Amplifying Business Value with AI through a Connected Omnichannel Ecosystem
In this session, we cover:
- What is the ultimate business value of omnichannel and how AI can help realize its full potential?
- What are the common challenges faced when building out your commercial software ecosystems?
- In the past 2 years, we’ve generated a huge amount of data. How do we ensure we’re using that data in the proper way when it comes to AI implementation?
- What does AI/ML’s role look like within a commercial strategy, both now and in a few years’ time?
Watch Aktana and MSD present at NEXT Pharma Summit 2022
webinar transcript
Moderator
All right. Next session, amplifying business value with AI, buzzword, through a commercial omnichannel ecosystem. Who do we have today, here, we have Jonny from Aktana, Jonny, how do you like it so far?
Jonny Rawlinson
Fantastic. Yeah. Saw a great quote in the lift from George Bernard Shaw, saying “If you want to find paradise on earth, come to Dubrovnik,” and I totally agree with that.
Moderator
Fantastic. And we have Erasmus from MSD Denmark.
Erasmus Holm
I haven’t thought about that, but yeah.
Moderator
Let’s kick off the conversation with the first question we prepared during our preparation call, which is what is the ultimate business value, or even call it purpose, of the whole omnichannel strategy and ecosystem?
Erasmus Holm
You want to go first?
Jonny Rawlinson
Sure. I can start. Absolutely. Yeah. I’ll start with a definition that’s similar to some ears in that omnichannel and what we’re trying to do with omnichannel is engage with customers through all of the current channels that a company might use, using the same connected strategy, essentially. What we’re aiming to get out of that is the conversation with a customer should be continuous conversation as Samir was referencing, that’s always relevant, always tailored to the HCPs preference and their needs. Essentially what you get out of that is enhanced customer experience. People have touched on that a lot throughout today. It’s all going to be about customer experience and omnichannel done right, it enhances that customer experience at every single touch point, along the HCP journey and myriad benefits of doing that consistently improving customer experience, HCP experience with brands and products and connected through R&D medical and commercial. You’re obviously looking at loyalty, you’re looking at retention, you’re looking at fast people, moving customers through campaigns more quickly, and everything that comes with that. It’s obviously really difficult to do. It’s easy to say, but then there are elements of optimization that needs to happen. You’ve got that long term optimization around strategy content, whatever it might be that’s often done in house or by consulting. And then you’ve got that real time optimization that needs to happen. And on what’s that HCP actually doing? How are they engaging with your content? How do they prefer to be engaged with? Where are they? Things like that. The balance between those two is absolutely crucial and the power of AI within omnichannel, I see that as the enabler of omnichannel and making that successful, because it’s not possible for humans to bring all the insights out of all of that incredible amount of data at a quick enough pace to act on it through commercial teams or medical teams. AI really is the enabler for that omnichannel, to work correctly because the amounts of data that we’re talking about.
Moderator
Excellent. And are we close to that state, like you explained currently, or?
Jonny Rawlinson
I don’t think so. I think we’re scratching the surface of the potential of the omnichannel ecosystem and connectivity between all of the different elements from, let’s say internal external data, data curation, data integration, strategic planning, omnichannel execution and say intelligence and then systems integration. I don’t think all of those. And if we asked the audience, I’m not sure everyone would say we have everything connected. All of our vendors connect seamlessly together right now. It’s not the case. I think the vision is getting to that. It’s a long way down the line, but I think we are on the path to achieving that but I’d say a lot of potential in the future for this type of technology. I think what someone mentioned it earlier, around the connectivity, talking to each other partnering is going to be absolutely crucial in this new era of medicine.
Moderator
Thank you, Jonny. Erasmus, same question to you and listening to Jonny, what makes you think about the current omnichannel strategy?
Erasmus Holm
I agree with most of what you’re saying. I don’t think it’s the enabler, AI. I think it’s one enabler. That’s the difference maybe I would take on it. I think the change management for us in the industry in Pharma is much bigger challenge. I think that AI can also, as you said, in the best buzzword as well, I think it can be a distraction, but I think it is the future as well. How do you balance those two things out? How do you balance that you need to change your organization and people and the way you work at the same time, not being distracted by potentially something that’s super cool and sounds very interesting, but can, maybe if you don’t have the fundamentals of omnichannel, if you know, some years ago we couldn’t even send an email at MSD ourselves and then I wouldn’t want to go and implement the chat bot because then everything just falls apart. You have to reduce the friction and our human interaction can’t be there all the time. And I think AI can be a great enabler in that removing those frictions that our customers have in many areas and help us also guide some decisions.
Moderator
Certainly, thank you Erasmus. Next question. What are the main obstacles, which certainly should be highlighted here today, when we encounter how to build out the commercial software ecosystem? That’s certainly a challenge. You know, it very well, very interesting example, GDPR, how many GDPR databases do we have? And they’re absolutely not connected at all. Erasmus, maybe we can start with you.
Erasmus Holm
Yeah. That’s many challenges. I think I have a session tomorrow, I also speak that digital transformation is only 20% digital, is 80% transformation. It’s the change management again of people. It’s also the compliance, it’s the data quality you have. Are you really customer focused? That’s also if you are implementing it for sales reps and others, it’s also the bonus scheme and how you are organized but there’s a lot of things in order to get adoption of any kind of investment.
Moderator
Thank you Erasmus. Jonny?
Jonny Rawlinson
Yeah, totally agree. I’d break it down into three areas that we see across our customers. One of the main ones being the people are sort of the element where we’re talking about alignment across stakeholders, across teams across our medical, commercial. That’s one of the main things really because the technology, as you say, can be absolutely fantastic. But if you don’t have that buy-in, you don’t have that engagement across the organization, then it’s going to go absolutely nowhere. Totally agree with that. And the other couple of things that I would say that we see across our customer base around data. There’s a perception of data being a challenge. And the other one is around the cost and resource associated with implementing an omnichannel solution essentially. The first one talking about data and I say it’s perception because often what we find, if we go through a data readiness assessment, you find really valuable data that can be mined to start tackling all your business problems immediately, this year for example, but often people get lost in the bigger picture of, we need to get all of our data completely sorted out, completely clean, and it needs to be this bigger challenge, which is often a 12 to 18 month project. But when you actually delve into it and look at the data that companies do have, they actually have this really rich data sets. And then if you’re matching that data to the business problems that you’re trying to solve in the next year, next three years, you can quite easily start somewhere. Let’s say it’s CRM interaction data on webinars or something like that. And you start there with a simple use case, really narrow, small team working really in an agile way and iterating on that product that you’re creating. Proof of concept and then after that, you can start looking to roll it out. That doesn’t have to cost that much, actually, in terms of the resource, you don’t actually have to put a huge amount of resource into it. Those data and the cost of a resource, they are perceptions. And I think there’s something that if you look deeper into it and look across your data, you do have, you can start addressing some of the key problems that you’re seeing.
Moderator
Speaking about data, I wonder, in the last two years, we’ve generated a huge amount of data, obviously, are we using that data in the proper way? And is this actually the perfect playground for proper AI implementation?
Erasmus Holm
Short, no. We are not using it enough at least I can only speak for myself and the teams that I’m part of. But no, I think, again, interesting point about, have we using the data we have and you come to a pharma company and they’re probably searching for so many other data sources and we try to, probably make another dashboard next month, but are we even the fundamental data that we have, are we even using that properly? And I think it’s also about us. A lot of our people doesn’t really know how to use it. They look at it and they think the data, or the AI, will help them to a decision, where right now we’re not there we are there where we can at best get some support to get a decision. And I say to my teams, “You have to have curiosity around data before you can even get to the next point. Just be curious about data,” and what I’ve seen in a lot of the teams with us, is that they are hyper focused on the data in the operational teams, like click rates, open rates and stuff like that. But then when I asked them, “Well, what is the business focus? What’s your business objective? Did you have data to support that?” Like, “No, it was just a gut feeling.” You need to grow 20%, like why 20%, what was the data? Have the curiosity up there as well, and look for the data there that will take you long way. And I think AI has a play there as well in the long run.
Moderator
Good to know Pharma needs more data, remember that. Jonny?
Jonny Rawlinson
I totally agree. The data that’s currently there is not being used. There is a lot and there’s the longer term plan of data looking at omnichannel solution for the next, let’s say five year plan, three year plan. Clearly you need to build, start with a smart foundation around data. Look at the things that you, that your business problems you solve, what data sources, data sets are going to are going to help you solve those. Start investing in those now, but at the same time, identify what you do have that works as to my previous point, because often you will have data that is really valuable. And then it’s about translating that into a usable format, setting metrics and KPIs around what you want that data to actually do. And then start off on a project, then start measuring it. And then you can see the value come through. Once you’ve proved it with a specific use case that is going to do whatever, increase your click through rate by 30% or something. And you can say, “Okay, well let’s apply it to another data source,” and build it like that. And it’s really building blocks. Building that foundation out for the future.
Moderator
What’s the starting point, when we speak about implementing or amplifying AI into the omnichannel strategy? Maybe some key takeaway for the audience when they return home, which software should I install or what should I do first? What is the concrete takeaway or generate more data?
Erasmus Holm
Which software should they install? Aktana obviously.
Moderator
Of course. Maybe the wrong question.
Jonny Rawlinson
Yeah, absolutely. It’s probably important to go back to what should this software do? What should your omnichannel solution actually do? The most important thing is that it helps you to improve your ability to solve your business problems. If it’s not doing that, then it’s not worth investing it. Potentially, you need to rethink it. Once you’ve identified that as the obvious main goal, it’s then about breaking it down into different elements of how you invest in that wider industry ecosystem. Things that I mentioned earlier are around identifying your current business problems. Then you can look at core competencies of vendors in each of the different buckets within the omnichannel ecosystem and match up what is going to actually answer your specific business problem and going to start helping you out immediately. Looking beyond that roadmap though, to the future, to say how are they thinking about their specific area? How they connect within the wider ecosystem? Do they enable seamless connectivity? All that kind of stuff that’s going to enable future or complex business problem solving, I think is probably the best way to put it. And then the second part is, what I’ve mentioned in the previous question, building out a smart foundation. There’s often the low hanging fruit that we’ve talked about, but start building now for the future and invest in data platforms, so they’re going to keep your data in the best possible way, keep it up to date, CRM systems, everything else that’s going to keep all of your interaction data in a usable format. So you don’t then have to go, “Right, we need to do a six month data cleanup,” before you start another project. And then finally, it’s really evaluating the ecosystem with the future in mind. Having a pragmatic plan in the short term, that’s going to start to help you solve your business problems that you’re focused on in the immediate short term. But then looking at the vision beyond that say, what’s going to happen in five years time? Technology is going to advance so quickly that we are going to very quickly be able to solve much more complex problems that you don’t even have no idea how to do that right now, but that will come in time. Have a vision of the things that you would want to ideally solve for your company, for your customers, for your patients. And by doing that, you can bring it all together and improve across R&D, commercial, medical and the outcome for the patient is going to be better because you’ve got that seamless information across all the elements of the ecosystem.
Moderator
Erasmus, starting point?
Erasmus Holm
Yeah, I think it’s how much mature are you? How mature are you as a business? Do you have the right organization set up? What about this system you have currently, are you using them to the fullest or is that your strategy for using that? I can definitely see teams that has ran too fast into new exciting solutions, but I also seen some that maybe move a little bit too slow, and sometimes you can throw something at them and that will be organizational catalyst as well, implementing of a tool. But it’s important to understand where you are, do you have your fundamentals in place?
Moderator
The basics?
Erasmus Holm
Can you do the fundamental interactions with your customers fairly frictionless? What about your people? Do you have the organization to also adopt as well? When I started about three and a half years ago from outside, I came from software, we did some training with the sales reps and the first training, two of them didn’t know what the space bar was on the laptop. And I was like, “I’m not going to put an AI solution right towards them tomorrow.” There’s some different things that also I feel that needs to be in place before you jump into this, but there’s definitely some companies that should do it also as a catalyst.
Moderator
Certainly. Did they learn at the end where the space button is?
Erasmus Holm
I think two of them did, yes.
Moderator
Okay, great.
Erasmus Holm
Maybe one of them.
Moderator
Okay. Fantastic. Last question. What role do see AI, and machine learning is also very hyped term, is playing within the omnichannel strategy, both now and for the upcoming future?
Erasmus Holm
Would you explain what the difference is between AI and machine learning?
Moderator
Well, that’s a good question, but there is a difference.
Erasmus Holm
Yes there is.
Moderator
Yeah. Machine learning the process. The brain behind AI. What I, we at NEXT, what we certainly see is that a very simple software everyone says, “It’s AI, it’s AI powered,” but it’s not a neural network. And it isn’t AI at all. It isn’t a proper AI. I would say that proper AI always comes with a proper machine learning because the machine should learn, improve and it’s a continuous actually learning curve. This is for my understanding, how we see AI and machine learning.
Erasmus Holm
And I can start maybe at least for us, in the Nordic’s and Baltic’s, the future there lies partnering. We don’t have the internal capabilities to do that ourselves, to be quite honest, if we want to create something that’s really meaningful for the customers, we need to partner up and we’re doing it right now with Microsoft actually on several projects, to create something with the customers, with hospitals, where you got AI, that can look through medical journals to simply find early stage lung cancer patients easier. And that’s a good business case. It takes a long time, of course, the compliance issue with that, but all that data that you also need to own. There’s so many compliance aspects of that is too difficult for us. We have a play in understanding our customers and we have some value to add there and all the new resources and people we got in as well in the company. We have a better chance of partnering with companies like that. But for me, going very advanced and those kind of solutions needs to be with a partner for us, at least in the Nordic’s.
Moderator
And is that a Denmark case or?
Erasmus Holm
It’s a Nordic case?.
Moderator
It’s a Nordic case and are data varying from country to country or not?
Erasmus Holm
No, it’s at the moment, it’s a Swedish case that we’re trying to build out. And we had already done it within a vaccine’s partnership with Microsoft. We actually do a Minecraft solution. It’s nothing to do with AI, but the next step is to do something around oncology and AI.
Moderator
Thanks Erasmus. Jonny?
Jonny Rawlinson
Yeah, totally agree again. I think now we are looking at machine learning, artificial intelligence engines help it helping to solve really quite specific business problems. But as I said earlier, I think we’re scratching the surface of the potential of that because the value of the ecosystem over the long term is that you start amplifying each element of the ecosystem. If you have really good internal data, it’s really clean, you’ve got great data integration and you’ve got each element absolutely sorted out on the VS, let’s say the execution piece, the strategy piece, the intelligence side of it, that starts then to amplify as you build these connections and the systems work together. I think that’s where, in the future, you’re going to see the ability to solve a much bigger problem that the business, your sort of your stretch goals and your vision of how you want to be in five, 10 years. That’s the kind of thing that’s a really well rounded omnichannel ecosystem, whether it’s powered or not by machine learning, whatever, however we speak about it’s all connected. One of the ways to do that is through the advancement of machine learning technology and AI algorithms that help that. I think another point is around explainability. One thing I think what we’ve seen over the last few years is, and it’s potentially a slight barrier to adoption is around the ability to explain the output of an artificial intelligence engine, for example. For our customers, explain ability is really key and that because that builds trust in the system, it then becomes you increase adoption, increase trust out there and you obviously you’ve then got that drive across the organization and it helps with alignment, all that kind of stuff. There’s a load of enablement that will probably happen through that. Finally, I think the ability to solve bigger problems and start focusing on more complex problems as we build these solutions out is probably the key.
Moderator
Thank you Jonny questions from the audience. Let’s start with the first one. Okay. No questions. Good. So then gentlemen, Erasmus, Jonny was a great pleasure, great conversation and looking for the next one.
Erasmus Holm
Thank you so much.
Jonny Rawlinson
Thanks very much.
Moderator
Thanks guys.