure providers anymore. The company’s recent partnership announcement with Amazon tells us all we need to know. Salesforce needs to focus on innovation, since its core product is old and the cost of maintaining the underlying delivery and development infrastructure itself is proving costly.

So, why is Salesforce potentially repeating past mistakes by trying to create a proprietary AI product for CRM?

Let me explain. What I see brewing with Salesforce’s Einstein concept is a hodge-podge of Wave analytics, generic machine learning (pieced together by several small pocket acquisitions), SalesforceIQ, and elements of Data.com – all components of Salesforce’s portfolio. In short, Salesforce is building yet another proprietary stack in AI.

By “owning” the entire stack, one could argue the profits (as noted, something perennially eluding Salesforce) can be much higher. But at what cost? By instead focusing on integrating industry standards and expert-AI platforms into its tools – a CRM provider can have more flexibility and be able to keep up with the rapid pace of change.

Today, companies like IBM with Watson, and Amazon with its AI platforms are opening these up to software manufacturers as a service. These companies have both the deep pockets and expertise to offer broad and even focused AI-tools for CRM usage scenarios – without CRM vendors having to do much if any heavy lifting.

Here at SugarCRM, we are taking a “best of breed” approach for a number of reasons. One, it will speed our time to market to leverage pre-built, highly scalable and proven AI toolsets and platforms. And, of course, the cost to bring AI-powered CRM offerings to our prospects and customers will be lower, which we can pass on to the user and remain a value-driver for our partners and customers.

And again, by leveraging larger platforms and standards, we will be more nimble than those building hulking masses of analytics engines, giant data warehouses, etc. We will be able to quickly hone our offerings to adhere to market demands, without having to re-architect massive purpose-driven AI stacks.

In short, it is becoming clear to me that AI is an arms race – and categories like CRM should not be trying to reinvent the wheel. Just as with cloud delivery – when you integrate and build upon expert, proven strategies – you can cut costs, speed time to market, and focus on building exceptional customer experiences.

 

Artificial Intelligence is all the rage right now, and it seems companies in every industry are talking about how the magic of AI will change everything. And, when I say every industry, I mean every industry.

Here’s the thing, the potential of AI has been something that data scientists have been touting since the 1970s. This time though, it does feel different. It feels like we are on the verge of AI changing the world forever.

I recently caught up with SugarCRM’s chief product officer Rich Green, a man who has lived through many technology crazes, to get his thoughts on what is different this time around.

Q: What’s Different About AI Movement This Time Around?

Rich: The biggest difference this time: the people who are leading innovation in AI are the same people who are using AI every day. Instead of a university of governmental lab working on AI, you have companies like Facebook and Google who have the deepest pockets, largest data sets and best data scientists. They are the ones working to improve AI and they have the easiest path to integrate what they are working on into today’s world.

There are a couple other big differences. For one, AI related techniques like machine learning get better with more data to interpret. Nowadays, the pool of data is so large that you can expand AI beyond narrow uses cases and do more interesting things, and the statistical accuracy if far greater due to big data. Secondly, the computing power required to do AI has caught up. We have cracked the “AI speed barrier” with hardware and algorithms. AI used to be clumsy and obtrusive, it’s now transparent and can be transparent to people, not having to internalize that many of their connected experiences is powered by AI technology.

Q: I think the industry is still debating what’s really AI and what isn’t. Where do technologies like machine learning, deep learning, neural nets fit into the AI category?

Rich: As you note, AI is a category, not a specific technology. Machine learning, deep learning and neural networks and many other technologies are all part of the AI category. The industry likes to debate what is AI and what isn’t. I argue the sum of technologies fundamentally required to create an evolving intelligent digital assistant, self-learning, self-driving cars and chatbots that incrementally improve their accuracy all fall into the AI category.  Tools like Google Translate  now use machine learning AI to rapidly improve and provide remarkably accurate translations. In fact in that particular case, the system did something particularly remarkable.

Q: In the past, many people working at the edge of AI technologies have grown disillusioned, and this has stalled progress. Do you think this will happen again?

Rich: That’s highly unlikely. Many of the world’s best AI researchers are no longer confined to pure research in academia. Instead they are spending some or all of their time with Facebook, Google, Amazon and others and are able to leverage the breadth of resources, data and access to accelerate their work. And while that is happening, they can test and validate their work using the largest data and computing engines in the world. With such access and the freedom to experiment and deliver, the pace of innovation is accelerating at an unprecedented pace.

Until recently, there used to be a significant delay in moving research to advanced development and ultimately delivering innovation to a wide range of users. That delay is now compressed because of the tight cycle between research and availability.

Q: Are we currently at peak hype for AI?

Rich: ‘Hype’ is an interesting term. It typically implies that the commentary and the reality are disconnected. Today there is a great deal of discussion and visibility but unlike the past, most of it is either true or will be true quite sooner than most people are able or willing to believe. But we are just scratching the surface of actual capabilities and utility of AI technology. Unlike the 70s and 80s, we are on a very steep slope of growth. There is no logical impediment to this hype cycle.