Orions Systems | So You Say You Want A (Digital) Revolution
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So You Say You Want A (Digital) Revolution

So You Say You Want A (Digital) Revolution

Newton’s laws of physics apply to business.  Particularly in the form of change.  More specifically, the resistance to it.  One could argue that the outside force needed to act upon a business to make it change seems greater than its inertia to stay in the current routine.  Without getting into the psyches of boards and managers responsible for the “we’ve always done it this way” approach, it’s pertinent to remind the business community that there is no longer a gap between those businesses which are “tech businesses” and those that are not.  Every business is a tech business (or needs to move that way) if it intends to survive into the future.  A driving factor in the non-tech business mindset is to adopt enough tech to support the company’s business model to still be able to compete in a digital marketplace.  One would think that the bigger businesses would be better at doing this, as they have more capital at their disposal to fund their Digital Transformation, but that’s not necessarily the case.  A major drawback to being big is that Newton’s law of momentum applies.  “An object in motion will continue on its path in a straight line until it is compelled to change state by forces impressed upon it.”  Bigger businesses have been in the same state of motion for a long time, and it’s worked for them thus far.  These businesses have higher momentum and, therefore, higher resistance to change.

Obviously, Google and Microsoft are giant tech companies.  They exist to provide technology to their customers, so they must continually innovate newer/better/faster tech capabilities to stay on top.  Their momentum drives them in the direction of innovation.  Just look at their internal R&D budgets to see what their boards value.  This article is not addressing tech giants. This is for the non-tech-oriented companies that need to be able to apply technological advantages to their existing processes.  That’s what a Digital Revolution is supposed to entail.  That’s who it’s supposed to help.  Point solutions offer some relief.  An easy example is the bar scanner in every major retailer storefront. 50 years ago, companies hired people to put price tags onto everything on the shelves and other people to punch in the cost of every item a customer purchased.  Human error in such a system is a painfully-obvious potential detriment to the bottom line.  It makes sense to employ a single tool that can provide a useful service that also saves time and money.  The point solution of a bar scanner allows the retailer to apply its business model of buying and selling groceries while saving time and money from labor costs. This innovation helps support the larger picture, but when the innovations are more expensive than a bar scanner and the advantages much less obvious, what’s a bigger business to do then?

A company cannot sit on its laurels and reap the fruits of labor put in 25 years ago.  The market, any market, is just too competitive. Businesses must keep innovating. Questions must be considered on a recurring basis to determine if changes must be made.  How will the industry work 10 years from now?  What must be done right now to still be a leader when that future comes?  How can innovation be afforded while protecting share price?  The advantage of smaller businesses is that it’s easier for them to change their models until something hits.  It’s far easier to be a disruptor than it is to continuously operate and at the same time work to improve the status quo.  The early Amazon business model (selling books) was such an inferior product to Barnes & Noble or Borders, it was laughable. However, Amazon kept at it until they got it right, and then they grew from there.  Being small, they were more able to capitalize on their ability to innovate quickly until something hit.  Big businesses can learn the following from innovative disruptors:  an inferior product will always take some market share from the superior product if the superior product’s providers refuse to innovate.  Herein lies the rub…how can a big business wake up tomorrow and decide to innovate?

In the first example of the bar code scanner in the grocery store, the ROI seemed measurable, the product is affordable, and the benefits foreseeable.  This isn’t a leadership decision as much as it is a quick press of the “Easy Button” especially in retrospect.  All of the low-hanging-fruit data has, by now, been plucked by most businesses who know they must innovate to survive.  Companies are now working on ways to view and structure their data in clear and meaningful ways so leaders can gain insight from it and move forward. While this is a step in the right direction, it’s only the beginning.  It’s the 2ndstep in any company’s Digital Revolution that is the tough part. This is when there’s no clear ROI.  This is when leaders must dedicate a portion of their annual capital towards R&D in the hopes the investment bears fruit over time. R&D budgets can be optimized in a number of ways to bring innovation to a business.  A company can have an internal or external accelerator, open up a VC arm, invest in a startup, or get into the acquisition business for technologies that seem promising, but there’s just no guarantee the ROI will be positive in any of the above innovation methodologies.  Many leaders simply hope that the gain is neutral, and that’s why they’re hesitant to start.  But when you think about it, there’s little difference between having faith In AI and a Digital Revolution and the earliest tech (bar code scanners) when it comes to disruption.  

Clearly, simplifying processes and procedures through any methods will save time in the long run.  It’s inherently obvious.  What’s also obvious is, like the scanner, productivity will decrease during the learning phase of operation, but once employees are fully trained, time savings will be measurable.  At the time the first bar code scanners came out, not every business adopted them early, as the ROI seemed a stretch at the time.  They just didn’t think it would save as much time or reduce as much human error as it did.  Similarly, with future AI research and employment, it is just as hard today to accurately project the time/cost savings, as the math isn’t as obvious.  With current management techniques (Lean 6 and others), we just don’t think there’s a ton more fat to trim.  But when considering  what digitization can do to help, the truly difficult part comes in a pair of problems that can be summed up as “Shiny Object Syndrome”.  First, we’re all used to cheap/free digital tools and apps on our phones that work fine for what they do.  However, the initial sticker shock over the development costs of AI, AR, ML, and algorithmic processing make it less obvious to see just how any long-term savings will occur over time.  If initial costs exceed $250k, how many labor hours must be saved just to justify the development costs alone?  Second is a problem of thinking that going digital means that steps, and their associated costs, can be skipped.  Algorithms and all associated AI are simply programs that do the work. They have to follow the same steps anyone else would need to accomplish to get the work done.  And the steps need to be accomplished in order.  The nice part about AI is that it can run through the steps associated with some procedures very quickly, but the steps still occur. Everyone understands you can’t skip steps when fixing your car.  It’s intuitive that you fix the dent in the fender before repainting.  It just doesn’t make sense otherwise, even for someone who didn’t grow up around cars.  But when it comes to data automation and considering how to proceed with a company-wide digital revolution, decision makers want a miracle.  They want to go directly to the good part of revolutionizing the training/operations cycle without having to spend the time and money analyzing and digitizing the data that makes up the potential solution.  You can’t (truly) have an answer that is NOT based on your own data.  You must first collect your own data correctly.  After doing so, you can then go through the steps to gain insight and eventually improve a process or fix a problem.  

Shiny Object Syndrome leads business to believe in a magic pill.  Decision makers are to a point where they don’t want to know what happened. They don’t want to know why it happened. They just want to know what to do about it so that it doesn’t happen again, and they would prefer if the way forward were simply automated and removed from their decision-making responsibilities.  As an old Army Colonel I know used to cynically say, “Task passed, task completed.” Digital Revolutions are hard. They are expensive.  They take time, patience, money, dedication, energy, and a thick skin.  Sometimes development efforts end in fruitless tools with nothing more to show for them than lessons learned.  Other times they lead to cheers across the boardroom when the new labor numbers come in.  One thing is for sure, the world of the future is a digital one, and if any business, tech business or not, wants to be competitive in it, they must keep researching and adding capabilities in any way they can.

Gabe Harris is the VP of Product Development at Orions Systems, a pioneer in the development of smart vision systems for government, sports, law enforcement, and anyone attempting to use unstructured data as a first-class data type. For further discussion visit us at http://www.orionssystems.com.