Big Idea: Artificial Intelligence and Business Strategy

Don’t Let Artificial Intelligence Supercharge Bad Processes

When artificial intelligence is used to expedite certain legacy processes, it can act more like a Band-Aid than a cure.

Sam Ransbotham Reading Time: 3 min 

Topics

Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

In collaboration with

BCG
More in this series
Bandaid Artificial Intelligence Processes Technology

Scenarios describing the potential for artificial intelligence (AI) seem to gravitate toward hyperbole. In wonderful scenarios, AI enables nirvanas of instant optimal processes and prescient humanoids. In doomsday scenarios, algorithms go rogue and humans are superfluous, at best, and, at worst, subservient to the new silicon masters.

However, both of these scenarios require a sophistication that, at least right now, seems far away. Our recent research indicates that most organizations are still in the early stages of AI implementation and nowhere near either of these outcomes.

A more imminent reality is that AI is agnostic and can benefit both good and bad processes. As such, a less dramatic but perhaps more insidious risk than the doomsday scenario is that AI gives new life to clunky or otherwise poorly conceived processes.

Consider faxes in the health care industry. Despite being obsolete in most places, “like the floppy disk or the CD player,” faxes are still a fundamental part of the medical infrastructure. Because of the long history and strong network effects, the medical industry still sends a staggering number of faxes every day.

Invented in the 1840s, well before the telephone, faxes illustrate how difficult it is to change an entrenched process.

Despite widespread use, sending faxes is, for the most part, a horrific way to transfer information. The process is typically (1) extracting and printing information from a computer system, (2) scanning it into an image, and (3) transmitting it somewhere else via fax. The burden rests squarely on the recipient to interpret a pixelated approximation of the original information. Structured digital information has become unstructured. With every fax, a data scientist gets their wings ripped off.

The conversion from structured information to unstructured and back is a waste. No one wins — a patient may be waiting for approval, medical staff may lack information, errors can creep in, etc. At a minimum, time and effort are needlessly spent.

Advances in AI and image processing are making significant progress in reducing this problem. Organizations can use AI to recognize images, automate the interpretation, and restructure the information. Certainly, this is a welcome improvement. No one gets competitive advantage or business value from wasted time spent restructuring information.

However, this improved image processing is a pyrrhic victory. While it may be more efficient, perhaps even significantly, the expended resources are lost forever. In the absolute best case, the original structured information is recovered, but it will be expensive and difficult to get close to that best case. The realistic case is far less promising.

The danger is when AI gets just good enough to let a less-than-ideal system like this limp on. Improved image processing can allow organizations to cut costs and automate much of the processing (this applies beyond faxes and beyond health care), but AI may also mask the symptoms of bad processes. It provides a bandage, not a cure.

If AI were to fail completely at interpreting the faxes, we might be better off: The system would be untenable. Costs would rise. People would notice. Change would be inevitable.

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Yes, it is better to reduce this annoying work than to continue doing it. This is a justifiable reason to apply AI in organizations and a great example of reducing the scut work. However, gains in AI applied to bad processes may staunch wounds, not heal the organization.

Successful organizations will continue to improve underlying processes. But, in many industries, like health care, with legacy systems and embedded processes that involve many people and many organizations, that will be difficult.

One risk we see is that upstart organizations, unencumbered by legacy processes, will be able to start fresh. They won’t have to make significant investments of time and resources to apply AI to processes that perhaps should not exist in the first place. These new entrants, possibly from outside your industry, will apply other tools and technology to completely bypass the process your organization is struggling to bandage with AI.

Are there processes in your organization that AI can improve? Great. But, be sure to ask yourself: Should these processes exist in the first place? No amount of gee-whiz AI used to improve a process beats not having to rely on that process at all.

Topics

Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

In collaboration with

BCG
More in this series

About the Author

Sam Ransbotham is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Artificial Intelligence and Business Strategy Big Idea Initiative. He can be reached at sam.ransbotham@bc.edu and on Twitter @ransbotham.

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Comments (7)
avinash misra
Spot on Sam !This is our thesis too at Skan . Automation is one of the end points of a larger transformation agenda. It needs to be preceded by discovery , optimization / re-engineering. Further just because we have been pointed in the automation direction of late let us not assume that that’s only way to transform. Infact automation forms only a small portion of the interventions that can be made in the work continuum. Again great article Sam !
Satya I
We completely agree with the insights in this article.  Without uncovering process variants, untangling the process pretzels, unleashing legacy processes with AI will not be a fruitful endeavor.  We suggest starting with computer vision-enabled process discovery, and then after understanding the process nuances, any interventions - re-engineering, automation, transformation, et al. - will be a net positive.
David Kazgar
Artificial intelligence makes easy to our work and reduces the human effort and time, Nowadays everywhere artificial intelligence are work in every field even to manage data of a company there is an accounting software "QuickBooks" that is also based on AI that analyzes your business and generates earning according to your spending money and you can contact QuickBooks support on web to get help 24*7
Ashish Sharma
I concur with this article, yet think it belittles the potential for hurriedly executed mechanization innovation to use little issues (e.g., squander, incapability) into huge, even vital, waste, botches and hazards. 

A significant number of the charitable Humaneering Technology Initiative's field trials for the rising biopsychosocial or "humaneering" connected science (now in v4.0 open beta) have concentrated on the revive or overhaul of human work in readiness for computerization. In light of what now appears to go for the "plan" of human work (e.g., some remnant rehearses and a speedy expected set of responsibilities) in associations, the operational and key effect and the "specialized obligation" of poor work configuration, once utilized with computerization, would far surpass any advantage that would have been picked up. Or maybe, upgrading the human work on account of the mechanization, and adopting a light-footed strategy to creating and actualizing the robotization innovation, appear to yield the best results, both operational and monetary.
anshul sharma
It was true that Technology increased every day by which future become artificial. Changing people and processes is hard, or at least usually harder than implimenting a bit of technology.
James Pepitone
I agree with this article, yet think it underestimates the potential for hastily implemented automation technology to leverage small problems (e.g., waste, ineffectiveness) into significant, even strategic, waste, blunders and risks. 

Many of the nonprofit Humaneering Technology Initiative's field trials for the emerging biopsychosocial or "humaneering" applied science (now in v4.0 open beta) have focused on the refresh or redesign of human work in preparation for automation. Based on what now seems to pass for the "design" of human work (e.g., some holdover practices and a quick job description) in organizations, the operational and strategic impact and the "technical debt" of poor work design, once leveraged with automation, would far exceed any benefit that would have been gained. Rather, redesigning the human work with the automation in mind, and taking an agile approach to developing and implementing the automation technology, seem to yield the best outcomes, both operational and financial.  

You can read more about this experience in these articles from a UK productivity publication: https://humaneeringtech.com/pepitone-new-frontier-for-increasing-workforce-productivity-msj-2016/ and https://humaneeringtech.com/pepitone-business-process-humaneering-msj-2017/ or contact me for futher insight at james.pepitone@humaneeringtech.com
Craig Armour
I 100% agree with this article, but I have also come to think otherwise.  There's a pragmatic view here which you point to:  An automated bad process is still better than a manual bad process.   

Changing people and processes is hard, or at least usually harder than implimenting a bit of technology.  Technology can then act as an agent of change to change the harder parts.  
First step, then becomes to automate the process.  Then you are less constrained by human or legacy influence to improve, or even remove it.