Why startups should solve a problem, not chase an opportunity

and other things I've been thinking about

Hey 👋

So moving is a nightmare (as many of you probably know all too well).

I won’t bore you with the details but I just had to endure a bit of a nightmare move that distracted me for the last few weeks. I’ve had very little time to read, reflect and write about what has been on my mind, but I’m back!

Last month, I came across an article by Brian Balfour that really resonated with me. (In case you haven’t heard of him, Brian is Hubspot’s former VP of Growth and the current founder/CEO of Reforge.) I haven’t had a chance with the move to write down my thoughts till now…

The article, titled Problem vs Opportunity First Approach, argues that there are two reasons why people start a company. The first is to solve a problem and the second is to go after a market opportunity. 

The starting point for solving a problem usually comes from personal experience (“I’m struggling with x and I figure I’m not alone”), whereas going after an opportunity usually originates from observing an unmet need in the market (“I noticed x trend is happening and I think there’s an opportunity here”). 

While neither approach is right or wrong, I agree with Brian’s hypothesis that most of today’s wildly successful companies are those that went after a problem. And that as a founder, it’s easier to find the stamina needed to be successful when you’re solving a problem rather than chasing an opportunity.

Let me explain using my own personal experiences.

At Charli, we started out because we saw an opportunity. For us, that opportunity was AI. As someone who loves tech, I was excited to develop our own AI that was interactive and that would bring specific capabilities to market in a new way. We were developing something we felt was missing from the marketplace.

I’ll admit that this posed challenges. We tried a few different approaches to find our product-market fit. But it never quite felt right. Instead of honing in on the true problem we were solving, we were dancing around it. Yes, Charli’s AI technology was – and always has been – something very special and unique, but we couldn’t quite figure out who our target market was or how best to apply the AI to them.

After considerable reflection, we decided to switch gears and instead go after a problem. 

That’s when everything in our company shifted. 

It was our moment of clarity. 

No longer were we trying to develop AI and fit it to the market. Instead, we had identified a very real problem and we were using AI to solve it. And that problem was content management.

Content management is something that has frustrated me to no end for years now. Why is it still so difficult to find what we’re looking for? Searching through emails, Google Drives, Dropbox, link clippers, and more, to find that one item you need...all of this takes considerable time. I’ve faced this problem on a daily basis while building my past companies while doing my home reno while running Charli, and so on. Finding, sharing, and managing content in today’s digital world is truly a hassle.

Once we honed in on this problem, everything fell into place. We’ve made some exciting changes to Charli as a result, and we’re getting a great response from our users. Rather than seeing Charli as simply an AI-driven content assistant, we now see it as a unified workspace for digital content and cloud apps. We know we’re on to something incredible, and I’m looking forward to sharing some big announcements with you in the coming months. 

Looking back on our journey, I completely agree with Brian Balfour when he says in his article, “Being a founder is a rollercoaster. When you are in a down moment, [your] source of energy and motivation is stronger when it stems from the problem vs the opportunity.” I also agree with him that going after a problem is more fulfilling and rewarding.

If you’re struggling in your business to find your product-market fit...or to keep your motivation up…or to progress to that next level of growth...or with any number of challenges, I highly recommend you revisit the problem you’re trying to solve. It might just be your golden ticket to success.

Say hello to Charli for teams

Introducing two new paid plans: Charli Pro and Charli Grow

Collaboration is the name of the game today. There are very few scenarios where we work in isolation. Yet, individual productivity is still easier to manage than team productivity.

Work has changed, our tools should too. 

At Charli, we’re changing that, we’ve designed a hub for modern teams who want to move fast.

We’re entering a new era of work, one that is more focused on the stuff that matters and less on the minutiae of admin work that plagues productive teams. In the coming weeks and months, we’ll be rolling out some exciting features that help teams, departments and companies organize and share their content. 

Our goal? To enable people to work together and with their business content, seamlessly. With these new features, Charli will become a hub where everyone can find, share and manage the “stuff” that powers teamwork.

If you work in a team, here are some features to keep your eye on:

• Content Canvas - This feature is already live and ready to be used. Charli’s Content Canvas is unlike organizing information in folders because it allows users to bring together disparate forms of content -- from docs to links to images -- and display them on one beautiful, shareable canvas. This is helpful for teams because it keeps all content related to a specific project or client in one place.

• In-app Sharing - Coming soon, this feature will allow users to share content with others directly from within the Charli app. That means no more clicking between windows, tracking links, or searching for shared docs again. It’s quick and easy, and it allows the receiving party to do what they want with the content.

• Integration with the tools you already use - Is your team addicted to Slack? Heavily invested in Google Chat? Loving the Shift life? Soon you’ll be able to integrate with the apps you know and love so that while you’re working, you can send info directly to Charli in-app and enable better find capabilities across platforms.

With Charli for teams, it’s time to say goodbye to static, siloed content, and hello to smart knowledge management!

Stay tuned for more details about these exciting new updates, coming very very soon.

Hint: check the Charli blog tomorrow for the announcement

Why knowledge management is broken

And how AI can fix it...

Many of my articles until now have been focused on productivity at the individual level. This month, however, I’m switching gears to look at it from an organization and company standpoint.

Organizations suffer from many of the same productivity challenges as individuals do... except they’re compounded. The more people that get involved, the more opportunities there are for productivity breakdowns. This is the same for knowledge management.

What is knowledge management?

Established in the early 90s as a discipline in its own right, knowledge management is defined as the “process of creating, sharing, using and managing the knowledge and information of an organization” and achieving organizational objectives by “making the best use of knowledge.”

While this concept sounds simple in theory, it is less so in practice. In fact, it could be that no organization -- from small businesses to enterprises -- has truly managed to crack the code on knowledge management. Knowledge management is simply broken.

Why is knowledge management broken?

When we look at the stats, we see that 19% of everyone’s workday is spent looking for “stuff”. That could be searching your emails, hunting for a file, or sifting through items on your desk. Moreover, 44% of the time, we can't find the information we’re looking for. That’s a lot of time wasted and a lot of frustration.

When we extrapolate this over a team, department, or entire business, the problem gets even more complex. Just as everyone has different working styles, they also have different knowledge management approaches. Some people are hyper-organized, others are barely holding it together, and everyone else in between has a different way of creating, sharing, using, and managing information. 

Throw the cloud in there, along with all the productivity platforms, and it’s easy to see how knowledge management can quickly get out of sync.  This can result in individual frustrations, organizational inefficiencies, business risks, and stress.

We’ve tried to fix this problem...many...many times

Centralizing and managing knowledge has been ‘solved’ many times over. When the category of Knowledge Management first emerged in the 90s, the discipline spurred many new technologies to ‘solve’ the challenges of managing knowledge within the enterprise. This included tools like enterprise asset management systems, document management systems, collaboration tools, enterprise portals, and workflow systems. 

As we moved into the 2000s and cloud storage became more widely accepted, the internet boom created more data, mounting an urgent need for tools that made it easier to find information.

Enterprise search emerged as another ‘solution’ to this problem. Enterprise search started in the 1970s primarily in academia but slowly made its way into the consciousness of business in the 90s. Many startups built businesses around enterprise search until the industry consolidated in the 2010s and Dassault, IBM and Oracle scooped up their flavor of enterprise search. 

If you go to any larger organization, they will likely have one of these enterprise search solutions but if you ask any employee how much they love using it...crickets. 

Why is the knowledge management challenge at a tipping point?

Despite the fact that we’ve tried to fix the problem with various solutions, I would argue that knowledge management hasn’t gotten better with technology, it’s only gotten worse. 

Plus, with more emphasis on collaboration, remote and hybrid teams, a tool for everything, we’ve grown the complexity of knowledge sharing exponentially. 

Remote work over the last year has highlighted this problem for every organization both large and small. . No longer can we lean over our desk and ask a colleague for a document. No longer can we walk to the company’s bank of filing cabinets and pull out the file we need. 

Now, we have to ask a colleague over Slack, try to use a half-baked global search tool, or explore with trepidation the cloud-based world of Google Shared Drives, Teams, or DropBox. Many times, we actually end up creating more content (like Slack messages, emails, etc) just to find where that one piece of missing content is. 

Yes, I think we’ve hit peak content chaos! 

With content siloed, some large organizations have tried to revive their old enterprise content management tools, but if you ask folks at these organizations how it’s going for them, you’ll probably hear “not very well”. 

More platforms, apps, and tools aren’t solving the problem. Instead, we’re in a situation where we’re creating lists in Notion to track Google Docs, and Google Docs to track workspaces in Notion.

How can AI fix these problems?

All of this to say: it’s time for a better approach. As someone who spends their days immersed in the world of AI, I see opportunities for this everywhere. Here are a few ways I think AI can do a better job than content management systems (or humans, for that matter) can at managing knowledge:

1. Embrace “find” versus “search” - I’ve written on this topic previously, but to summarize, AI has the capacity to ingest and understand huge amounts of data, paving the way for true “find” capabilities, versus merely “search” ones.

2. Break up with folder structures - Our brains have been conditioned to think and dream in folder hierarchies. That means, traditionally: A top-level folder, followed by sub-folders, containing even more sub-folders. Every document has its place, and it can only live in one folder at one time. It requires the cognitive ability to understand a document’s content and then put it in the right place. However, inevitably, not everyone sees eye to eye on the “right” folder structure, and each person has their own preferences or requirements for working. This is where AI is a game-changer. Even when information is spread across multiple platforms, AI enables organizations to pull together what’s needed, when it’s needed, and scrap traditional folder hierarchies.

3. Say hello to automation - Organizing content has typically been a very manual process. You must first read a document to understand it, then hunt to see if an appropriate folder exists to store it in, and finally, move the document to that location (or create a new folder first if none exists). This doesn't sound like a lot of effort, but it all adds up to time and brainpower wasted. AI changes this because it can scan the document’s content for you, generate appropriate meta tags to classify and store the document, and then quickly find it for you when you need it again. 

It’s exciting for me to see Charli moving in this direction of helping teams. Our AI will soon have the capabilities to mobilize smart knowledge management at the organizational level, making it a game-changer for organizations big and small. Charli will let individuals work as they prefer while keeping content organized, quick to find, and easy to share at the organizational level. 

👉 Stay tuned for more announcements on Charli for teams in the coming weeks.

AI & the Challenge of Scale

4 Tips for Approaching Scalability in Your AI

As you may have noticed based on the last newsletter, AI has been on my mind a lot lately, which makes sense, AI is core to pretty much everything we do at Charli.

In particular, I’ve been thinking a lot about scale lately. As a young startup, we focus a lot on features, functionality and tech, but then you hit an inflection point, where you MUST think about scale or you’re going to have a pretty difficult time taking the company where it needs to go. 

For a technology startup, scale is an obvious must (at some point). But for an AI like Charli’s that’s all about learning personal preference and automating for the individual user, designing it to be both user-centric/customizable and scalable didn’t come without many long nights and a few extra grey hairs. 🤯

The dirty little secret of the tech world is that AI has a ton of scale challenges. If you’re a founder considering starting an AI-centric startup, fair warning, scaling AI can be expensive if you don’t get creative.  Luckily, you’re not alone. Much data is needed to train the models, and the quality of the data must satisfy the goals you are trying to achieve. Data labeling (the exercise of providing data along with the metadata in order to train the models) is also strenuous because it requires enough people to help to satisfy the training requirements. And then, there’s the continuous training. That’s a whole other mountain to climb.

In other words, developing and implementing AI that can continuously learn across a diverse population of millions of users can be overwhelming, and general-purpose ML models won't suffice.

Having been down this path, and learned many things as we came to our solution, I wanted to share the following for those of you thinking of building AI products or adding AI to your existing portfolio...

If you’re struggling with AI-scale stress, you’re not alone. 🤗 Here are four scalability challenges many startups lose sleep over, as well as some suggestions:

1. Customization of AI models

General-purpose AI models do not have the necessary performance, nor the accuracy, to solve real problems. That means each AI model must be customized and trained to fit a specific problem, data set, and domain. In addition, maintenance and continuous learning of each model require a ton of work. This is a huge challenge when we start talking scale.

To approach this challenge, focus on making the design and implementation of the customization process as efficient as possible. At Charli, we’ve figured this out and can work closely with users across any number of industries and apply knowledge and continuous learning on a structured basis. We can do this through scalable methods that orchestrate AI models across users, industries, locales, and other factors. 

2. Data management

Data is a crucial component of each AI model. Labeling, processing, and managing a vast amount of data needed to utilize AI models at a production scale require tremendous effort. Moreover, AI models have to be continuously updated with new incoming data. This means we need to go through the lifecycle of data pre-processing, model training, and deployment again and again. 

The first step to approach the data management complexity is to take the time and define the data strategy endgame, i.e. think about what and when the data should be collected, processed, and incorporated. At Charli, we designed a robust scalable data management solution with the requirements of AI models in mind. Having an AI-infused solution facilitates implementing automated data processing steps.  In addition, we take extra measures to ensure the quality of data since any AI model is as good as its training data. 

3. Resource management

With the ever-increasing data and new complex architectures of AI models, scalable infrastructural resources, such as memory, computational power, and storage, are a must. Specifically, those that can easily scale without breaking the product. 

In addition, we have to make sure the AI models are efficient and customized to their specific task, otherwise, they might run slow and consume huge amounts of expensive computing resources. This is more and more important for models that need to be continuously updated/trained.

One way to approach this is to invest time and resources to design and implement an optimized MLOps process. To bring AI models into production at scale, MLOps practices bring Data Scientists and DevOps engineers together to increase automation and improve the quality of various steps in the ML lifecycle. At Charli, in addition to having an optimized MLOps practice in place, we are also mindful of the performance of AI models at scale while creating them. This is necessary to avoid creating AI solutions that appear to work well during testing, but are unstable in production.  

4. Unexpected behavior

For incorporating AI models in production, we need to have support for situations that are not designed or planned for. Like issues that did not appear in testing but could happen in real life. An example of this could be if the result of an AI model is not accepted due to any business logic. Supporting how to deal with these cases and learning from the experience automatically is another challenge for scaling AI.

To approach this challenge, at Charli we’ve learned that having various validation steps helps to capture these unexpected behaviors and activate the contingency options in time. Moreover, we’ve invested into continuous monitoring and “guardrail” techniques that are designed to provide confidence in AI decision making. Here at Charli, we see most of these unexpected behaviors as learning opportunities, to enhance the accuracy and performance of our AI solutions.

Want to learn more about Charli and experience our AI in action? We just released version 1.3 of Charli, try it out and start getting organized: https://www.charli.ai/

Dispelling the Myths of AI

Q&A with Charli AI's VP of Data Science, Elham Alipour

Just like “in the cloud” had its over-usage frenzy in the mid-2000s, “AI” has become the tech buzzword of the past several years. As the usage of the term has gone up, its clarity has gone down. Anyone building software right now had better find a way to massage AI into their marketing or risk looking antiquated.

At Charli, AI has been core to our architecture from day 1, no bolt-on solutions can be found here, our Data Science team works in tandem with engineering, which is pretty unique and not something you’ll see at other software companies. I wanted to help dispel some of the confusion around AI by sitting down with our VP of Data Science, Elham Alipour, and talk about what AI (actually) means today in the world of tech and help bust some of the more common myths you hear in the news. 

KC: At its core, what is artificial intelligence (AI) anyway?

Elham: I think the biggest mistake people make when thinking about AI is thinking of it as a single “thing”. The truth is, AI is a complex and sophisticated orchestration of various algorithms and processes. It’s the sum of its various parts. 

KC: Also, there’s a tendency to assume that we’re trying to replicate the human brain with AI. But that’s not it either because, to be frank, the human brain isn’t that good! 🤔🙃

At its core, AI is about a series of processes that have the ability to learn, adapt and apply its knowledge to new scenarios.

Elham: I would agree. At Charli, for instance, our AI is a collection of models and algorithms that line up with the capabilities we offer our users.

That means we’re applying different approaches at different stages of the game to enable the intelligent and coordinated processing of content and data all the way down the line.

KC: How are machine learning (ML) and AI, related?

EA: This is often where people start to conflate the terminology. In essence, ML is one method that can be used when developing the complete AI process. Think of it as one type of railcar when you’re assembling a train.

In other words, AI is an umbrella term that includes various technologies such as machine learning, deep learning, transfer learning, computer vision, natural language processing (NLP), recommender systems, and more.

KC: At Charli, we use ML in the development of our AI – in addition to a lot of other methods that Elham just listed. We also have our own proprietary techniques.

Machine learning essentially allows machines to learn from data without having to directly program it.

It can become highly complex but is only one method used when developing AI.

KC: I’ve heard people say you can get AI “off the shelf”. Is that true?

EA: If only it were that simple! Long answer short, no you can’t (and wouldn’t) buy AI off the shelf. You can buy ML algorithms off the shelf, but with AI it just wouldn’t work. If you want to get true automation built into your app or enterprise, AI requires extensive assembly, orchestration, and continuous learning.

KC: I think it’s also important to note that these off-the-shelf capabilities for ML have real limitations on their completeness and accuracy. The algorithms are trained to be “general purpose”, meaning they want to serve a broad audience.

Because of that, it’s highly unlikely they will address the specific needs within your organization or app – especially when it comes to designing automation and AI.

KC: Along a similar line of questioning, can you bolt AI onto an app?

EA: You can definitely bolt an ML algorithm onto your app or product. But, as we discussed, ML is not AI.

For AI to be incorporated into your solution, you must design it from the ground up. You need to understand where AI is going to be applied, how you will collect the necessary data, how you will train the models, how you will orchestrate the process and how you will enable continuous learning.

KC: That’s right. In many other cases, bolting on a solution has value. But it simply won’t address the need for automation and AI unless you take a close, hard look at the foundational elements of your solution and integrate other AI methods.

KC: There’s a lot of discussion about bias in AI and we’ve talked about it internally as well. Do you believe there is bias and if so how do we overcome it? 

EA: You’re absolutely right, AI is inherently biased. Let me explain. AI is dependent on models, orchestration, and training data.

These are developed by people who are biased, and so that trickles into the AI. Can we overcome the bias?

That’s a bigger question. At Charli, we approach this in two ways: using the bias to our user’s advantage when it makes sense to do so and being very conscious about introducing diversity. Just like we value diversity in business, we value it in AI. We know it gives us a natural advantage and so we see it not only as the right thing to do but also the best thing.

KC: Adding to that, something we’ve spent a lot of time trying to grapple with is how diversity adds a layer of complication to the design and implementation of Charli.

We want Charli’s AI to understand each individual user, hence we want their personal bias to be an advantage. For instance, if a user has a tendency to treat a certain type of content in a certain way, we want Charli to notice that and start doing it automatically.

However, when it comes to scaling Charli’s AI, one person’s individual bias might not be the same as another's. In that case, biases limit Charli and so by injecting outside influence and coaching, we can ensure that Charli’s AI will work for all users. It comes down to designing a scalable approach that enables the transfer of learning but still allows for individualization.

To learn more about Charli and try it today for free, please visit: https://www.charli.ai/

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