We are currently experiencing a boom in AI for the enterprise, and in that mad dash to ensure that your business remains competitive, it’s natural to focus on the technology itself rather than its implementation. No company has helped to deploy more AI solutions to the enterprise than Next IT, and over the last decade and a half, we’ve always emphasized the importance of human-in-the-loop processes of feedback and oversight in getting the full value out of AI deployments.
Today, and for the foreseeable future, 98% of the enterprise will be people, and 2% of it will be AI. When you adopt an AI solution, you are introducing a new technology into a human environment, not introducing humans into a technology. Like all new processes and technologies, deploying an AI solution is just the beginning. To succeed you need key stakeholders from across your organization to engage in the ongoing work of refining and upgrading your machine learning capabilities and core intelligence.
All too often businesses that want AI enhancements fail to appreciate the level of commitment required. They overlook the fact that human feedback and oversight are crucial, and assume they can enchant their products and services with AI “magic” simply by adding natural language capabilities to their current products and services.
For example, we find many companies attempting to apply the way they think about content and the mobile paradigm to the new AI paradigm; however, just as mobile required us to move beyond the web paradigm and adopt a new way of enterprise thinking and engagement, so too will AI. Previous strategies cannot simply be transposed onto AI strategy. That won’t deliver value.
So what’s the actual magic behind intelligence?
After years of driving value for both users and businesses, we can say that truly intelligent enterprise AI takes a user-first approach that goes beyond mobile-first, web-first, content-first, and even voice-first. To begin with, taking a user-first approach requires you to understand both your users’ needs and where they overlap with your business needs. In other words, if you want to build an incredible use case for your first intelligent assistant, you should focus on deploying AI from the bottom-up, rather than the top-down while also taking into consideration the ability to grow, expand and improve beyond the initial use case or cases.
To bring these use cases to life, you’ll need a platform that can bring your systems of record together with your content and understanding engine or, in some cases, engines. Finding a platform that allows your company the flexibility to add or change engines is key to future-proofing your investment. Then you need to think about how do you want the AI to respond to users, and you need to be able to change those responses on-demand, based upon context.
To achieve these levels of engagement, you need natural language generation (NLG) that is both flexible and easily accessible. How do you want to respond to a platinum member versus a new customer? How do you respond through SMS, Voice, Web or Messenger interfaces? With ready access to flexible NLG, you essentially have a content management system for digital conversations and interactions.
Finally, you need to figure out how you’re going to measure the value your system is providing, and how it’s going to improve over time.
Simply enhancing your systems with AI will not magically make them intelligent. Like any new technological implementation across an organization, AI must be deployed strategically with a core value and user-need in mind.
AI will be as transformative to the enterprise as the mobile and web paradigms before it, but it’s not a magic transformation simply executed through plug-and-play deployments. Your AI strategy requires a hands-on, user-centric approach focused on the people who power your company and the people it serves and then it will deliver a frictionless experience that feels like magic to users.