Archives For Artificial Intelligence

(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.