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The Great Spending Showdown: AI vs SaaS in 2025/2026 — What Every B2B Leader Needs to Know We’re witnessing the most dramatic shift in enterprise tech spending since the cloud migration began 15 years ago. The answer today is: very few. But that could change rapidly as AI tooling improves and more case studies emerge.
Cutting Through the Noise: Three Gen AI Pioneers Reshaping Enterprise Technology In a pivotal moment for generative AI, Vanessa Larco, partner at NEA, brought together three visionary CEOs convened at SaaStr Annual to share insights that are redefining the technological landscape.
The harsh reality: Most enterprises are adopting AI due to FOMO (Fear Of Missing Out) rather than for specific business outcomes. The actual tech stack matters. Yet there’s a massive gap between interest and implementation.
” I saw a term sheet the other day where a leading VC firm reserved $1m of the round … for hiring a “VP of AI” Leadership teams scrambling to post job descriptions for “Head of ArtificialIntelligence.” ” Recruiters cold-calling anyone with “machinelearning” on their LinkedIn.
This change is driven by advances in AI technology and changing customer expectations. The panelists emphasized that vertical software is particularly well-suited for AI implementation due to its contained workflows and specific use cases.
But perhaps more impressive than these numbers is how Co-Founder and CTO Arvind Nithrakashyap has positioned the company at the intersection of two of enterprise software’s most critical trends: cybersecurity and artificialintelligence. And more on Rubrik here: 5 Interesting Learnings from Rubrik at $1.1
Early movers who consolidate now will have cleaner data and faster cycles when the technology catches up. Timeline Reality : True end-to-end platforms are 12-18 months away from market maturity. AEs Want to Sell, Not Admin—AI Will Handle the Rest The Hard Data : Average AE spends only 28% of their time actually selling. The other 72%?
Two of the most misunderstood concepts in AI for B2B and SaaS aren’t technical—they’re human. “Human-in-the-loop” and “AI orchestration” have been watered down into feel-good phrases that make AI adoption sound effortless. The reality? It’s actually an organizational capability problem.
Artificialintelligence is everywhere from smart content generators to coding assistants and its changing how SaaS products are built and marketed. Terms like LargeLanguageModel (LLM) and AI tool often get tossed around interchangeably, but they arent the same thing. and What is LLM orchestration?.
It’s already enabled us to: Dramatically improve our content Review 200+ pitches for SaaStr Fund Review 700+ content submissions for SaaStr Annual Review 150+ sessions for SaaStr Annual And so much more. Content creation, data analysis, customer communications, pitch deck review—all truly transformed.
” When I dug into it, they hadn’t reviewed a single AI-generated email in 3 weeks. But humans still: Define the target criteria Review and approve all messaging Handle all follow-up conversations Manage complex deal progression Provide continuous feedback for improvement The AI handles the high-volume, repeatable tasks.
The generative AI revolution has driven explosive growth in LargeLanguageModel (LLM) applications. more efficiently, developers rely on LLM orchestration frameworks. What Is an LLM Orchestration Framework? This Retrieval-Augmented Generation (RAG) pattern enhances accuracy on private or large documents.
” and your AI will deliver a perfect technical answer instantly. QBRs and Check-ins Will Be AI-Driven AI will take over the quarterly business review and regular check-in calls. Meanwhile, mediocre reps who neither master the technology nor offer exceptional relationship skills will become obsolete.
This isn’t just a comeback story—it’s a masterclass in how enterprise software companies can reinvent themselves when new technological waves create fresh demand for their core capabilities. And it’s being driven by one primary factor: artificialintelligence. What Other SaaS Companies Can Learn 1.
Close behind, 40% are using technology like AI to automate key tasks. The technology integration game has changed. Thirty-seven percent of leading CMOs are integrating advanced technologies, including AI, to enhance efficiency. This isn’t just about having better dashboards. of budgets.
Largelanguagemodels (LLMs) like GPT-4, Claude, and open-source equivalents are now powering new featuresfrom intelligent chatbots to automated content creation. However, simply wiring LLM APIs into your application can create complexity. In effect, it makes managing multiple LLMs predictable and reliable.
Retrieval-Augmented Generation (RAG) is a cutting-edge approach in AI that combines largelanguagemodels (LLMs) with real-time information retrieval to produce more accurate and context-aware outputs. Think of a standard LLM as a very smart student who has learned a lot of general information.
Mike Valdepenas, Senior Director, Portfolio Management “What trends in data modeling are you most excited about, and how does data impact risk management in payments?” Mike’s key takeaway: Data modeling has become a cornerstone of effective risk management. Explore risk and compliance advice for platforms.
For SaaS and tech firms, the message is clear: mastering the full-stack AI (data pipelines, compute, ML models and application layers) is critical for innovation and growth. This confirms that mastering the full AI stack is a strategic priority for tech companies everywhere. Notably, user reviews of U.S.-born poured $109.1
Linear’s AI issue triaging doesn’t just categorize tickets—it predicts resolution time, suggests optimal assignees, and auto-generates technical context that saves engineering teams 2-3 hours per sprint. Invisible intelligence that makes your product fundamentally better. And that there isn’t “stealth churn” in your base.
The technology is amazing, but our assumptions and processes for understanding and leveraging AI metrics are very different from traditional support metrics. For better or worse, CSAT was a small enough sample size to review every comment – particularly unhappy ones. But what do you do with 1,600+ reviews across the org?
Hackers work diligently to identify application weak spots, and once found, steal app access privileges , intellectual property, credit card data, customer lists or other sensitive data. For example, the app might have security technology or processes that dont meet your companys requirements. Security vulnerabilities.
Early customers are often innovators and tech enthusiasts willing to try new solutions, even if the product is incomplete or buggy. It specializes in creating personalized shopping experiences for customers by leveraging machinelearning and AI technologies. At this stage, startups face significant uncertainty.
Theyve been writing market reports for years as the pioneer of tech adoption and market insights. They led a several hundred person team that ran the predictive machinelearning that personalized the Yahoo homepage. It includes a breakdown of the Sales AI landscape, adoption of GenAI and Sales software across buyer groups.
AI’s True Power in Finance : The biggest unlock of AI isn’t just automation, but its ability to reason over complex financial data, freeing finance teams from hundreds of hours reviewing ledger records. “The biggest fallacy in tech is that the only way to scale is through hiring and delegating,” notes Nangia.
Artificialintelligence (AI) tools are becoming more and more prominent, and many have functions specifically designed to help marketers better promote their products and services. How AI Works With Marketing: 9 Use Cases Because AI is a newer technology, you might not be sure how it works with marketing quite yet.
Tools like ChatGPT, Google Gemini, Claude and others (logos shown above) use advanced largelanguagemodels (LLMs) to generate text, images, and speech. We based our choices on criteria like ease of use, innovation, performance, customer reviews, and value for money. For example, we looked at benchmark tests (e.g.
Designed for non-technical users. HubSpots automation is often praised for allowing non-technical users to set up triggers and actions in minutes. The integration experience on HubSpot is often noted as very user-friendly and feels more curated and easier to navigate than Salesforces, which can be overwhelming due to sheer volume.
I’ve been exploring various AI tools, mostly as a hobby with a few professional POCs mixed, for almost a decade now and the technology has been through tons of ups and downs, but the volatility due to the flood of everyone trying to get in on the AI game from the last few years has been something different. Let’s get started!
Working with limited resources and keeping up with emerging tech are the biggest challenges for game developers today. From cloud-based applications to emerging technologies, SaaS platforms give you instant access to whatever you need for your game development project. On the other hand, not all SaaS tools have the same learning curve.
The promise of ArtificialIntelligence isn’t just about futuristic possibilities, it’s about present-day competitive advantage. AI models, algorithms, and applications evolve at a dizzying speed. A budget set today for a technology that will be vastly different in 12 months is inherently flawed.
FastSpring continuously monitors transaction flow via machinelearningmodels that are under the oversight of an enterprise-grade team of infrastructure and payments experts. We review expected spikes such as season resets, character drops, or regional promotions and align our infrastructure plans accordingly.
For the C-Suite of Today’s Competitive B2B Technology Landscape: Feature-driven product development is no longer enough. Todays B2B Technology customers demand tangible, measurable outcomes. The future of B2B Technology belongs to those who can define and deliver measurable value. Will you lead the way?
This G2 review shows that another user has experienced this gap: “The main problem with Segment is the analysis experience… if I want to see the conversion funnel of users who have activity 1 and only then proceed to activity 2, Twilio Segment does not have that functionality.” This composable model gives us a flexible PLG tech stack.
Payment processor – Handles the technical aspects of the payment. Integration capabilities Since you probably have other tools in your tech stack, you dont want to keep switching tabs or windows to reconcile invoices or transfer data. On top of that, regularly review your systems transaction logs and reconcile transactions.
This is called shadow IT and its a constant battle, but theres a new, more sophisticated invader on the scene: ArtificialIntelligence (AI). Enhanced monitoring, user education, AI discovery tools, and established vendor management practices are all crucial for successfully finding AI in your tech stack.
The Tactical Insight : Smart companies run dual AI strategies—cheaper models (like DeepSeek) for internal productivity, premium models (GPT-4, Claude) for customer-facing features. The report notes cost has jumped dramatically in importance due to “commoditization of the model layer with the rise of more cost-efficient models.”
Internet’s 23 years to reach this level) Why This Matters for B2B: Unlike previous tech waves that started in Silicon Valley and slowly diffused globally, AI hit the world simultaneously. ” – it’s the biggest infrastructure buildout in tech history. AI use will be part of what we evaluate in performance reviews.
While those consumer innovations may still be a few years away, 2025 is sure to be a year of creativity and change in the B2B technology space, particularly in customer success (CS). I, for one, cant wait to see what creativity and technology combined come up with in that regard. Which of these strategies will you implement this year?
Prompt Engineers Don’t Need to Be Technica l Some of Gorgias’ most effective prompt engineers came from customer service backgrounds rather than technical roles. Their deep understanding of customer interactions and support scenarios proved more valuable than technical expertise when it comes to crafting effective AI responses.
SaaS tools are fantastic, but keeping your tech stack from turning into a financial snowball can be tough. ets break down smart SaaS budgeting , so you can make your tech work for you, without blowing the bank. Therefore, shadow AI is just another thing to keep in mind when taking inventory of your tech stack.
” – Ron Gabrisko, CRO, Databricks Databricks scaled from under $1 million to over $3 billion ARR with a predominantly technical sales organization. .” ” – Ron Gabrisko, CRO, Databricks Databricks scaled from under $1 million to over $3 billion ARR with a predominantly technical sales organization.
” His real-world example is that when he gets a contract to review as the CBO of Perplexity in a buying role, he doesn’t immediately send it straight to legal anymore. Sales teams that comprehend the technical architecture of any buyer. Pipeline Reviews Became Data-Driven (Finally) This one hit us hard.
These marvels are powered by LargeLanguageModels (LLMs) giant AI systems trained on vast amounts of text. In todays AI-powered world, LLMs have become the brain behind many smart applications, from virtual assistants to content generators.
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