Ida 21.02 will be released and available for customers beginning at the end of this month. Here are the highlights in this release as we continue to fine-tune the incredible accuracy performance we see from Ida as well as make various bug fixes and improvements.

  • No-match text is now configurable
  • Feedback Loop language toggle button styling changes (see both English and native languages used in this tool)
  • Capturing auto-utterance & initiator analytics to better understand who is handing off to Ida
  • Add Oracle ODA 20.09+ NLP model support for improved accuracy
  • Manual chat-ui language setting to force a specific language vs. auto-detection
  • Pruning features for audit data
  • Pruning & archive features for chat data
  • Check skill version against IUC version prior to testing
  • FBL Row Padding Fixes
  • Clean up Thumbs UI/CSS/HTML
  • Support separate DE processes for Help FAQ and NLP Failure process
  • Easy On/Off for Thumbs user satisfaction ratings
  • Audit reports for blank lines in answers
  • New 80/20 split administration page for training/testing data sets
  • Feedback loop now filters at server for improved filtering
  • Fixed MS Teams Variable Error
  • Now Capturing MS Bot User ID values

Product Update Notes

The focus for this release was to continue to improve NLP and utterance matching performance even beyond the 90% mark most of our clients are seeing. The central part of this improvement is supporting updated NLP models. As part of this support, the automated regression testing was significantly changed to more closely model real life thereby ensuring better quality assurance.

A series of features were added to understand how Ida plays in the larger context of an enterprise by tracking any referrals it gets, where it is being used and what channels it is running on (such as Microsoft Teams).

Next we have features added for better multi-language support such as now having a choice between a configured language for a user vs. auto-detection. Additionally, the language being used can now be passed to integrated systems for an end-to-end experience in your language.

We continue to add more skills to the library of Ida. Clients can get up and running quickly by using this catalog. Recently we have been adding content around the return to work/campus. Finally, many bug fixes, performance improvements and other minor updates are included.

Contact us below to learn more and setup your own personal demo:

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Every industrial revolution has been defined by increased efficiency and reduced costs. The new digital revolution we are embarking upon is no different. Things that took days to do can now be done in seconds, and things that used to cost hundreds of dollars can now be accomplished by spending less than one dollar.

Conversational AI is cool, but that’s not why it will change the world. It will change the world because it will be better and cheaper than many of the things we pay humans to do today.

In this blog we will focus on the impact of digital assistants in the world of human resources (HR). And how it will change how organizations can service requests and questions from employees and managers in a way that reduces organizational costs and improves the level of service. We will therefore break down the two areas that should result in large reductions in operating costs: the HR help desk and HR staffing levels.

What you will see is that even the most conservative approach to saving costs with a digital assistant will realize between a 10%-30% reduction in help desk and HR costs in one year. And that can be doubled in two years. Plus, you’ll be providing better service to your employees and managers too!  

As Larry Ellison pointed out last year at Oracle OpenWorld. It’s not the software that is the most expensive item, it’s the cost of all the people who have to deal with all the ramifications of running the software. 

1.   HR Help Desk Costs

It has been said that help desks are the cost of (a lack of) quality. Scattered, and often misleading, information and complex processes inevitably force employees to reach out to live agents to help them solve their problems, answer their questions, or complete a task. Help desks are, often, the cost organizations pay for failures elsewhere in their internal systems. 

So, let’s break down the staffing costs of a help desk in order to drive to an expected cost saving:

The average number of service agents per 1,000 seats ranges from 5.4 in the healthcare industry to 21.9 in the financial services industry. This is the metric that defines the staffing levels of an organizations help desk. 10 per 1000 would be a conservative average number across all industries, and so we will use that for the model in this exercise. 

This means that for an organization with around 20,000 employees, the number of agents is around 20. In North America the average salary for a service desk analyst is $41,000. Multiple that by 20 and you get $820,000 per year. But that in itself is not the complete picture. 

The ratio of agents to total service desk headcount is a measure of managerial efficiency. The average for this metric worldwide is about 78%. What this means is that 78% of service desk personnel are in direct, customer facing service roles. The remaining 22% are supervisors, team leads, trainers, schedulers, QA/QC personnel, etc. And those people are even more expensive. This takes the headcount up to 25.

The average salary for a service desk supervisor is $61,000 and the average for a service desk manager is $75,000. Which means that those extra 5 people push the staffing costs up by at least $305,000. Driving the total cost of salaries staffing the service desk for an organization of 20,000 up to $1,125,000. And then when you factor in utilities, technology and facility expenses this raises the number to over $1,325,000. 

And one final statistic to keep in mind. While the average overall employee turnover for all industries is 15%, inbound customer service centers have a turnover rate on average of 30-45%. It should come as no surprise that service center turnover is at least double what you’d see in other businesses.

Based on age, the differences are stark: workers age 20-24 stay in the job usually just 1.1 years, while workers 25-34 stay 2.7 years on average. 

And the key metric here is that it costs on average around $12,000 to replace agents that leave. Why? The costs of turnover include the following:

  • Recruiting
  • Hiring time (HR time, interview time)
  • Training, including materials and time
  • Low-productivity time when employees first start out
  • Supervisory time
  • Overtime (remaining staff may have to cover extra shifts)

So, going back to our original metric of 20 service desk agents, if 40% leave each year, that equals 8 annual replacements at a total turnover cost of 8*$12,000=$96,000. 

So, as a grand total, an organization of 20,000 employees has to pay an annual cost of around $1,421,000 year to staff their service desk. 

In terms of how cost per ticket is calculated (a key metric), this also depends on the number of tickets closed per agent per year. Again, this varies a lot by industry.

Help desk tickets per agent, per month by industry

Figure 1: Tickets closed per agent per month

The average number of tickets closed per month per agent is around 120 cross-industry. So, per year it is 1,440. Which means that with 20 agents the expected number of cases closed (not always successfully) is 28,800. 

This means that the average cost per service ticket is around the $49 mark ($1,421,000 / 28,800) if you take into account a broader range of costs than just service agent salaries.  So, while generally published average costs per ticket are estimated to be around $19-$20 per ticket, the true cost is much higher, but with massive variance based on industry. 

The good news is that the actual logistics around achieving ROI are therefore pretty straight forward. Instead of hiring 40% new staff every year due to attrition, just have the digital assistant pick up the slack and do not hire any new staff. This immediately saves your organization $96,000 in turnover/onboarding costs. Plus allows you to drop the salary costs by around $450,000 (40% of a $1,125,000 payroll). 

It also allows you to reduce other costs associated with your help desk. Utilities, technology costs, and facility space (you can downsize based on the reduced headcount). 

For a digital assistant, the average cost per ticket is less than $1. Which means that if you replaced 40% of your help desk calls with digital assistant calls, this would result in digital assistant costs of less than $11,000.  Factor in a reduction in headcount and other expenses, plus a hiring freeze, and you would see an overall reduction in costs from $1,421,000 to $806,000 in just one year (see diagram below). And even greater savings after two years.  

Help desk cost infographic

Figure 2: HR Help Desk ROI using a Digital Assistant

Also, and just as importantly, the quality and accuracy of the digital assistant will continue to increase each subsequent year and will not plateau (as it does with humans). This is due to two factors:

  • Digital assistants don’t leave your organization. There is zero turnover. 
  • Digital assistants benefit from machine learning. The more they see and the better training they are given, the more accurate they get. As an investment, they are a win-win all round. You teach them something once, and they remember forever. And they’ll never leave you or call in sick. And they’ll work 24/7, 365 days of the year. And can even speak multiple languages. 

But this is not where the story of ROI ends, it’s really where it begins. Help desks are really designed to handle the easy, first level stuff. Once you get to the next level (where the agent can’t handle the ticket because it’s too complex for them), the costs are in the hundreds of dollars per ticket as you are now dealing with a more expensive level of staffing and more minutes required to solve the problem or meet the request. This is where HR staffing levels come into play. 

2.   HR Staffing Costs

In the world of HR, HR experts handle many of the day to day HR activities and employee/manager requests in an organization. In the same way that there are agent staffing levels per industry, there are also HR staff to employee ratios too. And this ratio does vary per industry. Typically, the more complex the organization the higher the staffing level. But size matters too. There are economies of scale that kick in once an organization gets really big. But being global, having a mix of full time and part time employees, union and non-union, blue collar and white collar, will dictate higher ratios than a company where most people fit a similar profile. 

But this does not mean that the ratio is stuck and cannot be changed. There is one key aspect of the staffing ratio that is in complete control of HR, and, therefore, has a huge capacity for change. And by change we mean reduced! 

The role of HR is a key variable factor that influences the HR staff to employee ratio. A highly operational HR department will do different work and require a larger HR workforce compared to a highly strategic HR department. So, what specifically does this mean? How can HR move from being mostly operational to being mostly strategic (a much more fun and productive role btw). 

The answer is to move traditional HR admin tasks from humans to a digital assistant.  HR admin work is probably the least popular thing that any highly educated and highly paid HR expert has to do, so removing this onerous work from their plate is a good thing! 

Running reports, answering requests for data, following up with managers to ensure key tasks were performed, entering data into the HCM system. These are all repetitive operational tasks that can be automated and handled by a digital worker. 

All this stuff is boring and repetitive to humans, and it takes a lot of time. But to a digital assistant it is fun and can be done extremely quickly. And the “right” digital assistant, with the proper skillset and training, can do almost all the HR administrative tasks that an HR expert can do. Often better, as they don’t forget obscure details and business rules, they don’t make mistakes, and they bring their “A” game every single second of the day. And, as stated before, they don’t leave your organization, turnover is zero, so wisdom is accumulated and not lost via natural attrition. 

So let’s get into the math of the ROI. Bloomberg Law’s 2018 HR Benchmarks Report states that HR departments have a median of 1.5 employees per 100 people in the workforce. At the time, this represented an all-time high as it had long been around 1.0 per 100. Both the Society for Human Resource Management (SHRM) and Bloomberg numbers were very similar, so this number is considered very accurate.

SHRM also noted a clear reduction in the ratio based on organization size (an economy of scale). However, as the size of the company rises, so does the average compensation to HR staff (which explains why published averages are very misleading). Working in HR for a large company can be twice as financially rewarding as for a very small company. The reason being complexity (on many levels). If you want HR people who understand complex organizations then you have to pay a premium. 

HR to Employee Ratio Graph

Figure 3: HR staff to employee ratio’s cross-industry

Using our example of an average organization with 20,000 employees and ratio of 0.4 HR staff for every 100 employees, the HR staffing level would be around 80. At this size of an organization the average level of HR compensation would be around $100,000. Making the total spend equal to $8,000,000 per year. Note: that’s a lot more than the $1,125,000 spent on the service desk salaries. 

In the world of HR staffing, turnover is more inline with other industries, around 15% per year. Though the cost of hiring HR staff is much higher than the $12,000 for service agents. For HR Staff it costs roughly $30,000 to replace the turnover (recruiting, interviewing, training, etc.).  So, in our example, 12 new staff are required every year at a turnover cost of $360,000. Making the total annual cost equal to $8,360,000.

The big question then is how much of this work can be taken over by a digital assistant? The answer isn’t quite as clear as with the service desk. It all depends on the skillset of the digital assistant, and HR taking a proactive approach to how it replaces natural attrition of HR staff. 

But the expectation, and based on the results of early projects, is that for the best digital assistants it is at least 10-30% of HR admin work that can be transferred from HR staff to the digital assistant. But that is just for 2020. This number should leap forward in bounds each year for the top digital assistant performers. 

Using a conservative approach, if a company decided to hire just 5% new HR staff each year instead of the usual 15%, and used the digital assistant to pick up the slack of the 10% of the positions left unfilled, the savings would still be considerable. Let’s examine the resulting cost savings and see how this looks in detail. 

In this scenario, HR costs would reduce in year one from $8,360,000 to $7,680,000 as the new staffing level would drop from 80 to 72 (12 people would leave and only 4 new people would be hired). While at the same time digital assistant operational costs would amount to roughly $360,000 to cover the slack. So, the total net saving in year one would be $680,000 (excluding implementation and configuration costs). But with a huge potential for much bigger savings in future years. 

Implementation and configuration costs for a digital assistant that could handle both help desk and HR admin tasks would likely cost in the realm of $100,000 to $250,000 to implement. But this would be a one-time fee and would result in a year one total saving of HR staffing costs between $800,000 to $900,000. 

Year two would see greater savings, as there would be no implementation costs, and the new hire rate would again be set at (a maximum) 5%. Taking the HR staffing level from 72 to 65. 

Year two HR staffing costs would therefore be $6,500,000. With a turnover cost of $90,000 (10 people would leave and only 3 new people would be hired), giving a grand total of $6,590,000. Because the digital assistant would be taking on more work in year 2, that cost would rise to $396,000. Which would result in a total cost in year 2 of $6,986,000 (see graph below for details). 

HR professional cost infographic

Figure 4: HR Staffing ROI using a Digital Assistant

In summary, correct implementation of a digital assistant solution that can handle both HR help desk AND HR admin requests is by far the best approach to achieve maximum ROI.  Done effectively it will also realize superior service levels by providing faster and more accurate turnarounds for your entire workforce in a way that is far more convenient for them. 

Welcome to the world of high ROI, and welcome to the next industrial revolution. It’s ready and available now. 

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It was a different world back in the 1960’s, and the TV series “Mad Men” did a great job of taking us all back to a point in time when people did things very differently than they do today. In the show, Don Draper (played by Jon Hamm) was fortunate enough to have Peggy Olsen (played by the inimitable Elisabeth Moss) as his executive assistant (aka secretary). Interesting note: by season 7 the tables were turned and Don reported to Peggy. 

Peggy’s job as an executive assistant was to smooth out Don’s life, to the extent that all he had to do was attend to the “important stuff” while she took care of everything else. And made sure that he did all the things he needed to do to become successful, and all in a timely fashion (“Hey Don, before you head out can you …. “). That was the essence of the executive assistant, and only the rich and powerful were assigned this luxury, as the cost would have been prohibitive to provide one for every single person in the organization. 

So, what exactly did Peggy do? Well, lots of things! Calendaring, scheduling, reminding (lots of this!), organizing, running of reports, troubleshooting issues, strategic counsel, managing expense accounts, implementing new processes, managing the data in multiple systems (especially payroll), onboarding, offboarding, communications, and coordination of events. 

And did we mention having to remind Don constantly, but delicately, of what he needed to do? That was a full-time job all in itself. And Peggy also helped Don complete things he was too busy to do himself, or could never do properly even if he did have the time (generally due to three-hour business meetings over lunch and cocktails)?

Being an executive assistant meant you had to be really smart, super organized, and have astonishing communication skills. It wasn’t like Don provided a detailed list to Peggy of what he needed each day. Generally it was a few words blurted out as he exited the door. Peggy then had to decipher a brief utterance and figure out everything she needed to get done. No wonder that eventually she ended up being his boss after he left on a leave of absence that she no doubt had to organize for him. 

So that’s what executive assistants did, and still do today. And what about digital assistants (the reason for this blog)? Well, a good one does pretty much the same thing as Peggy! Only they do it for every single person in the organization, 24/7 and 365 days a year. And for tiny fractions of money. 

This is why we are entering the next industrial revolution. Your entire workforce can now focus on the “important stuff”. With no need for departments full of people to help them when they get stuck, or head in the wrong direction.

But to understand the fork in the road we are at today, it’s important to differentiate between chatbots and digital assistants. And to do that we need to look at what happened, and the mistakes made, from the mid-90’s onwards. 

The advent of the internet in the mid-90’s sparked the beginning of an era of bad web site development and poor user experiences (especially in the Enterprise). From the infamous “link farms” that forced the user to guess (like whack-a-mole) which was the right place to click. To sites that were so confusing that the average person would just give up and call someone after wasting countless minutes searching for what they needed. And even then, things often went from bad to worse. The person (eventually) answering the phone often didn’t know the answer and would pass you to a department where they thought someone else would be able to help. All accompanied by massive wait times and mounting frustration. Being bounced from one support person to another was not productive and it certainly wasn’t fun. Ultimately it was an expensive waste of time where nobody was a winner. 

And this is why the most expensive aspect of Enterprise systems isn’t the actual software, or hardware required to run them. It’s the people who need to support these systems (functional and technical) that make up the greatest cost. 

And now we are headed into a new era of conversational UI. And, guess what? The people who brought you the “link farm” are now trying to sell you the “chatbot farm”. This is a long, long, way from Don and Peggy. Don had one executive assistant for a reason. She understood everything, and she could do almost everything. And for the things she couldn’t do, she always knew who could. And her job was to just make sure things got done properly and on time. 

So, what is a “chatbot farm”? A chatbot farm is an army of chatbots with super narrow knowledge of specific subjects. They will be (if organizations take the wrong fork in then road) the “link farm” of the future. Forcing the human to guess which chatbot knows how to help on which subject, and then hope for the best. And it gets even worse if the requirement to satisfactorily complete a task means that you have to discuss multiple subjects. If that’s the case, then welcome to the concept of digital dead-ends.  

But, imagine an alternate universe where, instead of an army of disconnected chatbots with crude functional capabilities, we have one super chatbot that knows everything about your Enterprise, and which also understands the nuances of human conversation. Well, ladies and gentlemen, meet the digital assistant (let’s call it Peggy).The answer to all the problems you never knew you were about to encounter. 

To illustrate this concept, in the figure below imagine the dots are skills (small groupings of “intents”, aka things the chatbot can do). The problem with delivering skills as stand-alone features tied to individual chatbots is that this just provides linear access to what that skill can do (which in itself may not be fully comprehensive of all the things associated with the topic the human is interested in). And also assumes that the human asking the question knows which chatbot to engage with in order to complete the task at hand. Hint: they won’t have a clue! This tower of babel is represented in box 1.

In box 2 we see a representation of how the actual brain works and how an executive assistant sees the world. And, therefore, how a digital assistant (properly tooled and configured) operates. Box 2 is the digital version of Peggy Olsen.The ultimate, as Gartner would refer to it, “downloadable worker”.

Chatbot Skills vs. One Digital Assistant

Figure 1: A tower of babel or one clear voice?

From an IntraSee perspective, we have spent years building a “digital Peggy Olsen”. And, even better, a configurable version that can be tailored to your organization.

“Digital Peggy” is configurable because it is auto-generated by a complex AI and meta-data driven middleware that resides on Oracle Cloud Infrastructure (OCI). Peggy also utilizes Oracle Digital Assistant (ODA) as its core conversational UI technology, which it auto generates at design time via a single push button. 

Then at runtime it interacts with both OCI, ODA, other AI elements, and a massive skills library (created by IntraSee, Oracle, and custom built by clients) which also plugs into Cloud and on-premise adapters (created over a 10 year period) to manage and interact with all the systems necessary. 

Peggy is also super-scalable, and handles things like disambiguation, such that Peggy never leaps to assumptions as to what someone is asking it, and instead can ask follow-up questions to make sure Peggy understands the “ask”. Just like humans do. Well, the best ones anyway. Don Draper didn’t always make himself completely clear in what he wanted, and Peggy just needed to make sure before she took action. Plus, digital Peggy also can quickly be taught to understand terminology and acronyms used in your organization. And starts on day 1 with a general understanding of common business terminology. Almost as if “it” had been working with you for the past twenty years. 

And one more thing. Digital Peggy can speak around a 100 languages. So, welcome to the world of digital assistants, and welcome to the next industrial revolution. It’s ready and available now. 

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We are at the dawn of an artificial intelligence (AI) revolution and there is a lot of confusion about how this technology should be used to provide support for students, faculty and advisors in higher education. Terms like chatbots and natural language processing are thrown around, but many projects are not really AI and are not much better than the old IVR phone systems we hated. Press 1 for a better experience! Worse yet is that if you choose the wrong platform, you will be fighting a bad reputation and adoption issues for a long time to come. So let’s examine the top 10 features an effective digital assistant (or as some mistakenly term chatbot) should be offering your users.

1. Personalized answers, not “one answer fits all”

Any chatbot can answer an easy question where everyone gets the same answer such as, “What is a FAFSA form?” or “How do I reset my password?” In higher education, however, rarely are answers so generic. Higher ed culture is built on autonomy such that there are very few of these one-answer questions. Each department and school tends to have their way of doing things. 

What if the user asks, “Do I need to sign up for a cap and gown for graduation?” The answer for a full-time student in the school of engineering will be different than the answer for a part-time student in liberal arts. If your chatbot can’t understand this difference, then it really is no better than pressing 1 on MovieFone.

2. True natural language processing, not just a crude search engine in disguise

A true AI-based digital assistant relies on AI modeling. Any chatbot that promises to crawl your existing web site and “just work” should be met with skepticism. 

Time and again our student focus groups have told us they don’t trust or use search engines found on university owned web sites. The results are often contradictory and lack relevance. So why would your chatbot crawl a web site just as these web search crawlers do? You really are just deploying another web search disguised as a chatbot and that will not produce the experience or results you are looking for. A true enterprise digital assistant works in a fundamentally different and better way.

3. Deeply integrated with the SIS and LMS, and not just serving up static content

Any digital assistant needs to be able to help the student, and in order to help them effectively it needs to be able to talk to the two biggest systems of record in higher ed: the student information system (SIS) and the learning management system (LMS). Not only should the digital assistant be able to look up and use data from these systems, but also assist the user in conducting transactions such as paying their bill. If your chatbot is only providing links, than there is a missed opportunity much like we saw in the age of printed mapquest directions.

The digital assistant should have a catalog of adapters for your SIS and LMS systems, and a configurable suite of predefined  intents like “Show me my grades” so you don’t have to build everything yourself. 

Configurability is important, so you can change the delivered functionality based on your organizations needs, because in higher ed, nobody fits into generic boxes. 

4. Understands the meaning of human language, not just a click through navigation tool

The promise of AI and natural language processing is that a user can type in anything and the bot will understand them just like a human would. Many people assume this is a given for all chatbots, but that is definitely not the case. 

Crude chatbot solutions resort to providing a series of buttons to push you along a pre-determined path. Does that sound a little bit like the old IVR phone trees? Press 1 if you agree!

We believe a user should be talking/typing and not clicking. And the better the bot can understand humans (in over 100 languages too!), the less back and forth and the less clicking needed. Which, ultimately, leads to a faster, more enjoyable experience.

A true digital assistant is a navgiationless solution where people just “say” what they want to do

5. Has the tools to allow you to grow its understanding, not a one-and-done solution

The whole point of artificial intelligence is that it gets better over time, right? How will your digital assistant get better unless it automates feedback and monitoring and allows you to model and test new training methods in multiple languages.

And the digital assistant provider should also deliver monitoring and training as a service. If your AI isn’t learning then it’s not AI.

6. Includes advisor and faculty “intents”, not focused only on students

There are a lot of student use cases that typically are catered for in early chatbot projects, but we find it a shame that faculty and advisors are often being ignored. After all, if you make the advisor experience more effective and comprehensive, the end result is that advisors can better service their students. Which, consequently, contributes to happier students who are graduating on time more often. 

The challenge, however, is that your technology has to be up to the task. Securely automating advising processes isn’t a simple task. Data protection, advanced language understanding and entity recognition have to be the basis of this kind of solution. And many projects, using crude chatbots, don’t meet this standard. 

Our digital assistants are being used by advisors to look up data on advisees based on PeopleSoft security settings, and even process tasks like advising notes, or approvals on course load. In one example, by using the digital assistant, a user can approve a course load in under 30 seconds, when previously it would require navigating and updating two web pages with about 10 fields and take multiple minutes (assuming they can remember to do it correctly). 

7. Speaks the language of your institution, not just canned responses that could apply to anyone

While it sounds like a good idea to purchase a basic chatbot with pre-delivered answers to common questions, you have to consider what that really means. Not only will none of the answers be personalized to the user, but it also means answers will not reflect the nuances of how your institution’s processes work. 

If one of your goals is to offer better service than human beings, 24×7 and in dozens of languages, than your digital assistant needs to provide better answers than a human would. Not canned responses that don’t fully answer the question.

When a vendor tells you, “You just plug it in and you are live in a week,” be skeptical. 

The key is to have a business-user tool to allow you to easily, and without coding, manage and extend a catalog of questions. Additionally, you need a platform that isn’t generic by design. One that allows for sophisticated questions and answers.

8. Highly secure and protects sensitive data, not an open invitation for data breaches

In the age of cloud applications and data breaches, security is more important than ever. We have seen institutions deploy a non-authenticated bot for a narrow use case and believe that data security is not a risk. Even if your chatbot can only answer basic questions about admissions, you can’t prevent the user from typing in a social security number, a test score or other sensitive data. If you are not using an enterprise digital assistant, where is that data stored? Is it secure? 

Further, the security of the bot channel is often overlooked. Channels like Facebook and SMS are used, but keep in mind, those channels are not secure, and there are no guarantees for how that data is stored or how it is protected

Using a secure channel like a web channel or MS Teams will protect your data and provide your institution the controls needed.

IntraSee uses the most secure cloud in the Enterprise: the Oracle Cloud. And for customers using Azure or Oracle Cloud for other enterprise data, there are even greater assurances

We have also built in features like abbreviated logging of conversations to protect sensitive data ensuring that we are never storing your sensitive data in the cloud. This type of data security is something that basic chatbot vendors almost never can deliver. 

9. Can handle an extremely broad range of questions, not a one-trick pony

Many chatbots live on their own islands. They are locked into narrow functionality specific to the service desk software they come with, or a particular vertical space like admissions or financial aid. As Amazon has taught us, people want a one-stop shop to serve all their needs. 

Imagine a student has a question, but first needs to figure out which of the 20 chatbots they need to go to. That is a recipe for failure and isn’t really achieving the promise of artificial intelligence.

High performing AI should be able to walk and chew gum at the same time, just like a human! It is important to handle a wide variety of student questions, but it is also important you can leverage delivered skills from enterprise software partners. IntraSee’s digital assistant can combine our catalog of skills, delivered Oracle or PeopleSoft skills and custom client skills into that perfect one-stop digital assistant.

10. Helps drive student success, not a crude FAQ machine

It is one thing to be able to answer questions 24×7, but the goal ultimately is to help students. The best form of help is proactive, timely help. 

A digital assistant should be capable of reminding students about their to dos and holds, as well as initiating a chat with key reminders that an assignment is coming up or their enrollment window is opening soon. 

While reducing the cost of human-based service desks is the easy ROI case for digital assistants, their role in student success can be much more impactful.

If you haven’t seen a digital assistant or chatbot than can do all of these things, then just reach out to us below and we will setup a demo to show you live!

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