in a world that is changing rapidly.
And most of the developers, they are not machine learning engineers.
They have limited AI knowledge behind the scenes to go and evaluate what's going on.
So my recommendations for them are don't be distracted by the technologies that are coming out every day.
It's going to leave a certain level of exhaustion for you.
Take one step back and identify what is the problem that you're trying to solve and be very intentional about where you spend your time, because the technology world keeps changing, but the time that you have spent on that piece of technology, you can't get it back.
Focus on the problem that you're trying to solve and use it as a frame and lens through which you can evaluate what is noise and what is an area that is worth your time to spend.
I think the boundaries between roles are going to dissolve.
There is going to be more focus on building the right product versus building the product, especially between product management and engineers.
Historically we were saying that for a typical group of product management versus engineers, a ratio of 1 to 6 to 10 is applicable.
But now we've been hearing the leaders talking about even 1 to 05 for engineers, which is indicating that how important it is to identify the right problem.
I'm less worried about the labeling of people, but I'm expecting the lines between these two roles are emerging big time with AI.
Creating great products isn't just about product managers and their day-to-day interactions with developers.
It's about how an organization supports products as a whole, the systems, the processes and cultures in place that help companies deliver value to their customers.
With the help of some boundary pushing guests and inspiration from your most pressing product questions, we'll dive into this system from every angle and help you think like a great product leader.
This is the Product Thinking Podcast.
Here's your host, Melissa Perry.
Hello, and welcome to another episode of the Product Thinking Podcast.
Today I'm excited to welcome Dr Miriam Ashorey, a Vice President of Product and Engineering with IBM Watson X.
Miriam is at the forefront of simplifying generative AI adoption for major companies and has a unique blend of expertise in computer science, user experience and interface design.
I'm excited to dive into the conversation with Miriam and explore AI agents, their potential for product innovation and how AI can transform the way we build products.
Welcome, Miriam.
It's great to have you on the show.
Thanks for having me, Melissa.
I am so excited about your background.
It spans so many different things.
Can you tell us about a defining career moment that led you to pursue AI so deeply?
I like the way you framed it.
It's a combination of so many different things.
So over the past 20 years I've been really working at the intersection of system design, AI and product strategy.
It all started out with a couple of advanced degrees in AI and robotics, focusing on multi-agent systems, way before AI.
LLMs, put them under the spotlight again.
And then for my PhD, I did system design interaction and human computer interaction.
So, throughout my career, I've operated as a user experience designer, as an AI researcher, as an engineer and ultimately, a product leader.
But throughout all of those, I've been focusing on designing AI system, making them trustworthy, making them usable and impactful.
More recently, I've been responsible for the product strategy for our portfolio, to make sure that we are not just moving fast, but we are also competitive and we are responsible by design.
That's the balance, the delicate balance between velocity and trust that I've been operating in.
And how has this background really shaped how you think about building AI products at WatsonX?
When people are a really good portion and center of the problems that you're solving.
Whenever you think about AI, you're looking to what problems it solves.
So that view is going to be independent from the technology.
But what is the problem that we are solving?
So, instead of looking into different technologies and trying to figure out how I can bring it to businesses, I do a reverse one.
I go to the business problems and I question, is this problem as well suited to be tackled by AI?
And if so, what are the advantage?
What are the risks?
And have a realistic view of what requires, but also what it takes to implement it.
Because I've worked as a designer, so I understand the pain points of our designers out there.
I've worked as an engineer so I have a good understanding of the dev sizing and effort that is needed to implement a solution.
And then last but not least, the product strategies.
Is this the right product that we are building?
I think that's so important in the age of AI because we're all just really excited about it.
But there's a lot of questions about, is this actually going to produce business value?
So when we talk about AI agents, you know, it's all the buzz out there.
Can you explain what an AI agent actually is and what you would use it for?
Very good question.
AI agents is an intelligent system with reasoning and planning capabilities that can automatically make decisions and take actions.
So now how can I use this?
Because it has some sort of reasoning.
We can question if it's reasoning or just rudimentary planning capabilities, but because it has those capabilities, it has the opportunity to break down complex problems and play the role of a partner for brainstorming, for market evaluation, for gathering and analyzing information and data.
And then because it has some sort of action taking actions, we call it in the AI word tool calling or action calling.
I can automate and some of the actions that is potentially possible to execute on for an agent.
For example, we are having this conversation on the podcast.
I can have an agent to automatically summarize everything that we are talking about, identify a list of actions depending on what we said.
So use those reasoning and planning capabilities to identify the actions and connect them to some internal systems that automatically enables it to go and execute those action items and get them started.
So, when we're thinking about AI agents versus a lot of the generative AI applications that we see out there, we've got your chat GPTs, your clods.
How is it different?
What do you do with an AI agent if you're in a business?
And like, why would you want one yourself?
Very good question.
So let's go back to, you mentioned ChatGPT, the story of GenAI in 2023.
So there's a series of use cases that GenAI was applicable to at the time was around content generation, code generation, content grounded question and answering summarization, classification and information extraction.
Mid 2024 we started seeing LLMs expanding to taking actions, the tool calling and function calling capabilities that we talked about.
So suddenly businesses saw an opportunity, a window of opportunity to connect all those acceleration that we previously got from LLMs and bring them to action calling and function calling.
Bring them to every single of their businesses, even the legacy systems.
You can think of automation that can come everywhere.
You can think of acceleration that can come everywhere.
So practically any use case that you can think of can be potentially benefit from some of the use cases that are enabled by agents.
And it's like Claude and ChachiPT, they're one type of AI.
They're like a generative AI LLM, right?
There's multiple different types of AI that you could possibly build into these agents.
Yeah, so if you look into, for example, Hugging Face today, there are probably 100000 models at this point.
There has been lots of development on the open market in terms of creating open LLMs, but there is also development on the closed market, like Anthropic Cloud or ChatGPT that you mentioned.
There is an LLM behind the scene that is powering up these agents.
But then around that, you have the libraries and framework to connect these agents to the off-site work, to the tools, to the APIs and to the data sets as part of the pipelines.
So that application can be some of the applications that are offered by those companies that you mentioned, or you can create your own.
Like.
You are a product manager, you are a developer.
You can just grab one of these models and go build your own for the purpose that you are targeting for.
Can you give us an example of when a company or something that you've seen in practice that developed an AI agent?
What did they use it for?
What did they do to put it together and how did it work?
Yeah, let's look at a very simple example.
I would call it content grounded question and answering.
A question comes in, let's say it's a customer service situation.
Someone has a problem with the camera is not working.
They go to the website of the camera provider and they ask a question hey, my camera is not working.
So now the agent.
The first thing that can happen is the agent goes to the instructions of that camera to see if the agent can troubleshoot the situation.
We called it retrieval augmented generation.
There is an LLM behind the scene that is retrieving information from that document and it's basically helping the agent not to hallucinate but respond based on the instructions that are available.
But then if the instruction doesn't come up now, we can say hey agent, if you don't find the instruction in your internal guidelines and manuals, go fire up an online search to see if other people have experienced that.
So through that API and action calling, the agent can automatically make a decision.
That hey, I'm not confident on the accuracy of what I'd retrieve from here.
So I'm going to fire up and search online.
To see what's going on over there.
And it can retrieve some information through API calling and action calling, bring it back potentially because it's external right.
Depending on how sensitive the situation is, it's probably represented and packaged to a human partner to validate.
And if it's okay, then it's going to be communicated to the end user.
So it sounds like there is some kind of like discretion level that is programmed into these AIs which is different than just let me search.
Oh, I can't find it.
Right.
Oh, I searched.
I can't find it.
But now I know to take a different step.
Is that like the difference between having an AI agent that can string all these things along versus just like one question and answer model, like you would do on Claude or something like that?
So then you use an LLM.
It's restricted to the knowledge of the LLM.
You can pair the knowledge of LLM.
We call them a body of knowledge, external body of knowledge.
Let's say, if I'm a camera provider, I can probably connect that LLM that is trained on public information on some proprietary domain specific knowledge that I own for my business and help the LLM to answer those questions.
So now the body of knowledge for the LLM is expanded from what the LLM was trained on to also that body of knowledge.
But that body of knowledge, we call them unstructured data, is text.
But for most of the organization, the data doesn't live in text.
For example, the customer data information that we track from customers.
It's not necessarily just text.
They might be in the database, a record that we want to query and all of those.
So, in this situation, we can define a series of tools that are available to the agent to properly, as part of the pipeline that we design, take the action and pull the information.
In this case...
I didn't find what I was looking for in this body of knowledge, which was text.
So now I can automatically query that database by converting the natural language, the query that came into SQL, and go interpret that code automatically on that database if I have access to, as the agent, retrieve the data that I'm looking for, bring it back.
Or I can go online and do a search online.
So there is a series of tools that are available with the agent, but the agent is going to use the LLM underneath for the reasoning and planning capabilities to pick which one is the right tool to pick over here.
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There's a lot of talk out there about how, one day, we're going to go to a developer with a bunch of AI agents and that will be it.
That will go out and do the things for them.
What are the risks that you see with AI agents that product managers need to be aware of, and leaders as well?
And where do you think our limits are right now in what AI agents can actually do?
I'll give you an example.
We talked to about 1000 developers across the US building enterprise AI applications and we asked them do you use AI-assisted coding?
And almost half of them, they said they frequently use that.
We said, how much time saving are you getting out of using these tools?
And most of them, they said one to two hours a day.
About 4%, they said, about six hours saving per day.
What it tells me is if you, as an engineer, know effectively how to prompt these agents or how to put together a system to get AI to work for you, you can maximize savings, your productivity and stand out.
So that's the opportunity that you can gain.
But on the other side, there are limitations associated with this.
And it's the mastery of developers to use the prompting or the system in a way that they are well aware of the limitations and they manage on this by going in, by effectively working with the AI and getting the AI work for you.
Why does it matter?
For LLMs of the past.
Like 2023, the absolute worst thing that could happen was AI.
That could generate inappropriate content.
The AIs are today, as you mentioned, agents, they take actions.
They can leak data.
They can wipe out your code for optimizing the code, right?
So it's essential to have transparency and traceability of actions of the agents.
And as the developer or as product manager or whoever that is interacting with, those have the sort of like set the strategy for the agent and move, supervise the actions, versus completely automating what needs to happen.
The second thing that I wanted to highlight is the limitations that are carried forward from the LLM wars.
Every agent is powered up with an LLM underneath.
So if the LLM is suffering from hallucination, lack of explainability and all of those, now it's applicable to the new world.
So if you're using it for the purpose of code generation, I just say generate this code and not properly prompting it.
It may hallucinate code that may not be necessarily what you want.
So may work for prototype, but it's not going to work for production quality.
So it's important for developers to understand those limitations.
And plan around to capitalize on that as a differentiation for various skills, something that not everyone can achieve and deliver.
This is a question, and I feel like you're the right person to ask this.
Hallucinations.
I know that AI can hallucinate.
Why does it hallucinate?
Because there is no reasoning, really.
There is no logic of thinking behind LLMs.
This is an unsupervised learning that basically, LLM is exposed to a body of information, very large body of information.
So when you ask a question, it basically calculates what's the probability of the next token.
It's just the word of probability of the next token versus the whole thing.
And because of this the model can hallucinate because, based on just following the probability rules, there is a high probability that the output that you have generated, It's sound, but then when you use the logic behind it, it's like hey, it looks good.
And most of the time they are confident, very confident, on generating this, that you can feel the confidence in the tone, but it's not an accurate information.
It's just a collection of words nicely put together.
So when we're trying to prevent hallucinations or get better accuracy or not, have our AI agent just delete an entire code base.
How do product managers or developers what tactically are you doing to put like guardrails or instructions or that strategy in there to make sure you can mitigate that risk?
There are two things that they can do.
One is using the technology to identify some of those.
So, for example, for agents, we have been developing guardrails agentic guardrails that identify context-relevant faithfulness to the contents, and something like that.
So, as a product manager, you can put together these guardrails in the flow of decision-making for the agent to make sure that they stay close to that truth.
The second thing that they can do is when they design the workloads, they make sure that the human is in the loop.
When there is sensitive information or when there is the need to very high, accurate output.
For example, if you're using AI to provide recommendations for where to eat, Maybe the confidence can be low and nothing can happen, unless we are talking about food allergies and stuff, which is a serious thing.
So maybe as a product owner that is providing recommendation, you can make sure that if there is something in that query about food allergies, you want to make sure a human is in the loop, or you want to make sure that there is an extra checking to make sure that the output, before it's communicated to the end user, is double validated.
And it sounds like this has to do with risk.
Like the higher the risk, the more you want to put these into place.
Exactly.
We call it calculated risk.
So, as a product manager, you need to know what is non-negotiable and what are the risks that can be jeopardized, and just work around that.
With your team, I'm very curious, like how are you using AI?
We've got vibe coding out there, we've got all these different tools.
What are you putting into place and where are you using it strategically?
It's still fascinating to see how AI has been enabling every single role out there.
My department, I have product managers and engineers.
When I'm talking to my product managers, they are using AI not just for white coding, for prototyping.
They have an idea and they come back with a fully fledged working prototype.
And sometimes I'm like, is it the prototype or is it fully functional tools that I'm looking into?
But also looking into AI as a partner.
You do evaluation on the market perception of your product.
You can just fire up a search on everything that everyone has said, let's say on Reddit or on search or analyst, compile all of them together from multiple communities and forums, bring them back and then you can format the output that you want to see from them.
So, for example, you can send an agent and say go do a market requirement analysis on this product that has been out there and organize it in the factors sections that I care about.
And in a few minutes, it's ready to go.
Obviously, the models hallucinate.
They make things up.
They may not understand things clearly.
So it's a back and forth to make sure through the prompt engineering you get the right prompt to get the model to deliver what you want.
I've also been seeing it heavily used as brainstorming partner.
We are looking for our ideas for a new feature.
What can work?
What is the competition?
Like what is the strengths of the competition?
What is the weaknesses of my portfolio?
How can I leverage?
What is the user problem?
So it can help you with those information.
I've also seen people using it as generating PRD for the features.
Okay.
So we settled on this.
Now I want to define this feature.
Who do you think is going to benefit from this?
What are the pain points?
And basically the whole thing.
It's still owned by the product managers, but it's accelerated big time.
Like I can't say 10X or 100X, but I can see the progress and the speed of delivery that we can enable if AI has been effectively used.
Back to your question about understanding the limitations, right?
If they don't understand the limitations, it might be misleading.
All those hallucinations that we talked about, it might be misleading.
Even the tone.
Some of these chat applications out there.
They are trained to please you because they want the customer to feel good.
So that comes back and say, oh, the product is well-received and it's really good and stuff.
So you want to make sure that you set the tone and say hey, stop praising me, and help me with product thinking to improve.
And this is just coming up out of understanding what is the limitation of this model and how to effectively work out this.
What do you think is the future of AI when it comes to productivity for product management?
I think the boundaries between roles are going to dissolve.
There is going to be more focus on building the right product versus building the product, especially between product management and engineers.
Historically we were saying that for a typical group of product management versus engineers, a ratio of 1 to 6 to 10 is applicable.
But now we've been hearing the leaders talking about even 1 to 05 for engineers, which is indicating that how important it is to identify the right problem.
I'm less worried about the labeling of people.
So maybe it's the developer that is now able to play the role of the product managers because they have access to the same AI, or it can be the product managers that now, through white coding and all of those, has access to go down in the development path.
But I'm expecting the lines between these two roles are emerging big time with AI.
How do you think designers fit into that too, in the shape of interface design as we move forward with more AI baked in?
Very good question.
I think that designers need to focus their expertise on asking the right questions and testing the right thing, versus building the UI itself.
They.
To me, they should be the master of defining the right promptings for AI to help the product managers and engineers achieve what they need, based on the user preferences.
Because we already see AI generating UIs, we already see them even creating codes out of that UI.
I think the role of the visual designer, UX designer they are going to be more involved to a user researcher and maybe design managers to set the right strategy and prompting and what defines the right experience for the user to build and then just get AI to implement such an experience for them.
The one thing I'm really interested about and I want your take on this is I love the vibe coding stuff.
I've used all the different ones.
I like to generate the same UI through all of them and see where they go.
And I feel like they all converge.
I'm wondering if it's going to get to this point where we have the same UI everywhere because everybody's using the same stuff.
It's the same patterns.
It's like a pattern library.
So it's just using a pattern library going for best practices.
Do you think there's going to be an advantage in the future of having a unique design or some innovation there that's going to set stuff apart?
So my hypothesis is that, because all these models are practically trained on the same internet data eventually, if you don't tune them or if you don't apply your nice designer touch to it, or what is unique to your firm or to your business, it's going to generate the same design that someone else has generated.
But what is going to win this competition is the ones that are grabbing that and add their differentiated touch.
And especially, I don't think I believe that like I believe in universal UI designs.
Maybe patterns yes, patterns or interaction patterns are different, but universal UI designs for all the users globally, or depending on just treating people the same, is not going to work.
So it's essential to apply that expertise to bring that uniqueness to AI, because the LLMs by default can't offer that uniqueness.
Yeah, it seems to me like having people who are super creative in this field of ai too, can help going forward, like being able to look at it from a lens of that's not just the way that we've always done it, but here's where i can add value, here's where i deeply understand the value pieces and that's why i'm taking away from you when you're saying, like the product managers need to really focus on this, the developers have to focus on the customers, the interface designers really have to come back to user research.
It's like how do we get back to that problem instead of just generating stuff?
Yeah, the way that i think about ai is really a tool.
It can be a sophisticated tool that you would treat it as a partner, but this is still a tool.
Um, and the metaphor that keeps coming to my mind is calculator.
When i was going to school, at some point calculators at the university were banned because they wanted us to focus on manually doing the very complicated math stuff, and now i feel like that word is gone, Because what the skills that they are teaching the students these days is how to leverage the calculator, even the advanced calculations of calculators, or maybe AI today to go solve bigger problems, versus how to manually do this calculation.
So it's the same for all of these roles that you mentioned.
AI is just a tool for them, to give them the acceleration to improve, prove the quality of their designs, to improve the quality of their code and to improve the quality of the product that they are building.
It gives you an option to bring agility to your design.
Create something, put it out there, do user testing and, in a few days, just test it out if it's the right thing, before investing a lot of resources on building it.
With that, I feel like we're going to see a lot more applications out there, a lot more domain-specific, targeted applications out there too, that are solving niche problems because the cost of development is going low.
I'm expecting to see a lot of open development in the open ecosystem.
Beyond firewalls.
So the power of community designers being part of a community, developers being part of the community, influencing each other on the designs.
These are the parts that can be enabled by AI, but not necessarily replaced by AI.
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I love the calculator analogy.
I totally agree with you on the community part.
So I've seen this, and you work with a lot of large enterprises, so I would love your take on this.
I think so many large enterprises want to do something with AI.
They're readily trying to adopt it.
But they struggle, I think, with security and how do we get approval for this?
How do we actually bake that in?
How have you seen large enterprises adopting AI and what ways that get it into their systems and are successful with it?
Start small.
Identify what is the problem that you're trying to solve.
I see a lot of POCs, proof of concept, happening in the market.
You can go back and experiment with other options that are available to you to create an optimized workload.
Especially for enterprises, we are talking about millions of transactions per day.
So if you think about using a very large general purpose models behind the scene, the compute it takes, the latency, the energy that it's going to consume, it's just not scalable at the scale of enterprises.
So I see a lot of enterprises thinking small, identifying the problem, but then quickly moving to what are the scaling requirements and how can I go back to scaling hand select the tools that are applicable to my use case.
Those thousand application developers that I told you about.
Like we asked them in average, how many tools do you use to build an AI application?
And the majority of them, they said five to 15 different tools.
Wow.
So you need to stick together five to 15 different tools.
In a word that is evolving rapidly, every three months in that word is a generation now.
And some of these tool providers may not be relevant startups that may not be relevant in a few months.
So you are pulling them in, stitching them together, and you are asked to optimize them for the scale of enterprises.
And that's the area that I see the developers especially has been challenging.
This is the areas that are not necessarily leveraged by AI, but these are architectural choices that are enabled by senior developers that have a very good understanding of what needs to happen to deliver such frameworks.
And I think AI is going to have a few years to get to the part that they can provide recommendations on the architectures or play the role of the architect.
It's, hey, use this new tool.
You go and you learn it and then something better comes out.
Then you got to go learn that one.
How do you think about adopting different AI technologies responsibly and with an eye towards the future?
Like, what should people be looking for?
We asked people how much are they willing to spend?
How many minutes are they willing to spend on learning a new piece of technology?
And the majority of them, they said not more than one or two hours.
So they can't afford spending more than one to two hours a day in a world that they need five to 12 15 different tools.
And in a world that is changing rapidly.
And most of the developers, they are developers.
They are not machine learning engineers.
They have limited AI knowledge behind the scenes to go and evaluate what's going on.
So my recommendations for them are don't be distracted by the technologies that are coming out every day.
You can't catch up with that.
It's going to leave a certain level of exhaustion and fatigue for you.
So that's not the right approach.
Don't chase the solutions.
Take one step back and identify what is the problem that you're trying to solve.
And be very intentional about where you spend your time.
Because the technology world keeps changing.
But the time that you have spent on that piece of technology, you can't get it back.
So basically be very selective.
Focus on your problem, the problem that you're trying to solve, and use it as a frame and lens through which you can evaluate what is noise and what is an area that is worth your time to spend.
I think our conversation keeps coming back down to having like a product strategy, right?
So like identifying the problem.
What do you think are the core components for a really good AI product strategy?
What do you need to have to say now we can go pursue a solution?
You're going to have a clear problem.
Clear goal.
What are you trying to achieve?
Whatever your goals are to achieve at the end of the day.
You're going to need to have a very clear understanding of the personas and users that are going to be impact with that AI application that you're building.
And you're going to need to have a very clear I would call it a technology agnostic view of how to achieve that.
And I make it very bold the value of the technology agnostic part, because the world is changing so fast.
And by the time that your product gets out, there's a good chance that this underlying technology is already outdated.
And I see a lot of enterprises or businesses that they stick to the old technology because the cost of upgrade is too high for them.
And basically not providing a chance for them to enjoy the best and the state of the art technology out there, just because they didn't plan it well.
So I would say any decision that you make, try to be technology agnostic.
Use the best that is available in the market for you, but have a plan of or a layer in your architecture to abstract away the business case from the technology part.
When you're thinking about emerging trends in AI, what do you think product leaders should pay attention to the most?
It depends on what they are using AI for.
Some of the product managers are using AI for the purpose of productivity.
I think for those people they should every day look into the community how other product managers have been using it, what are the sample prompts, and just experiment on a daily basis.
There is a group of product managers that are looking into using AI as a way to enrich the existing capabilities of their product.
For that one.
My suggestion is to look into really have a good understanding of the acceleration that Gen-I can give you and then go back to your product features and see which one can benefit from it and make you stand out.
The third one is I've seen product managers that are using AI as a new business opportunity.
What are the new product ideas that can come to mind?
One example is all of the startups that are in code development, that are using one of the powerful LLMs out there behind the scenes to provide applications for developers and they get very popular.
So that's a really major business opportunity that only product managers that have a good understanding of the potential of LLM can jump on it and be the first.
And last but not least, I see opportunities for the provider side of the business.
If you are a product manager and have access to AI researchers that are developing the state of the art technology, you can potentially package something on the provider side of the house that can be potentially adopted by thousands of or millions of technology consumers through embed or resell or OEM.
It represents a major opportunity.
So, depending on where you are operating, the paths to take forward in terms of where the future is going to look like for you is going to be very different.
Yeah, and there sounds like there's lots of different directions that are emerging, which is really exciting.
Miriam, I've got one last question for you.
If you had to give advice to your younger self before you started your tech career or early in your tech career, let's say what would you say?
Be very intentional about where to spend your time, because the board changes but you don't get that time back.
I think that is fantastic advice.
Thank you so much, Miriam, for being here.
If people want to reach out to you or learn more about you, where can they go?
LinkedIn.
Find me on LinkedIn.
I'm pretty active over there.
And my current product is WatsonX.governance.
Great.
So we'll put all those links at our show notes at productthinkingpodcast.com.
Thank you so much for listening to the Product Thinking Podcast.
We'll be back next week with another amazing guest.
And, in the meantime, make sure that you like and subscribe to this podcast so you never miss an episode.
If you have any questions for me, go to dearmelissa.com and let me know what they are.
I'll answer them on an upcoming episode.
We'll see you next time.
