Is Adding AI to Your Website a Viable Option for Your Business?

Know the pros and cons before you jump on the Artificial Intelligence (AI) bandwagon

There’s no denying that we’re living in a world that is speeding up daily when it comes to technology. Sometimes, there are so many new gadgets and apps out there that just trying to keep up with it all can start to get a little overwhelming.

When it comes to making the choice about what technology to incorporate into your website, take the time to weigh all of your options and choose what will make the biggest difference to drive your business forward.

Simply utilizing tech to “keep up” with the competition or to give the impression of being “current” or “tech-savvy” is not enough. You also need to know how to use it. This includes considering how best to incorporate it into your product development, website design, and marketing strategy.

Look at it this way, simply owning a high-end set of power tools doesn’t make you an expert handyman, any more than owning a car means you’re an excellent driver. You need to have the knowledge and skills to complement the equipment. Or at least have someone with the necessary know-how working with you, in order to use the tools effectively.

Some of the more recent advancements in tech have been the appearance of AI, or Artificial Intelligence, in various homes and businesses in the form of voice assistants, i.e. Alexa, Google Assistant, and Cortana, and the growing popularity of interactive customer support on company websites via chatbots. In fact, several years back Business Insider predicted over 80% of companies will be using chatbots on their websites by 2020. While I don’t know if we have reached the 80% mark today, chatbots have definitely become commonplace. But what exactly does all of this stuff do, and does it provide a significant advantage to warrant using it in your business?

To help answer those questions, take a look at the pros and cons of jumping on the AI bandwagon when it comes to your business website:

robotic looking women's face with data background

The PROS of Using AI on Your Website 

  • Faster Service – When there’s a “robot” behind the scenes dealing with customers the perks are quick responses and 24/7 accessibility. Your customers won’t get frustrated wasting time waiting for answers or trying to find a human to speak to. Your clients that are looking for information after normal business hours will be happy that they can still ask their questions. All of this can lead to a higher rate of customer satisfaction.
  • A Broader Audience – Providing voice recognition technology makes your site more accessible to users with visual impairments or mobility challenges, which broadens the scope of your business to reach a wider audience. Plus, the 24/7 customer support potential offers a very attractive feature to those people who are on the go and place a high value on convenience.
  • Low Costs, Higher Profits – The use of artificial intelligence equals non-human employees, which means you don’t have to pay a salary or benefits to a portion of your customer support team, or any other area where you’re using the technology, for example – taking orders or marketing. This helps keep operating costs low, which translates to higher profits for your business.

Using AI in different aspects of your business can help keep your operating costs low, which results in higher profits.

  • Catering Products and Services to Customers – Virtual shopping assistants can learn about customers’ habits and tastes as they shop online and use this information to suggest products and services they might find appealing. It’s like having your own personal shopper along with you as you explore the internet, to help you sift through the numerous options and zero in on the ones that are most likely to work for you.

There’s a definite trend toward this technology when it comes to consumerism because 43% of voice-enabled device owners use voice assistant technology to help them shop. Adding this tech to your web interface becomes another way of knowing your customer and adapting your marketing and business strategies to meet their specific needs. 

The CONS of Using AI on Your Website

  • cute robotic bug looking in mirrorLimited Abilities – Robots might be fast and never need to sleep, but that doesn’t mean that they have all the answers. When all is said and done, technology can only do what someone else has programmed it to do. In the case of chatbots, for example, predetermined responses provide limited support. Unlike a human, there’s no adapting, improvising or emotional responses present to handle individual situations.
  • Frustrated Customers – If customers can’t get the answers they’re looking for or are never able to actually speak to a real human being, this can lead to frustration. People crave attention, validation, and empathy, and chatbots don’t possess the capacity to deliver these feelings. 
  • Expensive – Wait, what? Wasn’t one of the pros all about saving money? It all depends on how complex you need or want your AI to be. The more complexity you need means the more money you’re going to spend. The trade-off for the higher expense is more intelligent bots that can deliver more targeted responses, thereby keeping customer frustration levels low, but this might not be viable for smaller companies. There’s a fine line between making the tech worth your while and having it work against your goals.
  • Reactive, not Proactive – A machine is still a machine, which means it needs to be told what to do. If you’re looking for someone to take some initiative to help you with a problem, odds are you won’t find it from a non-human source.

At their core, AI platforms respond to stimuli, like questions, certain keystrokes or specific speech. So if they don’t get the right input, they won’t deliver any helpful output. As for emotional reactions and understanding, an artificial source won’t give you any, no matter how intelligent the design. For customers looking for a personal feel, the lack of human interaction can be off-putting.

Oddly enough, a lot of the cons mirror a lot of the pros. It’s important to remember that every business is unique in its needs and operations. The decision of what is best for YOUR business ultimately lies with you.

By 2021, companies expect to shell out over $57 billion on AI platforms, but that doesn’t mean every business should. Now that you know some of the ups and downs of incorporating AI into your website, you can make an informed decision about whether or not it’s the way you want to go to move your company in the right direction.

If you’re looking to create a strong web presence, or get an evaluation of your current website, we’re ready to assist you with strengthening your brand and reaching more customers. Let our experts evaluate your business needs and put a plan into action that will keep you on a track toward growth and success.

Artificial Intelligence has been on the rise in the technology sector for years, and until recently was a very specialized tool that only the most advanced companies and researchers were using. Now, AI has managed to integrate its way into a more everyday application geared at making our lives, ability to work, and the capacity to analyze information far more efficient. One of the platforms that AI has taken to like a duck to water is the website experience. To better understand how AI can be used to enhance your website experience, we take a look at its two-fold impact; the customer experience is enhanced, and the company’s ability to use analytics to the fullest is pushed beyond the previously established boundaries.

The Customer Experience

So much of whether the company makes money or not through the website is determined by the customer experience. One of the largest catalysts that AI has provided to enhance this factor is voice- and visual-search based queries. In a study performed by Gartner, it is predicted that by 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase their digital commerce revenue by 30 percent. A major retailer that has already taken advantage of AI and its ability to enhance the customer experience is Amazon. It has been reported that customers actually prefer searching for items directly on as opposed to doing a Google search. While using the Amazon App from a smart device you can easily dictate your search into the information field. If you take a critical look at the website, many of the options given to you are represented by an image or graphic. Not only is this new way of searching convenient for the average customer, but it also gives customers with disabilities the opportunity to be more independent!

But search features aren’t the only way that speech interaction is improving the customer experience, Chatbots also make a customized experience more attainable. These AI channels are becoming so advanced that it is rapidly resembling the interaction with a real human consultant. It is even theorized that these Chatbots will eventually be able to read our emotional reactions to products and situations! Imagine walking or driving up to a coffee shop and having a chat pop up on your phone letting you know what your last order was, and asking if you would like to not only order it again but also if it can charge the same mode of payment you used previously as well. You could have ordered, purchased, and picked up your drink without ever having to stand in line.

The Business Experience

The business experience has a symbiotic relationship with the customer experience. While the customer is interacting with the AI and having a convenient mode to get what they want; the business is using all the information that the AI is collecting from the customer, and using it to better the customer experience. This allows for an increase in profit through becoming smarter about their business and products or services. An example of this is tracking information like the most searched for terms on the website, the item or service that is selling the most units, or the converse, the item or service selling the least units. It notes what parts of the website are being interacted with the most, which can help simplify the user interface in the future. The metrics that the AI can generate are endless, as are the possibilities to use this information for positive change in the future.

Integrating AI into your webpage can take your online experience to a new level. Here at Komaya, we love keeping abreast of emerging technology and ways that it might help enhance the web presence of our client’s brands.

Original article published at The Market Mogul May 8, 2018.

“Artificial Intelligence (AI) is the solution. What is the problem?” This conversation is representative of the pervasiveness of AI. Unnoticed, an AI revolution is upon us. Something has changed in last half decade to make this happen.

Student Becomes Master

Cat, the lion’s mentor in Indian folklore, had hardly finished teaching her student how to hunt when the lion pounced on her. She escaped by climbing a nearby tree, a trick she had not taught the lion. Like the popularity of this creation-creator story, events of AI matching or exceeding human performance are making big news. Last year AI beat top humans in poker, a strategic thinking game, where both the cards and bluffing matter. A similar feat was achieved in 2016 when a computer dethroned Lee Sedol, the world champion of go, a complex Chinese board game. Besides these shocking successes over humans in strategy and board games, there has been a significant improvement in the technical performance metrics of the machines. This is leading to breakthroughs in autonomous vehicles, visual recognition, natural language processing, lip reading, and many other fields, signalling the development of algorithms with skills superior to humans.

In the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC), machines showcased dramatic progress, improving from an image recognition error rate of 28% in 2010 to less than 3% in 2016. The best human performance is about 5%. Deploying a new computational technique (Deep Convolutional Neural Net) in 2012 brought this marked improvement in image processing and set off an industry-wide boom. Similarly, both Microsoft and IBM increased the accuracy of ‘Speech Recognition on Switchboard’ from 85% in 2011 to 95% in 2017, equalling human performance. Advances in machine learning, pattern recognition, and a host of other technologies will enable autonomous cars without human drivers to drive on roads this year. Alphabet’s Waymo leads the pack of several self-driving car makers in the race.

Classical Versus Deep Learning AIs

The biological brain consists of two different but complementary cognitive systems, the rational (model based, programmed) and the intuitive (model-free, learning through experience). The understanding of how the mind selects between these modes and the interface between intuition and rational thought is beyond the current technology. In the classical approach, AI, and its subsets Machine Learning and Neural Networks, followed the rule-based (if-then) systems imitating the decision-making process of experts ignoring the intuitive part. For complex problems, it was difficult to encode rules and make available the requisite knowledge base. For almost sixty years classical AI had been languishing as human cognition is not based only on logic.

A new approach, based on continuous mathematics, was developed. In deep learning, the new name given to artificial neural networks, the classical logic-based method has been passed over in favour of experimental computation using continuous mathematics. This change has been possible due to the growing infrastructure of ubiquitous connectivity, exponentially increasing computing power, powerful algorithms, and big data sets.

Artificial neural networks, which mimic the biological brain, are used for mathematical modelling. The human brain is imitated using electronically simulated interconnected neurons stacked on the top of each other in layers. The hidden layers perform mathematical computations on inputs. Iterating through the data sets, the output gets generated via ‘back propagation’, using a technique called ‘gradient descent’, which changes the parameters to improve the model. Maybe stumbling on nature’s design, the process works very well perhaps because of imitating intuition as in the human brain.

Astonishing Results and Possible Futures

The results are so astounding that these cannot be explained even by creators of AI programmes. AI systems are like black boxes taking in questions on one side (“Should this autonomous vehicle accelerate or apply breaks on this yellow light? “What is the next move in this board or strategy game? “What are the objects in this image”) and giving out answers on the other side. It will be difficult to explain how the black box works, but it does work. In some situations, it will be difficult to use such an unpredictable, inscrutable, and unexplainable system. This method, using neural networks inspired by the human brain, requires tons of quality data to be useful compared to the very little data needed by humans. Another flaw in deep learning is its inflexibility in using experience learned in one case to help solve another. Humans can learn abstract concepts and apply them in different applications.

The algorithmic, or specialised, intelligence, known as narrow artificial intelligence (NAI), has existed for years and has now got some teeth. It is benefiting humankind in many ways but is also capable of causing large-scale damage to an increasingly digital and interconnected infrastructure. General artificial intelligence (GAI) is a general purpose human-level intelligence and is perhaps a decade away. While GAI can meet challenges such as climate change, disease, and other problems which humans are not able to solve, it will cause economic and cultural upheaval. GAI, undergoing recursive self-improvement, will give rise to a superintelligence, the third flavour of AI. Superintelligent AIs will radically outperform humans in every field and may pose an existential threat to humans. They could appear in the next few decades, or not at all.

Human intelligence is to be understood first before it can be created. With 20,000 AI research papers being published annually, enrolment in AI programmes and investment metrics rising northwards, humanity may be close to accidentally creating a general artificial intelligence.

Here at Komaya, we love keeping abreast of emerging technology and ways that it might help enhance the web presence of our client’s brands.

We live in an incredible age where scientific advancement is progressing with leaps and bounds heretofore unseen in the history of humanity. As new technologies are developed daily, old ones are falling by the wayside. All of this change amounts to a very real and influential force in the way that individual companies and even entire industries do business. The key is to know what the march of technological innovation means for your business. Here are some helpful ways to think through interacting with emerging technology as an entrepreneur.

Know What’s Out There

If you are a really busy business owner, it can be easy to be so focused on what you’re doing (which is, after all, working just fine) that you don’t pay attention to ways that new technology can improve your workflow or business model. Look up from your own business every once in a while, and pay attention to what’s going on in the industry around you. If your industry has a trade magazine (or a few), subscribe to them. Follow leading bloggers who are active in your field. Coming across an idea in one of these forums can plant a seed in your head that you can at least begin to consider as you think about how to improve your business.

Don’t Get Tunnel Vision

While focusing on your industry is a good start, it can be useful to pay attention to emerging technology in other business that only relate loosely (if at all) to yours. How many times have you heard something called “the Uber of” this or “the Netflix of” that?  Some of the biggest entrepreneurial breakthroughs have occurred when one bold soul looked at an idea in a different industry and said, “Hey, I bet that would work in my space too!”

Use New Tech Efficiently, not Obsessively

It can be easy to become consumed with every new technology that has anything resembling even a tangential connection to your industry. Energy spent trying to keep up with the latest trends, is energy that could be spent growing your business in other ways. That’s not to say that seeking out new technological advances that could help your business is always bad. You just need to be selective and intentional with the attention you pay to the subject, and precise and calculated with how you implement new ideas.

The Early Bird Gets the Worm, but the Second Mouse Gets the Cheese

Being the first of your competitors to embrace a new technological breakthrough can give you a competitive advantage, but that advantage doesn’t come without any risk. Think back to the days of the Blu-ray vs HD-DVD battle. HD-DVD actually hit the market first. If you had thrown all of your eggs into that basket, you’d be pretty disappointed in the way that battle turned out, with Blu-ray winning out in the long run. Every technological advancement has a chance of failing, and at the blinding pace of innovation and corresponding obsolescence, has a chance to be surpassed just as quickly as it appeared.

By keeping your finger on the pulse of technology that relates to your industry AND knowing how to use it effectively, you can separate yourself from the competition.

Thomas CasselberryOriginal article published by Information Management, April 6, 2015.

Ever wonder how to migrate from a relational database management system (RDBMS) schema structure to a big data platform (NoSQL database implementation). This article (part 2 of an overall series; Big Data: part 1) offers some practical advice for such a migration.

In the following sections you’ll find information that will show you the ins and outs of migrating from a traditional RDBMS schema structure to a big data storage structure. While most of the examples are made to be fairly generic, the final structure will be created for use in the “Wide Column / Column Family Store” variety. There are many ways of getting to the end goal. The process I illustrate is one of many, but it does work well for what I’m trying to show.

Warning! Here’s where things will get a little deep, technically speaking. Not to worry though! Just like in American football when you know you’ll be breaking through the defenses line, buckle up your chin-strap and keep going. The reality is that even if this discussion causes you to twitch a bit (indicating that you may be on the upper end of the non-technical speaking population) you’ll probably come through it just fine.

Relational to Composite Structure

For the sake of narrowing the scope of this discussion, I’m going to limit the focus to the “De-normalize and duplicate” approach. We’ll be ignoring the “Balanced” approach because it takes too much time to explain correctly. We’ll also be ignoring the “Model it as-is” approach because that’s a poor a practice and doesn’t perform well.

With our discussion scope sufficiently narrowed, we’ll start by tackling a relatively simple relational structure. The very first thing we’ll need to do is to evaluate which entities can be de-normalized to become what I call super-classes. “Super-class” is not a standard big data term. It’s my term and I find it makes things easier for the initial discussion. I’ll explain why later. Each of these super-classes will be used to help define the new composite structure (an actual Big Data term). We’ll be using the following Entity Relationship Diagram (ERD) to lay out the steps needed to identify our super-classes.


In this example we have a relational structure that is used to manage information about Really Simple Syndication (RSS) feeds. Remember, there are no joins and no referential integrity in the No SQL database world. Thus, we’ll be adapting the relational structure represented in the ERD to one that matches more of a composite structure.

Finding and de-normalizing the entities in our diagram into our super-classes can be as easy as asking a few simple questions. These questions will help us identify the super-class candidates, and the de-normalization will help us create them correctly. Ready? Let’s go!

Does the entity contain foreign keys (references) to other entities?

If the answer to the foreign key question is no, we have to ask an additional clarifying question. Do other entities reference this entity? If yes, in most cases, we have a pure reference entity that will be absorbed into one or more super-classes. Reference entities become part of the super-classes that reference them (de-normalization).

If yes, this is an indicator that the entity could be either an intersection/junction or an actual super-class. To discover which, we’ll ask a clarifying question. Does the entity contain only foreign keys, foreign keys that all make up the primary key or foreign keys with an additional, separate primary key, dates or other superfluous information? A ‘no’ means we’ve identified a super-class. ‘Yes’ indicates that we have identified an intersection/junction entity. We’ll ignore any entities of this type (for now).

Let’s take a look at how this type of analysis works with our existing schema structure. Using the questions we just went through, it looks as though we have three reference entities: “categories”, “sources”, and “item_types.” We also have an intersection/junction entity: “feed_contents.” That leaves the two remaining entities: “feeds” and “contents” as our identified super-classes. That’s it, right? Not quite.

We haven’t touched on the ever-so-popular: (pause for effect…) common modeling jargon, lingo, you know – the terms you just have to know! Let’s get that taken care of now, shall we? The following table shows the common terms that we’ll need to understand to make that all important transition in our thinking when modeling from relational to non-relational.


See! That wasn’t so bad. The key here is that unlike in the relational world we have to being to think about structures in more finite terms. A relational column is broken down into a name-value pair. Primary keys become row keys. Tables are column families and databases are key spaces. Column Families can be related to one another but not in a traditional sense so the concepts of joins and referential integrity do not translate well at all.

Before we get into the resulting structure, we still have to address a few missing parts. Namely, we need to follow through with the de-normalization of the reference entities. Let’s start with the reference table “item_types.” Its information will need to be duplicated into both the “feeds” and the “contents” super-classes. That doesn’t mean simply adding the columns from one table to another. Instead, think of it more as nesting. I’ll use braces and brackets to illustrate table boundaries, nesting depth, and column ownership as follows:



You see, the “item_types” entity literally becomes part of the “feeds” entity. Using that same technique, let’s fast-forward and show how our “feeds” super-class looks after de-normalization.


Along with the modeling terms and the de-normalization technique we just learned about, we’ll also need to know about composite structures and how they can look in Wide Column Stores. Each has their uses, pros, and cons. While I’m going to introduce these structures and their uses, I will not get into all of the pros and cons as there are currently too many discussions for, and arguments against, one structure over another to list them accurately. That said, here we go.


The column family structure is what I would call a “class.” It’s a pretty generic structure and can be easily used to model simple entities.


The super column family structure is derived from what I’ve been calling a “super-class.” It’s the one structure that allows us to model our de-normalized entities and accommodates elements of the column family structure also.

Let’s look at an illustration of how our “feeds” super-class converts into the “feeds” super column family.


I know what you’re thinking: “what about that intersection/junction entity we’ve been ignoring?” You’re right. Now is definitely the right time to bring that up. How are we going to handle the many-to-many relationship? My preferred way is by modeling it explicitly.


While this may seem like a waste of space, this model allows us to efficiently query all the contents for a specific feed as well as all feeds that have a specific content without using the actual super column families making the whole lookup process more efficient. This form of modeling can also be called: Query Pattern Modeling.

How many lookup column families and which data columns you have in those families depends what you’re intending to use in your application data-wise and how you’d like to model your query patterns. Ultimately, modeling the data structure based on the type of data you’re after is the right way to go. I will note that some structures will require much less de-normalization than ours. In our example we opted for super column families to hold the bulk of our data and did our de-normalization for that up-front.

A note on performance: Modeling the intersection/junction explicitly caused us to create two lookup column families. It also caused some obvious, additional, de-normalization for both “feeds” and “contents.” Even with the extra duplication, this method is still very reasonable to populate lookup information for both feed and content detail pages. Both will perform well with this model. Both will cause two lookups: one to query feeds and one to query contents. This will remain constant regardless of the total number of feeds and the total number of contents (a good thing).

In this article, I’ve illustrated how to define a data model for a “Wide Column / Column Family Store” NoSQL database implementation. I’ve shown how to define the model and discussed an applicable modeling technique for our narrowly defined scope.

The information presented is relevant regardless of structure modeling technique you end up using for the “Wide Column / Column Family Store” NoSQL database implementation you choose.