Index

ARTIFICIAL INTELLIGENCE & YOUR BUSINESS: 3 THINGS TO KNOW

For Starters: This is Not Skynet

Artificial intelligence is all around you. You have been using it for a while, probably without even knowing it. Gmail finishes your sentences. Your phone corrects your spelling and grammar. Instagram decides what to show you next. Spotify creates perfect playlists of new music. Advertisements know exactly what you’re thinking. You use AI hundreds of times a day.

For some of us, this is an uneasy truth at first glance. We imagine computers ruling our world with cold efficiency, slowly robbing us of our freedoms. But AI is not the villain from our favorite dystopian movies. As fun as it is to get lost in the world of Terminator‘s Skynet, I,Robot‘s VIKI, or Captain Marvel‘s Supreme Intelligence, AI is far less sinister in real life. 

AI is now a necessity; it’s simply integrated into everything you do. Your CRM, ERP, website, and applications are all using AI. If you aren’t making the most of it, then this low-hanging fruit is spoiling inches from your hand. That is, if it’s not being snatched up by your competition.

AI is a tool, helping to solve problems that require enormous computing power. It’s lines of code that process millions of haystacks worth of data to pull out a single needle in a matter of seconds. 

The point: AI is everywhere, and it’s not the far-off villain of Isaac Asimov horror fiction. AI is a tool that is seamlessly integrated into hundreds of your daily experiences. It’s not just for nerds anymore. 

Especially in business, there are a few things you should know about this tool if you expect to remain competitive in the coming decade.

3 Things You Need to Know:

(1) AI is Now a Commodity

Until recently, artificial intelligence was mostly the subject of science fiction writers; today it is the subject of your average software engineer. The application of AI has come a long way.

The business community has witnessed an integration of AI on a grand scale. Ubiquitous in all markets, it is written into many of the functions that we use on a daily basis. Furthermore, companies like Amazon and Google have used unimaginably large collections of data to perfect AI tools, and have commoditized them in the form of products like AWS and Google Cloud.

Some have chosen to ignore AI, not seeing value in tools they can barely understand. Meanwhile, fields that lean heavily on AI (like data analysis and business intelligence) have expanded rapidly in recent years. For example, CIO.com lists “BI Analyst” as the most in-demand tech job of 2019. AI is changing business.

Failing to make the most of AI is not just a missed opportunity; failing to utilize AI is an increasingly significant liability. 

A great example comes from an interview with the Harvard Business Review, MIT Sloan School professor Erik Brynjolfsson. He describes an AI program that reviewed recorded conversations of successful sales, and then listened in on active conversations between salespeople and potential customers. While they were on a sales call, the program used the data from successful pitches to make suggestions about words or phrases that the sales person could slip into their conversation to help close the sale. This small application of AI boosted sales by 50%.

Brynjolfsson strongly believes that the only thing holding businesses back is a lack of imagination by business executives on how to use these new tools in their businesses.

(2) Your Competitors Are Using AI

Even if you have a few data analysts on staff, you’re most likely not getting the most out of your software. Since AI is everywhere, it’s hard for CIO’s, tech leads, or business owners to find and use the full range of the tools that are available to them. For instance:

your CRM could be generating leads for your sales department in places they wouldn’t have thought to look

your supply chain solution could be dramatically cutting waste by ordering supplies to be delivered for the lowest shipping cost at the exact moment they are needed  

your security solution could be identifying fraud and malware threats before they strike, saving you the time and money you would have spent recovering from one employee clicking one email

your ERP could be spotting spending trends and suggesting campaigns to your marketing team 

You might ask yourself, Is it worth all the hassle? Do I really need to do all this? I’m getting along fine without AI, why would I change? If you’re asking yourself this question, you’re looking backwards, not forwards. Failing to make the most of AI is not just a missed opportunity; failing to utilize AI is an increasingly significant liability. 

AI is a complicated tool, and getting the most out of it requires knowing how to use it.

The proof is in the numbers. Netflix claims that a machine learning tool saves it $1 billion a year. Amazon used AI to influence the decision-time of online shoppers and cut it by more than a third. HBR found that companies using AI for sales were able to increase their leads by more than 50%, reduce call time by 60%, and realize cost reductions of 40%. If you don’t take advantage of AI, you will lose out to someone who is.

AI is now a necessity; it’s simply integrated into everything you do. Your CRM, ERP, website, and applications are all using AI. If you aren’t making the most of it, then this low-hanging fruit is spoiling inches from your hand. That is, if it’s not being snatched up by your competition.

(3) The Catch: It’s Not Magic

AI is certainly low-hanging fruit, and it doesn’t take an enormous investment to get more out of it. But it’s not a magic solution that will fix everything. AI is a complicated tool, and getting the most out of it requires knowing how to use it. Utilizing AI takes work. And worse, if you don’t use it correctly, then AI could actually lead you in the wrong direction. Ray Dalio put it best, “Be cautious about trusting AI without deep understanding.”

If you don’t have a crystal clear understanding of what you need from an AI solution, then all that will change is the speed in which you receive unusable or incorrect answers to your business problems. 

AI is a tool, and just like any tool it can be used improperly. With AI, bad input means bad output. There’s an art to using this tool.

Here’s a simple illustration. At one time or another, most of us have used the online radio service, Pandora. The process is simple. Tell Pandora a song or an artist that you like, and it searches an enormous music library to play a song that is similar to your input. You rate the suggestion in order to help Pandora hone in on your taste. This is AI at work, learning from your preferences.

But a tool is only as good as its users. If you vote thumbs down on your favorite song, then Pandora won’t play it again. Or if Pandora hadn’t invested in a large and diverse enough music library, it wouldn’t be able to return songs similar to the ones you like. The tool needs to be used properly in order for you to get the most of it. 

AI solutions in business are no different: you need to use the tool properly in order for it to work properly.

So How Do I Do it Right?

There are three main components of a good AI implementation in business: know yourself, know what you need, and use the right data. If you don’t have all three of these components then at best you’re not getting the most out of AI, and at worst the tool will lead you in the wrong direction.

First: Know Yourself

An AI solution isn’t worth the investment if it doesn’t solve the specific problems facing your business. This makes sense in theory, but is hard for executive leaders to get right in practice. 

The reason for this is not hard to grasp. CIO’s or VP’s of Sales have deep knowledge of their own departments and the business verticals relevant to them, but good tech integrations require organization-wide implementation, and this always pushes beyond the knowledge of a particular individual or department. It’s hard to see beyond the boundaries of your silo.

We begin every project with a current-state assessment. This seems like a logical first step, but it’s often overlooked. It involves gathering requirements that clarify the current-state needs and processes that are affected by a solution. This gives you a clearer understanding of what you need in the future. Many executives assume they already know this, but even the best leaders have blind spots. 

A current-state assessment is the best starting point for any kind of project work, but it is especially important with AI. If you don’t have a crystal clear understanding of what you need from an AI solution, then all that will change is the speed in which you receive unusable or incorrect answers to your business problems. 

A worthwhile software integration must always begin with a careful look inward, with an up-to-date assessment of requirements gathering and process mapping. Failing to do this has its consequences. If AI is integrated into an organization’s workflow without this look at your current-state, the result is solutions that don’t fit your business or market. 

Second: Use the Correct Inputs

What sets real-life AI apart from fictional AI is one key aspect: general intelligence.  AI can solve some problems faster and better than humans, but it can’t think for itself. 

Well-defined and clearly-articulated problems are inseparable from successful AI integrations. The payoff comes once a computer knows how to do a task properly, and can do it at a speed and volume that humans could never achieve.

For example, AI programs have bested world champions in Chess, Go, Texas Hold’Em, and Jeopardy!. But there’s an important detail: the same AI that beat champions in chess can’t even play the game of Texas Hold’Em. Another example: an AI program has to sample tens of thousands of photos before it can identify animal pictures with any reliability, whereas a 2-year-old can correctly identify cats after only seeing one example. 

But it’s not just games and image recognition: there are darker examples of AI falling short in big ways:

Developers at MIT were excited about the accuracy of their AI facial recognition software, until they realized that they forgot to build inputs into the software that could identify darker skin tones.

Biases built into AI solutions in law enforcement yielded inaccurate results with huge consequences, such as falsely singling out minorities for recidivism or counseling police to target ethnic neighborhoods. 

Amazon used an AI recruitment tool that spent 4 years sorting out the resumes of female applicants, even specifically flagging the word “women” as cause for downgrading a resume. 

AI tools are narrow, specialized solutions: you can’t expect to solve problems without teaching it how. It takes work to shape the tool to work correctly. Well-defined and clearly-articulated problems are inseparable from successful AI integrations. The payoff comes once a computer knows how to do a task properly, and can do it at a speed and volume that humans could never achieve. The good news: this work is absolutely within your reach, and most off-the-shelf software has easy-to-use feedback loops built in to help you!

Third: Use the Right Data

Imagine searching through a deck of playing cards to find the midday market report. Or searching through a 4-pack of crayons looking for an exact match to Robin’s Egg Blue. If your data set isn’t large enough or doesn’t fit your questions, then you aren’t going to find meaningful answers. This is especially true for artificial intelligence. 

This can be daunting for someone new to AI. How do I know if data is high-quality? How do I know if I have a sufficient quantity? Without the help of experienced input, executives might be making data purchases that are unhelpful, or even harmful. The consequence of using AI with insufficient or bad data is inaccurate solutions and misdirection. 

One Last Consideration: Don’t Reinvent the Wheel

Your business is unique, but your problem is not. Why spend time and money custom-fitting an AI solution to your business when a tool has already been developed for just that problem? Finding the right solution might just be a matter of having someone who knows the market helping you find the solution that fits your business.

Using Artificial Intelligence Well: A Case Study

A client of ours was experiencing stagnation in their financial and customer growth for the first time in their history, and couldn’t identify the reason for the slowing growth. They turned to The Gunter Group to help them revamp their digital strategy in order to expand to new customers.

This client had years of data on their customers that they didn’t know how to leverage. They offered great service, but they didn’t understand their customers’ behavior. So we started there.

We began with collecting their data, which consisted of several different types that needed to be aggregated into one system. We helped them build a unified repository, so that any insights they sought maximized the value of their data. In addition to helping them improve the quality of their data, we also helped them refine the insights they hoped to gather. At the beginning of the process, we engaged our experienced Business Analysts to help them integrate their knowledge of their organization’s structure and business goals into the process. 

With the 3 important ingredients in place, we were ready to make the most of an AI integration to explore the data. Our team helped craft complex algorithms to create customer segmentation, cohort development, churn prediction, and market share analysis. They were able to use these insights to launch highly effective marketing campaigns, and began a path to predictive analytics to enable real-time interventions in the future. 

This kind of example abounds in the business community today. Artificial intelligence is quickly becoming a commodity, available to all. You can’t afford to stay behind the curve. 

The Gunter Group partners with organizations in Portland, Vancouver, Bend, Salem, Reno, and Sacramento, helping them to know themselves and seize the low-hanging fruit of AI. If you are interested in learning how we can help you to do the same, reach out today! 

EXCAVATING DATA

Archeologists seek to understand human behavior through stuff – physical stuff. These artifacts of human existence are often ravaged by time, leaving the subtlest of clues for us to examine. Without context, archaeologists piece together the puzzle and hypothesize why humans of the past made certain decisions and how they interacted with each other and their environment.

As a former archaeologist, I’ve transitioned from the academic world into management consulting. At first glance, the two may seem unrelated but I’ve discovered that my background gives me a unique perspective and a surprisingly transferable skill set. I certainly know my way around a transected excavation pit and a trowel, but the skills I’m talking about are more akin to business intelligence, behavior science, and organizational development. Additionally, the academic discipline itself, like most in the liberal arts, provided an incredibly strong interdisciplinary education and honed my ability to learn new things and synthesize complex data quickly.

I dug deeper to uncover the sweet spot at which wait times, volume, and customer loyalty could work in harmony to optimize results.

Archaeological education, training, fieldwork, and research commonly incorporate a wide range of disciplines, from botany and geology to sociology and economics. One day I might read a dense medical journal and the next study climate data, all while making novel connections to illuminate meaningful patterns. By now, I’m sure you can see how this would translate to a more corporate setting where business analysts, data scientists, and market strategists regularly flex the same muscles. What I love most about consulting is taking these insights and the scientific method and turning them into action so clients can better predict, understand, and adapt to their customers’ behavior.

For example, I recently helped a client understand how wait times impacted the customer experience and subsequent loyalty. Their assumption was that lower volume would reduce wait time thereby increasing volume again. I rolled up my sleeves to excavate their data and find out if this proved true. It turned out to be more complicated than that so I dug deeper to uncover the sweet spot at which wait times, volume, and customer loyalty could work in harmony to optimize results.

Such a dynamic situation can be difficult to articulate so I leveraged a commonly used framework developed by in-depth ecological research: the predator/prey model. When applied to the above business scenario, our analysis revealed a pattern that led us to actionable insights. We determined the retail equivalent of “ecosystem equilibrium” which indicated an ideal range for how long customers could be kept waiting without negatively impacting loyalty. We are currently testing our hypothesis of the maximum theoretical wait time in the real world. By engineering specific wait times, they will be able to collect additional data which will then be fed back into our model and drive continuous improvement.

Another recent example of archaeological expertise adapted to the business world comes from our work with a large retailer. This client was puzzled as to why one location showed double the sales compared to another only a mile away. Customer profiles, inventory, staffing, etc. were all so similar so why the disparity? 

Suddenly customer behavior made complete sense and store managers were able to direct their attention to the right issues and avoid unnecessary time and expense trying to solve the wrong problem.

For this scenario, I invoked another scientific model: least cost pathways. The least cost pathways model essentially helps us determine the path of least resistance using weighted costs associated with various routes. In archaeology, this model might help explain seemingly odd trade patterns or dietary choices. In a modern business scenario, the same approach can be used to map human movement, both physical and virtual, and optimize location of brick-and-mortar stores, button placement on a website, or product placement on a shelf. 

Through this lens, our analysis revealed that the under performing store required a U-turn and a left hand turn across four lanes of traffic. Meanwhile, the other store required only an easy right hand turn off a freeway entrance. Given this information, which location would you choose to do your shopping? Suddenly customer behavior made complete sense and store managers were able to direct their attention to the right issues and avoid unnecessary time and expense trying to solve the wrong problem. Going forward, executives have revised their strategy for key real estate and business decisions to incorporate these insights and avoid costly mistakes.

The potential of data analysis, especially in this age of “big data,” is immense but it’s important to use appropriate models to help explain that data and to ask the right questions in the first place. By thinking like an archeologist and working like a data scientist, I’m able to solve puzzles that save clients time and money.