How New AI Functionality is Getting Priced Q&A Part 3 More responses to questions from the May 23 webinar by Mark Stiving, Ph.D. from Impact Pricing LLC and Steven Forth from Ibbaka. We respond to questions on costs, metrics, valuations and what a move to continuous update and reporting will mean for pricing. https://lnkd.in/gwDKJneY #AI #pricing #SaaS #B2B #generativeAI
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B2B SaaS companies need to work out how they will price generative AI based innovations. A lot of money is being invested in this and it will only be sustainable if part of the value being created can be captured in price. https://lnkd.in/gWykNdCf #AI #pricing #innovation #AIpricing
AI Pricing: Shaping the Future of Business Strategies I Ibbaka
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More than 70% of B2B SaaS companies are investing in AI but only 15% have begun to monetize these investments. This is not sustainable. What questions should boards be asking about AI in 2024 planning? https://lnkd.in/g7pFT65k #AI #investment #governance #boardofdirectors #pricing #SaaS #AImonetization
AI Pricing: Questions Boards Need to Ask I Ibbaka
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More than 70% of B2B SaaS companies are investing in AI but only 15% have begun to monetize these investments. This is not sustainable. What questions should boards be asking about AI in 2024 planning? https://lnkd.in/ganZ4sNE #AI #investment #governance #boardofdirectors #pricing #SaaS #AImonetization
AI Pricing: Questions Boards Need to Ask I Ibbaka
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How will your firm monetize AI in 2024? What is the key way you will measure the value you are creating with AI? What approach will you take to pricing? We are seeing some very different strategies emerge. What is your company's approach? https://lnkd.in/gfQAVVc5 #AI #AIpricing #pricing #innovation #SaaS
AI Pricing: 2024 will be a year of AI Monetization I Ibbaka
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Only two more days until my webinar with Mark Stiving, Ph.D. on how companies are pricing their AI based innovations. Sign up and we will send you the recording and some additional resources! Pricing AI based innovation is the most pressing question for many companies. https://lnkd.in/gANB5Av5 #pricing #AI #SaaS #innovation #innovationpricing
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🧠 Many firms still see no concrete use case for generative AI, according to a NetSuite customer, even as the firm works to expand the use of foundation models across its offerings. 💸 ITPro's Rory Bathgate caught up with Fraser MacKenzie, CTO at Buster + Punch, a home fashion brand and NetSuite customer, to discuss the long-term value he sees in the technology. Read more here. #GenerativeAI | #SuiteWorld2023 | #NetSuite
SuiteWorld 2023 proves customers still don't know what they're supposed to do with generative AI
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Current chatbots have a last-mile problem. They read and regurgitate existing knowledge but can’t take action on dynamic data. If a customer of an e-commerce or SaaS tool comes with a query about a feature not working. AI should be able to fetch the user’s data and solve the problem without involving a human customer service rep. Some companies have to have both- which negates the whole point of subscribing to a chatbot. Now some complex feature bugs might not be completely solvable by AI but for a start, it can single-handedly execute the famous refund policy most SaaS tools have. If you have a no-questions-asked 7 days refund policy and a customer comes within 7 days to get his refund, an AI capable of handling dynamic data should: - fetch his email - check if the user’s subscription exists and the customer ID - check if the request is actually within 7 days - and initiate a refund Without a human customer service rep.
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My favourite thing about Clay’s AI agent 👇 It tells you where it finds information… And how confident it is about the output. Picture this... → You have a list of prospects (in this case, visitors of my website, picked up by RB2B) → You want to classify them by "type of business": - Agency - SaaS - E-commerce - Other Thus, you write a short prompt: Claygent browses the websites… …and starts classifying them. Here’s the issue; It’s not always straightforward to segment business models. The “fix”? Looking at Clay’s AI agent: [1] reasoning & [2] confidence score. You can then decide to trust its output above a certain confidence score, And manually check the reasoning if it’s below. Curious how else do you use it?
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SaaS is DEAD...... Of course it is NOT. #AI will replace it all. Not quite as a generalized statement. What a fantastic episode that dropped last week from Harry Stebbings with one of the best AI leaders out there Alexandr Wang So much to cover there but the primary thing from me was data for models has hit a wall and now "Frontier" data as Alex says is what is needed. Deep, complex, chain of thought reasoning tasks and so much more. Want to slap a chatbot UI on your app? Tablestakes at this point. It isn't to make it work properly and accurately but the simple function is. Ensuring you are becoming agentic is the new oil. Go read Raghvender Arni last few posts on this. A quick visual below from crewAI as they are building something pretty cool. Agents are going to make so much more possible. Be agentic so you win. So what does all of this mean for GTM? A lot....here are 5 key items to start: 1. You can now finally build an architecture that can sell "outcomes". If you read what Aaron Levie wrote this past weekend on this relative to seat based licensing you will see a massive TAM explosion. Everyone wants to "sell" a hard ROI based on an "outcome". Everyone also wants to "buy" that. But has this really been true? Not in a widespread manner. This is changing fast - so better the pricing model (seats or otherwise). 2. Content - Can't just explain why your AI is better. Why you are delivering the outcomes? I met with someone doing some wild things in Oil and Gas last week. Deep domain expertise delivering 10s of millions of hard ROI and 7 figure deals to match. Infuse your domain, process, architectural and marketing experts together and DO NOT use "just" AI to produce it. Remember - if you sell outcomes, you get more money. If you get more money on outcomes, the customer gets more value. This is a winning relationship. Both sides deserve this. We are here. 3. Systems to manage GTM in this world. Forget the myraid of ways having an agentic GTM "stack" would be amazing (finally we don't have to rely on an expensive database called the CRM no one likes) but how do you use all of this information to get the right "tiggers" for your ICP and personas? Much more available to us now. If you don't use this automation you will lose. 4. GTM Strategy. Perhaps this will involve some value added services. Some hate this. Some love it as it makes you sticky. If it is high margin, high value and delivers the outcomes - does it matter? Is it just pre-sales and part of your S/M expenses? Maybe but then the personas you hire are different. Lots to ponder - no right or wrong in any of this. 5. Recruiting - In the next evolution that what is your talent strategy? Some nail this now. Some use amazing partners. Investors are amazing at intros but you won't be able to rely just on that as competition is fierce and many of these "roles" are changing as we speak. Same old playbook won't work. Happy Monday.
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B2B SaaS companies everywhere are embracing new and emerging customer value opportunities brought about by artificial intelligence and machine learning (AI/ML), particularly due to awareness and accessibility brought about by generative AI. AI/ML isn’t exactly new, but as it enters the mainstream, SaaS product managers wonder what this means for embedded analytics…do they work separately or in harmony? Embedded Analytics has traditionally been about navigational journeys that lead the user to root-cause and explain why something has occurred…Answering Questions. AI/ML makes it possible for SaaS solutions to determine, without human intervention, actions that might be carried out automatically…Automating Answers. If the SaaS solution answers every question automatically, why do I need analytics? 2 primary reasons: Firstly, not every answer has been automated and it’s likely an open debate whether that will be the case for the foreseeable future. Should we automate democracy, for instance? Secondly, if every answer had been automated, wouldn’t we want some oversight and help in fine-tuning AI/ML and adjacent models for optimal efficiency? AI/ML and embedded analytics are partners-in-value for your B2B SaaS solution. Innovative product and development teams think about them together, not as if they are mutually exclusive. #b2bembeddedanalytics #b2bSaaS #SaaSProductManagement #SaaSAnalytics
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