Continual learning is integral to the human experience. People who can learn faster and better than others tend to do well in life. The same is true for successful organizations.
Machine learning, a branch of artificial intelligence, has shown the ability to help organizations large and small learn faster than before. It has demonstrated the potential to continually improve the effectiveness of decision-making and business processes at a rate unachievable by prior methods.
But achieving this tantalizing potential won’t be simple, and it won’t be overnight. It will be a journey measured in years, though one that will deliver tangible benefits each step of the way.
The result has been dubbed an “adaptive enterprise,” a new style of organization that can learn and adapt more quickly than its peers.
While machine learning itself can be unduly complex, the basic ideas are easy to grasp. Let’s use the example of a business process both familiar and highly important to most organizations: selecting and onboarding job candidates.
The basic components would start with a training data set: a complete history of all candidates selected and hired, their key attributes, how they were on-boarded, and their eventual performance in the organization. Next, an analysis engine would extract key features that contributed to candidates’ success and create a recommendation engine that would rate new applicants and their likelihood to thrive at the organization.
So far, this scenario is somewhat similar to data analytics, except that the algorithms decide which factors matter and which ones don’t. Machine learning goes one step further. It processes ongoing results of those candidates, and continually updates its recommendation engine rules over time.
It learns from actual experience, and thus it makes better decisions over time. Think of adaptive intelligence as data-driven learning at vastly increased speeds compared with humans.
Now, extend this capability to other high-value, high-frequency business processes. Timing and pricing of supply chain purchasing. Negotiating discounts on large orders. Measuring the temperature of your customers to determine when a small issue might become a big one. Today’s AI-informed recommendations become tomorrow’s advanced automation.
Adaptive intelligence has the potential to redefine how we do business.
With every transformational technology, there are adoption challenges, and the journey to the adaptive enterprise is no different. What must leaders consider as they plan for the future?
First, any reasonable use of machine learning must work with everyday business processes. It’s not productive to think of it as a magic black box that offers wisdom. Recommendations have to be delivered in context, with supporting logic, at the same instant decisions are being made. Conversational interaction becomes important.
Next, processes at successful organizations are connected, and any use of machine learning must be connected as well. A purchase decision might impact the supply chain, and thus customer satisfaction. It’s hard to envision an adaptive enterprise making impactful, actionable decisions in silos.
Better decisions demand access to better data, as a recommendation engine is only as good as the data it’s being fed. Having easy access to rich, relevant data sets from outside the enterprise will dramatically improve the impact of the technology.
And finally, you’ll need a roadmap to organizational adoption. Adaptive intelligence enhances and augments decisions ultimately made by human intelligence, creating more meaningful, high-impact work for people. But as with all transformational technologies, expect initial skepticism and distrust. If history is any guide, superior business results eventually will win people over.
The Road Ahead
Like with the internet, mobile, and cloud computing, business leaders should appreciate that a new, transformational technology is on the horizon. But in its current form, the core technologies can be difficult to consume and operationalize, making adaptive intelligence unattractive for all but the most compelling use cases.
Before long, however, adaptive intelligence will become an integral component of cloud financial, HR, customer experience, and other enterprise applications people use every day. Better yet, software developers will have ready access to the tools needed to transform familiar, static applications into ones that learn from experience, just as humans do.
When this happens, business leaders must move quickly to exploit these capabilities. Now is the time to start thinking hard about which core business processes would benefit most.
Because who wouldn’t want an organization that’s smarter than the average bear?