Thanks to COVID-19, every business should now realize they must rapidly adapt to not just survive these challenging times, but prepare for the future. However, more than six months into this pandemic, some say that executives are still sleep-walking through all the disruption around them. Visionary leaders like my former boss, Marc Benioff, have put out a clear call to action for CEOs to drive their companies in entirely new ways. He openly shares how the pandemic has fundamentally changed Salesforce, and passionately urges his peers to get out of paralysis and find ways to ensure relevance.
One of the best approaches organizations can take towards this goal is to lean heavily into AI as a force multiplier. Rather than pressing pause on complex machine learning and predictive analytics projects because of COVID-19, smart businesses are looking at their new market dynamics and actually accelerating those initiatives to help reinvent their digital offerings. In this way, they’re speeding through digital transformations that are bigger and faster than anyone expected.
Recently, my colleague Jamie Grenney shared his thoughts about how “The Amazon Effect” has intensified during COVID-19—fueling even more emphasis on business resilience. Of course, a key ingredient in Amazon’s secret sauce is its brilliant use of AI for things like product recommendations. But, to get going fast with AI you need to have lots of historical data. Data that often isn’t available from your SaaS applications out of the box.
How Good is Your Data?
All kinds of organizations are finding new ways to leverage the data at the heart of their businesses. Predictive models can unlock massive productivity gains and revenue opportunities, just keep in mind that AI’s recommendations are only as good as the data that inform the models. To extract maximum results from AI, data science teams require vast amounts of transactions and other information to train their models.
For example, Tesla ingests data from hundreds of thousands of cars in the real world. All of these signals continuously feed into the company’s AI and machine learning models, which then analyze each situation a human driver encounters on the road to train autonomous driving algorithms. Perhaps your business doesn’t need quite as big of a sample set as Tesla, but you’ll still require a meaningful amount of historical data for training. It is highly likely that months down the road, you’ll want other pieces of data to answer questions you aren't even thinking about today.
Much of the information you’ll need for analysis probably resides in a range of popular best-of-breed software-as-a-service (SaaS) apps, like Salesforce, Microsoft Dynamics, Workday, and others. But if you’re like most organizations, your IT team probably has limited resources and is too busy to prioritize snapshotting all that data on an hourly or daily basis. In this case, the full value of AI might be out of your reach for months or years while you collect enough data points to power effective predictive analytics.
Preserve Business Insights For a Competitive Advantage
When it comes to your organization’s most strategic asset (its data), look for ways to get some quick wins while rethinking the status quo. Imagine how many deeper insights you could extract if you had a perfectly preserved view of what happened to your business over the entire last decade, versus just what your data shows today. Data resilience is a critical component of overall business resilience, so all organizations should invest in backing up, archiving, and being able to recover their valuable information.
In this way, you can take advantage of the power of AI to get ahead of the competition and increase your team’s pace of innovation while proactively preventing data loss. You worked hard to capture your data, so why not harvest the wisdom hidden within? Don’t let it go to waste. Use all the information at your disposal to rapidly create more accurate predictive models that analyze the business’ past performance and anticipate the future. That’s how you go from getting disrupted to being the disruptor.
Trends like AI and machine learning are tectonic plates that have been shifting under our feet for a while. Now, the current pandemic has brought them back to the forefront of our minds, as businesses search for creative ways to build resilience (or risk obsolescence). Unrelenting disruption to operating models, customer demand, workforce needs, and more is our ‘new normal’ for at least the next several quarters, so it’s critical to use every tool in your toolbox—especially AI—in order to navigate these choppy waters.
