Editorial
In planning for the future, we’re trained to look to the past. As the adage goes, "Those who cannot learn from history are doomed to repeat it."
But when it comes to building digital platforms in the age of artificial intelligence (AI), that’s tough to do. New innovations emerge every day, but we’re still so early into this era the long-term results of our missteps and victories aren’t yet clear.
It’s difficult to determine the future direction of digital platforms and AI. However, there are some trends we can learn from, including the importance of simple interfaces, quality data and established AI tools. While the tech industry can pivot quickly, software builders can commit to embracing these three principles in leading the direction of their software initiatives.
Make Training Unnecessary
Creating a digital experience for employees was once a cumbersome process that required users to learn a lot before they could reap the benefits of digital tools. But as the user experience has become a higher priority, end users no longer want to be taught how to use a platform. A successful digital platform needs to be designed for intuitive and immediate use.
As software builders, this pushes us to empathize with our users. They’re looking to automate mundane parts of their work in order to apply their skills to more important tasks. Thus, spending time to understand a complex digital platform isn’t in their best interests.
Marketers, for example, use an average of 5.3 platforms to carry out their daily tasks. Becoming fluent in so many different platforms just isn’t feasible, so if you don’t make the user experience completely easy and obvious, your target audience will be quick to pass over your platform for another one.
Related Article: Brand's Still Haven't Tapped AI's Full Promise
Prioritize Data
The main mistake people make in building an AI-powered digital platform is to underestimate the role of data and the quantity of data needed. If you don’t capture adequate data to feed the platform, you’re “under-teaching” it and it won’t be as helpful or intuitive to users as it couldbe.
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ChatGPT and the Future of Conversational AI
This discussion explores various aspects of AI implementation, ethical considerations, and challenges for your organization.
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Learn proven effective methods and solutions to supporting an inspirational future of Employee Voice
Quality data is the game-changer, and the path to ensuring quality varies depending on what kind of data is involved. However, there are two constants you should prioritize in your data collection practices. First, you should have your method of capturing data embedded into the source — at both your platform and marketing engine. Second, you should ensure that data collection within those realms is always on, so that your AI features can constantly have access to the most up-to-date insights.
While expensive, investing in data analytics can deliver a significant return on investment. Plus, businesses seem ready to incur the costs: Marketers say they expect the amount they spend on data analytics to rise from 5.8 percent of their marketing budgets now to 17.3 percent in three years, according to the February 2018 CMO Survey (see page 81).
Related Article: How to Improve Data Literacy Across Your Organization
Don’t Reinvent the Wheel: Use AI Tools Already on the Market
Too often, I see tech companies looking to build their own AI offerings from scratch. Usually, this is driven by a desire to completely customize a product and retain control.
But given the tools at our disposal and the advances the industry at large has made in the realm of AI, trying to build your own tool is unproductive. It would never be able to match the capabilities of current offerings that have been refined over many years, and the effort you put into building it would prevent you from focusing on the original end goal of your platform. From Google to Microsoft to IBM, all the tech giants offer some kind of AI tool that enterprises can deploy within their networks. These tools enable your platform to best serve your end users.
It’s hard to see what’s on the horizon for AI, and that can make it tough to settle on a next step for building or growing your digital platform. We’re all in the same boat, and the best we can do is shape our strategies around a few objectives we’re sure of. By prioritizing the user experience and the quality of our data, and by trusting in existing blank-slate AI tools, you set yourself up to be flexible for whatever the digital experience brings next.
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