"Where ERP fails," says Roger Hockenberry, EVP for IT services provider Criterion Systems, "is when you try to apply best-practices business process that are empirically created to an organically grown enterprise. Replacing process that was developed internally, over a long period of time is not an easy thing for any organization."
Your choice? Either change your people and processes to match the software (good luck with that) or customize the software to match your business (hope you brought your checkbook).
As a result, implementing an ERP project is "like teaching an elephant to do the hootchy-kootchy," in the words of CIO magazine senior editor Thomas Wailgum. Millions of dollars later, most massive ERP projects end up half finished or largely ignored by the people they were supposed to help.
A 2006 report by the Cutter Consortium found that application software packages -- predominately ERP -- are fully implemented less than a third of the time. Forty percent of businesses reported that squeezing the promised benefits out of this software ranged from "quite difficult" to "extremely difficult." More than 90 percent of ERP projects take longer than expected to implement, and nearly 60 percent go over budget, according to the Panorama Consulting Group.
But many of ERP's failings lie with enterprises that weren't mature enough to handle the concept of integrated data flow, says Sue Metzger, management information instructor at Villanova University's School of Business. "The Y2K crisis of the late 1990s gave [ERP vendors] a great opportunity to come into enterprises and replace existing technology," she says. "Sure, some of them may have oversold and overcharged. Those were fat and happy times for many ERP providers. Organizations that had the fear of Y2K instilled in them felt ERP was the way to go, even if they weren't mature enough to handle it."
Though they never fully delivered on their promise, ERP systems are now common across large enterprises, as is the concept of data integration. "The challenge today is what to do with all that data," she says. "How do you make decisions based on that information?"
5. B-to-b marketplaces
Era: 1999 to 2002
The pitch: "By 2005, 35 percent of the Internet b-to-b trade volume will be conducted via a net market or a consortium of buyers or sellers. ... The value proposition of the Internet is on a grander scale for the b-to-b space; the sheer size of b-to-b trade, coupled with inefficient processes, makes the Internet migration of business strategies very attractive." -- Jupiter Research, June 2000
They were supposed to revolutionize the way organizations did business on the Net -- matching buyers and sellers in a dynamic, interactive bazaar that would dramatically cut procurement costs while boosting efficiency. According to Gartner, by the middle of this decade some $8.5 trillion of trades would occur annually via automated B-to-B exchanges.
This whitepaper explains the terminology and concepts behind Data Replication technologies and establishes some sizing rules through worked examples. Learn the new paradigm in disaster tolerance—protect data anywhere.
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Server virtualization is a popular option for dealing with mounting datacenter costs. Another equally promising approach is the use of an Application Delivery Controller. Citrix NetScaler provides a low-cost way for organizations to reduce their server count and accrue cost savings from a reduction in space, cooling, power and personnel.
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The emergence of WLANs has created a new breed of security threats to enterprise networks.
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Effectively address data protection challenges, implementing solutions that help store and protect business–critical data while cutting costs and improving efficiency and reliability.
Download now »As was pointed out in in the article, the merit or lack thereof in "Artificial Intelligence" lies in how you define it. Relative to the technology of the day in the mid 1980s a Huge amount of commonly accepted, frequently used, software of today IS the very feature sets and applications that were over hyped back then. For example, "Expert Systems". The over-hype talked about medical diagnosis, a pipe dream, but today there are maintenance assistance "wizards" using a basic expert system reasoning engine approach for an amazing array of complex products. Such as trade show kiosks that use a Lisp ES Engine to guide prospects through selection of products, or business process automation tools that capture BP "Rules" and enforce them in a framework that defines processes. And these are just from the "Expert System" branch of the tree.
From the key pattern recognition / pattern matching work of that era, comes a whole industry of web ad services, Google, AdAware, and much much more. Financial Services, as alluded to in the article, bank card and others do fraud detection with a sophistication undreamed in 1984. The FS world also commits serious capital to automated trading today with tools drawn from this same tool box. The whole "Business Intelligence" category of commercial software, not to mention Google, owes it's very existence to concepts that are AI in a very real sense. The list goes on.
Robotics and motion control is yet another area where the dreams of the 80s are on the manufacturing floor of this decade, as well as in many consumer products. Manufacturing robots function today within communications, control and process feedback loops that completely eclipse the most advanced available in the 80s. Yet another area in that world is "Fuzzy Logic". Bots and droids of all sorts today can come to a smooth and rapid halt after repositioning with a precision that makes even the most practiced human envious. In fact I daresay you would be hard pressed to find an autofocus camera on the market today that does not use this AI technique.
I am not even going to start on military applications. If I told you I'd just have to kill you, or some short-hair agent would come kill me. Suffice it to say in most cases you really do not want to know anyway.
So if AI is "bunk", I will take the approach the English have taken with their royalty for centuries. AI is dead. Long live AI!"
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