Decision Evolution

Automated systems are helping businesses make decisions more productively and consistently. But they're also making a lot of entry-level jobs obsolete. Executives had better be prepared to manage the transition

Have you ever known a family in which the child went well beyond the parents? One in which the parents didn't seem to have a lot on the ball, but they bequeathed just enough capabilities to their child for him or her to take off? That's just what happened in the world of automated decision making. The parents - artificial intelligence (AI) and decision support systems (DSS) - were ultimately disappointing despite lots of favourable hype. AI and expert systems required those pesky knowledge engineers to create them, and they were very difficult to maintain. Decision support systems also never really flourished, despite being the darling of academics for decades, perhaps because they required too much statistical expertise and too much human analysis for these lean times.

But their offspring, a technology called automated decision systems, is taking off, and it embodies the best attributes of each parent. Automated decision systems are rules-based like expert systems. And like DSS, they often involve statistical or algorithmic analysis of data. They typically make decisions in real time after weighing all the data and rules for a particular customer or case. Sometimes they also carry genes from another ancestor, business process management or workflow, leading some observers to classify them as "smart BPM" systems.

Their most salient characteristic is that they actually make a decision: what price to charge a particular customer, whether to grant a loan or an insurance policy, which delivery truck should be rerouted, what drug to prescribe to a diabetic patient. In many cases their decisions are made without any human intervention at all; in others - sometimes for legal or ethical reasons - they work alongside a human expert such as a doctor. For the most part, these systems are being used for decisions that must be made frequently and very rapidly using information available online. The decision domains are relatively highly structured, with well-understood decision factors.

In short, this is just what knowledge workers don't need - another reason why they're no longer necessary. If automated decision applications grow at the rate I believe they will, there will be far fewer entry-level jobs requiring judgment and computation. It's even easier and cheaper to automate these jobs than it is to send them offshore, and more of an issue from a job-loss standpoint. Economists say that productivity gains are largely responsible for 2.7 million US jobs lost since 2001, as opposed to the roughly 300,000 lost through offshoring over the past three years. I've even heard of one company that is using Indian programmers and statisticians to develop its automated decision systems - a double whammy for the local knowledge worker.

These systems are being used across a variety of industries and decision types, as my former Accenture colleague Jeanne Harris and I found after a year of research. They are more real-time, more complex and much more pervasive than the original versions: yield management systems in airlines that made seat pricing decisions in the early 80s. In airlines, for example, the technology has now been extended to a variety of operations issues, including flight scheduling and crew and airport staff scheduling. Yield management is also being combined with loyalty management applications to determine real-time pricing for hotel rooms. Casino operator Harrah's Entertainment, for example, makes several million dollars a month in incremental revenue by optimizing room rates for its hotels and offering different rates to different levels of members in its loyalty programs.

Although automated decision systems are common in travel and transportation, financial services is using this technology to the greatest degree. In banking, real-time mortgages and secured lending decisions are becoming common. For example, LendingTree.com uses automated decision making for two purposes: First, to decide which of its participating banks are most likely to issue a mortgage to a customer. Second, to actually offer four mortgage deals within a few minutes to a customer, using the banks' technology or its own.

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