View Recording: What works and what doesn’t in AI Adoption
Join us for an insightful webinar on “What Works and What Doesn’t in AI Adoption,” where we will explore the key factors that contribute to successful AI implementation and the common pitfalls to avoid. This session will be led by Nathan Lasnoski, Chief Technology Officer at Concurrency, and Brandon Dey, Head of Engineering at Concurrency. They will share their expertise on how to align AI initiatives with business strategies, prioritize AI projects, and foster a culture of experimentation and innovation. Attendees will gain valuable insights into the practical steps needed to achieve tangible results with AI, from envisioning and strategy to scaling and follow-through.
During the webinar, we will delve into real-world examples of AI adoption, highlighting the benefits such as increased revenue, improved customer experience, and operational efficiencies. We will also discuss the challenges and tensions that arise during AI implementation and how to navigate them effectively. Whether you are just starting your AI journey or looking to scale your existing efforts, this webinar will provide you with the knowledge and tools to drive successful AI adoption in your organization. Don’t miss this opportunity to learn from industry experts and take your AI initiatives to the next level.
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Nathan Lasnoski
OK. Hello everybody. Welcome to virtual edition of our AI symposium.
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Nathan Lasnoski
This is our kickoff session, and we’re gonna be spending some time today talking about what works and what doesn’t work in AI adoption.
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Nathan Lasnoski
It’s about about twoish years, maybe a little less than that since the Gen. AI.
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Nathan Lasnoski
Sort of exciting craze took off, but even feel like the holy cow that it’s been.
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Nathan Lasnoski
Almost that amount of time, and I’m really excited to talk about what we’ve seen be successful.
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Nathan Lasnoski
And what we’ve seen not be successful, so you can learn from that in your own AI adoption journeys and how you lead your organizations. And to do that, I’m gonna start by just introducing myself.
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Nathan Lasnoski
So my name’s Nathalie.
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Nathan Lasnoski
I am Concurrency’s chief technology officer.
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Nathan Lasnoski
I have spent the last 23 years in consulting.
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Nathan Lasnoski
I’ve been with concurrency for a long, long time and.
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Nathan Lasnoski
Especially over the last several years have spent time with over 70.
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Nathan Lasnoski
Executive teams helping them navigate the adoption of AI within the context of their organization, and it’s been a huge blessing to do that because, man, what have you ever seen a time where you’ve really been able to get to the heart of what an organization does where where?
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Nathan Lasnoski
The executive team has thought of technology as an asset for them and really challenged the IT organization and the rest of the organization to use technology to make them better. And some of those organizations have harnessed AI.
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Nathan Lasnoski
And leveraged it to to a way that’s really helped them to change and engage.
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Nathan Lasnoski
The mission of their business and other ones they’ve got, they’ve got it going.
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Nathan Lasnoski
And then maybe they’ve spotted out or they haven’t put the energy into it. And I’m really looking forward to sharing what differentiated those companies in this conversation today.
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Nathan Lasnoski
That is my QR code, so if you would love to scan that, I’d love to connect with you on LinkedIn. So I’m super active.
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Nathan Lasnoski
On LinkedIn, I have a weekly newsletter on AI leadership.
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Nathan Lasnoski
There’s about 20 something.
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Nathan Lasnoski
Past newsletter assets that you can take advantage of.
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Nathan Lasnoski
We’d really find value in in following that content and taking advantage of that content. I produce it on a regular basis for you to take advantage of. So please connect with me on LinkedIn, get that content, follow me in love for you to be able to take advant.
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Nathan Lasnoski
Of it.
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Nathan Lasnoski
Nice to meet everyone today, OK?
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Nathan Lasnoski
So what are we doing in the session?
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Nathan Lasnoski
First, what we’re going to do is talk about what successful AI adoption looks like.
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Nathan Lasnoski
What’s it look like?
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Nathan Lasnoski
What’s it smell like?
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Nathan Lasnoski
What does it feel like?
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Nathan Lasnoski
How do I know if I’m doing it?
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Nathan Lasnoski
Well, we’re gonna talk about what that adoption looks like, and then we’re gonna go into a little bit of ABAB of what works and what doesn’t work, and then we’re gonna talk about how to take action on some of those ideas.
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Nathan Lasnoski
Few things I’d love for you to do.
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Nathan Lasnoski
I’m going to I’m one man band on this call, but I’m gonna do my best to be watching the chat. I would love for you to be putting your questions in the chat.
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Nathan Lasnoski
I will do my best to answer those questions, if not throughout the session.
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Nathan Lasnoski
I will make sure to reserve some time at the end for us to answer them so liberally.
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Nathan Lasnoski
Use that Q&A feature in the context of the session. I’d love to just kind of get your reaction and get questions that you have as we as we go throughout.
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Nathan Lasnoski
So I’ll do my best on that.
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Nathan Lasnoski
OK.
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Amy Cousland
We have some people who can’t see the screen, but I don’t sometimes restarting or joining in a certain way.
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Amy Cousland
I have people who can see it and people who can’t see it, so this is getting recorded. If you wanna reshow it, share it.
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Nathan Lasnoski
OK.
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Amy Cousland
Hopefully it’ll help.
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Nathan Lasnoski
Will do.
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Nathan Lasnoski
Thank you for saying that.
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Nathan Lasnoski
Well, that’s look, this was actually a test.
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Nathan Lasnoski
This was a test of our our broadcast system. I’ve just gone to like full screen mode.
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Nathan Lasnoski
Hopefully this gives you gives you that ability to see my screen without any interruption that looking good there, Amy.
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Amy Cousland
OK.
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Amy Cousland
It’s looking good for me if other people can’t see it, maybe restart your teams instance, but it’s it’s showing up well for me.
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Amy Cousland
Hopefully this helps.
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Amy Cousland
Thank you so much.
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Nathan Lasnoski
OK, thanks. OK.
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Nathan Lasnoski
I’m glad we did that before I made this point.
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Nathan Lasnoski
So if you leave this session with only one thing to bring back to your business, I want you to remember this sentence.
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Nathan Lasnoski
Successful AI adoption is about actualizing the mission of your business.
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Nathan Lasnoski
Now, that may seem obvious, but it’s not obvious to many organizations as they gone down the path.
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Nathan Lasnoski
Successful AI adoption of actualizing the mission of your business.
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Nathan Lasnoski
Starts with understanding what the mission of your business is. Being able to translate that into strategic objectives that you probably already have, and then thinking about technology as an asset to make that true.
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Nathan Lasnoski
And it is an opportunity for us to be able to use a, a technology which is enabling technology.
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Nathan Lasnoski
Think about this as like a light bulb moment.
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Nathan Lasnoski
The electricity moment, maybe even closer to electricity than light bulb, right?
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Nathan Lasnoski
The Internet. If you thought a smartphone, if you thought about these moments where like these technologies became a thing, how long did it take us to really realize and actualize the Internet?
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Nathan Lasnoski
How long did it take us to really realize and actualize electricity, or even the smartphone?
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Nathan Lasnoski
There was a period where we knew it existed, but it really didn’t hit our social consciousness.
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Nathan Lasnoski
Because it was enabling technology that made other things true.
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Nathan Lasnoski
That except it’s moving a lot faster, so it’s an enabling technology. It’s changing the game.
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Nathan Lasnoski
It’s enabling our organizations to think about something differently, but you have to understand the mission of your business and how it relates to that rather than necessarily looking at it the other way around.
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Nathan Lasnoski
So this is a starting point for and how they’ve made this journey and how they’ve enabled that culture of experimentation, but done so in the context of actualizing the mission of the business.
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Nathan Lasnoski
So the challenge I would have for you, and I’m gonna go through some examples of this, is been a year and a half.
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Nathan Lasnoski
What have you achieved with AI?
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Nathan Lasnoski
What? What have you achieved in your organization?
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Nathan Lasnoski
You, meaning your organization, what is it achieved?
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Nathan Lasnoski
Has it achieved anything?
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Nathan Lasnoski
Has it achieved small things?
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Nathan Lasnoski
Has it achieved something really significant?
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Nathan Lasnoski
Maybe you did something even before the sort of ChatGPT moment. Maybe you’ve been engaged in traditional ML for a long time, and you’ve seen those results already. As before, even getting to this moment and you forced multiplied it through this swing.
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Nathan Lasnoski
Here’s some examples of what other organizations have achieved during that same period.
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Nathan Lasnoski
So if you say it’s been a year and a half, what have you achieved?
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Nathan Lasnoski
Have you increased revenue for your business by winning more deals?
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Nathan Lasnoski
Truly think about it that way. If I applied AI, whether it’s not the commodity level and the way that like my my individual team members are leveraging something like a copilot or a tool that helps them to be able to accelerate their work product or it’s auto.
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Nathan Lasnoski
Quoting engine that we’ve created, am I winning more deals because I’m able to bring those deals to my customers faster and more accurate way that?
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Nathan Lasnoski
Competes my competitors, or have I not done that and I’m still about where I was yesterday?
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Nathan Lasnoski
Am I able to ease frustration in my customer experience by applying AI?
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Nathan Lasnoski
There’s a a recent study that.
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Nathan Lasnoski
Over 90% of businesses that were surveyed are looking at AI to optimize their customer experience.
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Nathan Lasnoski
A small percentage have, but most are looking at it as an opportunity for that. Why?
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Nathan Lasnoski
Because we want to reduce the amount of time that our customers are sitting in that unhappy state. If I can take a customer that’s unhappy because of either their question or not sure how to use a product.
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Nathan Lasnoski
Either products broken or they have a question about like downstream activities within the context of something they’re working with us on. How can I ease that frustration faster for them?
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Nathan Lasnoski
Put them in a in a happy state more quickly. Many of our companies we worked with have used AI to be able to arm the customer service teams or arm their direct customers with answers to the questions or even.
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Nathan Lasnoski
Opportunities for them to be able to take action based upon something that they know.
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Nathan Lasnoski
Have you reduced inventory carrying costs by driving efficiency through the supply chain? This happens to be one of the older uses of AI. Even pre ChatGPT moment, but it’s one of the most powerful.
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Nathan Lasnoski
Can I optimize?
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Nathan Lasnoski
What really is my supply chain?
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Nathan Lasnoski
Can I optimize how much product or staff I’m applying to particular scenario in a particular location with particular skills or qualities to be able to optimize my cost at any given moment?
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Nathan Lasnoski
Tremendous opportunity, tremendous opportunity simply because.
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Nathan Lasnoski
The dollars here are so tangible. I was talking with an organization.
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Nathan Lasnoski
They said if you can reduce the purchase the the price they buy this particular commodity at at this bold cost by 1 cent, you’ll save us $1,000,000.
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Nathan Lasnoski
Save us $1,000,000 by driving efficiency of 1 cent.
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Nathan Lasnoski
This is the opportunity that stands before us in many organizations have taken advantage of.
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Nathan Lasnoski
I worked with an organization.
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Nathan Lasnoski
They saved $40 million a year.
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Nathan Lasnoski
They’re about a billion and a half organization.
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Nathan Lasnoski
They save $40 million a year.
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Nathan Lasnoski
Of carrying costs because of playing AI.
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Nathan Lasnoski
Have I set clear expectations of my customers that are measurably more accurate than before?
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Nathan Lasnoski
Can I set expectations such as?
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Nathan Lasnoski
Here is when your product is being delivered.
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Nathan Lasnoski
Here is and what?
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Nathan Lasnoski
Maybe there’s interruption to the supply chain.
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Nathan Lasnoski
Here’s when you can now expect that product to be delivered within one. Do I have a dominois pizza tracker?
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Nathan Lasnoski
And when there’s something wrong, does my Domino’s Pizza tracker adjust and give my customer service team the ability to to interact with them?
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Nathan Lasnoski
Can I even queue it in against that?
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Nathan Lasnoski
In an intuitive customer centric way.
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Nathan Lasnoski
I’m working with a company that travel agency.
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Nathan Lasnoski
You can follow them on LinkedIn, Fox World Travel. They just went live with a product called Colby and this product, what it enables them to do is their customers will buy their, their customers or other businesses and they will buy travel services. You know, at bulk right like.
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Nathan Lasnoski
I have 1000 people are going to be flying this year and I want to know what percentage of my flights are going through SW versus.
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Nathan Lasnoski
Northwest. Whatever and.
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Nathan Lasnoski
I’m gonna. I wanna ask a question. Have to return that information to me from the business system in AQA centric way.
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Nathan Lasnoski
They just went live with that. So cool.
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Nathan Lasnoski
Check them out.
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Nathan Lasnoski
So can I set those clear expectations?
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Nathan Lasnoski
Are my employees.
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Nathan Lasnoski
Do my employees indicate that AI that the availability of AI agents creates definitive efficiencies for them?
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Nathan Lasnoski
This is not am I turned on. Its do I indicate I’m getting value?
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Nathan Lasnoski
Am I seeing efficiencies?
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Nathan Lasnoski
So if I have someone enabled for copilot, for example.
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Nathan Lasnoski
And their delegating activities to copilot prepare this presentation or give me the the outcome of this particular meeting and summarize the action items.
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Nathan Lasnoski
Are they able to measurably show that they’re getting real efficiencies from that, or have I not achieved that because I haven’t?
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Nathan Lasnoski
Maybe trained them well or I haven’t?
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Nathan Lasnoski
I’ve just been dinking around with it.
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Nathan Lasnoski
Have I gotten through that Channel?
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Nathan Lasnoski
Do I have a new revenue streams that’s been created by using AI driven information?
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Nathan Lasnoski
This is truly about can I use data that I know about my customers to create new revenue so.
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Nathan Lasnoski
For example, I have a customer that they sell food products and those food products have extremely low margin, but they’re in every restaurant.
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Nathan Lasnoski
You know what?
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Nathan Lasnoski
They really know.
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Nathan Lasnoski
They know everything about the restaurant business.
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Nathan Lasnoski
One of the highest turnover, one of the highest areas of failure is restaurant businesses.
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Nathan Lasnoski
But they know a lot about what makes successful restaurants.
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Nathan Lasnoski
They can use that information to be able to sell.
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Nathan Lasnoski
You’re the top quartile because this is what you do.
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Nathan Lasnoski
Really, this is what the top quartile does.
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Nathan Lasnoski
Well, here’s how you adjust to be in that top quartile.
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Nathan Lasnoski
How do I use data to be able?
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Nathan Lasnoski
A new revenue stream or asset to create higher margin activities with my customers.
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Nathan Lasnoski
Have I found unintuitive insights into production processes that have been discovered? I was working with a company.
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Nathan Lasnoski
They happen to produce.
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Nathan Lasnoski
Batteries, so they learned was there is there was like this unknown part of their production process. They were able to use AI to discover essentially why they have this variability within their production. They’re able to achieve normal normalcy of that production by what they learned from the 0.
0:12:45.901 –> 0:12:46.821
Nathan Lasnoski
Data kind of like the.
0:12:47.401 –> 0:12:50.321
Nathan Lasnoski
I’ve heard of the OT data be like the undiscovered country, right?
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Nathan Lasnoski
Like it’s out there.
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Nathan Lasnoski
I’ve never used it.
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Nathan Lasnoski
How do I use that information to be able to be able to learn something about my my process?
0:12:59.701 –> 0:13:0.821
Nathan Lasnoski
So all these things, OK.
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Nathan Lasnoski
This is what many companies have achieved.
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Nathan Lasnoski
How do we take this forward?
0:13:6.901 –> 0:13:10.861
Nathan Lasnoski
So my question is, where are you on this process?
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Nathan Lasnoski
So meeting where are you?
0:13:12.621 –> 0:13:18.101
Nathan Lasnoski
So in the start of this you have to start with that mapping that envisioning and strategy.
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Nathan Lasnoski
Think about the step one right.
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Nathan Lasnoski
I need to know how I’m going to.
0:13:22.521 –> 0:13:36.441
Nathan Lasnoski
Mary, the mission of my business, the strategy behind it and using AI to force multiply those goals have I thought about that at the executive level and that bubble down into the prioritize alignment.
0:13:36.441 –> 0:13:39.841
Nathan Lasnoski
And that’s not just a lot of people think about this just in terms of use cases.
0:13:40.161 –> 0:13:47.441
Nathan Lasnoski
It’s not just about use cases, it’s about thinking about what the possible future is and working backward from that possible future.
0:13:48.301 –> 0:13:51.581
Nathan Lasnoski
To be able to translate that into action.
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Nathan Lasnoski
And not all of those actions are going to work.
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Nathan Lasnoski
We’ll talk more about that later this creating this culture of innovation is not about like this one thing.
0:14:2.301 –> 0:14:11.901
Nathan Lasnoski
It’s about translating that strategy into action and scaling across my organization that then measurable results we need to measure we need.
0:14:11.901 –> 0:14:16.581
Nathan Lasnoski
To prove it, we need to show it actually happens, and then scale that within the context of our business.
0:14:16.861 –> 0:14:21.861
Nathan Lasnoski
I venture that that many of you are probably still here, but if you’re not, I’d love to understand.
0:14:22.381 –> 0:14:25.781
Nathan Lasnoski
Where you are in the channel so we can start to talk about how we get you there.
0:14:27.421 –> 0:14:38.221
Nathan Lasnoski
So to start that part of the conversation, I wanna kinda make it just a general statement. You probably heard the statement the hardest. The hardest company disrupt is your own.
0:14:38.501 –> 0:14:51.581
Nathan Lasnoski
Maybe you’ve heard in the context of yourself, like the hardest person to disrupt is yourself. Like it’s always hard for us to turn inward and to accept something that may be true about us ourselves, or to change our own actions.
0:14:52.421 –> 0:14:53.581
Nathan Lasnoski
Same thing with companies.
0:14:54.341 –> 0:14:59.181
Nathan Lasnoski
Hardest company disrupt is your own, particularly if it is already making money.
0:14:59.821 –> 0:15:4.221
Nathan Lasnoski
Companies are I was a fantastic talk last week.
0:15:4.341 –> 0:15:12.901
Nathan Lasnoski
That kind of harkened to Clinton Christensen’s ideas, which is essentially that enterprise organizations, they drive toward efficiency, and they get really good at it.
0:15:13.261 –> 0:15:20.501
Nathan Lasnoski
But what? They aren’t really great at is disrupting that efficiency to create new business models that may not exist today.
0:15:20.801 –> 0:15:30.961
Nathan Lasnoski
So they may be really good at this incremental innovation which a lot of times when you look at the things that you’re trying to do in your business, you think of like well, you know, here’s a process I do today.
0:15:30.961 –> 0:15:32.121
Nathan Lasnoski
Can I replace that with AI?
0:15:32.121 –> 0:15:33.801
Nathan Lasnoski
And that’s not bad.
0:15:33.801 –> 0:15:38.241
Nathan Lasnoski
That’s just like you’re optimizing an existing process. But what?
0:15:38.241 –> 0:15:45.241
Nathan Lasnoski
Some companies need to do is they have to say what’s the possible future that could exist in my market space that I’m not harnessing today.
0:15:45.981 –> 0:15:47.581
Nathan Lasnoski
And what needs to be true for me to achieve that goal?
0:15:49.341 –> 0:15:50.61
Nathan Lasnoski
And then in looking at that.
0:15:50.661 –> 0:15:57.861
Nathan Lasnoski
You start thinking about what are the jobs to be done, not only from our organization but from my customers that I can serve more effectively.
0:15:59.421 –> 0:16:0.141
Nathan Lasnoski
Do I need for example?
0:16:0.141 –> 0:16:3.941
Nathan Lasnoski
I was working with a company that distributes like a commodity.
0:16:4.261 –> 0:16:11.341
Nathan Lasnoski
Do I need to quote my customers this or can they quote themselves? And what would need to be true for them to quote themselves?
0:16:11.861 –> 0:16:18.541
Nathan Lasnoski
And how would I enable them to get the best price and have certainty that they would get the best price if they quoted themselves?
0:16:18.541 –> 0:16:19.301
Nathan Lasnoski
What would happen?
0:16:19.301 –> 0:16:21.501
Nathan Lasnoski
What would need to be true for that to for that to occur?
0:16:22.631 –> 0:16:35.551
Nathan Lasnoski
Thinking about jobs to be done and able to happen, and this really then drives us to this idea of these Co innovation paths. This idea of sustained innovation that you’re always gonna be doing to run your business and disruptive innovation which essentially.
0:16:37.341 –> 0:16:37.501
Nathan Lasnoski
Disrupting.
0:16:39.101 –> 0:16:43.261
Nathan Lasnoski
It’s enabling your business to look at the market in a way which is different than it does today.
0:16:43.261 –> 0:16:44.621
Nathan Lasnoski
I’ll talk more about that in a minute.
0:16:45.661 –> 0:16:47.341
Nathan Lasnoski
So this is this tension, OK?
0:16:47.541 –> 0:16:49.141
Nathan Lasnoski
So imagine yourself.
0:16:49.221 –> 0:16:51.861
Nathan Lasnoski
Imagine your competitor yourself.
0:16:51.861 –> 0:16:55.101
Nathan Lasnoski
You have this revenue and you have a tug of war with competitive forces.
0:16:55.621 –> 0:17:12.341
Nathan Lasnoski
Against your competitor and you see here that as you gain efficiencies, you gain more in that competitive force against your competitor, whatever that percentage of market is, let’s say this is per 50% of the available market that spread between you and your competitor or maybe even smaller.
0:17:12.461 –> 0:17:13.781
Nathan Lasnoski
Like I was working with this company.
0:17:13.781 –> 0:17:17.501
Nathan Lasnoski
They’re like the leading company in the world, but the only own like 8% of the overall market.
0:17:17.501 –> 0:17:20.701
Nathan Lasnoski
Like it’s like you’re nowhere close to a monopoly, but you.
0:17:21.501 –> 0:17:26.261
Nathan Lasnoski
You’re you’re the biggest company in the world, so it’s a tug of war and these efficiencies you’re trying to drive.
0:17:26.261 –> 0:17:29.421
Nathan Lasnoski
This is where you’re you’re applying AI and other tools to grab these.
0:17:29.421 –> 0:17:30.461
Nathan Lasnoski
Grab, grab these efficiencies.
0:17:30.461 –> 0:17:33.261
Nathan Lasnoski
Maybe that’s like even the auto quoting situation.
0:17:33.261 –> 0:17:35.701
Nathan Lasnoski
Just trying to help like get more of that pie.
0:17:36.521 –> 0:17:36.961
Nathan Lasnoski
But what?
0:17:36.961 –> 0:17:38.321
Nathan Lasnoski
Probably exists.
0:17:38.321 –> 0:17:41.481
Nathan Lasnoski
Is this unharnessed opportunity that?
0:17:43.21 –> 0:17:44.101
Nathan Lasnoski
Neither of you are getting today.
0:17:44.501 –> 0:17:47.781
Nathan Lasnoski
What would need to be true for me to harness that opportunity?
0:17:47.781 –> 0:17:54.381
Nathan Lasnoski
What would need to be true for me to use technology to be able to change the way I engage that market?
0:17:54.381 –> 0:18:2.221
Nathan Lasnoski
And sometimes it means less features, less capabilities, a different way of engaging. But a broader market that I can then I can counter today.
0:18:2.221 –> 0:18:7.381
Nathan Lasnoski
So maybe I’m a $2 billion company today, but the overall market opportunity is $40 billion.
0:18:8.401 –> 0:18:12.801
Nathan Lasnoski
How do I get more of that 40 rather than fighting for the two I have right now?
0:18:13.561 –> 0:18:15.1
Nathan Lasnoski
Or how do I do both?
0:18:15.1 –> 0:18:20.961
Nathan Lasnoski
How do I keep garnering that that two, but then go after the 40 as a new opportunity?
0:18:22.461 –> 0:18:24.581
Nathan Lasnoski
This is where people who are being successful are thinking.
0:18:24.581 –> 0:18:26.381
Nathan Lasnoski
They’re thinking about both of those those ideas.
0:18:27.61 –> 0:18:35.701
Nathan Lasnoski
So if we go into this idea now of what works and what doesn’t, we’re gonna do a little bit of an AB between each of those so.
0:18:37.771 –> 0:18:39.51
Nathan Lasnoski
What works and what doesn’t?
0:18:39.51 –> 0:18:41.811
Nathan Lasnoski
Business alignment. What doesn’t work?
0:18:42.251 –> 0:18:46.371
Nathan Lasnoski
Is it by itself isolated on use cases?
0:18:46.371 –> 0:18:49.531
Nathan Lasnoski
It is really good at coming up with use cases in a vacuum, right?
0:18:49.531 –> 0:18:57.931
Nathan Lasnoski
Like if you gave it the opportunity to go after AI, they come up with their big list and maybe they share with the business and they try to go after some.
0:18:58.371 –> 0:19:0.811
Nathan Lasnoski
But what happens is becomes too siloed.
0:19:0.811 –> 0:19:3.571
Nathan Lasnoski
Becomes its objective, not the business’s objective.
0:19:4.381 –> 0:19:8.661
Nathan Lasnoski
You need to make sure the business is driving those AI objectives.
0:19:9.221 –> 0:19:24.821
Nathan Lasnoski
And are the number one supporter behind why they are being pursued. The most successful AI initiatives have been their been been successful because the business cared about them even more than the tech organization did.
0:19:25.501 –> 0:19:35.941
Nathan Lasnoski
And this is by enabling the priorities being mapped to the business by having an AI upscaling campaign and by having a direct experimentation culture.
0:19:37.101 –> 0:19:38.861
Nathan Lasnoski
That’s accepting not just accepting.
0:19:38.861 –> 0:19:39.261
Nathan Lasnoski
A failure.
0:19:39.261 –> 0:19:44.221
Nathan Lasnoski
It’s it’s expecting failure and looking to what they can learn from those moments.
0:19:44.901 –> 0:19:51.301
Nathan Lasnoski
How can I have lines in the water realize, oh, there’s no fish in that hole? I’m not gonna fish there again.
0:19:51.661 –> 0:19:58.741
Nathan Lasnoski
I’m gonna or I need to use a different fly on my rod because that particular like that hole’s got. That’s a good place. There’s it’s deep.
0:19:58.741 –> 0:20:14.621
Nathan Lasnoski
It’s there’s, there’s still water, but I need to use a different type of lure for it to desire to hit that particular particular cast that I get right. But I need to be able to react to those moments and expect failure and be able to have the res.
0:20:15.381 –> 0:20:17.141
Nathan Lasnoski
To be able to take those next steps.
0:20:18.701 –> 0:20:31.661
Nathan Lasnoski
So you need to be able to have a ability to create and validate alignment to the business and understand what’s there and understand the opportunities that exist before you. So this is an example of that for like software digital.
0:20:33.261 –> 0:20:34.821
Nathan Lasnoski
Software digital platforms company.
0:20:34.821 –> 0:20:37.101
Nathan Lasnoski
So this idea of thinking about what are the things we do.
0:20:37.101 –> 0:20:38.101
Nathan Lasnoski
What are the categories?
0:20:38.101 –> 0:20:39.861
Nathan Lasnoski
What are the names or descriptions?
0:20:39.941 –> 0:20:46.261
Nathan Lasnoski
Where is going to impact our business and then even how hard is it like I might not go after the moon shot opportunity on day one.
0:20:46.751 –> 0:21:4.421
Nathan Lasnoski
I might go after some some lowers like lower capability type of things to get my feet wet to experiment, get some quick wins, but that’s not to distract me from this idea that I’m building muscle to attack the goal. The the failure is when companies think of going.
0:21:4.421 –> 0:21:16.351
Nathan Lasnoski
After the quick win as end in itself like that is that is a start. That’s like a A if that’s like your AI strategy, you’re missing this idea of like, how am I gonna engage the business?
0:21:16.951 –> 0:21:21.551
Nathan Lasnoski
And transform in in two years, I can look back and say I really did something.
0:21:21.671 –> 0:21:33.711
Nathan Lasnoski
So starting with this map, but then getting to a point where I’m following through on it in multiple lanes, but then still picking some picking some that are really focused on achieving measurable outcomes.
0:21:35.301 –> 0:21:39.701
Nathan Lasnoski
So this pairs with this idea of Co innovation in Co innovation.
0:21:39.701 –> 0:21:42.261
Nathan Lasnoski
What doesn’t work is all AI efforts.
0:21:42.261 –> 0:21:48.61
Nathan Lasnoski
Ride on that single use case success. So if you go back to that previous slide, you pick one. You’re like, this is the one we’re going after.
0:21:48.931 –> 0:21:51.931
Nathan Lasnoski
And that one kind of hits a roadblock.
0:21:51.931 –> 0:21:55.211
Nathan Lasnoski
You’re like, oh, like, that did not get to where I needed to get to.
0:21:56.211 –> 0:21:56.771
Nathan Lasnoski
Oh, I’m sorry.
0:21:56.771 –> 0:22:1.11
Nathan Lasnoski
AI is a failure because that specific use case didn’t get to where it needed to get to.
0:22:1.371 –> 0:22:8.771
Nathan Lasnoski
That’s that’s really like underfunding under engaging under scaling. The idea of how we’re gonna pursue AI within our organization.
0:22:9.131 –> 0:22:10.891
Nathan Lasnoski
Don’t put all your eggs on one basket.
0:22:10.891 –> 0:22:18.531
Nathan Lasnoski
Don’t think that just because one weren’t use case is not going to be successful or needs to pivot that suddenly you are going to.
0:22:19.71 –> 0:22:20.151
Nathan Lasnoski
Not see value from AI.
0:22:20.231 –> 0:22:22.751
Nathan Lasnoski
This is where we need to see value.
0:22:22.751 –> 0:22:40.551
Nathan Lasnoski
In experimentation, we need to be comfortable with this idea of reacting to what’s happening within the market. Being able to continually see that this innovation is happening outside and inside our organization, creating that culture where we build team momentum vs this one and done long game vs short.
0:22:40.551 –> 0:22:45.471
Nathan Lasnoski
Game. Now. That also doesn’t mean we just tinker, right? Like I’ve seen some companies like.
0:22:46.261 –> 0:22:48.981
Nathan Lasnoski
You can see some companies that they put a lot of value in.
0:22:49.631 –> 0:23:6.591
Nathan Lasnoski
And it sort of rides or dies on that one big thing. I’ve seen the flip side, tons of energy on innovation, but none of it really gets to production right, because that that culture of innovation is impaired with the stick to autiveness of pushing things out of that.
0:23:6.591 –> 0:23:12.551
Nathan Lasnoski
Experimentation into a production use case or into like a channel that gets to production.
0:23:14.101 –> 0:23:17.261
Nathan Lasnoski
So I’m gonna pause here for a second and ask another question.
0:23:17.541 –> 0:23:20.821
Nathan Lasnoski
And I think this is really important question for all of us to ask ourselves.
0:23:21.761 –> 0:23:26.81
Nathan Lasnoski
Can this be a moment where every person can be the best version themselves?
0:23:26.81 –> 0:23:27.521
Nathan Lasnoski
Where we enable them to be.
0:23:27.521 –> 0:23:35.801
Nathan Lasnoski
That, and I ask that question because in every major technology movement there are winners and losers.
0:23:35.801 –> 0:23:46.601
Nathan Lasnoski
There are people who are impacted. There are individuals who receive the benefits and those that are in a sense that can be taken advantage of. Unfortunately to receive those benefits.
0:23:47.341 –> 0:23:49.21
Nathan Lasnoski
You saw that with the Industrial revolution, right?
0:23:49.101 –> 0:23:51.261
Nathan Lasnoski
There was a transition from point A to point B.
0:23:52.1 –> 0:24:1.281
Nathan Lasnoski
You saw a dramatic improvement in the sort of general like populace’s accessibility of of goods, right?
0:24:1.281 –> 0:24:16.81
Nathan Lasnoski
Like we could produce more of the same amount of people, we could produce more outcome. We can enable more more economic GDP. If you look at the hockey stick that happened after electrification in the Industrial Revolution and then travel.
0:24:17.21 –> 0:24:23.661
Nathan Lasnoski
You saw dramatic changes, but you also know that there was dramatic changes to the way people work in those scenarios.
0:24:24.431 –> 0:24:28.671
Nathan Lasnoski
You had people who were in these sort of factory settings that weren’t treated appropriately.
0:24:28.791 –> 0:24:35.711
Nathan Lasnoski
You also saw that in something we all probably lived through is the advent of the smartphone, or just simply having a smartphone. Getting your first smart phone.
0:24:35.711 –> 0:24:36.951
Nathan Lasnoski
What does that mean to us?
0:24:36.951 –> 0:24:38.711
Nathan Lasnoski
Like, how did that change the way we work?
0:24:39.231 –> 0:24:46.551
Nathan Lasnoski
We know that if I had like knowing what I know today, what would I tell someone who’s getting a smartphone for the first time?
0:24:46.671 –> 0:24:53.151
Nathan Lasnoski
How do I prevent them from having the same kind of like addictive qualities that maybe I have with a smartphone that I don’t want them to have?
0:24:53.471 –> 0:24:54.391
Nathan Lasnoski
Do we just let that happen?
0:24:54.621 –> 0:24:56.261
Nathan Lasnoski
With us, or do we lead through it?
0:24:56.461 –> 0:25:2.821
Nathan Lasnoski
This is a moment for us to lead through it, to know that every person in your organization is going to be using AI in some capacity.
0:25:3.61 –> 0:25:8.821
Nathan Lasnoski
How do we enable them to be able to have the skills to function in this new world of work?
0:25:10.381 –> 0:25:11.981
Nathan Lasnoski
And that goes with scaling. OK.
0:25:11.981 –> 0:25:16.61
Nathan Lasnoski
So when you think about scaling an AI engagement you think about.
0:25:16.151 –> 0:25:35.741
Nathan Lasnoski
Now is my AI strategy just to enable a small team and then everybody else just keep doing what you’re doing? Or is it to understand that I have diverse lanes of AI engagement across my organization that enables various types of capabilities and engagement that are centric to the?
0:25:35.841 –> 0:25:36.401
Nathan Lasnoski
Type of user.
0:25:36.401 –> 0:25:43.401
Nathan Lasnoski
So for example, I might have individuals in my organization that are just. They’re information workers, right?
0:25:43.401 –> 0:25:46.961
Nathan Lasnoski
They’re doing all sorts of work all day long and they’re on all these calls, right?
0:25:46.961 –> 0:25:54.681
Nathan Lasnoski
How can I enable them with, say, copilot for them to record those calls and capture action items and then not have to spend the time manually typing all of that right?
0:25:54.681 –> 0:25:56.521
Nathan Lasnoski
Like how can I amp up how they work?
0:25:56.641 –> 0:26:3.561
Nathan Lasnoski
How can I name my factory workers to have access to AQ and a agent that enables them to get their HR questions answered faster or to?
0:26:4.301 –> 0:26:7.101
Nathan Lasnoski
Be able to participate in the culture of the organization in a different way.
0:26:8.611 –> 0:26:15.131
Nathan Lasnoski
This is about scaling and encouragement, engaging a broad group, challenges and hackathons. Executive buy in.
0:26:15.131 –> 0:26:20.691
Nathan Lasnoski
This is if your executive team isn’t talking about this and it’s just an it thing. You’ve missed it.
0:26:20.891 –> 0:26:23.291
Nathan Lasnoski
This is about scaling across the organization.
0:26:23.291 –> 0:26:29.371
Nathan Lasnoski
You might say like like. OK, like I realize we’re gonna hit this through a missed expectations and so on.
0:26:29.491 –> 0:26:30.371
Nathan Lasnoski
Like, how does that?
0:26:30.371 –> 0:26:33.411
Nathan Lasnoski
How’s that gonna impact us realize that?
0:26:34.341 –> 0:26:37.501
Nathan Lasnoski
This same thing with the smartphones. Same thing with the Internet, right?
0:26:38.101 –> 0:26:45.981
Nathan Lasnoski
You’re gonna have this channel that happens this ups and downs throughout this transformation, but it will dramatically change everyone’s work.
0:26:47.541 –> 0:27:0.981
Nathan Lasnoski
And your leaders are going to be excellent leaders if they work. If they move through that Channel in a way that enables every person to be able to be more and especially that rounds out the curve doesn’t have quite as high of a high, doesn’t have quite as.
0:27:0.981 –> 0:27:13.501
Nathan Lasnoski
Low of below and enables you to be able to get that broad engagement engagement meaning like everyone feels they’re on the bus, everyone feels they’re on the boat rowing in the same direction and not left on the shore.
0:27:14.311 –> 0:27:15.111
Nathan Lasnoski
Being left behind.
0:27:16.661 –> 0:27:17.701
Nathan Lasnoski
And that means follow through.
0:27:17.821 –> 0:27:23.821
Nathan Lasnoski
That means what doesn’t work in follow through is when you have that small effort, you get that cold feet.
0:27:23.941 –> 0:27:34.741
Nathan Lasnoski
So I had a customer or have they have something like 500 something sales persons we built auto quoting engine for them and initially they released that auto quoting engine.
0:27:34.741 –> 0:27:43.581
Nathan Lasnoski
We knew it worked and you had the sales people and they’re like, I don’t know, I’m faster doing it myself or I’m. I’m not sure how to use it.
0:27:44.111 –> 0:27:48.791
Nathan Lasnoski
And that VP of sales could have been like whatever, just just adopt what you want to adopt.
0:27:48.791 –> 0:27:50.271
Nathan Lasnoski
It’ll be fine, you know.
0:27:50.271 –> 0:27:51.671
Nathan Lasnoski
Or maybe I won’t follow through on this.
0:27:53.221 –> 0:27:58.341
Nathan Lasnoski
And I had the same thing with like a customer service scenario where it was like it was answering some of the questions.
0:27:58.341 –> 0:28:1.941
Nathan Lasnoski
Well, I was answering them all. Well, like, well, maybe it’s not successful.
0:28:2.501 –> 0:28:3.941
Nathan Lasnoski
You need to have the follow through.
0:28:4.261 –> 0:28:12.741
Nathan Lasnoski
These efforts are going to POC, you’re going to see initial success, you’re going to see Edge cases where these things don’t work, or if major cases where they don’t work.
0:28:13.531 –> 0:28:23.611
Nathan Lasnoski
And you have to have organizational follow through both on the adoption side and the business and in the development side in building solutions to bring it all the way through.
0:28:25.261 –> 0:28:27.661
Nathan Lasnoski
The the the channel of of adoption.
0:28:27.741 –> 0:28:43.341
Nathan Lasnoski
So ultimately that 500 sales person organization adopted this across the entire organization and one of the biggest benefits they received from that was it levelled up everyone. Someone who’s been there 20 years and someone who’s been there five weeks have a lot of the same assets now.
0:28:43.341 –> 0:28:44.141
Nathan Lasnoski
Available to them.
0:28:44.821 –> 0:28:48.541
Nathan Lasnoski
In creating this level playing field, how they can engage their customers?
0:28:48.541 –> 0:28:55.621
Nathan Lasnoski
Basically, the average ability to engage a customer went up because they were able to take advantage of AI that brought that to the table.
0:28:55.781 –> 0:29:1.261
Nathan Lasnoski
So commitment on follow through is a critical capability to enable you to be successful.
0:29:2.261 –> 0:29:4.621
Nathan Lasnoski
So another pause.
0:29:4.781 –> 0:29:14.421
Nathan Lasnoski
I think this is another really important point, and it’s almost important is understanding the mission of the business and enabling a person to be the best version.
0:29:15.311 –> 0:29:28.671
Nathan Lasnoski
If you translate that down, I want you to remember that we are currently under hyping AI upscaling. We’re under hyping AI upskilling, an impact and you can say, well, we’re under hyping something like AI is pretty hyped right now.
0:29:29.31 –> 0:29:38.791
Nathan Lasnoski
We are under hyping it and the reason why I know we’re under hyping it is you have to look at the actions of the organizations as though they are living, what they believe to be true.
0:29:39.431 –> 0:29:44.911
Nathan Lasnoski
Does your organization truly believe, like truly believe in their heart that like AI?
0:29:45.71 –> 0:29:52.231
Nathan Lasnoski
Is going to change the nature of the work that they do like there’s a hurricane coming. I gotta get out of the way, right?
0:29:52.231 –> 0:29:57.151
Nathan Lasnoski
I gotta get out of town if I don’t believe that, that’s going to have that impact.
0:29:57.151 –> 0:29:58.791
Nathan Lasnoski
I’m not getting out of town, right?
0:29:59.151 –> 0:30:7.231
Nathan Lasnoski
It might hit me anyway, you know, but I if I understand that that’s a threat and I need to move. I’m gonna get out of town.
0:30:7.431 –> 0:30:14.951
Nathan Lasnoski
We need to understand that this is not only a threat, it’s an opportunity and it’s something that is going to impact every person.
0:30:15.191 –> 0:30:31.981
Nathan Lasnoski
Organization. The organizations that are skilled and capable with AI tools are going to be tremendously more effective than those that are not even just commodity by itself. The ones that are able to use that well are going to be tremendously more effective simply because it takes less time.
0:30:32.71 –> 0:30:38.311
Nathan Lasnoski
To do anything that they need to do. I was working with a company over the last couple weeks and.
0:30:39.861 –> 0:30:43.581
Nathan Lasnoski
I I was starting on their like idea registry and you know what I did?
0:30:43.581 –> 0:30:44.541
Nathan Lasnoski
I went out to.
0:30:44.541 –> 0:30:47.301
Nathan Lasnoski
I went out to copilot and I said here’s their website.
0:30:48.661 –> 0:31:2.21
Nathan Lasnoski
Here’s my old registries index that website and give me a table of scenarios that align to their functional towers, categories and return on investment associated with them.
0:31:2.261 –> 0:31:4.901
Nathan Lasnoski
And I got a great start list on it wasn’t perfect.
0:31:4.901 –> 0:31:8.941
Nathan Lasnoski
It wasn’t what I was going to show to them ultimately, but it got me going, right?
0:31:8.941 –> 0:31:12.781
Nathan Lasnoski
And you know how much that helped me? Immense. Immense because.
0:31:13.901 –> 0:31:17.901
Nathan Lasnoski
It allowed me to be able to make intentional changes and not spend time on the busy work.
0:31:19.611 –> 0:31:21.51
Nathan Lasnoski
So what does that?
0:31:21.51 –> 0:31:22.931
Nathan Lasnoski
What’s required to enable that?
0:31:22.931 –> 0:31:27.691
Nathan Lasnoski
It’s upscaling and what doesn’t work is not acting like AI is gonna be transformative.
0:31:27.691 –> 0:31:28.971
Nathan Lasnoski
It is transformative.
0:31:28.971 –> 0:31:31.251
Nathan Lasnoski
It is going to change the very nature of work.
0:31:32.821 –> 0:31:44.101
Nathan Lasnoski
And that requires dedicated and substantial upskilling process. And it particularly reminds you that this is not about technology skills, it’s about growth mindset. So what?
0:31:44.101 –> 0:31:59.781
Nathan Lasnoski
Every person in your organization having to challenge themselves of what they do today and how those tasks will be replaced and changed in the future and how their work will be augmented by AI agents. You’re seeing the infinite, remember, weren’t like Internet 1.0 right now.
0:32:0.901 –> 0:32:2.101
Nathan Lasnoski
Of what this means?
0:32:2.101 –> 0:32:10.981
Nathan Lasnoski
You’re seeing the infancy of the delegation to AI agents and a lot of it is like give me information back. It’s moving to go do something for me.
0:32:11.221 –> 0:32:24.21
Nathan Lasnoski
It’s like having an intern next to you that just start in and you’re having to understand their skill set and know what they can do and not do like I have my my son power washing my deck and then staining my deck right?
0:32:24.101 –> 0:32:26.21
Nathan Lasnoski
I I had to teach him on a power wash to deck.
0:32:26.821 –> 0:32:31.501
Nathan Lasnoski
And as he’s improved his skills, I could better delegate that activity to him.
0:32:31.541 –> 0:32:33.661
Nathan Lasnoski
Same thing with like the staining process right like.
0:32:34.291 –> 0:32:38.131
Nathan Lasnoski
As he better understood the skills and how to do it, he could better delegate.
0:32:38.131 –> 0:32:42.811
Nathan Lasnoski
This is what’s happening with AI agents, as we’re better able to collaborate and communicate.
0:32:42.811 –> 0:32:49.851
Nathan Lasnoski
Understand the capabilities of the AI agent. Build the AI agents capability to be able to be delegated to. It gets better and better.
0:32:51.421 –> 0:32:53.541
Nathan Lasnoski
But this is where AI enablement skills come in.
0:32:53.621 –> 0:32:58.781
Nathan Lasnoski
Really, really importantly, they become part of the nature of how we execute on the work that we’re doing.
0:33:0.981 –> 0:33:20.261
Nathan Lasnoski
So as we move from this idea of what works and what doesn’t work, I wanna give you some flavor of how companies are tackling some of these important objectives with really well structured sort of development frameworks. And one of the ways that we’ve been thinking about this is.
0:33:20.341 –> 0:33:21.861
Nathan Lasnoski
A lot of the AI pursuits.
0:33:22.341 –> 0:33:30.901
Nathan Lasnoski
It’s it’s not all that different software development or adoption, right? If you’re we’ve had to adopt things in the past, but now we’re adopting something really substant.
0:33:31.261 –> 0:33:41.141
Nathan Lasnoski
It’s like if you said the smartphone’s going to drop tomorrow. This is going to be impactful to you and people get the first smart phone. And like, I remember, my first smartphone was like the Palm 3C.
0:33:41.141 –> 0:33:46.981
Nathan Lasnoski
It was like the color version of the Palm and I was like, this is so cool. But I was like, the only person walking around with that palm.
0:33:48.541 –> 0:33:50.101
Nathan Lasnoski
I was tracking my help desk tickets on it.
0:33:50.421 –> 0:33:55.821
Nathan Lasnoski
Now it’s everyone’s got it right. But if if you told someone, then this is going to change. This is going to rock your world. You’re not going to.
0:33:55.821 –> 0:33:58.21
Nathan Lasnoski
In fact, you’re going to be addicted to this thing they brought.
0:33:58.181 –> 0:33:59.501
Nathan Lasnoski
They probably looked at me like why?
0:33:59.501 –> 0:34:1.381
Nathan Lasnoski
Really, like I’m not going to carry that thing around.
0:34:1.381 –> 0:34:1.981
Nathan Lasnoski
Like that’s for. That’s for geeky people.
0:34:2.261 –> 0:34:3.941
Nathan Lasnoski
I’m not going to do that like this is.
0:34:4.471 –> 0:34:6.511
Nathan Lasnoski
That same kind of level of thing.
0:34:6.791 –> 0:34:11.191
Nathan Lasnoski
So how do I think about this in the context of like building these kinds of solutions?
0:34:11.991 –> 0:34:14.511
Nathan Lasnoski
So really what this means is you’re doing something hard.
0:34:14.511 –> 0:34:15.791
Nathan Lasnoski
You’re doing something complex.
0:34:15.791 –> 0:34:18.231
Nathan Lasnoski
Let’s talk about what that is.
0:34:18.631 –> 0:34:23.31
Nathan Lasnoski
So if we had to say, what do the winners do? Well, what do the winners do?
0:34:23.31 –> 0:34:29.951
Nathan Lasnoski
Well, the first things they do well is they do really rigorous problem selection.
0:34:30.191 –> 0:34:31.471
Nathan Lasnoski
They pick the right things.
0:34:32.261 –> 0:34:33.541
Nathan Lasnoski
Small internal AI friendly.
0:34:34.541 –> 0:34:46.61
Nathan Lasnoski
For replacing bad existing solutions, they’re looking for opportunities that are are clear. The ones that stick out, the ones that business willing to support and they’re following through on.
0:34:46.501 –> 0:34:50.981
Nathan Lasnoski
But they have a belief that failure is good, and that’s a hard thing to say.
0:34:50.981 –> 0:34:59.21
Nathan Lasnoski
Like failure is good because we can react to it and they’re pragmatic about what’s going on and they’re they’re willing to stop and start and move and shift.
0:34:59.821 –> 0:35:4.781
Nathan Lasnoski
And focus on the things that are going to be the right objectives and then in execution.
0:35:5.821 –> 0:35:10.901
Nathan Lasnoski
They think about this being engineering activity and engineering in different ways, right?
0:35:10.901 –> 0:35:15.101
Nathan Lasnoski
So we have the adoption lane. We have this idea of like low code, no code.
0:35:15.101 –> 0:35:30.901
Nathan Lasnoski
You’re seeing that with copilot Wave 2, for example, where you see these like agentic AI scenarios start to pop out, and then as you get to your big, your big hairy audacious AI solutions that support your business in like these huge follow through’s, this is this idea.
0:35:31.201 –> 0:35:34.241
Nathan Lasnoski
Of products, not projects, products, not projects.
0:35:34.331 –> 0:35:43.331
Nathan Lasnoski
The idea of I’m creating product teams that enable me to be able to support the objectives of the organization and they’re be able to react to the needs of the business.
0:35:43.331 –> 0:35:48.251
Nathan Lasnoski
They’re not just spinning up one time projects to be able to respond to something.
0:35:49.11 –> 0:35:54.371
Nathan Lasnoski
Projects sometimes are to stack too static for us to be able to really be successful at as an organization.
0:35:55.921 –> 0:36:3.881
Nathan Lasnoski
So I think it’s important to think about once your business like starts to tackle an objective and this is centric to building them, OK.
0:36:3.881 –> 0:36:5.881
Nathan Lasnoski
So if you think about those three lanes.
0:36:6.531 –> 0:36:17.411
Nathan Lasnoski
Especially on the like high build scenarios where you’ll probably be going after some really important objectives. A lot of companies they think of like, OK, you’re gonna go build an ML system.
0:36:17.971 –> 0:36:18.931
Nathan Lasnoski
What is that made-up of?
0:36:18.931 –> 0:36:25.411
Nathan Lasnoski
What they think of I’ve got a data scientist and they’re using generative AI or they’re using something else and they’re creating this ML code.
0:36:25.651 –> 0:36:26.691
Nathan Lasnoski
And that’s it’s true.
0:36:26.691 –> 0:36:27.571
Nathan Lasnoski
There is ml code.
0:36:27.571 –> 0:36:36.451
Nathan Lasnoski
There is the the the the capabilities of the the machine learning system, the generative AI system that we’re building, but then surrounding that are all these other things.
0:36:37.201 –> 0:36:49.161
Nathan Lasnoski
And that’s where the idea of like the data I’m using or the serving infrastructure, how I bring it to my customers like am I building this from scratch or am I creating a power app or a building in copilot studio?
0:36:49.441 –> 0:36:51.881
Nathan Lasnoski
Am I doing it in a self-service?
0:36:51.881 –> 0:36:52.441
Nathan Lasnoski
Kind of way.
0:36:54.1 –> 0:36:56.201
Nathan Lasnoski
Is there a model validate validation process?
0:36:56.201 –> 0:37:5.201
Nathan Lasnoski
Have I enabled, for example, Microsoft’s AI safety and security layer that’s looking for different types of compromises against my AI systems?
0:37:5.851 –> 0:37:14.771
Nathan Lasnoski
How am I monitoring that to validate that that continues to be true, especially if I launch and land AAI agent that my customers are interacting with?
0:37:14.931 –> 0:37:18.811
Nathan Lasnoski
Have I built the right layers between that customer and the information?
0:37:18.811 –> 0:37:21.131
Nathan Lasnoski
The business system that sits behind that AI tool.
0:37:21.691 –> 0:37:35.731
Nathan Lasnoski
So building AI systems becomes very rigorous, and especially when you’re thinking about, you’re thinking about creating systems for your customers directly, or that your customer service or sales or engineering teams are using.
0:37:35.961 –> 0:37:47.41
Nathan Lasnoski
Be able to serve your customers. You need to think about it rigorously, as very much is a software development exercise and then below that exists the data and exists the infrastructure and exists the capabilities.
0:37:47.121 –> 0:37:53.81
Nathan Lasnoski
So when you think about building AI solutions underneath, an AI solution is essentially software engineering.
0:37:53.601 –> 0:37:56.121
Nathan Lasnoski
It sits on the data and sits on the availability of the cloud.
0:37:56.121 –> 0:37:57.401
Nathan Lasnoski
Why bring this up?
0:37:59.41 –> 0:38:16.121
Nathan Lasnoski
Is so many organizations have been starting to dabble in the cloud, starting to leverage the cloud for different purposes. And the organizations that didn’t do the work up to this point to get themselves ready skilling wise capability wise data wise, they’re going to be behind, they’re going to.
0:38:16.121 –> 0:38:24.41
Nathan Lasnoski
Be challenged to be able to take advantage of the data and the availability of the cloud and some of the muscle memory of software engineering.
0:38:24.41 –> 0:38:26.721
Nathan Lasnoski
Tune land AI capabilities within the organization.
0:38:27.771 –> 0:38:37.571
Nathan Lasnoski
In the build lane because they haven’t done the homework ahead of time, and you can think about that outside of the build lane in the adoption lane in this way.
0:38:39.81 –> 0:38:40.361
Nathan Lasnoski
Imagine you have a family member.
0:38:43.41 –> 0:38:48.121
Nathan Lasnoski
Who does not have significant technology skills? OK, like just in their personal life.
0:38:48.121 –> 0:38:57.361
Nathan Lasnoski
Just think about like I think about one of my family members that like still struggles with their smartphone still calls you with every question.
0:38:58.131 –> 0:39:1.611
Nathan Lasnoski
Maybe they still call the airline rather than online booking, right?
0:39:1.611 –> 0:39:4.411
Nathan Lasnoski
Like there’s like, there’s these. They just don’t get it right.
0:39:4.411 –> 0:39:5.931
Nathan Lasnoski
They didn’t live through it.
0:39:5.931 –> 0:39:7.971
Nathan Lasnoski
They they struggle with the technology.
0:39:7.971 –> 0:39:16.611
Nathan Lasnoski
Today they haven’t been able to make the pivot and they’re just at that point. Maybe it’s your grandmother. Maybe it’s your mother. Maybe it’s your sister. Whoever it is, right?
0:39:16.731 –> 0:39:17.811
Nathan Lasnoski
Or your your brother.
0:39:20.431 –> 0:39:21.991
Nathan Lasnoski
We’re gonna live through that moment right now.
0:39:23.601 –> 0:39:36.881
Nathan Lasnoski
And those of us that are getting access to these tools and are growing along with it or digital native land in which adopted because it always existed as part of our life, are going to take advantage of these tools really quickly.
0:39:38.521 –> 0:39:43.121
Nathan Lasnoski
But we’re going to have a set of people whose businesses drag their feet aren’t engaged.
0:39:43.121 –> 0:39:55.81
Nathan Lasnoski
They don’t think it’s going to be a thing and then you left behind and they won’t have done the homework. So we’ll get to a .5 years from now and they’ll get an AI agent on their desk when they go to the new job, they’ll be like.
0:39:55.401 –> 0:39:58.721
Nathan Lasnoski
How do I use this thing and how do I interact and what should I do and?
0:39:59.241 –> 0:39:59.801
Nathan Lasnoski
It’s gonna be.
0:39:59.801 –> 0:40:6.681
Nathan Lasnoski
It’s gonna feel so foreign because they’re not used to delegating to an AI agent the same way another person might be very comfortable.
0:40:7.401 –> 0:40:10.841
Nathan Lasnoski
So one of the things that’s on us is to do the homework right?
0:40:10.841 –> 0:40:26.921
Nathan Lasnoski
So in the build lane, it’s about getting all this ready, picking the right solutions to to deliver on AI. But in the adoption lane, it’s about how do I bring my people along for this ride and enable them in a culture of innovation that helps them be to.
0:40:26.921 –> 0:40:27.601
Nathan Lasnoski
Be successful.
0:40:29.571 –> 0:40:35.131
Nathan Lasnoski
So when you’re building these systems, you’re always looking at opportunities to derisk.
0:40:35.531 –> 0:40:39.131
Nathan Lasnoski
So what we do is we divide them into three different pieces.
0:40:39.131 –> 0:40:48.971
Nathan Lasnoski
You have a proof of concept phase where you’re just doing the minimum capabilities necessary to to validate the scenario. Actually works like will this work?
0:40:48.971 –> 0:40:50.971
Nathan Lasnoski
You know, let’s ask that answer that question first.
0:40:51.251 –> 0:40:53.251
Nathan Lasnoski
Like can I get here from here?
0:40:53.291 –> 0:40:54.91
Nathan Lasnoski
Can I get there from here?
0:40:54.881 –> 0:40:58.841
Nathan Lasnoski
But then you get to a point where you’re then building a minimum viable product.
0:40:59.361 –> 0:41:17.561
Nathan Lasnoski
Minimum valid products that may even no are aware is the minimum product to deliver value. The minimum product to deliver value in some way and then also is like missing some elements because you’re not going to over invest until you’re actually delivering value. You’re getting it to a.
0:41:17.561 –> 0:41:29.361
Nathan Lasnoski
Point where you like. You can test it out on X number of customer scenarios or X number of use cases and validate it’s doing what not only can it do it but like does it do it in a in practicality way remember.
0:41:29.491 –> 0:41:30.371
Nathan Lasnoski
Said pragmatic.
0:41:30.491 –> 0:41:33.851
Nathan Lasnoski
Like does this pragmatically do what I I needed to do?
0:41:35.401 –> 0:41:51.521
Nathan Lasnoski
Which then leads us to this idea of machine learning operations in building this operational resiliency into everything I create and this and this is one of the things that many companies leave off going back ten years in the AI space, I’ve seen organizations build, you know, ML mod.
0:41:51.521 –> 0:42:0.41
Nathan Lasnoski
For machine, for forecasting of their supply chains, but have really weak resiliency on the phase three capabilities.
0:42:0.41 –> 0:42:1.321
Nathan Lasnoski
Really weak resili weak resilience.
0:42:2.221 –> 0:42:8.141
Nathan Lasnoski
On how the machine learning operations is built in the context of their operational state.
0:42:8.141 –> 0:42:9.21
Nathan Lasnoski
So something breaks.
0:42:9.21 –> 0:42:12.381
Nathan Lasnoski
They put the wrong data in or. They’re not even evaluating the data.
0:42:12.981 –> 0:42:23.221
Nathan Lasnoski
All those surrounding components become too brittle and by the time it gets pushed into production, that guy leaves or the wrong data gets pushed in.
0:42:23.621 –> 0:42:24.141
Nathan Lasnoski
This isn’t.
0:42:24.141 –> 0:42:27.261
Nathan Lasnoski
This is a production service. I need to have that resiliency.
0:42:27.261 –> 0:42:28.101
Nathan Lasnoski
That exists here.
0:42:28.221 –> 0:42:31.501
Nathan Lasnoski
This is why follow through is so important, but also constant evaluation.
0:42:32.111 –> 0:42:40.271
Nathan Lasnoski
Is so important to to this this channel, so this is a phase comparison of what elements are going to exist in each.
0:42:40.271 –> 0:42:41.591
Nathan Lasnoski
You can see this a little bit.
0:42:41.591 –> 0:42:43.151
Nathan Lasnoski
This is a good one to screen capture.
0:42:43.151 –> 0:42:57.551
Nathan Lasnoski
You can see the POC leaves off with just those first couple components, but then MVP and the ML op start to build on that as you’re creating that muscle memory and as you’re getting it further into the the deployment process.
0:42:58.361 –> 0:43:4.601
Nathan Lasnoski
Why this is important is this is not just important. If you go back to that, that arrow of adoption, this is not just important.
0:43:5.191 –> 0:43:13.991
Nathan Lasnoski
In the context of building a solution, it’s important in governing what’s necessary to be in production within our organizations.
0:43:14.151 –> 0:43:32.31
Nathan Lasnoski
So as you look at creating your artificial intelligence center of excellence or you know steering group, that steering group has to have responsibility not only to ensure that enablement is happening, especially ensure enablement is happening in the right ways against the context of the business both in the.
0:43:32.71 –> 0:43:33.191
Nathan Lasnoski
Commodity lane.
0:43:34.41 –> 0:43:37.641
Nathan Lasnoski
And the sort of by lane like I’m buying AI solutions and adopting them.
0:43:38.431 –> 0:43:56.31
Nathan Lasnoski
Or in the lane of building AI solutions, whether it’s low code, no code, or it’s some of the things we’re talking about here that that governance exists and that’s always a balance. You can’t govern something that doesn’t exist, and you can’t in adopting something without governance is a.
0:43:56.31 –> 0:43:57.111
Nathan Lasnoski
Mistake, right?
0:43:57.111 –> 0:43:58.991
Nathan Lasnoski
It’s like a. It’s like a road with no speed limit.
0:43:58.991 –> 0:44:2.391
Nathan Lasnoski
Sometimes it can be OK, but most of the time you need a speed limit somewhere.
0:44:2.391 –> 0:44:3.471
Nathan Lasnoski
You need some science, right?
0:44:3.471 –> 0:44:7.71
Nathan Lasnoski
Just keep us going the right directions to pause at the right places.
0:44:7.721 –> 0:44:9.761
Nathan Lasnoski
To enable us to be highly aware.
0:44:11.321 –> 0:44:29.241
Nathan Lasnoski
Have the right traffic patterns, all of that matters to us being being very successful in creating AI systems. So as you’re driving toward using and adopting AI systems or building them, supporting know where it exists on the channel, like on the site like the the range of cap.
0:44:30.41 –> 0:44:37.641
Nathan Lasnoski
Of AI that exists that we might be adopting. So on the far left hand side you can see this idea of no AI. OK, no AI.
0:44:38.161 –> 0:44:42.681
Nathan Lasnoski
Simply meaning that like, well, Doc, we’re not using AI for anything, right?
0:44:42.801 –> 0:44:48.721
Nathan Lasnoski
And it gets to AI as a tool that we’re able to automate simple tasks.
0:44:48.721 –> 0:44:49.801
Nathan Lasnoski
We’re able to.
0:44:49.801 –> 0:44:51.921
Nathan Lasnoski
I kinda feel like this is where.
0:44:53.561 –> 0:44:57.481
Nathan Lasnoski
Many of the AI solutions had been in the public space for for some time now.
0:44:57.481 –> 0:44:58.921
Nathan Lasnoski
It’s like, OK, cool.
0:44:58.921 –> 0:45:3.721
Nathan Lasnoski
I’m like I’m creating an image or I’m I’m it’s responding with some content.
0:45:3.721 –> 0:45:8.1
Nathan Lasnoski
I’m I’m getting optimized sentence from Grammarly you know?
0:45:8.681 –> 0:45:13.441
Nathan Lasnoski
And it’s a tool it’s providing me with an asset to do something, but it’s not.
0:45:13.881 –> 0:45:16.161
Nathan Lasnoski
It’s not boosting me above that right it’s not.
0:45:16.161 –> 0:45:19.441
Nathan Lasnoski
It’s not moving me past what my ask initially is.
0:45:21.11 –> 0:45:25.51
Nathan Lasnoski
Which then moves in this idea of AI as a consultant, this idea that.
0:45:26.601 –> 0:45:34.721
Nathan Lasnoski
It’s it’s not only doing something. I’ve asked it to do, but it’s it’s kind of taking the next step beyond that and saying, did you think of did you do X?
0:45:34.971 –> 0:45:39.691
Nathan Lasnoski
Did you is providing recommendations back based upon a body of knowledge?
0:45:40.211 –> 0:45:45.171
Nathan Lasnoski
And that’s still more on the person than it is on the AI tool.
0:45:45.251 –> 0:45:47.571
Nathan Lasnoski
But you can see it’s a next step beyond the tool, right?
0:45:47.571 –> 0:45:49.731
Nathan Lasnoski
The It’s asking the next question.
0:45:49.771 –> 0:45:59.51
Nathan Lasnoski
It’s it’s infusing knowledge beyond what it was initially asked, but where we’re going to arrive at is this idea of AI is a collaborator.
0:45:59.171 –> 0:46:5.411
Nathan Lasnoski
OK, AI is a collaborator, which is this. This idea of AI and humans playing equal roles within the process.
0:46:6.211 –> 0:46:10.811
Nathan Lasnoski
And they’re bouncing ideas off of the other. You’ve seen the infancy of this with reasoning systems.
0:46:11.461 –> 0:46:22.61
Nathan Lasnoski
The infancy of this with some of the custom systems that have been built, they play this complementary path within the channel, getting then to this idea of AI as an expert.
0:46:22.301 –> 0:46:25.461
Nathan Lasnoski
AI controls tasks and uses human for feedback and input.
0:46:25.821 –> 0:46:29.741
Nathan Lasnoski
And they can execute those simple sub tasks, but they are the expert.
0:46:29.741 –> 0:46:30.741
Nathan Lasnoski
They’re the one providing.
0:46:30.781 –> 0:46:32.781
Nathan Lasnoski
So if you look at this in like terms of supply chain.
0:46:34.331 –> 0:46:38.411
Nathan Lasnoski
This might be like you know, no zeroes, no supply chain tool.
0:46:38.771 –> 0:46:43.931
Nathan Lasnoski
One might be I now have an AI supply chain tool that is going to give me information.
0:46:45.291 –> 0:46:47.731
Nathan Lasnoski
About my my.
0:46:49.331 –> 0:46:49.931
Nathan Lasnoski
Predicted demand.
0:46:49.931 –> 0:46:57.691
Nathan Lasnoski
It’s not prescribing something yet, but it’s giving me predicted demand. OK, as a consultant might predict what I should do about it, right?
0:46:57.691 –> 0:47:6.651
Nathan Lasnoski
So it says it’s not just predicting the demand, it’s saying here’s the inventory you should buy based upon the the prediction of the of the needed demand.
0:47:6.651 –> 0:47:13.571
Nathan Lasnoski
So it’s like taking the next step and then AI is a collaborator might be like it’s going to bounce off the the potential.
0:47:14.531 –> 0:47:15.11
Nathan Lasnoski
Possibility.
0:47:15.581 –> 0:47:33.861
Nathan Lasnoski
Future, let’s talk about what could change here or here or here or here based upon that. That idea Ai’s expert is almost coming back to the business and functioning as its own agency within the context of the organization. Saying the the AI agent is the A to.
0:47:33.861 –> 0:47:39.661
Nathan Lasnoski
My B on the the supply chain, the supply chain challenge, right?
0:47:39.661 –> 0:47:40.821
Nathan Lasnoski
It’s recommending back.
0:47:40.821 –> 0:47:45.501
Nathan Lasnoski
It’s saying this is what we now understand. I’m would recommend doing this business you make these.
0:47:45.691 –> 0:47:57.251
Nathan Lasnoski
Choices. But it does so with more agency than the tool or the consultant. It has the agency, and it’s simply asking for permission, and it says an autonomous AI is really like it’s completely on its own.
0:47:58.171 –> 0:48:1.291
Nathan Lasnoski
Think about this is like truly self driving cars.
0:48:1.291 –> 0:48:6.51
Nathan Lasnoski
Do we get to a state where we have truly self driving cars at some point?
0:48:7.611 –> 0:48:9.411
Nathan Lasnoski
That’s would be an example of autonomous AI.
0:48:9.531 –> 0:48:10.691
Nathan Lasnoski
Why is it so important?
0:48:10.691 –> 0:48:12.251
Nathan Lasnoski
It’s extremely expensive.
0:48:12.291 –> 0:48:14.771
Nathan Lasnoski
It’s extremely expensive right now to build autonomous AI.
0:48:15.781 –> 0:48:19.501
Nathan Lasnoski
Realize that most of the things you build are gonna be in this space.
0:48:21.11 –> 0:48:22.291
Nathan Lasnoski
Maybe eventually getting to here.
0:48:23.931 –> 0:48:27.971
Nathan Lasnoski
Short term, at least these are the spaces you’re building in. That’s OK.
0:48:28.51 –> 0:48:31.291
Nathan Lasnoski
The difference between that and no AI is substantial.
0:48:33.961 –> 0:48:35.281
Nathan Lasnoski
Cool graphic. OK.
0:48:35.281 –> 0:48:36.441
Nathan Lasnoski
So where do we go from here?
0:48:37.761 –> 0:48:46.521
Nathan Lasnoski
Where do we go from here is. I want you to take action and I want you to realize where you are on that Channel. You may already be taking action. May already have your ideas.
0:48:46.521 –> 0:48:48.41
Nathan Lasnoski
You may already know how to map that.
0:48:48.41 –> 0:48:48.921
Nathan Lasnoski
Or maybe you don’t.
0:48:49.321 –> 0:48:59.481
Nathan Lasnoski
So the ways that we help with this channel is a couple different things. We help by doing executive AI envisioning. I do this all the time.
0:48:59.521 –> 0:49:3.601
Nathan Lasnoski
Our team is does it all the time the the results of this are substantial.
0:49:4.101 –> 0:49:14.381
Nathan Lasnoski
This is a spot to engage us, to help you to get going on the journey in the appropriate ways to help engage your executive team to help understand what ideas are working, which ideas won’t we’ve garnered.
0:49:14.701 –> 0:49:17.301
Nathan Lasnoski
Seriously, we have so much background.
0:49:17.301 –> 0:49:20.301
Nathan Lasnoski
Know what ideas are good ideas and what ideas are not?
0:49:20.301 –> 0:49:30.141
Nathan Lasnoski
Simply that by itself is helpful as you’re starting to do that exploration because there’s some that just simply work, some that simply are challenged to work based upon the capabilities that exist today.
0:49:30.421 –> 0:49:34.91
Nathan Lasnoski
So that’s a great area for us to engage right at the start is executive AI envisioning.
0:49:34.531 –> 0:49:40.851
Nathan Lasnoski
The second is if you’re going down the lane of commodity adoption, especially copilot. They just released copilot Wave 2.
0:49:41.771 –> 0:49:49.491
Nathan Lasnoski
We have a readiness workshop for that we have an adoption program that’s that’s very provable in terms of its ability to drive success.
0:49:49.491 –> 0:49:59.651
Nathan Lasnoski
We’re working with organizations all the way up to very highly regulated customers as well as non regulated kind of anywhere in that space for an M365 copilot adoption.
0:50:0.451 –> 0:50:6.51
Nathan Lasnoski
If you are thinking about going down the the lane of of copilot adoption option, we are a great company to work with there.
0:50:6.901 –> 0:50:20.891
Nathan Lasnoski
And then the third is this idea of like chat bot use case exploration. This idea of like I’m just I’m looking to think about not only what I should build, but maybe I’m in the middle of building it like we are interesting enough, some very highly regulated chat.
0:50:21.91 –> 0:50:29.131
Nathan Lasnoski
Scenarios have come to our table recently that we’re helping companies collaborate with. I can’t give you the details of them, but some amazing use cases.
0:50:30.771 –> 0:50:36.251
Nathan Lasnoski
And even expanded beyond chatbot to be the idea of just an AI agent that’s performing an activity within your environment.
0:50:36.571 –> 0:50:38.331
Nathan Lasnoski
So many opportunities for next steps.
0:50:38.411 –> 0:50:42.131
Nathan Lasnoski
I would love for you as you’re leaving today to do 2 things.
0:50:42.131 –> 0:50:50.611
Nathan Lasnoski
We get your questions, by the way, if as you’re leaving today, I want you to fill survey, please select one of those and give me feedback. I want to know.
0:50:51.231 –> 0:50:57.271
Nathan Lasnoski
What you loved about the session, what you didn’t love about the session, what what we can do better, what we can do better.
0:50:57.591 –> 0:50:58.511
Nathan Lasnoski
Give me that feedback.
0:50:58.511 –> 0:50:59.151
Nathan Lasnoski
I wanna hear it.
0:50:59.151 –> 0:50:59.911
Nathan Lasnoski
I wanna know it.
0:50:59.991 –> 0:51:9.391
Nathan Lasnoski
I wanna be able to react to what you’re learning and not learning so we can do more. These next sessions are other sessions coming from our our in person events.
0:51:9.391 –> 0:51:12.151
Nathan Lasnoski
So why you shouldn’t invest in Gen. AI?
0:51:12.871 –> 0:51:17.671
Nathan Lasnoski
This is a great session. Brandon’s gonna do a great breakdown of just like how to even have a decision matrix.
0:51:17.671 –> 0:51:20.191
Nathan Lasnoski
How do I choose what to do versus not do there?
0:51:20.931 –> 0:51:22.131
Nathan Lasnoski
And then on the 24th.
0:51:23.671 –> 0:51:39.931
Nathan Lasnoski
We get into next Gen. agents which is comparing semantic kernel which is one of the AI agent infrastructures and copilot studio which is a local no code vehicle for building AI agents that you’re already seeing light up in M365 copilot as well that is a.
0:51:39.931 –> 0:51:40.391
Nathan Lasnoski
Very interesting session.
0:51:40.391 –> 0:51:41.991
Nathan Lasnoski
Tons of value in that as well.
0:51:41.991 –> 0:51:43.631
Nathan Lasnoski
So both of those are going to be worth your time.
0:51:43.631 –> 0:51:44.791
Nathan Lasnoski
Make sure you sign up for them.
0:51:45.111 –> 0:51:46.871
Nathan Lasnoski
They’re on our website right now.
0:51:47.271 –> 0:51:50.71
Nathan Lasnoski
OK, I promised you I’d take some time for questions.
0:51:50.851 –> 0:51:54.851
Nathan Lasnoski
I would love to answer them now, so I’m going to go check out the Q&A and chat.
0:51:56.131 –> 0:51:57.651
Nathan Lasnoski
And we’ll see what is.
0:51:57.811 –> 0:51:58.771
Nathan Lasnoski
See what is there.
0:52:0.291 –> 0:52:0.931
Nathan Lasnoski
Give me a moment.
0:52:2.491 –> 0:52:2.691
Nathan Lasnoski
OK.
0:52:2.691 –> 0:52:6.571
Nathan Lasnoski
Oh well, I thank you for the screen thing. I see that OK.
0:52:8.121 –> 0:52:13.121
Amy Cousland
I don’t see anything yet, but if anybody wants to add any questions, we still have a few minutes.
0:52:14.531 –> 0:52:15.691
Nathan Lasnoski
Right. I will just chill.
0:52:17.251 –> 0:52:19.331
Nathan Lasnoski
Please drop them in there if you have questions.
0:52:19.331 –> 0:52:21.891
Nathan Lasnoski
I’m. I’m here for you would love to answer them.
0:52:39.121 –> 0:52:39.481
Nathan Lasnoski
OK.
0:52:39.481 –> 0:52:42.841
Nathan Lasnoski
That means you learned everything, and I’m so happy about that.
0:52:43.241 –> 0:52:52.881
Nathan Lasnoski
But if you didn’t and you wanna have more conversations and we would love to as well, please fill out the form and we love to talk to you after the session is over.
0:52:53.321 –> 0:52:54.161
Nathan Lasnoski
Yes, exactly.
0:52:54.161 –> 0:52:54.841
Nathan Lasnoski
Thank you, mark.
0:52:55.201 –> 0:52:59.401
Nathan Lasnoski
AI experts please fill out the form we love to chat with you.
0:52:59.401 –> 0:53:0.441
Nathan Lasnoski
I’d love to connect with you.
0:53:0.441 –> 0:53:4.441
Nathan Lasnoski
Hit me on LinkedIn and let’s let’s go on to the next steps and talk more about this.
0:53:4.441 –> 0:53:8.281
Nathan Lasnoski
So I’m just thrilled that you spent some time with us and looking forward to more.
0:53:8.361 –> 0:53:9.41
Nathan Lasnoski
Have a great afterno.
0:53:9.261 –> 0:53:11.61
Nathan Lasnoski
Have a great afterNoon and great day and we’ll see you soon.