Business agility is becoming a strategic necessity. Greater globalization, increasing regulation and faster cycle time all demand higher service levels at lower cost. Companies cannot be competitive if they’re not staying ahead of their customers’ expectations.
You can see the effect of this when Apple introduced Siri in 2011 with the release of iPhone 4S, changing the customer experience. Since then, Google, Microsoft, and Amazon have all come up with their own A.I. concierge services to assure that they are meeting the customer expectations.
Look at the current robotic interactive voice response (IVR) systems that require you to navigate through layers of menus to retrieve a simply answer: “Has my claim been paid?” If you are one of these companies employing this type of technology, then you’re failing to understand how your customer experiences and expectations are evolving. This can leave you vulnerable to your competition, losing your customers to a better experience.
A.I. concierge services are changing customer experience: reducing complexity and providing a competitive advantage. This is why market-forecasting services are calling for tremendous growth over the next five years in the field of cognitive services.
A.I. concierge services benefit a company on two fronts. First, they address the need to meet your customers’ expectations for a better experience. Second, and more importantly, they allow you to increase your employee’s productivity.
Published industry studies indicate companies are losing millions of dollars in productivity from employees unable to find relevant information. You can see this pain point magnified in the area of eDiscovery and litigation process. At $300 an hour, attorney costs can add up quickly.
Productivity losses in large companies are amplified by their size. When you multiply the number of people employed by a large company (say a company with 100,000 employees) you could be looking at productivity losses that are substantially in the 100s of millions of dollars a year!
Where to start?
If you are a technology decision maker looking to realize an A.I. concierge services, where do you start? Do you develop your own solution, or contract out to a cognitive service provider? Either way, you will need to invest months or even years getting your system to be cognitively conversant for your market.
With the majority of corporate America living quarter-by-quarter driven results, the long-term investment approach may be a hard pill to swallow. However, contrast the development of a cognitive solution to hiring a new employee.
With a new hire, you will need to find someone who has a college education (or equivalent experience). Then you will need time for on the job training to develop the individual to a level of subject matter expert (SME) to your corporate processes. Depending of the complexity of your processes, the total time required to find the right person and train them vs. developing a cognitive solution could end up being equivalent.
It might be tempting to shorten the cognitive learning curve by using a cognitive service provider. If you decide to go with a service provider, what is your exit strategy? A long investment time will be required to bootstrap a cognitive service to your business needs. And the proprietary nature of a cognitive service provider, the effects of this decision are that you have inadvertently acquired a business partner not a service provider. On the other hand, if you develop an on-premise solution, you risk the technology becoming obsolete before you can get a cognitive solution into production.
You can take the position of sitting on the sidelines and waiting for a more favorable solution to come along. Alternatively, you can initiate a strategy that would mitigate the risks, regardless of whether you use a solution provider or develop an on-premise solution.
What does it look like?
What would this strategy look like? The strategy is to develop a linked data solution. In “How Do I Describe My Information So Others Can Understand It?” I illustrate the challenge of describing information. There is no standardized information framework, no fidelity or full context of information defined at the enterprise level. Linked data principles are the foundation building blocks for knowledge acquisition.
In “Linked Data – The Foundation for Interchanging of Information,” I discuss what an information framework would look like by abstracting your data to a common linked data model. Implementing a common model using a Resource Description Framework (RDF), a W3C open standard allows interoperability with any cognitive service solution. You would be able to either leverage a third party cognitive service provider or use an on-premise solution. By defining your data against W3C open standards, you have in effect implemented an insurance policy on your data!
It seems the benefits of implementing a linked data framework might have gotten lost in all the big data hyperbole. A number of consulting firms that specialize in the implementation of linked data frameworks are reporting significant benefits. Case-in-point: Reduction on maintenance costs by 90 percent, reduction in operational costs by 30 percent. The overall total cost of operations has shown to be reduced by 60 percent.
The ability to access information in natural language (English or any other language) at the enterprise level is a game changer. Think of the advantage you will gain over your competitors. Disruptive changes are indiscriminate, and no one is immune from its effects. General Eric Shinseki, Retired Chief of Staff, U.S. Army captures the true essence of disruptive changes with his quote, “If you don’t like change, you’re going to like irrelevance even less.”