Barry Pellas
Four Rules to Make Smarter Choices With Cognitive Technology
Barry Pellas
October 5, 2017

Four Rules to Make Smarter Choices With Cognitive Technology

Every day seems to bring a new cognitive technology advancement – from AI that can detect your political leanings to software aimed at preventing dangerous driving. It’s easy to get caught up in the hype surrounding machine learning and deep-thinking capabilities… but how does the average company actually get to the point of executing these updates?

Successful cognitive technology implementation cannot happen without first thinking through its practical usages, both internally or externally. While 72 percent of organizational leaders see AI as a business advantage, if these solutions do not first and foremost improve experiences for users, businesses will never drive full value from their investments.

From our work helping businesses embrace new technologies, here are four rules we’ve identified to not only inject cognitive technology into existing services but to then make better use of it once incorporated. This list is by no means exhaustive, but should serve as a starting point for businesses committed to a digital future:

1. Don’t Forget About Users

Users must remain at the heart of decision making. While cognitive capabilities open businesses to a host of new and exciting possibilities, stakeholders should focus on those improvements that users are comfortable with and interested in.

Talking directly to those who will interact with the technology surfaces valuable insights about personalization desires, points of friction and more. We need to ask ourselves how technology can enable our strategy instead of the opposite. When we determine what users want and how an intelligent system can improve their experiences, we can select the right kind of intelligent technology to accomplish those goals. We need the system to give us the right information to satisfy the user’s request, and for the answer to have the system’s maximum level of confidence.

We also cannot forget about the negative use cases where there simply isn’t enough data to satisfy the user’s request, or perhaps there’s nothing at all. Intelligent systems can react to these types of conditions and provide the user something to work with. This could be as practical as showing older weather data if the third-party service stops responding, to a more robust system that chooses which third-party service to use based on cost, response time or the quality of its data.

2. Think Macro and Micro

AI can improve every discipline and touchpoint in your system – whether that’s back-end software, integrations with third parties, APIs, front-end experiences, etc. As in society, businesses must think through how these improvements benefit the individual and the overall system.

The new wave of intelligent APIs need to think about both self-improvement and what they offer to their overall societal system. Take, for instance, a service that utilizes just a single third-party provider. The system is very transactional, and if I give it a request for some information, it provides me back a formatted response of the data it received from the provider. Given there is no feedback loop to let the system know if it provided an adequate response, the system will never provide better and more meaningful information over time.

Instead, we could use analytics gathered from our API management system (combined with user interactions through the application) to provide feedback to the system about the quality of its data. Over time, this system becomes more and more intelligent and confident in its ability to provide quality data. The system has achieved the ability to improve itself.

Now that the system has become intelligent, it will need to consider both future-proofing its service and utilizing everything other contributors can provide. In this case, the service can use multiple service providers and choose which one to use based on a set of rules. This could be anything from speed of the response to the system’s own confidence that it’s providing something meaningful. More so, it will need to change which data set it utilizes based on the application that is asking for the data.

By taking these steps, the system can achieve a higher level of user- and data-driven intelligence. The system can provide what is best for itself in the long term, as well as what is best for the consumers of its data.

3. Clue in Designers

The way designers think about the user experience change as new tools are added to their toolset. Having an intelligent system is no different. However, for designers to create experiences that take full advantage of the intelligent capabilities, they first have to understand what they have to work with and then how best to apply the technologies.

When working with clients to map out user journeys, doing user research or even sketching out user interactions with a system, designers are in a unique position to determine what is possible through an intelligent system. UX teams need to listen for specific cues during research and determine which areas of interaction they could augment via these intelligent systems.

Additionally, UX teams are in a unique position to determine what analytics should be considered when a user interacts with the system. This is not because they are determining the data models themselves, but rather that they can determine how personalized or dynamic the system could be based on the possible feedback loop of data from those analytics.

4. Bring in Developers Early

As with designers, the ability to see through ideated architectural updates depends on developers’ abilities to execute them.

If your developers aren’t already taking advantage of cognitive technologies, now is the time to develop practical strategies for injecting a cognitive culture into your dev team. Creating a cognitive primer for your team to understand the basics of what these systems can be an effective approach and reduces the barrier of entry.

However, we can take it one step further by changing the culture of these teams to emphasize close collaboration with designers and strategists. This brings these systems’ intelligence concepts full circle. When developers are able to understand the way in which designers are creating dynamic and personalized interfaces, they have the ability to create code expecting the inputs that make up those experiences. This ultimately drives a very dynamic system created from strategy and user experience rather than driven from technology.

In 2016, companies invested upwards of $40 billion on AI, three times the external investment growth since 2013. These spending trends are exciting, but they don’t come without concern – businesses are investing, but are they being smart about their tech choices?

By remembering tips like the four above, your business can answer yes with confidence. Interested in learning more about making smarter cognitive technology decisions? Contact us for more information!

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