16-Mar AI/ML & Data Readiness Teardown Roundtable

iSPIRT is announcing the first AI Teardown roundtables. The focus of this first teardown RT will be an honest review of the data readiness for your machine learning strategy.

The AI Teardown Agenda

Of the many problems startups on the AI/ML journey face one of the most critical is with their data readiness.

Most startups on the AI journey struggle to get sufficient data to build effective ML models. Further, data privacy has increased the complexity of sharing data, which now resides in distant silos. While internal proprietary data is a rich source of patterns, often times it is incomplete.

This teardown roundtable will primarily focus on helping founders resolve many of the queries like:

  • Do I have the right data?
  • How can I source/collect more data to get completeness?
  • How to clean & prepare data for training?
  • How much data do I really need?
  • Are we using the right model/algorithm?
  • Do I have a virtuous cycle of data in my product?

If you are a B2B SaaS startup in the early phases of building an AI-enabled product value then this playbookRT will be a great opportunity to get critical feedback and resolve many queries from our Mavens and fellow peers.

Click to Register for the AI Teardown PlaybooksRT. (limited invites)

Our Mavens

 

 

 

 

 

Ramesh Loganathan IIIT Hyderabad

Shrikanth Jagannathan PipeCandy

Puneet Jindal Eduwaive Foundation

 

This is a product startup founder/CXO (+1) invite-only events. Venue details will be sent along with the confirmation of your registration.

RoundTables are facilitated by an iSPIRT maven who is an accomplished practitioner of that Round-Table theme. All iSPIRT playbooks are Pro-bono, Closed room, Founder (+1), invite-only sessions. The only thing we require is a strong commitment to attend the sessions completely and to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

9-Mar AI/ML & Data Readiness Teardown Roundtable

iSPIRT is announcing the first AI Teardown roundtables. The focus of this first teardown RT will be an honest review of the data readiness for your machine learning strategy.

The AI Teardown Agenda

Of the many problems startups on the AI/ML journey face one of the most critical is with their data readiness.

Most startups on the AI journey struggle to get sufficient data to build effective ML models. Further, data privacy has increased the complexity of sharing data, which now resides in distant silos. While internal proprietary data is a rich source of patterns, often times it is incomplete.

This teardown roundtable will primarily focus on helping founders resolve many of the queries like:

  • Do I have the right data?
  • How can I source/collect more data to get completeness?
  • How to clean & prepare data for training?
  • How much data do I really need?
  • Are we using the right model/algorithm?
  • Do I have a virtuous cycle of data in my product?

If you are a B2B SaaS startup in the early phases of building an AI-enabled product value then this playbookRT will be a great opportunity to get critical feedback and resolve many queries from our Mavens and fellow peers.

Click to Register for the AI Teardown PlaybooksRT. (limited invites)

If you are interested in the 16-Mar Hyderabad roundtable click here.

Our Mavens

 

 

 

 

 

Adarsh Natarajan Aindra

Shrikanth Jagannathan PipeCandy

Puneet Jindal Eduwaive Foundation

 

This is a product startup founder/CXO (+1) invite-only events. Venue details will be sent along with the confirmation of your registration.

RoundTables are facilitated by an iSPIRT maven who is an accomplished practitioner of that Round-Table theme. All iSPIRT playbooks are Pro-bono, Closed room, Founder (+1), invite-only sessions. The only thing we require is a strong commitment to attend the sessions completely and to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

27-Oct MiniRoundTable on WhyAI for B2B SaaS

The MiniRT Agenda

  • Seeding & creating an active discussion on Why AI/ML? What is the higher order value being created?
  • How to identify the value & opportunities to leverage AI?
  • How to get started with an AI playground (if not already running)?
  • How to think of data needs for AI/ML investments,
  • How to address the impact on Product & Business…

This is part of our AI/ML roundtables for B2B SaaS. Insights from these sessions are meant to help refine our approach & readiness to leverage AI/ML for building higher order value products. And in doing so building a vibrant community focused around navigating this shift.

Click to Register for the AI/ML Playbooks Track. (limited invites)

Our Mavens

Adarsh Natarajan

Founder & CEO AIndra

 

Notes

This is a product startup founder/CXO (+1) invite-only events. Venue details will be sent along with the confirmation of your registration.

RoundTables are facilitated by an iSPIRT maven who is an accomplished practitioner of that Round-Table theme. All iSPIRT playbooks are pro-bono, closed room, founder-level (+1), invite-only sessions. The only thing we require is a strong commitment to attend the sessions completely and to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

18-Oct MiniRoundtable on AI/ML Tools & ML/DL challenges

The MiniRT Agenda

  • The current state of AI/ML tools & frameworks.
  • Share & discuss ML & Deep Learning challenges in your products.

Insights from these sessions are meant to help refine our approach & readiness to leverage AI/ML for building higher order value products. And in doing so building a vibrant community focused around navigating this shift.

Click to Register for the AI/ML Playbooks Track. (limited invites)

Our Maven: Viral Shah
Co-creator of Julia Language,
Co-founder Julia Computing

Notes

This is a product startup founder/CXO (+1) invite-only events. Data scientist leads in a product startup are also welcome to register. On confirmation, the venue details will be sent along with the confirmation of your registration.

RoundTables are facilitated by an iSPIRT maven who is an accomplished practitioner of that Round-Table theme. All iSPIRT playbooks are pro-bono, closed room, founder-level (+1), invite-only sessions. The only thing we require is a strong commitment to attend the sessions completely and to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

6-Oct MiniRoundTable on WhyAI for B2B SaaS

The MiniRT Agenda

  • Seeding & creating an active discussion on Why AI/ML? What is the higher order value being created?
  • How to identify the value & opportunities to leverage AI?
  • How to get started with an AI playground (if not already running)?
  • How to think of data needs for AI/ML investments,
  • How to address the impact on Product & Business…

Insights from these sessions are meant to help refine our approach & readiness to leverage AI/ML for building higher order value products. And in doing so building a vibrant community focused around navigating this shift.

Click to Register for the AI/ML Playbooks Track.

Notes

This is a product startup founder/CXO (+1) invite-only events. Venue details will be sent along with the confirmation of your registration.

RoundTables are facilitated by an iSPIRT maven who is an accomplished practitioner of that Round-Table theme. All iSPIRT playbooks are pro-bono, closed room, founder-level (+1), invite-only sessions. The only thing we require is a strong commitment to attend the sessions completely and to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

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AI/ML Shift for SaaS Companies: Insights from SaaSx Fifth Edition

Early stage SaaS startups typically struggle with one of two things. When you are just starting out, the first struggle is all about mere survival. Will we find customers willing to use and pay for our product ? Good teams typically manage to find ways to negotiate that first challenge. The playbook has been sufficiently commoditized that if you execute well enough, you can actually succeed in getting those early customers. Its a challenge for sure, but is getting easier and cheaper to overcome — which takes me to the second challenge. Once you survive that initial phase, how do you continue to stay relevant and grow? For if you don’t grow, you’ve only prolonged the inevitable and will likely get disrupted into irrelevance by the next upstart that comes along. When you play in a commodity market, that’s the sad reality.

If you find yourself gaining customer adoption, you can be fairly certain that competition isn’t far behind. Unless you find a way to establish sustainable differentiation while you have that head start, you will ultimately die. And that differentiation now increasingly comes down to the value of the data flowing through your platform and how you are able to leverage it better than your competition. In other words, if you are not thinking about constantly learning from the data that you are gathering and enabling implicit intelligence via your products, the odds of survival are going to be stacked against you. Given the significance this topic carries for us at Swym, I was really excited to have the chance to sit in on Ashwini Asokan and Anand Chandrasekaran’s session on AI/ML for SaaS at SaaSx5. And they most certainly didn’t disappoint. With a lucidly laid out argument, their talk served as a strong wake-up call for the SaaS founders in the room that weren’t sufficiently worrying about this topic.

SaaS growth is slowing

Ashwini started out by underscoring the fact that SaaS growth was slowing in general. There’s no denying that most solutions are rapidly becoming commoditized — building a good product has gotten fairly prescriptive, costs have come down and barriers to customer adoption are a lot lower than they used to be. That inevitably leads to markets getting very crowded, making survival increasingly difficult. If you don’t stand out in very defensible ways, you will perish. To make matters worse, AI is slowly but surely causing entire categories of work to disappear — Customer Support, SDRs, Financial/Market Analysts, to name just a few examples. If those workers were your market and you were helping them be more efficient, you are in trouble because your market is disappearing with them. You better be evolving from being software that’s serving those people that in turn serve a function, to actually serving the function itself. Of course you do this with human assistance, but in a progressively intelligent fashion that makes you indispensable.

Embrace the platform mindset

In order to stay relevant, you really need to create a viable roadmap for yourself to graduate from being a simple feature that’s part of a larger platform (No one likes being told they are nothing but a feature, but this really is where most early stage SaaS products sit today) to becoming the platform itself over time. It can most certainly be done because the opportunity exists, and the access you have to your data and how you are able to leverage it is likely to be the most effective weapon to get you there. Think really hard about new use cases you can light up, automations you can now enable, important solutions that hitherto weren’t possible or practical — enabling those capabilities is what will give you stickiness. And you can in turn leverage that stickiness to allow others to build on the data platform you’ve created to expand your moat. Easier said than done of course, but it is the only path to staying relevant. Alexa, Salesforce, Adobe, Hubspot, and most recently Stripe with their just announced app store, all come to mind as stellar examples of execution on this strategy.

How should I be thinking about Data Science?

Anand followed that up with some really good advice on how to go about this, especially touching on what not to do, and it was clearly resonating with the audience. For instance, when he highlighted the fact that most AI initiatives that start with “Here’s the data I have…what can I do with it?” are doomed from the get go, a lot of heads in the room were nodding in agreement — seemed like a pretty common trap that folks had fallen into. Instead, his advice was to identify the end goal that mattered first, with the caution that this could be deceptively challenging. Once that goal is well understood, then focus on the data you have and the gaps that exist — and your challenge basically boils down to filling those gaps and cleansing/validating your data. Those are your most critical, time-consuming steps in the process for once you get the data quality you want, it becomes much simpler to build and iterate your model around that and figure out how to engineer this into a repeatable part of your workflow. The sub par data quality is one of the most common causes for AI projects “failing” and no amount of modeling proficiency will save you from bad data or a poorly understood problem statement.

Get on the train, but don’t lose sight of what got you here

I’m really glad to have had the benefit of listening to their talk in person, and now that I’ve let the arguments sink in over the past couple of weeks, a few truths have become indisputably clear in my head. The AI shift is not one you can ignore as a SaaS founder. If you don’t get on the train, you’ll likely end up under it. And no, getting on the train doesn’t mean simply attaching a “.ai” to your domain name and claiming success. It really comes down to internalizing your vision for why you exist, identifying in very clear terms how your roadmap to making that vision a reality will need to evolve given the AI shift. How do you see your problem space changing in the the next 2–5 years thanks to AI, and what does that mean for you? And given your existing strengths, what can you do to make the most of that shift?

Its important to remember that a lot of the fundamentals of a good SaaS story still don’t change. For instance, a sound distribution strategy is still very much necessary, for without sustainable access to customers, the rest of it is moot. Likewise, you want to be able to protect the access you have to your most valuable asset, your data) and lower the barriers enough for adjacent players to be able to work seamlessly with your offering. All those advantages you have still very much matter. Really, the biggest mental shift you need to make is thinking very deliberately about how the world around you is changing because of AI, and how you leverage those strengths so you continue to have proprietary access to the data you need and become an integral part of that change.

The article is authored by our volunteer Arvind Krishnan, CEO & Founder – Swym Technologies.

Posted on

First AI/ML Playbook Roundtable – Playing With the New Electricity

This is a Guest post by Krupesh Bhat (LegalDesk) and Ujjwal Trivedi (Artoo).

AI is seen as the new electricity that will power the future. How do we make the best of the opportunity that advancements in AI technology brings about? With this thought in mind iSPIRT conducted a symposium roundtable at the Accel Partners premises in Bengaluru on March 10th. Accel’s Sattva room was a comfortable space for 20+ participants from 11 startups. There were deep discussions and a lot of learning happened through subject matter experts as well as peers discussion. Here’s a quick collection of some pearls, that some of us could pick, from the ocean of the deep discussions that happened there.

Products that do not use AI will die soon. Products that use AI without natural intelligence (read common sense) will die sooner.

– Manish Singhal, Pi Ventures

Starting with that pretext, it isn’t hard to gather that AI is not just a promising technology, it is going to be an integral part of our lives in near future. So, what does it mean for existing products? Should everyone start focusing on how they can use AI? Are you an AI-first company? If not, do you need to be one? After all, it does not make sense to build the tech just because it appears to be the next cool thing to do. If you are building AI, can you tell your value proposition without mentioning the word AI or ML? have you figured out your data strategy? Is the need driven by the market or the product?

Before we seek answers we must clarify that there are two types of products/startups in the AI world:

First, an AI-first startup – a startup which cannot exist without AI. Their solution and business model is completely dependent on use of Artificial intelligence (or Machine Learning at least). Some examples of such startups in local ecosystem are Artifacia and Locus.sh.

Second, AI-enabled startup – startups with existing products or new products which can leverage AI to enhance their offering by a significant amount (5x/10x anyone?). Manish has a very nifty way of showing the AI maturity of such companies.

The session was facilitated by several AI experts including Manish Singhal of pi Ventures, Nishith Rastogi of Locus.sh, Shrikanth Jagannathan of PipeCandy, Deepak Vincchi of Julia Computing.

Maturity Levels of AI Startups

After a brief introduction by Chintan to set the direction and general agenda for the afternoon, Manish took over and talked about the various stages of AI based companies. Based on his interactions with many startups in the space, he said there are roughly four growth stages where different companies fall into:

Level 1 – No Data, No AI: An entity that solves a business problem and is yet to collect sufficient data to build a sustainable AI business. The AI idea will die down if the company fails to move to state 2 quickly. Business may be capturing data but not storing it.
Level 2 – Dark data, No AI: The company holds data but is yet to build solid AI/ML capabilities to become an AI company. There is a huge upside for such companies but the data strategy needs to be developed and AI capabilities are not mature enough to be considered as an AI/ML company.
Level 3 – Higher automation driven by data and AI: These are the companies that have built AI to make sense out of data and provide valuable insights into the data using AI/ML, possibly with some kind of human assistance.
Level 4 – Fully autonomous AI companies: These are the companies at the matured stage where they possess AI products that can run autonomously with no human intervention.

Manish also noted that most companies they meet as a VC are in level 1 and 2, while the ideal level would be 3 and 4. He noted that AI comprises of three important components: Data, Algorithm & the Rest of the System that includes UI, API & other software to support the entire system. While it is important to work on all three components, oftentimes, the data part doesn’t get enough importance.

Do You Really Need Artificial Intelligence?

A whole bunch of solutions are smart because they are able to provide additional value based on past data. These are not AI solutions. They are merely rule based insights. Nishith from Locus added that there is nothing really wrong with rule based systems and in a lot of cases AI is actually an overkill. However, there are two cases where it seems apt for startups to look at AI for their predicament:

  1. Data is incomplete: An example of this is Locus who gets limited mapping for gps coordinates and addresses.
  2. Data is changing constantly: A typical case was of ShieldSquare where bots are continuously evolving and improving and the system deployed to identify them also needs to learn new patterns and evolve with them.

It is important to have clarity on your AI model especially when you communicate with your internal teams. Figure out what is the core component of your product – AI, ML, Deep Learning or Computer Vision.

What’s Driving Your AI Approach?

There are two major driving forces that can help one in deciding whether to AI or not to AI.

  1. PUSH: The internal force when decision can largely be taken if your business is sitting on a lot of useful data, may be as a side effect of your key proposition.
  2. PULL: The external market driven force where clients expect or ask for it e.g chatbots. We are already observing that AI can be a great pricing mechanism.

However, take great caution when using Customer data or Derived data, it depends on legal agreement with clients and can get you into legal troubles if it violates any terms.

Is Your Data Acquisition Strategy in Place?

Anyone interested in AI should have a data acquisition strategy in place. Here are a few points that can help you get one in place:

    • What data do you collect, How do you validate it, Clean it and store it for further analysis?
    • Surveys and chatbots can provide a steady stream of data if built correctly
    • Think of data as a separate entity (has its own lifecycle), it may help to think of it as a currency and plan how you would earn, store and utilise it
    • Capturing location, user interaction data can be insightful. This may include the interactions user has committed and the ones they have not committed (deleted/skipped/hidden)
    • It makes sense to invest time, resources and people to gather data properly
    • Have a unified warehouse (can start with economical options like Google Analytics and AWS)

It is also important to give some thoughts on how you are using aggregate data across the platform. In case, if your AI model uses a combination of customer specific data and the sanitised aggregate data available in the platform (“Derived Work”), then you should make sure that you have the permission to use such data. Without such clarity, you may run into legal issues.

Deepak Vincchi explained how Julia Computing is emerging as the programming language of choice for data scientists. The platform can process 1.3 million threads in parallel and is used by large organizations to crunch data problems.

In all this was an extremely engaging 3 hours without break. Guiding the session with real examples by Nishith, Shrikanth and also shared learnings from Navneet and others really helped bring to life Why AI and How AI. This symposium is part of an AI playbooks track was aimed at kickstarting cohorts of startups ready to jump with AI and help them get traction with AI, more will emerge on this shortly.

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10 startups attended this mini-roundtable session – Acebot, Artifacia, Artoo, FusionCharts, InstaSafe, Klove, LegalDesk, Rocketium, Rubique, ShieldSquare.

Thanks to volunteers Rinka Singh and Adam Walker for their notes from the session and Ankit Singh (Mypoolin/Wibmo) for helping coordinate the blog post & note

* All iSPIRT playbooks are pro-bono, closed room, founder-level, invite-only sessions. The only thing we require is a strong commitment to attend all sessions completely, to come prepared, to be open to learning & unlearning, and to share your context within a trusted environment. All key learnings are public goods & the sessions are governed by the Chatham House Rule.

Featured photo by Matan Segev from Pexels https://www.pexels.com/photo/action-android-device-electronics-595804/