Higher Education is going through a major shift as institutions attempt to align to changing environments and student demands. Today’s student is looking for options outside of, or coupled with, a traditional four-year degree. The desire for life-long education and demand for lower-cost options has many traditional schools turning to online offerings. It has been a shift met with resistance by administrations, but minds are starting to change. Education, which was once believed to only be effective in a classroom, is now available in an online medium. Not to mention 24 hours a day, seven days a week, all over the world.

“This year 73% of schools made a decision to offer online programs based on growth potential for overall enrollment”

– 2018 Online Education Trends Report

Online education can open many doors for students who otherwise would never be able to attend a certain school. For example, Stanford this year rolled out 150 courses online. Students who never dreamt of attending Stanford, due to cost or distance, can now do so. The accessibility to high quality education is changing before our eyes. 

This wave was never more apparent than when Purdue University purchased for-profit Kaplan University in 2017 and turned it into Purdue Global with the aim of serving post-secondary education. A traditional, nonprofit land-grant university is shifting to meet the demands of present-day students. Being mentioned in the same press release as a for-profit education company was unfathomable, until it wasn’t.

We aren’t talking about only adult students here either. The trends point to traditional aged students (18-24 years old) turning toward online education as well.

”Students aged 18-24 saw the greatest year-over-year increase in online education enrollment at 115%”

– 2018 Online Education Trends Report

Unintended Consequences

However, no good deed comes without unintended consequences. Learning from a remote location can be a challenge. There is no building to walk into. There is no teaching assistant (TA) to sit down with. There is no residence hall advisor to check in on you. And rarely any other students to remind you of deadlines and schedules. 

Being online means you need online self-service mechanisms for support and help. A student’s success depends on it.

Many institutions have attempted to use their traditional telephone-based student support services. However, phone support is slow, it produces inconsistent answers, it isn’t available 24×7, and is mostly an experience students dread.

Also, help desks are not cheap to operate. Each call can generate a cost of at least $5 and oftentimes much more. 

Time has always been a student’s scarcest commodity. Whether it is balancing a full course load, or juggling work and family, students simply can’t waste time on hold waiting for someone to answer a basic question like: when am I allowed to register?

If perceptions weren’t bad enough about phone support, imagine how your worldwide student body feels about that support only speaking English. English second language learners require a special approach.

A 2018 study showed how many international students struggle in their relationships, with their finances, feelings of isolation and belonging, all of which affect their educational experience. For example, regarding isolation, only 35% of respondents reported feeling a part of the university.

– The International Journal of Higher Education Research

Digital Assistant to the Rescue!

Superman chatbot with a graduation cap

Able to help thousands of students in a single bound!

Digital Assistants are a type of enterprise chatbot that not only can answer questions and provide support, but they can assist you in completing tasks such as changing your email address on file with the registrar, or even signing you up for a class. They know who you are and can personalize their service to you. Today’s digital assistant is not your parent’s MovieFone.

Your digital assistant can be that self-service help, 24 hours a day, 7 days a week. Whether it is questions for the registrar, financial aid office or student services, the digital assistant provides consistent answers at speed. 

Average response time from the digital assistant is usually sub-second. With student demand for instant answers and their dislike for waiting on hold, chatbots are not only an essential tool, but oftentimes the preferred communication method of students.

Supporting students in their native language can bring a comfort to someone reaching out for help. Digital assistants can provide that multi-lingual help.

Figures speaking multiple languages

Speak all of your student’s languages

Our chatbot can speak over 100 languages automatically. That is a level of service that would otherwise be very expensive, and almost impossible, to provide with traditional support centers.

No conversation about student systems can be had without considering the impact to student success. Digital assistants open up all sorts of ways to help the student along their academic journey. While we have touched upon support functions already, sometimes students needs a more proactive nudge.

The digital assistant knows, for example, when a student has an assignment coming up or an advising appointment so it can make sure the student is reminded. If the student has a hold placed on their account which can interfere with graduation, the digital assistant can pop up and help them resolve the issue. 

You may be wondering… what about complicated problems with nuanced solutions or those that really need the personal touch? Chatbots don’t replace all personal interactions. The chatbot can sense when the student is stuck and transfer them to a live person or have someone such as their advisor follow up with a phone call or personal visit. 

An even better consequence of deploying a digital assistant is that it frees up time from key roles like academic advisors who no longer need to answer common, mundane questions. They can refocus on activities that help students be successful. Plus, it also negates the need to increase help desk staff to support an increasingly online student body.

And, of course, digital assistants don’t go to sleep, and never call in sick

Because digital assistants are extremely cheap to run, they are the key to keeping operational costs down, while student enrollment rises. 

AI is driving, and supporting, a new era of technological disruption

When you see the big names such as Purdue University, Stanford, MIT and Harvard getting into online education, you know the winds of change are blowing. And with those winds, we can’t lose sight of supporting our students in these new models and ensuring they are successful. Digital assistants can address many of the real problems presented by changing models. Consider that in the next decade, the incoming class of students will have never known life without Alexa or Siri or Google Assistant. This group will expect AI to be in place to support their needs.

This wave of change and the promise of cost savings, expanded enrollment, and better student success are compelling enough that CIO’s in higher education surveyed by Gartner designated artificial intelligence as the top game changer for 2019. 

Getting started with a digital assistant for your institution is as easy as our 12-week pilot program which has no long-term commitments. Contact us to learn more and see a demo for yourself. 

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There’s a very old joke in the software industry:

What’s the difference between a car salesman and a software salesman?
A car salesman knows when he is lying.

Unfortunately, there’s a huge amount of truth to this joke, and the explanation for why this is true is simple. Software is pretty complicated, and cars are pretty straight forward. With a car you can generally read up on everything you need to know in a matter of hours (enough to sell it anyway). While software can sometimes take months to really understand. Then factor in the myriad ways it can be used, and what business requirements people may be asking of it, and even the best sales people can be stumped at how to answer a question.

Oftentimes they really do believe they do know the answer. And that’s the source of the joke.

And this leads us to the new era of software: Artificial Intelligence (AI). And, of course, this means a whole bunch more woefully inadequate answers to very reasonable questions.

How does the chatbot know what to do when we ask it a question?
Sales Person:
It learns using AI.
But how?
Sales Person:
It just does. It’s called deep learning.
But what if it makes mistakes?
Sales Person:
It learns from its mistakes.
But how?
Sales Person:
It uses deep learning.

Obviously, this isn’t how any of this works at all. But given the mystery that shrouds all things AI, it’s not a surprise that these types of conversations take place.

So, to add transparency to what will be a very challenging subject to evaluate for many organizations, we’ve created a list of five facts that are critical to aid the understanding and implementation of a chatbot solution in the Enterprise.

Fact 1: AI is like a garden, it needs seeding & cultivation

Robotic hand gardening

Figure 1: Automation of nature and nurture

Out of the box, all chatbot engines (.ai) come with a general understanding of language and grammatical constructs. They also have a limited understanding of entities. Ex: I can ask a chatbot to do something “next Tuesday” and it will know what that date is, because it has knowledge of an entity that defines what a date can be. It also understands “today” and “tomorrow” too. It may also understand people’s names and cities in a country. “Is the Chicago office open tomorrow”?

What chatbots generally don’t know out of the box are the things particular to your domain. They don’t understand HR jargon, or campus terminology. They don’t know which departments you have, or job titles.  Terms like “leave of absence”, “expense reimbursement” and “travel auth” aren’t considered entities that have specific meanings, in the way that “next Friday” or “tomorrow” do.

So, it’s important to “seed” the AI on day one of your implementation. In many ways it’s just like how a farmer will grow a field. The farmer doesn’t just hope that nature will turn the field into a spectacular crop of wheat. Nature can only do so much, the farmer needs to do his/her bit also. The soil must be prepared, the seed planted, and each day it needs to be inspected and tended to ensure growth is according to plan.

For AI, it’s critical to plant the seed of domain knowledge on day one. And then monitor usage to identify areas it needs to be expanded, and also the specific areas it needs additional training and seeding in.  If the chatbot is HR focused then it needs an entire vocabulary injected and trained, in preparation for usage by actual humans.

If your chatbot doesn’t understand the difference between an adoption reimbursement program, and an adoption leave program, it will be destined to disappoint.

Fact 2: It’s not deep learning and big data that will be the key to success, it’s smart algorithms and neural networks

Last year we wrote a blog on AlphaGo Zero, and talked about how it wasn’t deep learning that made it so smart. The same thing is true of Enterprise chatbot implementations. Deep learning is a very powerful tool, but it isn’t the answer to everything. Neural networks and smart algorithms are the real engine behind a successful chatbot implementation.

Figure 2: Monte Carlo Tree Search in AlphaGo Zero, guided by neural networks

The lesson AlphaGo Zero taught the world was that AI is at its most powerful when it can map out its own neural network, while also readjusting decision points based on actual outcomes. This is why creating an incredible Chess or Go master is much easier than creating AI that cures cancer. 

In the Enterprise chatbot world, sophisticated decision networks don’t just create themselves, and deep learning doesn’t build them. They need to exist on day one, and they need to have been pre-built with domain knowledge and stacked with business rules that determine flows.

In the same way AlphaGo Zero needed to be aware of the rules of Go, your Enterprise chatbot needs to be aware of the best practice rules of the Enterprise. Only then can it be trusted by your employees, managers, students and faculty members.

In the Enterprise chatbot world, this equates to massively complex and sophisticated dialog flows that come pre-built and configurable for your business requirements. And that have over a decade of domain knowledge built into them.

Fact 3: AI is not a data warehouse

ones and zeros being inspected

Figure 3: AI is not a data warehouse

Those that remember IBM’s Watson winning Jeopardy may be disappointed to know that what they were really watching was a massive data warehouse stored in memory, with a search feature that had been built manually in order to meet the needs of one game.

There’s a lot of reasons why putting sensitive data into a proprietary AI engine in the Cloud isn’t a good idea. Security, data privacy, dual maintenance, and conversion effort, are just some of them. Your data belongs where it is right now.

It’s the obligation of the AI vendor to be able to plug into your data, not the other way round. As always in life, the tail should not be wagging the dog.

Of course, there are reasons why many vendors require this: laziness and lack of knowledge top the list.

Creating sophisticated data adapters that are a broker between your data and the AI isn’t easy and takes lots of domain knowledge. We know this because we’ve spent ten years doing it.

But with many vendors looking to jump into a market that they have no knowledge of, shortcuts have been taken by many of them. But that doesn’t change the fact that your data needs to be protected, and chatbot implementations shouldn’t be turned into massive integration projects.

Fact 4: A concierge chatbot is a requirement, not a nice to have

Tug of war between robots

Figure 4: Avoid chatbot confusion

Does anyone remember what a link farm is? Yes, they were awful. Quite possibly the worst manifestation of web-based technologies. And the problem was obvious, all those link farms did was sow confusion and frustration with the poor users who had to deal with them.

It’s 2019 now, and we face a similar conundrum. One chatbot that knows everything. Or hundreds of chatbots that know bits of information, but no way for the user to know which ones know what. Imagine being handed 100 help desk numbers and being asked to guess which one was the right one based on your area of need.

Fortunately, the problem has a solution. A concierge chatbot can be used as the focal point for all questions. All the concierge is responsible for is knowing which chatbot knows the answer to which question, and then seamlessly managing the handoff in the conversation, such that to the human it feels like one conversation with one chatbot.

This way the humans only ever need to start a conversation with the concierge. The ultimate one-stop bot.

Having worked extensively with Oracle’s chatbot framework, we not only can recommend it very highly, but we can also attest to its concierge capabilities. So, while Oracle is rolling out lots of small function-focused bots (they call them skills). All these bots/skills can be managed with one concierge chatbot automatically. Meaning you can have one concierge that includes Oracle delivered bots, IntraSee delivered bots, and also custom bots created by you.

Oracles uses the term “Oracle Digital Assistant”. What this is, is concierge chatbot capability under one technology stack. 

Fact 5: AI that requires massive human intervention, and coding development, isn’t AI

Storm trooper legos with guy at laptop

Figure 5: We like Joe, but Joe shouldn’t be creating Enterprise chatbots

AI that requires 95% of all its functionality to be created by the human hand isn’t really AI. It’s cool, but it’s not AI. It’s also not supportable or maintainable. The weakest link will be the human hand that pieced it all together. And if that hand has no domain knowledge, it won’t just be buggy, it will be stupid.

The real key to AI is not just automation of the task a chatbot can complete. It’s automation of the creation of the chatbot itself.

This blog has been about the facts as we see them at IntraSee. So let’s look at the facts of what a sample chatbot pilot generated. The background being: 200 FAQ’s, 16 view data intents, 6 transactions (promotions, transfers, etc.), 10 reports (yes, a chatbot can run reports). Here’s the technical numbers:

  • 24 Custom Entities
  • 690 Custom Component Invocations
  • 2,696 System Component Invocations
  • 3,386 States
  • 101,609 Transitions between States

We firmly believe that automation of creation is the key to AI success. Manually coding over 100,000 state transitions creates inherent instability, and leads to what we would call, a Frankenbot.

At IntraSee we have automated the creation of a chatbot, such that with one push button we can generate hundreds of thousands, even millions, of chatbot states, transitions, invocations, and entities. We do this for multiple reasons:

  • We remove human error from the equation.
  • We simplify the management and maintenance, such that a business user can easily deploy any changes.
  • We massively shorten implementation times down to a just a few weeks.
  • We can deliver more in four weeks than would normally be possible in over one year.
  • We can deploy mass changes, risk free, to a chatbot in a matter of minutes.

Please contact us if you’d like to learn more…

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It was a pleasure to attend another fantastic Gartner conference in Las Vegas (November 26-29, 2018). And while Amazon had their own mega conference (re:Invent 2018) down the road at the Venetian, the smart set were taking a broader look at the future with the team from Gartner.

So, what we’d like to do is break down the key messaging that we got from the conference, based on the tracks that we followed.

So here we go: Gartner’s key messaging from November 2018.

“IT organizations need to stop thinking in terms of projects, and start thinking in terms of products.”

– Gartner

This was the keynote theme for 2018. Last years theme was that IT needed to discover the word “yes” in their vocabulary. This year Gartner focused their messaging on redefining how IT needs to partner with the business community. Primarily, the advice was that IT needed to stop seeing the world in terms of projects, and instead embrace the concept of products as a means of implementing solutions. And, of course, it wouldn’t be a technology conference without the introduction of new a catchy phrase: PRODUCTology.

The concept is pretty straight forward and seeks to explain a lot of bad history when it comes to how IT has attempted to implement the dreams of the business community over many decades. And can be summed up as: projects are bad, products are good.

And the explanation makes a lot of sense. Projects are, by their nature, things of a finite duration. Risk needs to be managed. Scope needs to be controlled. Expectations need to be set. And then, when the project is complete, everyone moves on to the next project. Leaving in their wake a sterile, half-baked solution, that in a matter of months begins to age and crumble. The technology equivalent of a potted plant that never gets watered.

Meanwhile products are forever (well, at least until their replacement comes along). By their nature they are born of innovation and designed to be an entity that continues to grow and morph over the years as demands change and new ideas come to mind. Products have owners that care about them and lovingly tend to their good health, while also making sure they are meeting the requirements of the people using them. Products have roadmaps, they have interested parties, and they have a purpose.

In the consumer space, products are what make the world go round. While in the IT world it’s projects. And that is what needs to change according to Gartner (and we agree). If the business world wants to see their Enterprise systems become more consumer-like, then PRODUCTology is where it all starts.

“IT needs to come to the business community with ideas, as a trusted partner, and not be seen as an order taker.”

– Gartner

Gartner also spent a good deal of time urging the IT community to be more proactive with how they engage with the business community. Instead of waiting to be told what they needed to deliver, IT should be coming to the business community with innovative ideas on how to meet the demands of the era of disruption that we are now entering. Once the business community sees the IT group as an engaged and enthusiastic partner, then the nature of the relationship will completely change. And in ways that will benefit the entire organization. Gartner’s observation was that when IT and the business community collaborate well together, good things happen.

“IT can shape demand and become a thought leader.”

– Gartner

For IT to become a thought leader, Gartner recommended the use of external sources for inspiration. From Google, to Github, to Gartner themselves. There’s a plethora of information that IT can make use of, plus lots of vendors only too willing to demonstrate what they can bring to the table (which includes IntraSee by the way). IT should be looking to bring these resources and vendors to the attention of the business community as a means of creating a dialog about the art of the possible.

“If IT focuses on successful delivery, without trying to create everything themselves, then the business community will fund their initiatives.”

– Gartner

Gartner also believes that IT needs to stop trying to recreate wheels that have already been built. It’s not the job of IT to build anything. But it is the job of IT to ensure that “things” (ideally products) are built, and implemented, correctly. And that may mean a collaboration with a vendor that has a solution, but which needs configuration and extension that IT needs to be involved in. But that does not necessarily mean that IT needs to be building the code (which now has to be maintained). Once IT gets out of the code maintenance world, and into the innovation and enablement world, great things will happen for the organizations they support. What’s important isn’t how things get done, it’s what gets done that counts.

“Don’t try and build chatbots yourself. Building bad chatbots is easy. Building great chatbots is very hard. Find a vendor that understands your domain and can demonstrate excellence.”

– Gartner

And if there’s one thing that Gartner strongly recommended IT should not be trying to build, it’s chatbots. Instead, IT should be evaluating chatbot vendors and by a process of evaluation and demonstration figure out which ones truly match the hype, have the domain knowledge, and work securely with your existing Enterprise systems. Trying to build a “brain” from scratch may lead to a “Frankenbot” that consumes your organizations resources for many years.

The more research that IT does in this area, the less the chance that expensive, embarrassing, and time-consuming mistakes will be made.

“Don’t create ‘Technical debt’”

– Gartner

This isn’t a new concept. “Technical debt” refers to any code added now that will take more work to fix at a later time—typically with the purpose of achieving rapid gains. Shortcuts, hacks, and poor design choices will all lead to huge costs later on. Costs that aren’t just financial, but also reputational too. IT often creates technical debt for itself because of a desire to build things it doesn’t need to build. Then gets sucked into a maintenance and rewrite cycle that stymies its ability to take on new requirements from the business community.

Gartner very strongly believes that taking on unnecessary technical debt causes IT many issues that it needs to avoid.

“95% of bots in the market are s***”

– Gartner quoting Chatbot Summit 2018

Microsoft Clippy

Figure 1: Don’t implement your own chatbot version of “Clippy”

At IntraSee we would concur with this statement by Gartner 100%. The chatbot market right now is flooded with vendors who have massively subpar solutions. Many of them don’t have any experience in the Enterprise space, and have no domain expertise at all. Even an industry stalwart like IBM, with its Watson product, has failed to take a good idea and turn it into a viable Enterprise chatbot.

At IntraSee we firmly believe that a chatbot that is built by automated means, that can plug into your existing Enterprise systems, and comes delivered from day one with domain expertise, is the only way to deliver a chatbot solution.

And, we would say that this is something you need to see to believe. So, while you are looking at other chatbots in the market (which we encourage you to do), we would strongly advise you look at what we do too. You’ll see the difference immediately.

So please contact us to arrange an online demonstration of an Enterprise chatbot in action. And welcome to the world of PRODUCTology!

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The week of October 22nd was a fun time to be in San Francisco at Oracle OpenWorld. As usual there was an overriding theme that dominated the conference, and this year it was robots. Robots that manage entire Cloud architectures, and robots (aka chatbots) that engage in complex conversations with humans.

2019 appears to be set as the year autonomous robots take hold of the Enterprise, making it more secure than ever, and cheaper to operate than ever.

As is often the case, Larry Ellison led the charge by calling out all the features that differentiate a gen 1 Cloud vs. a gen 2 Cloud.

“Today I want to talk about the second generation of our cloud, featuring Star Wars cyber defenses to protect our Generation 2 platform. We’ve had to re-architect it from the ground up. We’ve introduced Star Wars defenses, impenetrable barriers, and autonomous robots. The combination of those things protect your data and protect our Generation 2 Cloud.”

– Larry Ellison

Having worked with Oracle’s Cloud architecture for a number of years now, we can say that we’ve seen a massive change from Oracle’s gen 1 (aka classic) Cloud architecture, to todays automated gen 2 architecture. As Larry went on to say:

“I’m not talking about a few software changes here and a few software changes there. I’m talking about a completely new hardware configuration for the cloud. It starts with the foundations of the hardware. We had to add a new network of dedicated independent computers to basically surround the perimeter of our cloud. These are computers you don’t find in other clouds. They form this impenetrable barrier. It not only protects the perimeter of the cloud, these barriers also surround each individual customer zone in our cloud. Threats cannot then spread from one customer to another.”

– Larry Ellison

And of course, the key to all this is AI and autonomous bots.

“Then we use the latest AI machine learning technology to build autonomous robots that go out, search and destroy threats. We’ve added lots and lots of more robots to protect every aspect of the cloud. It’s got to be a case of it being completely automated, completely autonomous.”

– Larry Ellison

Naturally, it wouldn’t be an Oracle conference if Larry didn’t call out Amazon for all their failings (price, performance, reliability, and security).

“They [AWS] don’t have self-tuning, they have no autonomous features, it’s not available. They don’t have active data guard. They have no disaster recovery. They have no server failure recovery. They have no software failure recovery. They’ve got no automatic patching. They’ve got none of that. We automatically patch and the system keeps running. In that case, we are infinitely faster and infinitely cheaper.”

– Larry Ellison

This is, most definitely, important stuff. As organizations are recognizing now, infrastructure matters. With Oracle owning the SaaS, PaaS, and IaaS layers, it can ensure security and reliability at every level.

What is also very significant is Oracle’s commitment to innovation and empowerment of its client-base. It now has a massively advanced PaaS layer that customers can take advantage of to flourish in an era of change. Which is in complete contrast to Workday’s approach, which is to lock their clients into a technological alley that stifles any attempt at UX innovation via automation. Workday’s euphemism for this is to describe it as “curation”. But in an era of change, curation is the enemy of progress.

And this brings us to the other hero of Oracle OpenWorld: Chatbots! In this new era of automation Oracle has now released its gen 2 chatbot technology. Now wrapped up in a package called Oracle Digital Assistant. This is a lot more than just a rebrand of what was called Oracle Intelligent Bots (OIB). It’s now a technology platform that enables true chatbot concierge capabilities.

This means that one Oracle chatbot can now seamlessly be a broker (concierge) for many Oracle chatbots. Such that the human user need only converse with one chatbot for any question it may have, regardless of how many chatbots and systems there are “behind the scenes”.

At IntraSee we specifically chose the Oracle chatbot framework for this and many other reasons (including being able to run on a secure infrastructure). Because we can automate the actual creation of an Oracle chatbot, we can also automate the creation of a concierge chatbot, while also being a service chatbot to another Oracle concierge chatbot.

In summary, we couldn’t be happier with Oracle’s direction for its infrastructure (IaaS), and also it’s chatbot technology framework (PaaS). 2019 will undoubtedly be the year for automation in the Enterprise. And for that you need automation at all layers of the Enterprise, and Oracle now has that (IaaS, PaaS, and SaaS). So, we would say that this was a terrific conference that sets the stage for an absolutely fascinating 2019.

Our prediction is that by the end of 2019 that chatbots will be considered the standard UI for the Enterprise for almost all self-service and help desk features, and that web-based applications will start to be seen as the province of the “back office”.

Also, on a personal note, we did get to speak at the conference jointly with Oracle on the subject of chatbots. It was a fun time and if you’d like to get hold of a copy of the presentation, you can now request it.

And, of course, if you’d like to see a demo of what the future (2019) looks like, please let us know and we’d be happy to oblige.

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The “build vs. buy” debate has a long and storied history in the Enterprise world. The big question being, “do we buy something that is close to what we want, but requires us to change how we do things? Or do we build something from scratch that does something exactly how we think we want it to work?” Then the Cloud came along, and suddenly the question was switched to “subscribe” vs. “build”, and the drivers to “build” became even more difficult to justify.

The advent of the Cloud was like “subscribe” slam-dunking on “build”.

The common consensus in the Enterprise world is to now always buy/subscribe, unless there’s a huge and compelling reason to build. These days, “build” has to justify itself, as “subscribe” is now the default decision.

Most organizations would prefer to focus on their main business objectives, and not get side-tracked into becoming Enterprise software development companies.

Building your own payroll system may sound exciting, but someone else has already done it, and you’re better off using what they built (and which the vendor will be committed to maintaining and upgrading forever).

OK, building a payroll system actually doesn’t sound exciting at all. But what does sound exciting is the idea of building a brain. But, builder beware…

Humankind’s fascination with the concept of AI goes back a very long way. Also, as anyone who has used Enterprise software will tell you, a “brain” for the Enterprise is a much-needed addition. In fact, the call center industry is built entirely on the fact that Enterprise software is too difficult for the average person to use – primarily because it seems to lack any kind of intuitive brain.

Today we are seeing some organizations embark on a journey to “build a brain” that understands and can interact with their Enterprise systems and the people in their organization. Not to rain on anyone’s parade, but we at IntraSee can tell you that this is not a simple task. And we know because we’ve done it. The brain is a highly complex organism and it’s a multi-year undertaking to replicate even parts of what it does.

While it might be tempting to open up LUIS (Microsoft), Watson (IBM), or DialogFlow (Google) and start to build your own Enterprise brain, we would suggest that you look at subscribing to a brain that is already built and understands how the Enterprise operates, and that can be configured to understand your particular Enterprise implementation.

R2D2, Frankenstein and Mr. Potato Head

Figure 1: What kind of brain do you want?

So, before we get into the mechanics of chatbot creation and the complexities it deals with, let’s discuss the types of chatbots that you could end up either creating or buying. Note: Don’t assume that what a vendor is selling is anywhere close to actually meeting your needs.

In doing so we’ve characterized chatbot implementations into three types of outcomes:

  • Basic: aka Mr. Potato Head
  • Frighteningly complex, unstable & unpredictable: aka Frankenstein
  • Intuitive, reliable, knowledgeable: aka R2-D2

To paraphrase an old proverb, “The road to chatbot hell is paved with good intentions”.

  1. Mr. Potato Head
    This is the most prevalent chatbot today. A chatbot that can do a handful of things with a moderate level of precision. But with absolutely no hope of ever becoming anything sophisticated enough to operate at the Enterprise level. Initially there will be some excitement at the novelty factor that Mr. Potato Head brings to the table, but ultimately he’ll be perceived as nothing more than a toy. And most definitely not the advent of a new era of AI.

    Mr. Potato Head is most likely just a very basic version of what’s called an FAQ chatbot, and not even capable of integrating with your own knowledge-base(s).

  2. Frankenstein
    This is, literally, the most dangerous of all chatbots. Typically, the Frankenstein chatbot takes many years to build given its epic scope. Plus, costs lots of money (bye-bye ROI), and consumes masses of resources. The problem with the “FrankenBot” is that from a distance it looks like it might be able to do the job. And for a while it may actually function decently. But, like all ill-conceived monsters, eventually it will fall apart and begin to wreak havoc among your organization. Wrong answers, corrupt data, security breaches, instability, and an angry mob of employees are the future of the “FrankenBot”. And the temptation will always exist (given how much money was pumped into it) to somehow make one more fix that finally resolves everything. But the problem is that the more you add to it, the more unstable and unmaintainable it will become.

    Ultimately the “FrankenBot” will take your organization down a rat hole that will set you back years, and will also sow organizational distrust that the next chatbot implementation team will have to deal with.

    Simpsons Riot Scene

    Figure 2: The inevitable end of the “FrankenBot” project

  3. R2-D2
    What’s not to like about R2-D2? A great example of how AI should be done. Let us count the ways:

    • Understands your Enterprise systems out of the box
      (ex: it even knew how to fix the shield generator on the Naboo ship)
    • Multi-lingual
      (actually, we’d need to add C-3PO’s linguistic skills to check this box)
    • A means of extending its knowledge-base by hooking into your knowledge base
      (ex: R2-D2 was able to plug directly into the Death Star to answer the question, “where is the Tractor Beam power source located?”)
    • Reliable
      (ex: always clutch in any situation)
    • Does more than just answer questions
      (ex: was able to assist Luke Skywalker by fixing the stabilizer and adding power to the X-Wing)
    • Built in a modular fashion
      (ex: was able to use some of C-3PO’s circuits after taking some damage in the battle of Yavin)
    • Highly secure
      (ex: even when captured, R2-D2 would never reveal anything to the wrong people, though it could have used better encryption of its databanks)

    R2-D2 tapping into computer system

    Figure 3: A good chatbot needs to know how to plug into your systems and access your data in your formats

In order to understand why building a chatbot from scratch is a daunting process (better suited for a “buy” decision). Let’s walk through what a chatbot actually needs to do, which can be broken down into three core competencies:

  • Communication
    • Understand the command of a human, and also understand how to relay a message back to the human. In potentially over 100 languages.
    • Chatbot terminology: This means Intents, entity definitions, utterance training, and language translation.
  • Thinking
    • Contemplate the “command” and consider what the appropriate course of action should be. Taking into account what is “allowed” to be done, what “can” be done, and what “should” be done.
    • Chatbot terminology: Dialog flows are used to determine which logic paths the chatbot should consider, and what decisions it has to make. Intelligent/intuitive branching logic is used to figure out both context and applicability in order that the optimal branch is taken.
  • Instructing
    • Ensure all the appropriate functions of the command are handled and any difficulties are dealt with in an elegant fashion.
    • Chatbot terminology: API awareness, coordination and execution. Note: The chatbot can’t actually do anything itself (in the same way a brain is useless without a physical body to respond to commands). But it does need to know how to interact with an entire suite of Enterprise API’s (REST, SOAP, RPA, custom, etc.) – which for some commands can be very complex.

Chatbot development tools come delivered with a lot of capability in the “communication” realm of the chatbot. And, of course, some better than others.

Where many fall short is with the “thinking”and “instructing”. For various reasons chatbot vendors think it’s a good idea for everyone to have to create their own “thinking” and “instructing” components. At IntraSee we think that’s a cop out, and that’s why we built these capabilities for you, and deliver them out of the box.

Also, because chatbots rely so much on API’s, they require a rich catalog of API’s for every system the chatbot needs to communicate with. Oftentimes this catalog does not exist, or is woefully inadequate, which requires someone (you) to build it. Which is why we spent the past ten (10) years building Enterprise Adapters and API’s for the major Cloud and on-premise Enterprise systems. Just so you don’t need to.

Remember the golden rule: without all the appropriate Enterprise Adapters and API’s, the chatbot is just a “brain in a jar”.

Brain in a jar

Figure 4: It’s just a brain in a jar until it’s hooked up with your Enterprise systems

IntraSee has spent over ten (10) years building an extensive catalog of Enterprise Adapters that are API enabled. Even for systems that are “API-challenged”, like PeopleSoft.

The question, before any such project begins, isn’t can you do this (though very few people can)? It’s, should you do this? Is your IT organization ready to become an AI development shop, and spend the next 10 years trying to create the perfect chatbot that understands your entire Enterprise suite?

Frankenstein on a table being assembled

Figure 5: How NOT to build and maintain a chatbot

If someone has already done this, wouldn’t that be, as Gartner advises, an easier and better solution?

The unfortunate truth is that the vast majority of chatbot solutions were built by hand from the ground up. Every dialog flow is hand-coded, entities are manually defined, and integrations with data and content are created by a coder with no knowledge of how your systems actually work. Even business rules, and often core branching logic, is hard-coded, forcing dual maintenance in a system you can barely understand. The chance of this business logic being wrong is massive. And there’s no simple way for you to ever know without constant regression testing – every single day.

What has been created isn’t just spaghetti code, it’s multi-dimensional spaghetti code.

Neural Network

Figure 6: Is this something you want to manually build and maintain?

AI that is driven from manually coded IF statements is not AI

At IntraSee we don’t hand-build any chatbot solution. Everything is automated and generated from an engine that is already Enterprise-aware, and a pluggable architecture that operates like a neural network, communicating across multiple lobes of the “brain”.

In a similar way to how neurons communicate across the synapse by switching protocols (electric to chemical), we have adapters that manage protocol differences in the many systems the chatbot needs to communicate with.

If you are considering implementing a chatbot solution for your organization and don’t want Mr. Potato Head, or a “FrankenBot”, then please contact us to learn more…

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