9 software areas where AI can impact mobile strategy success
As more and more developers incorporate artificial intelligence into their mobile software products, organizations are quickly discovering that AI needs a formal management strategy to live up to its true enterprise potential.
If an IT program lacks the ability or resources required to make a compelling business case that supports AI’s business value, there’s a good chance that organization will be slow to incorporate it into existing operations. Unfortunately, that means falling behind an ever-growing list of competitors.
However, that’s not the only major mobile strategy challenge AI creates. As one can imagine, automation involves numerous complex—and potentially confusing—workflows, technologies, and processes. Since these pieces are often referred to as AI interchangeably, it’s easy to understand why some businesses struggle to find their ideal management solution.
Even if an enterprise fully understands the term, AI’s impact is likely to fall flat if it’s not included as part of a larger, more strategic technology picture that details exactly how it fits in with and enhances the rest of the company’s IT investments. By including AI in a formal mobile strategy, companies don’t just accelerate the development of future infrastructure; they optimize digital transformation efforts, too.
This year, however, AI-driven devices and technologies will make most organizations reconsider their currently implemented mobile strategies. As more and more AI is rolled out at work, here are three innovations to consider adding or addressing:
1. Diverse Data Collection
It’s no secret that effective AI solutions require a massive amount of quality data to function. After all, this technology can’t grow smarter unless it’s collecting and analyzing real-time enterprise feedback. Therefore, it’s important to update mobile strategies to include specific details for how and why these data-driven insights are being collected.
2. Contextual Services
Today, most businesses rely on AI-infused mobile technology to deliver location-specific customer services. Since this trend will only continue to grow more and more popular, mobile strategies should be updated to include a formal process for collecting global customer metadata—and not just simple customer behavior and location data, either. Analytics like device type, OS version, and app downloads, for example, can be used to baseline and monitor future trends once there’s a plan in place.
3. Domain-Specific Design Intent Metrics
AI doesn’t depend on just any data set; it needs labeled data based on domain-specific insights to break down problems and make machines even smarter. Therefore, any updated enterprise mobile strategy should outline exactly how data categories are structured to make it easier for AI to classify and monitor user experiences.
In addition to these trends, the next few years promise to introduce a variety of new, automated enterprise mobility solutions as well. Let’s preview each technology’s potential business value:
4. Simple AI
For many employees, virtual personal assistants like Apple’s Siri, Windows’ Cortana, Amazon’s Alexa, and Google Now have already come in handy at work. Simple AI is the technology that powers these digital assistants behind the scenes, giving them the ability to recognize user speech patterns and adapt conversations according to previously learned data.
5. Machine Learning
Automated mobile technology solutions can only survive if they continue to learn and evolve whenever new information is presented. While Facebook’s News Feed is just one example of machine learning in action, failing to include this innovation in a comprehensive mobile strategy can lead to more ineffective interactions and inaccurate data analysis down the road.
6. Natural Language Processing
This term encompasses how AI-driven technologies analyze, understand, and learn from human language. Things like email spam filters and language translation apps scan vast inputted data sets to identify patterns in both written and verbal formats.
7. Predictive Application Programming Interface
Unlike a traditional Application Programming Interface (API), these proactive programs process enterprise mobility data to spot hidden patterns, predict outcomes, and recommend resolutions. If incorporated into a mobile strategy successfully, these next-gen APIs can create flexible hosting options for machine learning tasks, giving AI developers and IT professionals more freedom to work with whatever tools they prefer.
8. Machine Vision
AI is also being used in advanced cameras and lenses to capture machine vision—visual information used to automate tasks. After outlining the specific role and scope machine vision plays within a mobile strategy, businesses can use this technology to conduct image-based inspections and analyses.
9. Context-Aware Computing
If it’s not already being used, this unique, AI-driven process will help organizations manage situational and environmental data in a way that allows them to proactively identify problems and anticipate future needs. In fact, some mobile apps and technologies are already using it to serve different users unique content and features depending on where they are or how they feel.
If your business plans to adopt any form of AI this year, make sure to revisit your enterprise mobile strategy first. That way, your technology is capable of not only collecting real-time data as interactions occur, but learning from this data to find instantaneously satisfying solutions as well.