Podcast + Transcript: The Middle of the Funnel, AI vs. Humanity, and The Automate Everything Trap
- Where we want to go as a society, the questions are moral and ethical, not technical
- The problem with AI adoption is that we only trust humans
- How the cloud changes the cost benefit analysis of applying algorithms to any problem
Steve Woods: It’s interesting, that sort of balance in this triangle of automation, AI, and humanity is a really interesting piece because there’s a lot you can do with raw automation and the majority of marketing acts at that scale. Just automate the message, the ad buy, the content. Automate all of that based on a set of rules at whatever level of intelligence, but the one thing that really does not translate well out of the realm of computer is trust. You don’t have a relationship with anything that you don’t perceive as being 100% human.
John Wall: Hello and welcome to Stack and Flow. I’m John Wall.
Sean Z.: And I’m Sean Zinsmeister.
John Wall: And today our guest is Steve Woods. He’s CTO and co-founder at Nudge.ai. Anybody who follows this space knows him as one of the founders of Eloqua. We’ve talked to him over at Marketing Over Coffee on his book Digital Body Language. Steve, thanks for joining us today.
Steve Woods: Thanks for having me. It’s been a while we’ve been in touch. It’s great to be back.
John Wall: Yes, it’s great to catch up. Now Sean, to get us rolling, you had an article about, you’ve been watching Hardware. There’s a lot of stuff going on with folks gearing up for AI, especially some stuff going on with Nvidia. What have you seen over there?
Sean Z.: Well yeah, I mean it’s a bunch, you know being in the heart of Silicon Valley, you’re starting to now see more focus by people who are working, and especially in the AI and deep learning space, the connective power to the hardware and the ability for it to process because one of the things that they’re realizing, in terms of looking at sort of performance and data, is that they’re gaining better performance out of algorithms that are using deep learning and neural networks, but they’re requiring a lot more processing mower.
So now you have, think, big data combined with really complex, deep, sort of large neural networks and that’s another reason that you’re starting to see more emphasis put on the hardware, which is, you know, from one of the articles that we’re picking out from CNBC, reason that Google and Microsoft are already starting to sort of make their own chips, you know this is a hot space that Nvidia has been in with their GPU processing chips. It also makes sense because if you’re thinking about speed in general, you know, localizing that processing power, as it were, makes a lot of sense.
And I think a great example of this was the announcement everybody’s been raving about, which is the iPhone X and the iPhone 8, where they talked about neural network involved with that, but also the sort of AI chips. One of the use cases there, obviously looking at, is sort of a facial recognition and how that’s getting processed really fast instead of sending that to the cloud, have it be processed by a big powerful data center and then processed back.
So I think this is gonna be an area to really watch. Like I think that the blueprints around infrastructure for a lot of companies that are building themselves out as AI first, this is gonna be something that’s gonna be really exciting to watch, but I have to, I gotta chance, I have a couple inside friends who work on these phones and I got a chance to handle the iPhone X. It’s a pretty slick device. I mean, it feels very expensive. I guess it’s priced right.
Steve, to warm it up, I’m curious, anything that you took away that excited you from a consumer standpoint about the iPhone announcement, what Apple has been working about in that innovation? Just curious, your immediate thoughts.
Steve Woods: Yeah, I mean there’s so many things kind of, to unpack there. To your point on the hardware, we seem to be in this really interesting time as we go from Von Neumann architecture of doing transactions in sequence and getting faster and faster and faster at that and then if you look at what the brain does, it’s kind of slow, but it does everything massively, massively parallel. So, you know, do we fake it with the existing chip set? Do we come at it at a sort of partial way with sort of the GPU approach or do we take a completely new approach with some of the Tensor chips and the neuromorphic chips.
We’re definitely at an inflection point. I don’t, I’m not hardware centric enough to predict the future there, but it definitely feels like a time where there’s a path and I think future generations will look back at this and say “Wow, that must of been an exciting time.” And at the same time, to the point on face ID, we’re very much at a point now in technology in society with face ID, with the algorithms for ranking and rating people in various facets of life with the sort of change in the job situation and in driving. We can do stuff with AI, it now becomes the question of what do we become as a society and do we want to go down those paths?
Which is much more of a, it’s a philosophical, not a technical question in a lot of these cases? Do we want face ID? How does this play against some of the questions that have been raised to do with the involvement of authorities in peoples’ personal business when you’ve got a face ID recognition system… etc. etc. There’s so many questions there that are non technical, they’re societal and philosophical and it’s, again, a very fun time to be a technologist in the middle of this combination of decision points.
Sean Z.: Yeah and this is why I get really excited about this because AI is not only a product category, but it is a cultural category as well and I think that some of the questions that we’re starting to ask ourselves around, I mean look at some of the news now breaking about Facebook and how it was able to see use these psychographic algorithms to sort of target hate groups and things like that. Where it starting or quit questioning the ethics behind some of the technology, behind some of this advertising.
One of the questions, actually Steve, that plays on your background, co-founder and CTO from the beginning of Eloqua, getting back to the hardware, it’s something. There’s a train of thought that is always said that sort of the original marketing automation systems around probably late 90’s entering the early 2000’s were really thought of as first email service providers. Better ways to sort of send mass communications and then the data caught up with it and now we’re sort of playing that catch up game.
First of all, is that sort of the main thought true in your mind? Was that something that you guys were thinking about from the early standpoints in Eloqua and then my other question is how far caught up is it, where we’re at with marketing automation on the business side today?
Steve Woods: Yeah and it’s an interesting point because if you look right back at the founding of Eloqua, we did not start as an email vendor. We kind of backed into email based on some misadventures in becoming a proactive chat vendor, which was fascinating and funny in its own right, but what ended up happening for us was because we tried to do this online chat thing and then backed into email so that the people chatting knew who they were communicating with, we ended up in this interesting position where we knew a lot of rich web detail about the people and could map it to who they actually were.
And that core of online behavior combined with CRM type analytics is really the crux of marketing automation. So I think, yeah, there were a lot coalescing trends then and a lot of pressure on marketing to deliver more to the sales org, to become more analytical, to become more data driven and less kind of artsy, craftsy as a discipline and we were just lucky enough to find ourselves in a position of having the set of tools that that nascent market needed at that time.
John Wall: Steve, where you surprised at all through your journey with Eloqua and looking at the marketing automation industry in particular, about the amount of data that sales and marketing teams in particular would be really, really good at gathering? Did that sheer sort of volume and complexity surprise you at all?
Steve Woods: It definitely surprised us on the technology side. The tools obviously back then were nowhere near what they are today in terms of handling ridiculous amounts of high volume, high velocity data, it just didn’t exist. So we were trying to do it with fairly antiquated tools and it was technologically difficult to do that. We were probably one of the first kind of mass scale, high transaction SAS systems out there and sort of created a lot of approaches that band-aided our way through it.
So definitely a bit of a shock and then sort of fast forward from there to today, there’s now much better set of tools to say “Okay, you’re getting this much data coming in. How do you look at it in bulk? How do you look at it in stream? How do you think about it? How do you reason about it?” That tool kit has evolved from so many directions as different industries wrestled with similar quote unquote “Big data problems” and the tool kit evolved to be able to handle that.
John Wall: Yeah, that serves it up for us perfectly or so, talking about today’s tools now, tell us what you’ve got going on at Nudge and how does that fit into today’s ecosystem?
Steve Woods: Sure, yeah. So coming out of the marketing space and really seeing that decade long transformation as marketing went from unmeasurable and non-systematic and then looking over the wall at sales and really seeing two macro-changes in sales. One, the buyers are changing. Buyers don’t need sales people in the process. You don’t need sales for information, you don’t need sales for kind of access points or references. So the value of sales has morphed substantially and at the same time the pressure on sales to handle that middle of the funnel, where you’re trying to stay in touch with an organization, you’re trying to get broader and deeper with the relationships, you’re trying to find opportunities to get your product to be thought about as a high priority.
That middle stage of the funnel is becoming bigger in most organizations, harder, and it was unmanageable. There was no way to look at that and say “Okay, here’s the data that I need to manage, that relationship building and maintaining phase.” And so we looked at it and we looked at the technology and said “There’s a way that technology can understand what’s going on, on masse, across your entire team and the world that you’re selling into and really give you this new lens into how your team is doing at developing, at creating, at growing, at building and maintaining those relationships that get the deals through that middle stage of the funnel.”
And obviously it uses a lot of that suite of better tools and better hardware for tackling the bigger data challenges out there. But it just seemed like a very opportune time in sales to say – the old ways of approaching sales just aren’t working anymore. We’ve gotta have a new approach that contemplates current reality a little bit better.
Sean Z.: And it looks like one of the first approaches that you guys are taking are sort of, of course correct me if I’m missing it here, but being able to find the intersections between your social networks and I know that that’s something that guys, you know, when you talk about how do we build that network of relationships. I’m curious, what in your guys mind was broken about it, obviously like the large professional LinkedIn and Facebook and maybe some of the others out there.
Was it sort of just a usability thing or what were some of the challenges that you looked at to say “hey, you know, I think that this is something that we can add better technology and know how and make it better”.
Steve Woods: Yeah, no, and I don’t think anything out there is broke per se, everything kind of has its place. What was missing though was the ability, you know, if you’re a VP of Sales and you’re sitting there and saying, okay, we’ve put in place this BDR team. We’re knocking down the door and we’re getting that first meeting. We kind of got that nailed with today’s tools, moderately okay. We’ve got a CRM system in there and we’re doing okay managing that deal from 25% chance at close, marching it through the process and getting it closed.
What you quickly realize though is most of those first meetings turn into “Hey Sean, that’s interesting, good stuff, really glad I learned about it. We should totally keep in touch. Not gonna buy right now.”
Sean Z.: Right.
Steve Woods: And what do you do? You can’t march them through an automated cadence of “Hey, just wanted to keep in touch. Again.”
Sean Z.: Again.
Steve Woods: Or, you can’t do that. You’re not a deal yet. It’s not like, you know, Sean, in the CRM system 10% chance of close and we’ve gotta take these steps, you’re not ready to buy yet. So what do I do? That’s classic sales. I have got to understand when I’m losing touch with you. I’ve gotta look at what’s going on in your world. I’ve gotta think about how I can help you. I’ve gotta think about how I can stay present without being annoying. And that’s hard work. That’s the crux of being a really good professional sales person. And that’s hard for me to do as an individual.
That’s hard for me to look at across a team as a VP of sales. So sure, I mean there’s tools out there like LinkedIn that give you good ability to communicate and decent data sets on people, but doesn’t really help you with your sales process to say “How am I gonna keep in touch with 250 organizations in my territory that will buy, but aren’t ready to buy yet and how do I as a VP of Sales manage the fact that I’ve got 10 reps trying to keep in touch with 250 organizations in each of their territories?”
There just wasn’t a tool kit for answering that problem.
Sean Z.: And it kind of thinks like, if shaping your thinking, you really looked at the better way of automation, especially for the enterprise at scale through the work at Eloqua. Is Nudge kind of your play to say, hey, we should now look at how we’re sort of applying automation to those tedious things in the sale side? Is marketing still a part of your vision as that sort of continued road of automation there or are you sort of just like, hey, we really gotta focus on sales because there’s so many pain points and problems for us to solve?
Steve Woods: Yeah, it’s interesting, that sort of balance in this triangle of automation, AI, and humanity is a really interesting piece because there’s a lot you can do with raw automation and the majority of marketing acts at that scale. Just automate the message, the ad buy, the content. Automate all of that based on a set a of rules at whatever level of intelligence, but the one thing that really does not translate well out of the realm of computer is trust. You don’t have a relationship with anything that you don’t perceive as being 100% human.
If you perceive something as being 100% human, you can trust it, you can have an assessment of it as a person, you can understand that it has thoughts and it has feelings and in today’s world, in 2017, the only thing that acts human is a human. We’re not there with automation, we’re not there with AI contrary to what Hollywood movies are showing, If you want to trust something it’s gonna be a human and trust and that relationship is such a core part of selling because without trust you can’t, you can’t push someone to change their perspective and say “What if you think of it this way? What if we tackled it in a different frame of reference? Wouldn’t that change your mind a little bit?”
Okay, let’s think of it about that way, let’s prioritize it differently. We believe that humans and humanity are a critical part of selling, but humans don’t need to be doing all of the grunt work behind understanding their buyers and what’s going on in their world and keeping track of all of that detail. They should be just focused on taking that research and building relationships with it.
John Wall: Okay, we have to pause for a moment. We’d like to thank G2Crowd for their support of Stack and Flow. You can get the right software and services for your business when you’re adding to your stack. G2Crowd has over 225,000 validated user reviews to help you make smarter decisions. You can check our show notes for the G2Crowd reviews of all the tools mentioned in this episode and for more information visit G2Crowd.com
Sean Z.: And of course if you’re a Nudge user, then we’ll throw that out there. They should go over to G2Crowd and leave Steve and his team a review. I’m sure they would appreciate that.
Steve Woods: We very much would.
John Wall: So Steve, to sort of continue and to play on that riff there, here’s something around product adoption, especially for sales folks because I’ve gotten a chance to sit down with a number of CMOs and business leaders especially of larger enterprise and you’ll definitely have those seasoned reps, those seasoned enterprise sales reps that have been doing their job the same way for X amount of years and then introducing this change can be felt with a lot of friction and resistance.
I’m curious about your philosophy as a technologist about how you’re sort of taking a battering to some of that resistance or providing the education that, hey, this is a better way that we can help with some of this tedious, tedious task. I’m curious about how you, what your sort of approach is there.
Steve Woods: Yeah, it’s really interesting because it’s almost like there’s sort of three generations at play. You’ve got folks in sales who’ve been around for awhile who really are true relationship builders and they get it. We’re basically saying we’re gonna do the grunt work of your relationship building and we’re gonna leave it to you to have the conversations, build the relationships, take what we present to you and say “Okay, I can run with that.” They’re great.
Then you got this middle tier that really is sort of on the last cusp of this kind of automate everything. And so they’re kind of saying “Okay, this relationship stuff is great, but can I just click a button and run it automatically?” And when we say “No, you can’t”, that’s a bit of a challenge. And so that group, you know you’ll often see organizations go through this curve where it’s like, okay, we’ll automate everything in sales.
At first glance it appears to work okay and then it kind of starts to smolder a little and then it lights itself on fire and crashes into a brick wall. It’s a maturity cycle. It goes through that. But then you’ve got this third group which is at the end of that and they’ve said “Yeah, we went through that maturity cycle. That was an experience. Now how are we going to tackle sales in a more modern way? How are we going to be actually thoughtful and build relationships rather than just automating everything?”
And that third group, again, very modern, very thoughtful, and completely gets it, so it’s interesting. This really, there’s really those three communities out there and we do best with kind of one and three and those that are in the throes of stage two, you know, we’ll just wait. They’ll be back.
John Wall: Yeah, Steve, what’s the biggest problem with those organizations that are doing that over automation? Do they just feel like throwing tools at everything is gonna solve all their problems or is there anything that those guys all have in common?
Steve Woods: It’s one of those interesting things where I’m reminded of the expression ‘A stopped clock is right twice a day.’ If you start a business and you solve a particular pain point and so you say “Okay, we’re just gonna automate everything.” And you hammer the world, hundreds of thousands of people and say “Here’s the pain we solved. Do you want to buy it?”
You’ll actually get some hit rate. You’ll get a few people who are like “I’m so glad, I, right now, this moment in time, I need to solve that pain point. Thank you for reaching out and for reminding me of the email that you sent two days ago that I hadn’t read yet. That’s great. Lets go.” And so on glance one you say that that’s working wonderfully. Let’s double it. Let’s keep going.
Well now you’ve hit everyone that had that immediate need and your effectiveness just *scratching sound*, starts to plummet. And now you’ve ramped up to a team of five or 10 or 20 reps who are just pounding away at every list imaginable and your effectiveness is just dropping through the floor and at that point you realize “Well, wait a minute. Maybe the initial success that we got was just because those people had been pent up for so long.” That’s not a way to really build a market and generate demand, it’s just a way to tap that very, very small sliver of latent, unsatisfied existing demand out there, but by that point you’ve really started to burn out your audience and your list and you really need to come back to a much better relationship building strategy if you’re gonna get the other 99.9 thousand folks into the fold.
John Wall: Yeah, I can’t speak for Sean, I’ve never lived through that though.
Sean Z.: But Steve, what about your current Stack? I mean you have a series right now that you’ve been rolling called “How I Buy” I’m curious, what was the inspiration? Was it just, you were tired, you were sick and tired of getting berated by so many sales development and representatives? But I’m also curious about, how is that shaping your current thinking about the technologies that you’re bringing to help grow your own current business, not just looking externally?
Steve Woods: Yeah, absolutely. That series sort of started with a rant, if you will … I’m sort of two minds. I’m a CTO, but I’m in the sales space so I read a lot of stuff and I read a lot of sales people talking to sales people and sales consultants talking to sales people about how to sell. And it was all about this very precise things that you should do as a sales person and I though about it and I spend … Don’t ask my co-founder how I much I spend on technology. It’s far too much. I spend a ton of money on various tools, various technologies to build our product.
And zero of the techniques and approaches and tips and tricks and best practices that were being talked about in the sale space had any meaning for, no, they don’t work. That’s not … I don’t see the third thing in a sequence and think “Wow, maybe I did forget to look at your email.” No. No I didn’t. I saw them all. I deleted them. I’m deleting this one too.
Sean Z.: Wait a minute. Wait a minute. The fake reply subject line, that didn’t cash in for anybody?
Steve Woods: You know, I think the first one that I got, I don’t know, like seven years ago, maybe it caught me, maybe I “Ooh, these may, this is really …” No. Turns out when you get the 10th one that day, you clue into some of the tricks. And so that sort of, like all things, kind of devolved into a rant and then I put the rant on paper and I threw it out there on LinkedIn and said “Okay, this is how I actually buy.” And it went nuts. The article has just done phenomenally well.
And that led to, okay well, how about other executives and other people, maybe less cynical than me. So it turned into a series, we had a lot of fun with it and we’re getting sort of, I’m trying to but one a week that I get out there where I talk to a exec that, you know, buys a lot of stuff and really drill in on how they actually buy and it’s been really interesting to really drill in on that and see the patterns and see the regularity with what buying in 2017 looks like.
Sean Z.: I think that that sort of profiling an archetype of the real buyer is just something that I feel is so lost on go to market teams where I think we … Especially, maybe it’s in the Silicon Valley bubble or the tech community bubble that just sort spends a lot of time navel gazing and I think that that information goes through the echo chamber and we lose sense of like, hey, this is not actually how I’m gonna buy this $20,000 piece of technology that I’m bringing into my company to rely on.
So no, I look forward to more of that from you. We’ll be on the lookout. In terms, you know, I always like to move from the stack to the flow part of the conversation around data. I’m just curious, from a very high level, what are you seeing that sort of gets you excited around some of the work in data, whether, you know, obviously there is a lot of hype around AI right now and some of the, you know, now it’s all deep learning and all these other nomenclature that’s falling into there.
But I’m curious, like, what are you sort of seeing from the data flow side and some of the innovations there.
Steve Woods: Yeah, I think it’s an interesting topic and kind of V1 of SaaS really enabled a lot of that ability to bring a bunch of data sets together and pull them altogether in the cloud in a way that becomes accessible to AI, which is the most important and most foundational thing because in a lot of cases, if you look at solving a particular small tactical problem with an algorithm versus either ignoring it or solving it manually, you get this really interesting kind of cost value bar there. Where if I’m in one organization looking at one data set and there’s a thing that can be improved with an algorithm, well, it’s a bit of work to build that algorithm and train that algorithm and tune the algorithm and the pain point of not having that particular thing solved, it’s a bit of an annoyance, but I can get around it manually.
When you then move to the cloud, if we assume that computing power is relatively free, your capital cost to fix the problem with an algorithm is the same, but now your benefit goes across the world rather than one company. Your kind of cost benefit ratio analysis on looking at even the tiniest of problems – we’re not talking like self driving cars and sentient beings – the tiniest of problems suddenly become meaningful to tackle in ways that you just wouldn’t of bothered before because the cost benefit would never have worked it’s way through.
Sean Z.: Part of the quality of the way that these systems run today is obviously the acquisition of labeled data and I think something that I look towards is, listen. One of the ways to get – when I was working as a sound design and audio engineer – you want to get the best sounding thing, get the best raw inputs. It wasn’t a bunch of processing power that we were putting on in the back end. In fact you’d always reach a point where you just couldn’t make it any better. I mean, I like that analogy because from a data standpoint, I mean especially because you’re looking at things on the sales side.
Do you see AI leading to better data governance? Better guidance in terms of all the labeled data we’re creating, whether it’s sales people inputting things into system or other databases? Do you see that as a sort of next horizon?
Steve Woods: Yeah for sure. I mean I would interpret the concept of labeling to include essentially implicit labeling, I think is important because I don’t think you’re ever gonna have a sales person sit down and go through a thousand data points and tag them, getting them to click a button sometimes is challenging enough, but that being said, when you roll that all up and you look at, you look at the data that you have in terms of selling and you map that across thousands and thousands and thousands of professionals and how they sell and what titles and levels and roles they need the relationships with in order to get the right level of deal to happen, they’re essentially tagging that data implicitly.
And suddenly you can building patterns out of that and saying “Well wait a minute, this deal here, you’re saying that it’s got a good chance of closing, but for deals that generally close, you’ve got strong relationships with at least a VP level person in the finance function and I don’t see that on this deal.” You can pop information like that out based on patterns that you’re seeing much more globally.
And that gets really interesting even though it relies on essentially implied tagging rather than explicit, go click on a button tagging.
John Wall: Steve, going back to stack a bit here too now, obviously you guys are using Nudge for yourself to work accounts. What other tools do you have plugged into it and are there any other kind of just shout outs you can do to great tools that you’ve got in your stack that are working for you right now?
Steve Woods: Yeah. So for stack, where to start? So one of the key things that we’ve got, if I can kind of build up from this, is we’ve got a freemium, an individual premium, and then a team tier of our product. So we have a huge number of users on the free tier and that’s a core part of our go to market strategy is how do we get them more successful and then from there how do we then enable them to move up to the various tiers of the product. So the way that our stack looks, we’ve got Intercom as a heavy piece of it, using intercom to kind of guide and then analyze the interactions that users have.
We’ve got obviously our own data set on who they are and what they’ve been doing. Forming a key a part of that. We’ve got full story looking at really the kind of behavioral lens on those users and what have they had success with, what have they had not had success with? And those things then really give us, I wouldn’t call it a persona view because it’s a little more fine grain than that, but it gives us an understanding of where each and every user is on their journey from the earliest days when they sign up through the sort of setting themselves up well and success and repeat success and then bringing in their colleagues.
Where are they on that journey and what message or tactic can we put in front of them to guide them along that process and those messages can be delivered in app, through Intercom. They can be delivered by email, we use Sendgrid heavily for that. They can be delivered in advertising. We’ll use Terminus for account-based marketing there. But it’s all about where is that user on their journey and what is the next step that we should put in front of them in order to move them further and further towards success.
John Wall: That’s great and then how about as far managing the rest of the pipe too? More of the flow side of things? Do you guys reach a point where you give up on their journey or do you have it just kind of set where they ultimately reach some kind of point where they ultimately reach some kind of point where they get touched very infrequently? Kind of where do you go after you’ve made initial touch?
Steve Woods: Sure. Yeah, I mean, obviously like any product there’s people that try it, kick the tires, and it’s not for them so they’ll tune out and we try and map our tuning out to that. It’s interesting, we have a lot of people, so one of the things that we do is send a – well, you can think of it as a Wall Street Journal of your network. So all of the research on your entire network and what is going on with it and so we’ll have users where their main interaction with us really is just reading that Wall Street Journal of their network. And they’ll do that, even though we don’t, we don’t necessarily see them as active, they’ll do that for months and suddenly, boom, something catches their eye and they dig in further and we’ll see them kind of spike in activity again.
But you’ll get users going into this sort of long phase of quiet at times. On the other end, you know, as we see people really show a lot more activity and bring a small group with the organization on, we do have a fairly normal CS and sales process where we get engaged with them more directly, give them kind of that day in the life training view. Get them onto a trial, guide them through the trial and show them what could be if they got onto the paid versions of the offering.
So there it becomes a little bit more standard in terms of SalesForce.com in the back end of the technology stack etc. But that sort of runs into, it’s a fairly normal sales process at that end.
John Wall: And Steve, I know we’re running sort of towards the end of the show here, but I did want to ask you sort of what are you watching in general for the future? Is it all about Canada as the future of the AI technology hub as more businesses are looking to invent there? Is that what were we’re looking or, I’m just curious, what’s sort of catching your eye that you’re looking ahead towards?
Steve Woods: Yeah, the technology and the AI scene in Canada has been fascinating to watch. I mean I started Eloqua here in early 2000 and it was a desert. There were no other startups that I could name in Toronto at the time. No events, nothing. No venture capital around. And now it’s every week, there’s a major event and it draws multiple, multiple thousands of people. It’s completely flipped.
Heavy, heavy AI focus. Lots of great leadership from the academic scene. Lots of great organizations tackling that and I think a talent pool that is deep and wide and really engaged in tackling the long hard problems that sometimes are posed by AI. So I’m very, very bullish on the Canadian scene. I think, I mean AI itself, it’s a great tool, but so much of it is what business problem do you solve and if you’ve got a wonderful AI widget, but it doesn’t really solve any particular business problem, it’s not gonna go anywhere and likewise if you’ve got a great solution to a business problem, but it doesn’t happen to use AI, you’ll do just fine.
I think over time we’ll come right back to that: are you solving a real problem for real people who are willing to put money behind it? And that’s the crux of business and entrepreneurship.
Sean Z.: Alright, that’s great. Yeah my HQ2 bet for Amazon is Canada. So I’m gonna throw out there just as future watch for myself.
Steve Woods: As a Canadian I love that. Rah-rah Canada. As a technologist, I’m not really sure that I’m so keen on the intense battle for talent that will ensue.
Sean Z.: Yeah, yeah. In your own backyard. That could be tough to take.
John Wall: Steve, if people want to learn more about Nudge or anything else that you’ve got going on, what’s the best way to get in touch?
Steve Woods: I’m always there on social media at @stevewoods99. Definitely reach out and https://nudge.ai/ is our website. You can try the product for free. Great way to get started and learn a lot more.
John Wall: Alright and Sean, I won’t put you on the dime of offending any cities for your HQ2, but have you anything else that you’re plugging or working on now?
Sean Z.: I’m still putting my money on, a lot of theories, I have to say, I’m having a lot of fun poking at my friends from Chicago and I know that Atlanta is on the board so everybody wants the 50,000 Amazon jobs that are landing, but anywhere that it goes, I think it’s gonna be good for the, I honestly think it’ll make a huge impact on the economy and sort of help whatever budgeting tech scene that they have.
As long as they the land to put it, so they’re apparently gonna require a lot of space. Stuff for me that I’m watching, I have a piece in a MarTech series coming up, I believe that’s scheduled for the beginning of October, whenever this airs. So that’ll be something to keep a watch on.
And otherwise, no, I’m just following some of the big company announcements right now. I’m very interested, obviously, in following closely what IBM is doing. SalesForce Einstein is now starting to have some interesting releases. I saw that they had three things, including a new $50,000,000 startup venture fund that they have. So really investing in the ecosystem. I love seeing that from larger companies and I expect to see more sort of investing in that space.
But the run up to Dreamforce will obviously be exciting as that show is only about almost, depending on when this airs, it’ll be November, so not too far away. But John, what about you? Anything else that you’re looking at?
John Wall: Oh, just the regular stuff over at Marketing Over Coffee. Just had an interview with Christopher Lochheed, three time CMO, so that’s a great interview to check out. I’ll have a link to that over in the show notes, but that’s gonna do it for now and thanks for listening and we’ll see you in the stacks.
Originally on Stack and Flow.