Contracting in a Changing Landscape: Redlines, Versioning, Negotiation, and the Role of AI

Welcome and introductions (0:00)

Janior Valle: Okay, welcome everyone. It’s two o’clock. There’s a few folks that are trickling in so we’ll give everybody a minute to go ahead and join us. In the meantime, there is a QR code on the screen that contains the supporting materials that we’re providing during the session today. If you want to go ahead and scan that, it’ll take you to our resources where you can download that.

Janior Valle: We’ll give it one more minute for folks to join and then we’ll get going. For those of you that just joined us now, again, there is a QR code on the screen. If you scan that with your phone, it’ll take you to our resources where you can download additional materials from today’s session.

Janior Valle: It’s 2:02, two minutes after, so let’s go ahead and get going. So again, we have the supporting materials here on the QR code, so feel free to scan those. It will be shown again later on in the session, so if you miss it now, it’s totally fine. We’ll show it again during the session. So let’s get started. So just to kind of preface what we’ll cover today during the session, we’ll go over some welcome and introductions, talk a little bit about some of the basics regarding AI. And then from there, we’ll turn it over to our guest speaker today, Melissa, who’s joining us from a leading academic medical school. And there will, of course, be time for Q&A. So if you have questions, feel free to put them in the chat.

Janior Valle: We will try to answer those as we go along today in the chat. And then for anything that we’re not able to answer via chat, we’ll hold those for our dedicated Q&A session. Unfortunately, Irma was not able to join us today, so I will be your host as well. My name is Janior Valle. I work for Streamlyne. I’m the chief technology officer. I’m a little bit more technical in nature, so what I’ll be covering today is mostly around AI, things that you should look out for, how do you work with these tools, and then our functional expert today will be, and guest speaker, will be Melissa, would you like to say a few words?

Melissa: Sure. Hi, everyone. So as Janior mentioned, I work at a leading medical school. I’m associate director for grants and contracts. I’ve been doing this type of work for about 12 years. Before that, I was a corporate attorney for a couple of years. And I’m just really excited to be here partnering with Streamlyne for this webinar. And I’m very active on LinkedIn. So if anyone wants to connect with me or give me a follow, that would be great.

Janior Valle: Awesome. Thank you, Melissa. Okay, so today’s session is going to run for approximately one hour and 15 minutes. My portion will take approximately 20 to 30 minutes, and then I’ll turn that over to Melissa. And whenever Melissa wraps up, we’ll dive right into the Q&A session. Okay. So the first thing that I’ll mention is This is not the first webinar that we’ve been doing. This is actually a series of webinars that we’ve been doing since June. And even before then… Like throughout last year, we were giving workshops at different institutions where we kind of realized that there’s a broader need for the community. And so that’s why you’ve been seeing a lot of these announcements on the ResAdmin listserv, on LinkedIn and other areas because we genuinely want to provide, you know, this kind of workshop and training to the community.

The series so far (4:34)

Janior Valle: So in June, we covered a little bit about different AI agents on the market. So we covered things like ChatGPT, Claude, Gemini, and Copilot. So it was kind of tuned for, you know, how to work with those different platforms and which ones have their strengths and what their weaknesses are. In July, we covered how to create reusable workflows. So how do you create workflows that you can kind of trigger on any kind of cadence or whenever it’s needed? And in August, we covered better prompting with Tricia Callahan from Emory University. So in general, what you’re going to notice today is a lot of what we’ll cover or what I will cover is going to build upon the foundations that we set up for June, July and August. And so if.

Approved tools and what to keep out of them (5:30)

Janior Valle: If you miss the June, July, and August sessions, don’t worry. We do have those posted and publicly available on our website. A member of our team, either Yavuz or Hector, will go ahead and post that in the chat so that you have access to those prior sessions. All right, so let’s go ahead and dive in. So the first thing we want to talk about as… As we always mention during these sessions is you want to make sure that you’re using approved tools. So I know that Copilot, ChatGPT, Claude and a lot of other AI providers do have free. usage available right so like even ChatGPT if you go to their website you can kind of start chatting with it right away without an account definitely double check with your legal with your it to make sure that you’re using approved tools a lot of these tools definitely if you’re using it for free will train on any prompts or data that you provide so you know if you upload a document in there and it’s You know something from your institution those platforms will train on that data And that also applies for even paid accounts.

Janior Valle: So even if you pay for like the $20 plan on any of these tools, they will still train on that data. So really you want to make sure that you’re using something that’s available for your institution that has that data retention disabled so that you’re not submitting private information in there.

Janior Valle: With that said, some institutions do allow you to use these tools even as a free option, but you definitely want to be careful in what you’re entering in there, right? So generally, and again, this still goes back to your legal and your IT, generally, anything that’s publicly available is okay, right? So if you have, let’s say, an opportunity that you pulled from grants.gov and you want a summary on that, right? That’s publicly available information. So that’s generally okay to go ahead and put into that platform. And as usual, I’m going to say this often a lot today, check with your IT and legal, right? So if you have something that’s like private research, you definitely do not want to post that or paste that into any kind of prompts.

AI in contract work: the prompt (8:02)

Janior Valle: All right. So we’re going to be talking about AI in contracting today. So that’s our main focus. So I have a little bit of a hands on kind of presentation here for you to kind of get two perspectives from two different AI systems and how they handle a clinical trial agreement. So let’s talk about what we’re going to be doing. So I have one clinical trial agreement. It’s a public agreement that I pulled from online, so nothing private in there. I’m going to ask it to summarize. the agreement for me. And I’m going to ask it to flag any red flags that may be an issue as part of that agreement. And then I’m going to have it draft the reply.

Janior Valle: In this case, my reply will be a red line version of that agreement that I can then review and make decisions on. Right. So like I said, we’re going to be building upon a lot of the foundations that we established in the. pushing the prior webinars so in August we talked about Tricia’s craft framework and so Tricia Callahan covered you know how do we craft better prompts right so we have the concept of better good better and best so if we come here to good I could say review the attached clinical trial agreement for problems. And that would be a good prompt, but we can make it better with adding a role. So act as a contracts negotiator in a research institution’s sponsored programs office and review the attached clinical trial agreement a sponsor sent us for problems.

Janior Valle: But we can make that even better by adding.

Janior Valle: The kind of additional information, right? So like who, what is the format for the task? and you know what do we actually ultimately want out of this so let’s say act as a contract negotiator in a research institution’s sponsor programs office a sponsor sent us this clinical trial agreement we want you to review it with and return the word document with track changes moving each problem clause to our position with a comment on each change giving our reason in one sentence and a person will accept or reject each change in word forward and we’re going to be doing this live here shortly okay so that’s that’s a that’s the best prompt that’s available right because we’re talking we’re setting up the context we’re setting up the role we’re adding additional information on like what the task at hand is the format for what we want the output to be and what the task is at hand we can of course Build a prompt.

From a prompt to a reusable skill (10:53)

Janior Valle: What if you have to review? another clinical trial agreement? Are you going to rewrite that prompt, right? And this is where, you know, in July, we talked about the concept of skills and creating reusable workflows. So we’re also going to be creating a reusable workflow out of this today. So that way, you know, if we get another clinical trial agreement, I don’t have to type in the same prompt again, right? Kind of saves us a little bit of time, a little bit of automation, right? Now, if you’re not familiar with skills, And again, we’re going to cover all this hands-on shortly. If you’re not familiar with skills, skills are a way to save prompts such that you can invoke them whenever the need arises.

Janior Valle: Skills can also be triggered by the AI automatically. So it’s not like you have to say, hey, go run this skill, go run this workflow. You know, maybe the trigger. For a skill is I need help reviewing a clinical trial agreement. And so the trigger is reviewing a clinical trial agreement, right? So that AI will automatically trigger that skill. As part of the skill, we need to provide some inputs, right? So that input could be a wide range of information. You know, if we go back. to one of the slides here, our prompt slide, we want to provide context on the task at hand. What is it reviewing, right? In this case, it’s a clinical trial agreement.

Janior Valle: Maybe we can mention the sponsor or any additional supporting information, right? And then what process do you want the AI model to follow? You know, you can’t just say, although in this case in the prompt, we kind of are, you can’t just say, go ahead and review this agreement, because what is it reviewing, right? Maybe you have policies or procedures or rules in place at your institution that you can give to the AI model so that. it knows exactly what to kind of look out for, right? And then in terms of the output, you know, what is it that you’re expecting back from the model, right? You want to be specific in that. So in this case, we mentioned a redlined agreement in Word with comments so that we can review each change.

Janior Valle: And then, of course, we’ll review the output from there. But we don’t want the model to make any kind of decisions. Right. So when we talk about what stays with the person, obviously, we never want the AI model to go ahead and negotiate or communicate with the funder. We don’t want to commit to any promises or anything like that. Right. We just want the model to kind of. help us with our day-to-day. We want the model to surface issues, make recommendations, but never solidify or make decisions, right? Okay, so let’s go ahead and we’re going to run this live here. I’m going to stop sharing for one second and I will go over to my web view and I’m going to reshare.

Live demo: the skill in Claude (14:27)

Janior Valle: Sharing my screen again now in the interest of time because this can take a little bit of time i’ve already drafted the prompts and everything and i’ve started the chat so that we can kind of follow along together and that way we don’t eat too much time into melissa’s portion so here i’ve got two tabs open I’ve got ChatGPT in one tab and I have Claude in another tab. Now what I pasted, I’ll show you, is a skill and this is our skill. Now this is my skill. This doesn’t have to be your skill. This is an idea for kind of how to go about skills in general and create these reusable workflows. So first of all, I want to show you how you create a skill for those of you that use try GPT or Claude and really this works the same way if you use Gemini or Copilot works pretty much the same.

Janior Valle: So in Claude, I’m going to go to settings. There is a skills option here, and I already went ahead and created this skill. It’s called Clinical Trial Agreement Review. I can look at the contents and I can see that this skill should be used whenever a clinical trial agreement, sponsored research agreement or similar sponsored drafted agreement is attached and you are asked to review it, redline it or find problems in it, apply it without being asked to. And so if we look at what’s actually in the skill, this is the same thing that’s in my markdown file here, right? You know, regardless of whether. I think in Gemini, you know, it’s called gems or something. So regardless of whether it’s Claude, ChatGPT, or any other platform, they support skills in some way. It may be called something different like Gemini calls it gems, but the point stands that it’s a skill. It’s a reusable workflow.

Janior Valle: So what’s in here? So you always want to give a description of what the skill is and when it should be triggered. Like when we saw on our slide, you know, there’s a trigger, right? Because as I mentioned. you can certainly invoke the skill. So if I show you what that looks like, and I go to the very beginning of my chat here, all I did was I typed slash clinical trial agreement review.

Janior Valle: Just by doing that, the AI knows that I want it to invoke this workflow. I want it to run this workflow and do these tasks that are in the workflow. And I also attached this clinical trial agreement template. So if we go back to what’s in the skill, we have our trigger. Now. Who is the AI model working for? Well, it’s the institution-sponsored programs office. The institution is the site. The sponsor drafted the agreement, and every position below is the institutions. What are the inputs? Well, the inputs are an agreement, either in Word or PDF, the protocol, the budget, if it’s attached, and if not, just proceed without them and say so. Of course, you know, this may change according to what your inputs are, right? So, you know, you can always tweak these, right?

Janior Valle: What are our positions? And what I mean by what is our positions, these are like, you can treat these as rules or things to look out for, right? Now, I put 14 points together. Yours may look less, maybe more than what I have, right? But really what you want to put in here is what are the things that the AI model should look out for? What should it watch out for? Maybe it’s, hey, in our institution, we always want an identification clause that’s at least a minimum of $500,000 or whatever that number is, right? Maybe it’s… When you’re dealing with sensitive data, we always have to make sure we have, and this is a clinical trial agreement, so we need to make sure we have HIPAA authorization forms, BAAs in place, business associate agreements, or whatnot, right?

Janior Valle: So whatever those requirements are for your institution, that’s what would be under our positions. Maybe it could even be like, for example, if you have like policies and procedures or. SOPs, standard operating procedures, maybe that could be part of your position, right? And then what is the process that the model should follow when it’s using this skill? So in my case, I ask that it walks each position in order. So it’s going to go through all 14 points and it’s going to find a related clause, quote it, see if there’s any issues and then provide a reasoning back to me. And then what is the output? So what do we expect back from the model?

Janior Valle: And in this case, I said, well, we want a table. That table should be numbered. There should be a position, a status, a quoted clause or section, and then a reason why. We should also have something, you know, titled as needs a person, so maybe flag it with like absent or unclear or something that calls my attention. And then if asked for track changes, produce a Word document and flag each clause and whatnot, right? So just. Kind of show you what this agreement looks like I’m going to go ahead and pull this up here It’s a Word document. It’s basic. It has some templated items. As I said, this is a template that I pulled from a public website.

Janior Valle: It’s got information in here at the top relating to the institution, the sponsor, what the study or protocol is, the PI, the study drug or study device, all that information. It’s roughly about 13 pages. There’s things in here like the scope of the agreement. What the study drug is, confidentiality, ownership and use of the study data, informed consent, debarment, records maintenance. There’s a whole lot in here, right? Let’s go back. So how would we invoke this skill? Well, here using Claude, I went ahead and Did slash clinical trial agreement because I went ahead to my settings, I went to skills, and I added a clinical trial agreement review skill, right? And so now I can invoke this anytime with Claude just by typing this in.

Janior Valle: What if I didn’t though, right? What if I said, we’ll try this now. What if I said, Here’s the attachment and Here’s my prompt. So this is the the craft format, right, that we went over earlier where I said act as a contracts negotiator in a research institution sponsored programs office and we’re going to paste that here and we will send that message. So let’s see what happens. Look at that. So it’s already saying it’s loading the clinical trial agreement skill, right? So I didn’t have to do slash clinical trial agreement. I simply created the skill. It now knows when to trigger it. And so because my prompt includes the triggers, it automatically triggers that workflow for me, right? So it’s a very, skills are a very nice way to kind of. You can never say that an AI model is always going to give you the same information because it’s not like a typical program where it’s deterministic, right?

Janior Valle: AI will always produce something different, but skills are a nice way to, at best, try to get the same outputs, the same reviews, the same kind of output back from the AI, right? And so it invoked my skill and now it’s doing everything that I asked. It’s loading the Word document stuff, so it’s putting all the red line changes and everything together. All right. Now, like I said, this can take a little bit of time, right? When I ran this a few days ago, it took roughly about 10 minutes. So, you know, at this point, I can wait, I can go do something else, and then I can come back when it’s finished. So again, in the interest of time, I went ahead and automatically did this for us here.

Janior Valle: So we have a final solution. So here it went ahead and gave me the table that I asked for with the number. the position, what the status is. So it looks like I have a ton of flags in this clinical trial agreement. And it’s giving us the quote from the agreement itself and why it flagged it, right? So from here, it gave me the table. And it gave me kind of a breakdown as to what I have to review and why it needs a person, right? Again, this is what I asked for as part of my outputs. And then it finally asked me, you know, if you’d like, I can prepare the track changes word version with each flagged clause revised to our position and a one sentence comment explaining the change. And I said, yes, do that.

Janior Valle: And so I finally produced. a redlined version. So let me go ahead and pull that up now. And here we are. So we still have our template. Obviously, that’s not going to change. And it’s not going to change because I didn’t ask it to change, right? So maybe if we did want it to change, one of the steps in the skill could be if you recognize any templated fields, go ahead and populate it with our institutional details, right? So you can kind of give it additional information or steps to follow and include that. But if I scroll down. You’re going to see that we have some red lines here now. So it did change some verbiage around.

Janior Valle: It made comments to what it’s changing and some reasons why, right? So in here, we change this to 30 days and saying a reasonable period of time given understanding that failure to do so does not constitute a designation of non-compliant reality. So it’s giving us information as to why it made that change. And then, of course, you know, now that I have this red line document, I could, you know, either accept these or not. Right. Or maybe I can go back to the model and request further changes. You know, when you work with a lot of these AI models, it’s very much. an iterative approach. You know, kind of like when we talked about crafting prompts last month in our webinar, it’s you’re never going to send something to the AI and get something back that’s concrete the first go around.

Janior Valle: It’s typically going to be something that you have to work with the model, you’re going to have to ask for changes and it’s definitely an iterative approach. Some models are you know when it comes to Claude like Claude is pretty good at generating documents so WordDocs Excel PowerPoint you can do the same thing with ChatGPT and so I did the same thing with ChatGPT here now the way that you create a skill with ChatGPT is a little bit different so what you have to do is you have to go to plugins And then there is a skills option. And so this is where you can kind of create it at the ChatGPT side. And I attached the same exact workflow that I provided to Claude, right?

The same skill in ChatGPT (26:25)

Janior Valle: The downside with ChatGPT is There’s not a nice way to invoke the skill. So with like Claude, I can just do the slash or I can just talk to it and it’ll trigger. With ChatGPT, you can talk to it and it’ll trigger based on those inputs that we provided. You can say slash clinical trial agreement review. But as you notice, there’s not really anything that’s telling me that that exists, right? whereas with Quad, I could do the slash and I see that it’s pulling my skill. So that is one downside with ChatGPT. It does support skills in these reusable workflows. It’s not as intuitive as Quad, unfortunately. But in either case, it does work.

Janior Valle: So as you can see, I invoked it here. It went ahead and did the same review with the same table. Now, A few differences that I noticed, you know, whereas Claude was a little bit more direct in following my instructions. So you’ll notice that it put the number like I wanted and the flag with the quoted clause and why ChatGPT kind of did this a little bit differently. I like that it gave me the sources of where things are, so being able to click on that and see it right away is nice, but it did definitely miss certain things. So ChatGPT seems to not follow directions as well, something to keep in mind. And finally, it asked me if I wanted to produce a redline version, and I said yes, go ahead and do that.

Janior Valle: And it really didn’t do that. It went ahead and gave me kind of the original and the new, which is not what I want. I want a redlined version of the document. And so I kind of had to go back to it and say, I expected the output to be a Word document with red line changes via track changes mechanism and related comments, and said I was right, and it went ahead and produced that document. So let’s take a look at this real quick here. We’ll see if it did okay. All right, so this is the ChatGPT version. Let’s see how it did. Yeah, so it went ahead and redlined things, you know, it did kind of go ahead and do that.

Janior Valle: It just took a little bit of prompting to get it to do what I wanted, right? So all in all, it did redline the document, just took a little bit of back and forth, right? All right, Melissa, I think you’re up. So at this point… You know, I’m going to show while Melissa gets prepared, I’m going to show the slide one more time with the QR code that has our supporting material here. Again, you can scan that with your phone and you’ll be able to access those from the session today. All right, Melissa, take it away.

Melissa: All right, I think I’m all set. Let’s see.

Contracting in a changing landscape (29:32)

Melissa: Maybe that one. Okay. So can everyone see my slides? Does that look good? Okay. Great. So here’s sort of my agenda for the next 30 minutes. I’m going to be talking about some more of the details around contracting and kind of what the landscape is and how some of this relates to some of the different AI tools that Janior was talking about. So the first thing I’d like to talk about is just the really complex landscape that we’re finding ourselves in. Contracting and research administration has become so much more challenging and complex. And, you know, it’s important to keep in mind when using AI because you just want to make sure you’re using those tools wisely and appropriately.

Melissa: These are, you know, there’s a lot to talk about here. So the first thing is, you know, it feels like every day there’s some new law or requirement or regulation or something that’s coming out governing research administration. So that may be an exaggeration, but sometimes it feels that way. And this can be on the local level, the state level, the federal level, and even on an international level. There’s just so many rules and regulations and guidelines that we need to be compliant. be complying with and there’s new ones all the time sort of relating to that we also are seeing you know there are certain frameworks regulatory frameworks that that we’re very accustomed to and now we’re seeing a lot of efforts to change those and some of those you know are you know, really in process and happening, you know, there’s the revolutionary FAR overhaul, you know, there’s also the very significant proposed changes to uniform guidance and that’s kind of on hold that may or may not come to pass, but even if that doesn’t in its exact form.

Melissa: You know, we are seeing a lot more efforts to kind of change the way things are being done, and that requires a lot of adjustment and certainly adds a lot of complexity. Another sort of piece of the regulatory environment is that when these laws are changing or when new laws are being passed, you know, it’s not entirely clear, you know, at the outset how they’re going to be applied and interpreted by the… the, you know, government agencies, you know, so for example, you know. You may not know how it’s actually going to be enforced, like how it’s going to be, you know, how it’s going to work on the ground in the real world. So what I see a lot in this space is, you know, maybe the first six months or a year after a new regulation or law is passed, sometimes I see a lot of over compliance because folks are worried about, you know, making a mistake, doing the wrong thing.

Melissa: And, you know, the law doesn’t spell out exactly, you know, all the minutiae of how to. how to comply so people will kind of go a little bit further than they need to add more burden to themselves and then once they kind of see how it’s being interpreted and applied and what’s really happening they might pull back on that a little bit or they may keep the more stringent requirements but of course all of that is going to add more complexity as well and then with all of the regulatory changes you know your institutional policies are going to have to change as well to adapt to that so you have to not only be aware of you know what’s happening in the larger your regulatory landscape, but then how that’s impacting your own institutional policies and how that’s going to impact your contracting.

Melissa: And then the last two things kind of around, you know, again, the regulatory environment, especially I would say the federal environment right now, you know, there’s a lot of increased scrutiny. You know, some of it is on specific topics that may be, you know, more of interest to the government right now, like research security and international collaboration, you know, but just in general, I think there’s a lot more scrutiny. There’s a lot more, you know, the government is really looking carefully. It’s, you know, some of these reports that we’re filing, they’re not just kind of checking a box that people are really looking at them, looking what we’re doing, making sure that we’re in compliance with all of the policies.

Melissa: And regulations. And then kind of the last piece of that is I think, you know, this is kind of my personal opinion, but I think in the past if we made a mistake, you know, and were non-compliant with something, but it was kind of clear that we were doing the right things and it was just kind of an honest mistake. I think there might have been a little bit more, you know, benefit of the doubt and a little bit more willingness to kind of cooperate, you know, figure it out, move forward, kind of make sure it’s not happening in the future. There wouldn’t really be necessarily a huge penalty or fallout from that if, you know, if kind of you were doing the right things.

Melissa: You know, now I think there’s a little bit more concern and we’re seeing maybe some more severe penalties for folks who get it wrong even. I mean, if they have kind of made all the right efforts toward compliance, you know, you might see certain funding reduced or an award terminated outright, things like that. So I think, you know, there’s just more, you know, concern and stress. And I think in our profession, like all of us care very, very much about getting it right and we want to be compliant, but we’re still human and we make mistakes sometimes. But I think there’s more pressure and stress about getting it right and not making mistakes. not making those mistakes, which again adds a layer of complexity.

Melissa: Another area where there’s more complexity is just kind of in the nature of the research itself. So, you know, we’re seeing because of kind of the technology that’s available right now, you know, you can jump on a Zoom call with someone, you can email, you can, you know, WhatsApp or, you know, texting on your phone, whatever. There’s so many ways to communicate with people who are far away, whether it’s across the country or across the globe. And that means that there’s more collaboration with people in different places. You know, at the same time, we’re seeing more restrictions on things like cross-border collaboration. You know, some may not be permitted at all or some may come with a lot of additional restrictions or approvals that are needed, things like that.

Melissa: And then, of course, if you’re working with folks, you know, in a different location, you know, there may just be different ways of doing things, different cultural expectations, you know, just practical issues like time zones. And if, you know, if you jump on a call, it may be hard to schedule that, things like that. So if you’re contracting with folks, you know, across the country or across the world, you know, that can also be more challenging and there’s kind of more to think about and more to deal with there. And then, you know, similarly. You know, the technology that we have and, you know, has also enabled really large collaborations and we’re seeing this more and more where there’s, you know, three or four or five or a dozen even institutions collaborating on one project and, you know, all of these parties kind of have to agree on the same terms.

Melissa: And that can be really challenging because it’s hard enough to get two parties to agree on the same term sometimes. So getting, you know, nine or 10 or a dozen to agree is a very special challenge, you know, and depending on where they are, like they’re going to have their own institutional policies, they’re going to have maybe be subject to different state laws, different, you know, international laws, and you kind of have to take all that into account because everyone’s going to have to. maintain compliance while still finding a middle ground and for those especially you know unless you’re an institution that does a ton of these and maybe you have a template but I would say a lot of the time a sort of a typical template won’t won’t really adequately address these sorts of really complicated multi-party collaboration so you kind of have to piece together different agreements or different templates to kind of come up with something that’s going to fit.

Melissa: And, you know, just the, you know, if you’re kind of the person leading the negotiation, it requires a lot of specialized skills. You know, you have to have a lot of patience, you have to be very strategic about what changes you’re requesting and how you’re going about it, you have to be, I think, confident in your skills as a negotiator. So there’s just a lot there. You know, so I think that’s the proliferation. Sorry, proliferation, you know, more collaboration and larger projects with more parties can, you know, can again make contracting a lot more difficult and then, you know, that’s something, again, to keep in mind if, you know, as you’re using different tools to support your negotiation.

Melissa: So another, you know, thing that I’m noticing at my institution, and I’m sure is probably the case at others, is there’s just a lot more data-driven research happening. You know, data, I feel like it’s just, it comes up all the time with different projects. So, you know, in some cases it may be collected as part of the work, or other times you may be getting third-party data and analyzing it as part of the work. But there’s just more, you know, more research that involves or depends upon significant amounts of data. And then, of course, you know, the impact of a breach can be very significant. So as you’re thinking about your contract terms and kind of how you’re setting up the project, you know, you want to make sure that you’re not setting yourself up for any kind of a breach.

Melissa: In terms of the, say, the data subjects, obviously a breach, depending on what kind of data it is, could cause them significant harm. You know, if a third party is giving you data, they could get some heat for giving you the data and maybe not having sufficient protections in place. But, you know, if you are the one that has the data breach, you’re really going to be the one bearing a lot of the cost and a lot of the challenges of that, whether it’s legal fees and, you know, fees for travel and local council, if it’s somewhere far away, you know, the actual, you know, there might be significant penalties. GDPR, for example, has enormous penalties for data breaches and then of course the reputational damage that you know no one wants to be in the newspaper for you know for a massive data breach so that’s that’s something that you have to be thinking about in your contracting you know and again regulation the regulatory environment This is, I think, a hot topic as well and has been for, you know, in recent years, there’s a lot more regulation coming out about data privacy and security.

Melissa: You know, there isn’t really, you know, there’s certain ones that apply to federal research. There are certain international laws that are, you know, the GDPR, the PIPL, all of that stuff. And, you know, a particular project involving data, you know, it could be subject to more than one. regulatory framework you know so you kind of have to really be aware of you know aware of what applies and and how that’s going to impact your contracting and another piece of that as well is like you have to be familiar with the data flow I’ve actually had situations where I had to you know tell someone to explain it to me really slowly and I actually drew out a map of which way the data was flowing you know who’s trading data with whom and and you know where is it being stored and all this and I had sort of a you know a chart of with arrows and things like that showing the way that the data is flowing and that’s really important to understand because you know then you can make sure that you’re including the proper terms in your contract and you know handling that appropriately and I like to say that that contracts have to both comply with the laws and also facilitate compliance.

Melissa: So what I mean by that is, you know, for certain types of data, there might actually be provisions that you’re required to put in the contract or certain types of language or, you know, maybe it’s not. specific sentence but it’s like you have to have language that addresses this this and this you know these points and that has to be included in your contract and then the other side is that you also want your contract to facilitate compliance in the sense that you want to contain the appropriate safeguards and terms and conditions that are going to enable you and facilitate your ability to comply with the applicable regulations. And then, you know, I also just wanted to mention that a lot of times, you know, there’s added complexity because use of data with research projects typically involves a lot of coordination across different offices. So, you know, your IT, your research IT department might need to be involved.

Melissa: There might be research security professionals or data security professionals that need to be involved. with the PI of course and the research team so you know you might be coordinating with a lot of different folks and you have to make sure that the sort of promises that you’re making in your contract are actually things that you can do make sure that the expert has kind of looked at those terms so if it’s you know oh you’re going to use a system with this level of encryption and these security protections you know unless unless you’re you know in that technology every day you’re you’re probably not going to know that right so you’re going to have to talk to your IT person and say, hey, can we actually meet these standards and are these reasonable and appropriate for the situation?

Melissa: You know, I guess the takeaway here is that just the proliferation of, you know, all of this data and research, you know, again, adds more complexity, more things you have to be thinking about, more things to worry about and then address in your contract. And then finally, you know, I did want to sort of address the fact that, you know, with the environment that we’re in, I think there’s also just, you know, an expansion of the responsibilities that we’re taking on with maybe less resources. to deal with those responsibilities. So, you know, research is kind of under strain right now. A lot of institutions are having to cut back, reduce budgets, reduce, you know, lay people off, which is obviously very unfortunate.

Melissa: And you might also lose some institutional knowledge in the process, you know, and then add that to the fact that there’s, I think. sometimes more contracts that are needed or just more complex contracts and then you know the results of kind of adding those two things together is that you sort of that phrase that you hear all the time doing less with or doing more with less and I think a lot of us are kind of in that position or we know people who are in that position where you know you might be asked to take on a new type of contract that you’ve never done because they had to consolidate a team or you know you may just have less resources available because no one has Everyone has time to, you know, to draft them or to update them because they’re busy negotiating or, you know, there may not be, you know, as many people, again, with that institutional knowledge who can help guide you through something.

Melissa: So you kind of have to start, start over again. Kind of all of these things lead to us just being an environment where there’s more complexity and a lot of times more volume, and we may have less resources and, you know, we’re still trying to get it all correct. And so if we’re, you know, an AI tool can certainly be helpful and can help us maybe find areas to Streamlyne what we’re doing. But of course, we want to, you know, use it appropriately. So now that we’ve kind of set the stage of all that complexity, let’s talk a little bit about some of the other aspects of contracting. So one important, you know, important thing to consider is context, right?

Context comes first (44:13)

Melissa: So when you’re starting a negotiation, the context of the project is going to be really important. So… So first is sort of why context matters. So when I say context, what I mean is kind of the, you know, the facts and circumstances of the project. So you get a contract, you know, yes, you’re going to read the contract, of course, but you also want to know what’s going on around that. You don’t want to just read the contract because then you’re going to be missing out on a lot of important information. So you want to understand, you know, what you don’t need to understand the nitty gritty of the science, right, if you’re not a scientist, but you kind of need to generally understand.

Melissa: Understand what is the project that’s being done where is it being done who’s involved you know are there is there human subjects research are there you know reagents being used is there a material transfer or data transfer is there international travel or collaboration all of those things can impact sort of how your contract is going to look so it’s really important to kind of start with that foundation you know Maybe you take a passive agreement first and then look at those things or maybe, you know, it doesn’t the order doesn’t matter, but it’s really important to keep those things in mind. So why, you know, first of all, why is context important? There’s a lot of reasons.

Melissa: So first, the structure of the contract is going to depend a lot on that information. So just as an example, you know, a sub award and a vendor agreement, you know, on its face might seem like, well, what’s what’s the difference? They’re kind of doing the same thing, right? They’re actually kind of very different and it really depends on the facts and circumstances and the relationship and what type of work is being done. And, you know, it’s really important to use the right template and to treat it correctly. So, so that’s important. And then also you want to make sure, you know, just factual correctness, you want it to have the correct dates and dollars and period of performance, all of that.

Melissa: Another really, really critical thing is you want your contract to match what’s happening in the real world. So, you know, an example of this would be if your project is, you know, all about working with human subjects and doing some kind of analysis of the data that you’re that you’re collecting from these subjects and that’s you know kind of the main focus of your project you want to make sure your contract doesn’t say you know human subject work is forbidden under this contract and if you do any you know we will terminate your contract and not pay you you know that’s like an extreme example but the point is you want it to match what you’re doing and not impede you from doing the research that’s actually planned so you need to like I said have a general understanding of a statement of work And make sure the terms are actually supporting that.

Melissa: Another thing is to make sure that the contract terms are, you know, fair and appropriate for the work that you’re doing and aren’t adding unnecessary risk or burden. So a way to think about this is, you know, if you’re working with very low risk, maybe it’s like semi-public data or it’s, you know, de-identified data, you know, of course you want to use appropriate data. protection standards, but you don’t want to agree to standards that you would use for, you know, super high risk, you know, personally identifiable information because that level of security just really isn’t necessary or appropriate for what you’re doing and that’s just adding unnecessary burden. So that kind of match between what’s happening in the terms are really important.

Melissa: Another thing is that contracts can support, you know, risk assessment. So when you’re entering into a contract, kind of one of the, I guess, purposes of a contract is to allocate risk among the parties, you know, who’s going to be liable for what and how are we going to share this risk and is that an appropriate allocation of risk, you know, and how can we mitigate that and those things. And so, you know, understanding what’s happening in the project can help you to do that risk assessment. So, you know, different types of work will have different risks that need to be addressed differently. You know, work with human subjects is going to have a different risk from working with a very volatile chemical or, you know, a very contagious virus or, you know, what have you.

Melissa: So it’s important to know those things. And then finally, of course, as always, we want to be compliant with applicable laws and rules and regulations. In order to do that, you kind of have to know what’s actually happening with the research so that you can make sure it’s compliant. So, you know, the takeaway here is just that, you know, understanding kind of the full picture of what’s happening can really help you to create a contract that. That matches what’s happening and gives you a solid foundation and roadmap for your project. And then in terms of how to find contextual information, there’s a lot of places where you can find it. You know, so I’m just going to give a few examples here.

Melissa: But, you know, the proposal documents that were submitted are a great place to start. And of course, you want to make sure that if there’s any discrepancies, you’re asking some questions, you know, emails between. your institution and the other institution. Even the funding solicitation can sometimes have some helpful information buried in there that you might want to be aware of. So if you are able to look at that, you know, any historical information you have about negotiations with that same party, you know, have you done a contract with them before? And if so, you know, is there information that you can take a look at to kind of help with this negotiation and make it go faster?

Melissa: You know, you want to be looking in whatever systems or tools you have. you know, your system of record or, you know, document repositories, all of those things. And, you know, depending if you’re using some kind of, you know, AI tool that saves what you’re doing and you have, you know, kind of contract reviews or redlines saved in there, that can be another source of, you know, some of that contextual information as well. And then, you know, people are also great resources as well. So. If your colleague Bob has done, you know, 12 agreements with a sponsor and now you’re doing an agreement. with a sponsor. you know, go talk to Bob and say, hey, you know, how was it negotiating with the sponsor?

Melissa: You know, how did it go? Are there any quirks I should be aware of any anything just, you know, any advice that you can give me for this negotiation to make it go more smoothly. So essentially, you know, you probably have a lot of contextual information at your fingertips, you know, in different locations. So just being aware of that and making sure to use it can really improve the, you know. the outcome of your contracting and hopefully make it go more smoothly and kind of give you a better end product. So the next topic is kind of some of the nuts and bolts of of contract negotiation and kind of how AI can fit into some of those activities.

The mechanics of negotiation: redlines and versions (50:45)

Melissa: So I like to call this the mechanics of negotiation, but it’s really just, you know, the things that you’re actually doing as you negotiate. So one of the first things that you might do if you get a new contract to review is you might run a comparison. You might compare, you know, the new draft to a previous contract from that sponsor or that data provider. and kind of see if anything has changed. You might also, as you’re negotiating, compare, you know, say you’ve had four or five red lines at this point, and you want to kind of see, okay, how far have we come and, you know, how far do we have to go? You can compare version one with version five and just kind of see, OK, this is we’ve actually come really far and now we really just have these few things, but it can kind of just help you give you a, you know, a good picture of the negotiation so far.

Melissa: And then, you know, another possible uses at the end, you know, once you’ve kind of finalized your agreement and the other party, you know, say they’re sending you the final PDF or whatever for signature. sure you can compare that and just make sure nothing has changed you know something didn’t get added in there accidentally or someone forgot to delete something or you know I mean hopefully this wouldn’t happen but someone didn’t try to add something without without you realizing it things like that to make sure it matches what you actually agreed to and you know as honey were mentioned this is something that you know comparisons comparing documents is something that AI is particularly good at so this might be an area where some of these AI tools could add some value and perhaps make that a little easier that’s you know I like to think of that as kind of the bread and butter of what we do as negotiators right like that’s kind of the mechanism by which we actually conduct our negotiations at least until we get to a point where we either agree on a contract or we’re like okay we need to hop on a phone call or a zoom call and kind of pass out the rest of these terms.

Melissa: Yeah, so this kind of means, you know, I think we all know, you know, redlining is basically we’re going through, we’re flagging, you know, problematic terms, terms that are missing, terms that we don’t want in there, and then we’re kind of using that to mark it up, you know, cross things out, add things, add comments, and, you know, produce a document that we can then send to the other party and say that these are the changes that we would like. And, you know, again, this is something that AI can often do pretty well, you know, so this could be another area where AI is potentially adding some value. That said, I definitely echo what Janior.

Melissa: Was saying about you know you definitely don’t want to run you know run a red line in your AI tool and then just send it off to the other party in fact please don’t do that make sure a human is reviewing that and deciding what changes to to keep you know what to add or change because because AI you know it can be a very useful tool but it may miss things and you know or hallucinate or all those things you want to just be double checking it and you want to be You know, you the human want to be the one making the actual decisions about how to negotiate. Another thing, and this may sound very simplistic, right, but saving documents is actually really important because, you know, if you are in a three, four or five month negotiation and then all of a sudden you have to take a leave of absence, you’re unreachable and all of your documents are in your email and nobody can access it, you know, then they’ve lost all of that work and kind of either have to go to the other party and say, can you send me these red lines or.

Melissa: Or you know kind of start it makes it a lot harder for them to kind of pick up where you left off and even you know a year or two years in the future there might be reasons to go back to a negotiation to kind of see what happened what the what the decisions were and again you want to have that information available so it is really important to just you know make sure that you’re you know kind of have, you know, decide as a group or as a whether it’s your institutional has a policy or institution or your team has a policy or a process, but you want to kind of have a coherent way of saving documents, you know, are you saving your red lines or, you know, which versions are you saving, what are you saving, where are you saving it, is it accessible to everybody who might need to find it, are the naming conventions clear so that people can find what they’re looking for and all of that.

Melissa: All right. Now, I don’t think this would necessarily be a primary reason why you would use AI, you know, but it could be kind of a side benefit that if you’re doing some of your redlining or, you know, other things in an AI tool, you know, depending on the capabilities and I guess what type of account you have, you know, you might some of that information might be automatically saved in there. You know, if it’s a personal tool, it may not be accessible to others, but if you have an enterprise license. and everybody’s kind of using the same tool then some of those things may you know you may be able to select an option to make those things accessible to other users so that’s you know if you’re using that kind of comprehensive AI tool something to think about Another, you know, I guess component of what we do as negotiators is just keeping track of everything, right?

Melissa: And it can be complicated. So you can have a negotiation with a lot of rounds of back and forth, you know, hopefully if you’ve gotten three, four red lines deep, hopefully you’re jumping on a call or something, but maybe sometimes that’s not possible or whatever reason you may just, you know, have a lot of red lines. You also may have red lines with different parties, so you might be, you know, it might be a multi-party negotiation or, you know, you may have one external red line with the counterparty, but then you may have some internal red lines with colleagues, you know, who are subject matter experts or approvers or things like that, and you want to make sure you’re keeping track of what version you’re sending to whom, and, you know, you want to make sure that you’re not sharing any comments that are kind of for internal eyes only. Maybe it’s…

Melissa: It’s confidential information or something. You don’t want to share that with an external party. So you kind of have to find a way to organize yourself and keep the negotiation. organized and keep track of like I said the flow and you know again this is probably not something that you would you know have reason specifically to use AI but depending again on what tool you’re using you know if it’s saving certain things automatically and saving your red lines you know there may be options that you can choose or you know there may be a certain way to organize it that it could do some of that automatically for you. And I think the critical piece is just knowing how it’s going to do that, knowing what settings you can change or what settings your institution has enabled and, you know, kind of understanding the tool and how it works and then how you can track or find things.

Melissa: And then finally, kind of going back to the context, right, there’s the historical record. So if you have… If you have done your negotiation and saved all of your documents, then you have, you know, have a coherent record of the negotiation, which may include red lines, may also include other things like emails, you know, to internal partners who may have reviewed something and given you some advice, things like that. You know, once you kind of have that record, it can serve as a basis, you know, and context for future negotiations and be really helpful. So later on, you know, and in some cases, for example, you may, you know, let’s say you get a new award from this sponsor and it contains what you would consider, you know, a terrible indemnification term.

Melissa: But you look back and you see, oh, we actually accepted that two years ago. Like, why? Why would we accept that? That seems against our policy. You might be able to go back to your, you know, your records, your red lines and things like that and kind of. kind of see some of the back and forth like okay first we proposed this alternative they rejected it but proposed this other thing and then we kind of landed in the middle and here’s all the explanatory comments there is a note in here that there was an email from our legal team that’s in a file somewhere and we can go look at that email and they kind of approved it and things like that so kind of tying it back to the contextual piece you know having that historical record can have a lot of value and again probably not not the sole reason to use an AI tool but it might be a side benefit that if you’re if you’re using a tool that saves some of your information there may be you know that can then become part of your historical record Perfect.

AI as a workflow partner (59:08)

Melissa: So AI as a workflow partner. So how can AI support, you know, different tasks and stages of your negotiation? Just checking for time. Okay, I think we’re doing pretty good. So, you know, Janior talked about several uses of AI tools that I think are great uses, you know, but I want to just expand on that a little bit more and talk about a few others. And, you know, again, these can be sort of like a ChatGPT or a Claude or, you know, you might have, you know, something different like an enterprise license for sort of a tool that everyone in your organization is going to use. And it’s, you know, it’s much more like robust and it’s, you know, kind of different from those one off tools that you might just occasionally type something into.

Melissa: So some different uses that that I thought could be. Interesting. You know, again, Janior mentioned comparison, you know, that’s kind of an easy one that’s pretty common and something AI does well. You know, again, reviewing agreements against other documents, templates, policies and so forth. Another thing I wanted to mention for that is if you’re going to use one of these more robust, comprehensive tools, you might develop what some people refer to as a playbook, like a negotiation playbook, which would essentially contain. I mean, you know, the whole, you know, in a, I guess, a condensed way of your organization’s approach to negotiating a certain type of agreement, you know, all of the policies that are going to be applicable, all of the preferred terms, fallback positions, all of that.

Melissa: And, you know. It’s definitely an investment of time. It’s kind of a big undertaking to do that. And, you know, kind of similar to a prompt, it has to be done in a certain way to make sure that it gives you the output that you want. But that’s, you know, if you hear that term playbook, that’s what that means. And there are definitely webinars and courses out there that can kind of show you how to optimize and create a playbook. But that’s another thing that you might be comparing. against when you’re having AI flag terms or create a red line for you. Generating supporting arguments, you know, I think it could be interesting to, you know, if you’re, you know, if you have a term that you really want to push on, you know, the other party is wants to change it but you really want to keep it and you want to kind of try to explain to them why it’s important to you but you’re having trouble maybe coming up with a coherent argument or just wording it the right way you could put it in the AI tool and say hey can you can you give me some sample you know arguments for why I should keep this provision and then that might help you to craft a response.

Melissa: Similarly, you could even take it a step further and say, what are some potential counterarguments that my counterparty might come up with and how could I respond to those? And then, you know, that way you’re kind of walking in prepared, especially if you’re going to be, say, on a call and you have to kind of make some snap decisions, you might at least have some ideas of things that the counterparty might bring up. So another thing I think you could do is just asking AI to provide, you know, an analysis or some background information on a contract term. If it’s something you’re not as familiar with or you’re kind of like, you know, I’ve seen this term, but I don’t think it belongs in this type of agreement, like why would they include this or, you know, what could the thought process be before, you know, before you actually ask them, you know, you can have AI just kind of take a stab at it and see what it comes up with, you know.

Melissa: You know, why is this term typically included in an agreement? You know, can you provide some background information and maybe analyze some of the potential risks or implications of accepting it? And then, you know, of course, again, a human should be looking at that and making the decisions, but it can at least give you a jumping off point if, you know, if you’re trying to get a better understanding, you know, and of course, you always want to verify what the AI is telling you as well. Another thing I really think is, you know, kind of an interesting use is if you’re, you know, let’s say you’re negotiating, you know. IP terms, right? And, you know, you have position A, this is what your preferred term is, and the other party position B is like, you’re miles away, right? You’re nowhere close to agreement on this term, you know, and you’re struggling to kind of come up with a way to compromise.

Melissa: You can, you can put them both in AI and say, give me some ideas. How, what is, you know, what’s the middle ground here? What’s, how could we potentially come to agreement? Give me some samples, and then use that as a jumping off point. Okay. And then finally, another thing Janior mentioned, you know, again, assuming it’s permitted by your organization and it’s appropriate and, you know, you might be able to upload, say, a final contract into an AI tool and have it give you a summary. And you can even ask it for a summary, you know, for a different audience. You can say, please give me a summary tailored to the PI and here’s what I’m looking for.

Melissa: Give me a summary tailored to my finance team and this is what they’re looking for. and you can you know quickly and easily you know just of course you want to check it before you send it to anybody but it can maybe save you some time there and then I’m not going to spend too much time on this because I want to make sure we get through everything but you know there’s a lot of different factors for choosing what AI tool you might want to use for contracting you know so This, so the materials that are provided for this webinar, that sort of QR code that Janior provided at the beginning, that actually will take you one of the documents, I’m not sure if there might be others, but there’s a checklist for kind of walking through the decision making and some of the factors that you should consider for choosing an AI tool.

Melissa: So I would encourage you to take a look at that, you know, and it basically includes some of the things here, but really expands on it and goes into a lot more detail about all the different important considerations. So I’m going to going to skip that part of the slide and, you know, just encourage you to take a look at that tool. And then finally, just to kind of reiterate some of what Janior already said and just expand on it a little bit, we always want to make sure, especially with, you know, contracting and, you know, things that are, you know, working in a highly regulated environment, all of that, we want to make sure that we’re using AI safely and securely.

Before you use an AI tool (65:22)

Melissa: One thing, you know, the first thing I think that you should always do is, does your institution have a policy on AI? Hopefully the answer is yes at this point, but if not, hopefully they’re working on it. You know, I think we, most places are getting there or hopefully almost there. So the first thing you want to do is familiarize yourself with that policy. Don’t touch AI tools until you’ve read that policy and understood it. And then, of course, if there isn’t a policy, I would ask, you know, is there one in place or is there one? everyone you know that’s that’s in process do you have a draft is there are there any principles you know like what are the guidelines in the meantime and then in terms of available tools you know I know you’re touched on this as well but you want to make sure that you’re using tools that are permitted by your organization so that may be covered in the policy hopefully But, you know, you want tools that are vetted by your IT department, vetted by legal, anyone else who needs to approve that tool, and that you’re only using those tools, you know, that you’re allowed to use in your workplace.

Melissa: The next three that I’m going to mention are kind of all tied together a bit. So data security, data privacy and training data. So it’s important, you know, when you’re using these different AI tools to understand, you know, how secure is your data? Is it, you know, do they have appropriate protections in place to protect from breach or misuse? How private is your data? What are they going to use it for? Are they going to take your data and share it with other people? So, you know, who, what, when, where, why, you know, you want to know the answers to all those questions. training data you know as as honey were mentioned you know if you’re using a public tool of any kind or you know even if you sign up for an account they’re almost definitely going to be training on your data the only exception to that might be is if you have an enterprise license you can sometimes get an agreement with the AI provider that you might have like a separate kind of segregated you know instance of the tool and they will not use your data for training training, but that’s like a special term that you have to negotiate with them.

Melissa: Now, ideally, hopefully, you know, the data privacy, security and training, those should be things that your IT team has looked at as they’re vetting these tools. And so hopefully you don’t have to think too, too much about these things because hopefully your policy will kind of spell out, you know, here are the appropriate uses of this tool and here are the things that you shouldn’t do with this tool, right? Like this tool is okay to put a signed contract in, you know, or maybe even you have data security levels like level one two and three data is okay to put in this tool level four and five data don’t put it in this tool and hopefully they’ll spell that out for each tool and make it really clear what you can and can’t do and so you don’t have to get too into the weeds on this stuff but If, you know, especially if you don’t have a policy or you’re not sure, it never hurts to ask and you always want to ask and just make sure that you’re using these tools, you know, again, safely and appropriately.

Melissa: That’s especially true if you’re using, you know, third party data that you’ve received from another party, you know, you definitely want to make sure I would probably advocate if you’re going to put third party data in any kind of AI tool that you’ve. that you’ve gotten their buy-in and their agreement to that before you do it and that you’ve made 100% sure with you know your IT folks or other professionals that you know that you’re going to be able to meet meet whatever contractual obligations you’ve promised to meet and then kind of the same idea if you’re working with kind of legally regulated or you know data that has specific requirements around it you want to make sure that For that.

Melissa: Any use of an AI tool with that kind of data is going to comply with the applicable rules and regulations, you know, depending on where you’re getting it from or who’s involved, you might be able to ask, you know, ask your sponsor, ask your federal sponsor, is it okay to put this, you know, to use this data in this tool and kind of have that discussion before you start doing it and just be really, really careful. So then kind of the last thing to wrap up. To wrap it up is, you know, again, reiterating what Janior said, because I completely agree, you know, don’t make assumptions, don’t guess, you know, of course, AI tools can be incredibly helpful, valuable, you know, save us time, make things more efficient, and, you know, it’s not that we shouldn’t use them and experiment with them, but, you know, if we’re not sure how something works or what the safeguards are or what the policy is, you always, you never want to assume because it is an area where you where you could kind of get yourself into trouble pretty quickly or it could have negative consequences so it’s important I think to just be, you know, I’m personally pretty risk averse, my institution’s pretty risk averse. So, you know, I would say err on the side of caution and ask if you’re not sure.

Melissa: So that concludes my portion. So, Janior, I guess I can get it back to you. I haven’t had a chance to look at the chat, so I don’t know if there’s questions or…

Q&A and wrap-up (70:20)

Janior Valle: Yeah, no worries. I’m going to just display the Q&A here. And we did have some questions. Most of it was just kind of like the. Questions regarding the slides, the recordings and whatnot, and then we had a few questions with relation to where we could find skills for Copilot. So all of those are answered at this point.

Melissa: All right, okay.

Janior Valle: If there’s any questions from the participants, you know, feel free to post those in the chat. You can also unmute and ask directly. So whatever is preferred.

Janior Valle: And in case you didn’t see it while we wait for any potential questions, in case you didn’t see it, after the call, we will send out the recording once that’s available and we’ll include the skill that we covered earlier, the prompt, Melissa’s checklist and the slides, of course.

Janior Valle: Thank you. All right. Looks like we’re just getting a bunch of things. And I want to thank everybody for joining us today. I hope that this was informative and that you’re able to leave here with some additional knowledge and some additional resources that could help you in your day to day. So I want to thank Melissa personally. Thank you so much for being our guest speaker today. We do normally run these every single month. We are not going to be doing one next month as we have our user conference at the end of the month. So we will be skipping next month. We will be sending out further announcements for webinars in November and December once we’ve got those lined up with our guest speakers.

Melissa: Thank you, Janior. I really enjoyed collaborating with you and appreciate this opportunity to connect with folks and share this information.

Janior Valle: Well, doesn’t look like we have any questions, so we’ll go ahead and wrap up. We’re right on time. So again, thank you everybody for attending today’s session and we’ll forward on those details as stated. Okay. Take care.

Melissa: Great.

Janior Valle: Thank you, Melissa.

Melissa: Bye-bye.