The HIUG Interact Conference is an annual user-driven meeting of Oracle application Healthcare users. The conference comprises 600+ attendees, 40+ vendors, and more than 175 educational sessions/clinics.

Join us for this session hosted by Gideon Taylor:


PeopleSoft Power Tools to Boost ROI: Chatbots, eForms, and RPA

Presented by: Paul Taylor (Gideon Taylor) and Andrew Bediz (IntraSee)

Chatbots, eForms, RPA. There is no better return on investment in enterprise software than automation. Not cloud, not SaaS, not upgrades. The single biggest cost to enterprise software is the people needed to run it. Automation at all levels will drive efficiency and save your organization money.

Automation can be applied to various pillars, but the three most common are (1) automating work your backend users perform, (2) automating work your self-service users are asked to do, and (3) automating the work to support all of your users. For PeopleSoft customers, all three of these automation opportunities are available today! Join us for a crash course on the tenets of automation ROI and see how you can maximize the value, productivity, and performance of your PeopleSoft system. We’ll show you how you can automate user tasks and support with world-class digital assistants; automate business processes with Fluid eForms; and automate heavy backend work in PeopleSoft with RPA technology.

Eliminate clicks, simplify processes, increase visibility, and accelerate transactions. You will spend far less than on a new SaaS and have much greater returns. If you’re looking for more productivity, more efficiency, and more value, we will show you how to unlock that today from your HCM / FSCM / Campus Solutions systems.

If you missed these sessions, the slide decks are now available upon request.

Don’t forget to check out even more sessions from our sister divisions. If you would like to prebook a personal demo with us click the Contact Us button below and we will get you scheduled at a time that works well for you.

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The next release of Ida, our digital assistant, will be available April, 2022. Clients can talk to their account teams about a deployment schedule that works for you.

22.01 in Summary

There are two big, new features in this release of Ida. The first is called Ida Suggestions. Users of digital assistants tend to ask questions only when they have a problem. It is a very reactive pattern that is hard to break. This behavioral trend can be a barrier to discovering new ways the digital assistant can help you. We are focused on flipping this dynamic to a more proactive model where Ida routinely adds value to your user’s days. Ida Suggestions is a new, proactive feature which will suggest to users ways in which Ida can help. Whether it is what is popular lately to what is seasonally relevant, Ida will give you that little nudge to solve your issue before you even know you have a question.

The second feature of note is a new Feedback Loop mode called, High Value Mode. When High Value Mode is enabled, Ida will algorithmically target certain interactions where you can focus ratings/annotations for maximum value to the machine learning. We expect this mode to provide 10x more value per hour spent rating which ultimately will save our clients a ton of time while keeping the accuracy very high.

The release also includes routine fixes, new reports, training materials and catalog updates.


Release Notes

  • PeopleSoft on-prem environment refresh guide
  • New 22.01 Training Videos
  • Breakout collision fix when running in DA
  • Improve security packaging for on-prem
  • Dynamic location entity and answer source
  • Improved remote call request error logging
  • Added “Who built you?” intent
  • Feedback loop High Value Mode
  • Improved error handling when no questions available
  • Friendlier admin previews for remote answers
  • Thumbs Results Report
  • Long Term Trend KPIs
  • Cloud based, realtime thumbs satisfaction data collection
  • Update ChatUI to support embedding in ServiceNow
  • Add DA specific metadata fields to automated deployment
  • New live NLP data reports
  • Updates Convo Dashboard to use Convo Log Summary Table
  • Updated Convo Log reporting table
  • Added Question Type component for client use
  • Resolved an issue where phones/addresses weren’t using self-service display flag
  • Better handling of step-up authentication when user doesn’t exist in IUC
  • Added “incorrectly presented” to auto test output
  • Improved performance of chat locations report
  • Mobile MS Teams task module fixes
  • Ida Suggestions (what’s new, not tried, popular)
  • FBL simplified ignored outcome option
  • FBL filter by topic option
  • Report: Monthly Active Users (by Org)
  • Fixed an issue with an excessive margin on reporting pages
  • Support for groups in FAQ import file
  • Corrected FBL match calculation in Metrics Report
  • Added clarity to some intro text
  • Removed dependencies on IntraSee WebUX modules for address in-chat form
  • Added consistency to labels and naming
  • Ability to add an FAQ directly from FAQ Search page
  • Updated non-auth-to-auth handoff response

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

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Today we have a big announcement. IntraSee has joined the Gideon Taylor family. Both companies have been stalwarts in the Oracle ecosystem for more than 15 years. While IntraSee’s focus has been on the user’s experience in the enterprise, Gideon Taylor has been known for the automation of business processes. It was natural to join the two together. Our customers now benefit from the back end to the front end with a focus on driving real ROI whether you are on premise, in the cloud, or on SaaS. 

My co-founder, Paul Isherwood, and I started IntraSee in 2005 and what a ride it has been growing from a consulting company, to a software company and ultimately a SaaS Cloud company. We have successfully navigated through major shifts in the enterprise software market, the financial crisis of 2007, the beginning of the cloud era and most recently the pandemic. No matter what was thrown at us, we adapted to serve our clients. 2021 was no exception with the sudden passing of Paul.

In this next chapter of IntraSee, we become a new division of Gideon Taylor where we will continue to serve our existing clients and with our digital assistant, Ida, carve out an exciting path for both companies. I will lead that division and look forward to a long partnership with Paul Taylor and his leadership team. You can read all about our announcement in the press release issued today. 

I would like to take a moment to address all the important people who got IntraSee to this point.

To our customers:

Thank you for believing in IntraSee. It has been an absolute pleasure to help you improve your experiences for your employees, managers, students and faculty. We are only getting stronger from here with a broader cloud portfolio, the benefits of scale, and even greater investment in Ida, our digital assistant. We know many of you are planning major investments in the next ten years. We are excited for your future and to help get you there.

To our employees:

The IntraSee family is the reason we are here today. Each one of you, past and present, has contributed to our mission of bringing great usability to enterprise software. I owe you all a heartfelt thank you for your hard work and dedication. The support from the current team over the last year in particular is more than I could have imagined. I, and our clients, have been lucky to work with you and I look forward to continuing on the InstraSee journey with you as my colleagues.

To Paul Isherwood:

I remember the first presentation I saw you give back at PeopleSoft. The entire presentation was built with dynamic HTML and this was about 1999. When I asked you, “Why not use PowerPoint?” you simply responded with “Why would I use PowerPoint? This is so much cooler.” Throughout the 15+ years we were partners, you always helped us imagine something so much cooler. In your memory, I and the rest of the combined InstraSee/Gideon Taylor team, are going to push this mission to the next level like only we know how.

Sincerely,
Andrew Bediz

We are back and in person for Alliance 2022 in Seattle, WA! It has been so long since we have been able to connect with you and we are so excited to get back out there. We have been really pushing the envelope of user experience and AI driven chatbots/digital assistants.

Visit us in the Exhibit Hall at Booth 427 to find out what Gideon Taylor and IntraSee have been up to this past year, including PeopleSoft managed services, cloud hosting, RPA, chatbots, and, of course, really awesome automation with GT eForms!

We also have a full slate of customer case studies this year. Come and learn about our projects and the value they are bringing directly from these school’s representatives.

If you missed these sessions, the slide decks are now available upon request.
Session Title:Journey to the AI. 23 Community Colleges, 1 Digital Assistant
Session Number:8725
Track:Student Information System (SIS)
Session Type:Presentation
Sub-Categorization:Other
Room Assignment:Tahoma 4 => Tue, Mar 15, 2022 (08:30 AM – 09:30 AM)  
Session Title:Meet LUie, Loyola University of Chicago’s Digital Assistant, One Year Later
Session Number:8806
Track:Technical & Reporting
Session Type:Presentation
Sub-Categorization:Emerging Technologies – Digital Assistant, Machine Learning, etc.
Room Assignment:Room 612 => Tue, Mar 15, 2022 (10:00 AM – 11:00 AM)
Session Title:State of Chatbots for PeopleSoft Customers
Session Number:8792
Track:Exhibitor
Session Type:Presentation
Sub-Categorization:Campus Community
Room Assignment:Room 612 => Wed, Mar 16, 2022 (12:45 PM – 01:45 PM) 

Don’t forget to check out even more sessions from our sister divisions. We are always very busy at the Alliance conference, so we recommend clicking the Contact Us button below and we will get you scheduled for a personal demo in our lounge area at a time that works well for you.

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If 2021 has taught us anything, it is that what sounds like a consensus online is often not. Our world is dominated by algorithms whose output has shown the ability to skew our realities. Bad actors have discovered they can influence algorithms and they do so for financial gain or just a laugh. Artificial Intelligence (AI) can provide great value, but AI with bias and/or inaccuracy is something we must actively guard against. This post is going to explore the traps related to user feedback and how over reliance on that dataset can result in poor outcomes for any AI, but especially for chatbots and digital assistants which are your first line of support for your users.

For the purposes of this post, we will focus our examples on use cases we typically see our customers facing. Users, in this context, are the ones chatting with the bot and looking for support.

What is User Feedback?

User Feedback is a broad term meant to cover both direct and indirect feedback. Direct feedback is when the user is asked for their opinion directly and they reply. You will see this in various forms. For example, the thumbs up and down icons are meant to collect user feedback. You may be asked, “Did this solve your issue?” or “How would you rate this experience?”. Have you seen those buttons at a store’s exit where there is a smiley face, a sad face and something in between? That is a form of direct user feedback.

customer satisfaction buttons

The other type of feedback is far more subtle and indirect. We can look at a user’s actions and from those infer some level of feedback. These patterns can also be called user cues. An example of such a cue is when the user gets an answer and they respond, “you stink!”. The implication is that the user is unhappy about the previous answer. Another cue can be the circumstances under which a user clicks a help button or even asks to speak to a live agent. All of these indicate something may have gone wrong.

The Feedback Challenge

There is no problem with asking for feedback. In general, it is a good practice. There are some challenges, however, so let’s explore those.

Interpreting User Intent

Interpreting the user’s intended meaning is no easy task. Let’s focus in on a simple interaction to illustrate this point. With many help desk systems, upon completion of the experience, the user will be asked: Did this solve your issue?

Let’s imagine a digital assistant experience…

How much PTO can I borrow?

All our policies can be found in the Policy Center.

The user gives a resounding “NO” to the follow up question, “did this solve your issue?”. The problem is, we don’t really know why it didn’t solve their issue. If we present them with a big, long survey trying to find out why…well, you know no one is spending time on that. Back to the point at hand, there could be all sorts of reasons for the “NO”. For example…

  • They are annoyed because the bot didn’t answer directly. It simply gave a link and it is now the user’s problem to find the answer.
  • The user may have found the policy on borrowing PTO, but disagreed with the policy itself, thereby not solving the issue at hand.
  • The user may be unhappy that they are getting an answer about policies seemingly unrelated to the question which was about how much PTO can be borrowed.
  • The user is a bad actor and intentionally provides inaccurate feedback.

Experts say the key to effective user feedback is acting on it. However, the confusion around user intent puts you on a steep slope when trying to act.

Selection

The next problem with user feedback is that many studies suggest the data is not representative of the user community.

Anecdotal evidence from across the web suggests a typical response rate for an online survey is much lower than 10%. That means the vast majority of your customers (90%+ ) are not telling you what they think. You might be able to argue that away statistically, but in reality are you happy that so many of your customers don’t have a voice?

customerthermometer.com

We know user feedback tends to have a self-selecting effect. That is to say, the people who participate skew the data away from a true representation of the whole community. The most basic example of this is that unhappy people provide more feedback than happy people. This makes it very difficult to act on a dataset which lacks representation.

Intentional Manipulation

Famously, Microsoft released a bot using their AI to Twitter in 2016, a time when we didn’t fully understand the world of unintended consequences in AI. Without too much detail, let’s say this experiment did not go well. “The more you chat with Tay, said Microsoft, the smarter it gets.” Have you heard this before?

It is a case where users figured out they could influence the AI and they knowingly did so. We know humans are capable of this manipulation. Despite our speculation as to their intentions, we need to actively guard against it. So how does one know the difference between this manipulation and genuine user feedback? If Facebook and Twitter haven’t been able to tell the difference, we should be cautious in thinking we can.

IntraSee’s Feedback Data

Across our customers, many have deployed quick feedback mechanisms like thumbs or star ratings. This feedback is non-interruptive, and the user is not forced to answer. For this type of asynchronous feedback, we are seeing a 3%-4% response rate.

We will also collect feedback that is more synchronous, which the user can ignore, but it is not readily obvious they can continue without providing feedback. This method is getting about a 40% response rate +/- 7%. Clearly, more feedback is gathered with this method, but it can be annoying. To counter that potential frustration no user is asked too often. There is a delicate balance between getting feedback and being bothersome, but we feel throttling is necessary here even though it reduces the data significance.

For one customer, the asynchronous feedback (thumbs/stars) happens 7.5 times as much. Doing the math, we get almost the same amount (+/- 5%) of feedback data from both models!

Automating AI with User Feedback

We now understand that feedback, while valuable, can produce bad outcomes if you are not careful. It is hard to collect, it is often not representative and interpretation is rife with miscalculations. In the chatbot industry, there is a technique which will take user feedback data and feed it into the AI model, but doesn’t that sound problematic when our confidence of this feedback is on shaky ground? Remember how Microsoft said to just use it and it will get better?

Machine Learning AI is the most powerful type of engine behind enterprise-grade digital assistants. That AI uses a model that is trained with data just like a Tesla uses pictures of stop signs to understand when to stop. When we hear, “just use it and it will get better,” what is really happening is the training data is improving which should yield better outcomes. That is, of course, if the training data is of high quality.

How does training data improve? Two traditional ways: manually by a data scientist or automatically. How do you automatically update training data? You need to draw upon data sources, so why not use user feedback? For example, if a user clicks the thumbs down, we can assume the AI had a bad outcome, right?

It sounds like a good idea, but it can be a trap! As previously discussed, we see this data collected < 4% of interactions. Imagine you have 1,000 questions in your bot and get 10,000 user questions in a month. If every question was asked an equal amount of time, that would be 4 pieces of feedback per question! How many months do you need to wait before the feedback has data significance? This effect is even more pronounced if the question is not a top 20 popular question.

It's a trap!

Now consider you wait 6 months to have enough feedback to act on it automatically. What has changed in 6 months? The pandemic has taught us that everything can change! By the time you have enough data, that same data may be stale or, worse, incorrect.

This math all assumes feedback data is good and evenly representative, but as discussed above, we know it is not. Oh my, what a mess! We now have limited data, and it is overrepresented by the unhappy and we are considering automatically amplifying their voice into the AI model?

Time for another practical example.

Do I need another vaccine?

Information about health and wellness can be found by contacting the Wellness Center at 800-555-5555.

This answer isn’t wrong, but there is a better answer which specifically talks about booster shot requirements. The user doesn’t know this answer exists, so logically they click thumbs up or answer “yes” to the question, “did this answer your question?”

If we took this indirect user feedback and automatically fed it into the AI, we would be telling the AI you were right to give this less-than-perfect answer. The system is then automatically reinforcing the wrong outcome. Now amplify this by thousands of interactions and what happens? The AI drowns out the more helpful answer about booster shots. The end result of this slippery slope is continual degradation in the quality of service the user receives.

What’s the Solution?

This is a nuanced problem we spend time thinking about so our customers don’t have to. One solution is to not abandon the human touch. The dirty little secret about Alexa and Siri is that they have thousands of people contributing to the AI by tagging real life interactions. If Apple and Amazon still need the human touch in their AI, then it is probably for good reason.

When teachers teach students, they are curating the experience. Teachers don’t simply ask students, “do you feel you got this test question correct?”. They are grading those tests based on their expertise. Asking students to be the grader is flawed.

While we cannot discuss all our tricks, at IntraSee we will be introducing some new technology in 2022 directly aimed at this challenge. The lesson learned here is that while automating the data that feeds an AI model can be powerful, it is a power that comes with great responsibility. Ask your AI vendors how they solve this challenge. For our customers, these challenges are our problem at IntraSee, not yours. Rest assured, we are all over the challenges so you don’t have to spend a minute on them 😀

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