Agentic AI: A $267 billion opportunity for partners by 2030

GenAI and Agentic AI will be huge everywhere, and the partner ecosystem is no exception. Hat tips to Jay McBain, Lisa L., and Omdia for this research! Two things that jumped out: * From cloud marketplaces to agent ecosystems, every touchpoint across the industry is being recalibrated. For partners, this means moving beyond project-by-project delivery and into repeatable IP, agent development frameworks, and hyper-specialized vertical capabilities with new, value-based packaging and services. * Agentic AI is evolving as a partner-delivered service; the partners that lean in will capture long-term relevance and value, and those that wait risk marginalization in an increasingly agent-driven market. The $267 billion services opportunity is growing at 35.3% CAGR, making it one of the largest partner opportunities in the next 3-5 years.

#IT #Financial systems will need to evolve quickly to track and optimize micro #pricing and #consumption in a #Agentic #AI ecosystem world. This is why #Nicus and #ServiceNow are partnering in this area to deliver a modern Technology Business Management #TBM,#ITFM and #FinOps experience. See Jay’s excellent post below for details.

🚨 AI isn’t slowing down — it’s accelerating. New research (shared by Jay McBain) highlights the shift from GenAI to Agents, and the massive partner opportunity this creates. Some point to the MIT report on high AI project failure rates — but keep in mind, that study was based on a very small sample. The lesson isn’t to pull back. The lesson is to lean in with the right foundation: strong data readiness, partner collaboration, and upfront planning. 👉 Call to action for partners: Now is the time to double down on building AI practices, refining your go-to-market plays, and preparing your customers for the agent-driven future. The ones who do the groundwork now will be the ones leading when AI adoption hits scale. #AI #Partners #KellyOnAI #ScanSourceAI

How Do AI Companies Make Money? What Should be Your Platform Strategy? How do the top #AI companies make money? 1. #Nvidia: Sells the GPUs, systems and the software to run its hardware. They make a lot of money and invest in many other AI value chain participants. 2. Microsoft: Copilot subscriptions, Azure cloud usage as AI shows up in Office, Windows, Microsoft 365 and devices. 3. Alphabet/#Google: Protect Search (ads), moving developers and users to Gemini, cloud usage, Workspace offerings and usage of Vertex machine learning services. Alphabet has also invested in many AI companies. As OpenAI is disrupting the Google search ecosystem with direct purchases (e.g. direct purchases from Walmart, Shopify,....) and apps access from inside their own platform, the question is what will Google do? Be disrupted or disrupt its own business model at the right time? 4. Amazon: Cloud usage, the only neutral cloud provider, model agnostic. 5. #Open AI: Subscriptions from assistants at scale and enterprise API plans. Transaction fees from direct purchases, access to apps and from big enterprises' consulting. 6. #Anthropic: Enterprise/API usage, consulting fees and all with strong governance features. 7. #XAI: Subscriptions, API access and growing enterprise channels. 8. META: Ads at massive scale; New business through open source Llama. 9. #Mistral: Enterprise/API especially for EU friendly regulatory environment. 10. SAP: ERP Software sales and usage of SAP’s platform services potentially including AI agents. 11. Salesforce: CRM software with AI upgrades, services and #agents coupled to actions and outcomes. 12. #Databricks: Keep AI next to your lakehouse data. Charge for agents where the data already lives. The enterprise strategy for platform products to choose depends on the business nature and a few other parameters like: - #Trust/Governance - Product capabilities - Price - data use limits, opt outs, portability - Roadmap - Closed or open source - Region Most enterprises will land on one core enterprise product e.g. Microsoft 365, Google Workspace, SAP, or Salesforce & one data enabler e.g. Databricks or Fabric and two AI models to choose from OpenAI , Anthropic, Llama, DeepSeek, Qwen or Mistral among others. The open source models are for independence, cost control, and sensitive data. If your core enterprise product include AI model like Microsoft or Google it is easier to use this AI model. Any other criteria to choose for your AI platform components? Any other comments? In the attached you see a list of many AI agents.

Google is taking aim at Microsoft's AI productivity services with the launch of its new Gemini Enterprise platform, aiming to capitalize on its massive AI investments. The platform, which starts at $30 per user per month for Enterprise Standard and Enhanced editions and $21 per user per month for Gemini Business Edition, is designed to compete with Microsoft 365 Copilot. It will enable customers to analyze enterprise data using Google's Gemini platform and access AI agents in a single platform. "Google Cloud's unique advantage is that we provide all the technical layers people need to use enterprise AI at scale," said Google Cloud CEO Thomas Kurian. "Starting with the foundational infrastructure, we provide the infrastructure, including GPUs and our own [Tensor Processing Units], and the Gemini platform is optimized on top of this stack to give you exceptional performance, superior quality, and low latency," he added. Google says its products benefit from several key features, starting with its Gemini AI models. "You need a super-powerful, large-scale language model that can form the brain or nervous system of your interaction with enterprise systems. That's what Gemini is," Kurian said. Gemini Enterprise also includes what Google calls a workbench, which allows users to orchestrate AI agents to automate certain tasks, and a "task force" of pre-built Google agents that customers can use to perform tasks such as deep-dive research on a topic. Google says users will be able to connect Gemini Enterprise to existing data sources, including Google Workspace, Microsoft 365, Salesforce, and SAP. Customers can also track and audit agents to ensure they are functioning properly and using the right data. "What we're doing is bringing AI to every user, providing them with a single portal through which they can communicate with all enterprise data, search for information, and use agents... to perform various tasks on their behalf," Kurian explained. According to Google Cloud's CEO, companies across industries, from consulting services, telecommunications, and hospitality to software firms, are already using Gemini Enterprise for a variety of scenarios, including improving customer service and increasing the productivity of individual teams. But Google's offering isn't the only option. Microsoft also sells its own suite of AI tools, allowing customers to chat with its Copilot bot to search and organize proprietary company data. OpenAI (OPAI.PVT)'s ChatGPT has its own enterprise plan, allowing businesses to use the bot to search, analyze, and organize business data. Anthropic, the company behind Claude, also offers a similar service.

The adoption of agentic AI is no longer a distant possibility. It's here. Together with Google, Deloitte is helping organizations navigate the shift. With the arrival of #GeminiEnterprise, businesses now have a single, secure platform to unify data and orchestrate agents. In this read, we detail how we're leveraging this, along with our 300+ proprietary agents, to drive real business value across industries. Stop debating and start deploying. See how to build a scalable AI adoption journey that quickly translates your data into real business value. #AgenticAI #AIAdoption #BusinessTransformation #FutureofWork #DeloitteAI Matt Lacey Gopal Srinivasan https://lnkd.in/d2Ydt5kz

After 11 months at sea, building AI and fighting enterprise sea monsters, I'm back with some news from afar. Most leaders are operating on flawed assumptions. Here are some truths you need to know. This is a tectonic shift, not a trend. It might be the biggest in our lifetime. It's as big or bigger than the internet or SaaS or cloud. This is a double exponential threat and opportunity. Human brains don't understand exponentials and try to take linear actions. This means that slow linear action or inaction is really "going backwards exponentially faster" relative to your competition who understand what's happening. AI companies (Meta, Microsoft, AWS, Google etc) are spending 20%-40% of annual revenue to build out their AI capabilities. Leading SaaS vendors like ServiceNow and Salesforce are spending 8%-10% to embed AI in their platforms. Non-AI/SaaS "regular company" leaders are spending up to 2.5% of their annual revenue on AI. AI is strategic. Fund it accordingly. Your employees are way ahead of your leadership team but they are probably leaking your company data in free AI tools. The goal is not to lay people off, it's to make them superhuman. Then you can grow revenue without adding headcount. This increases your labor leverage ratio and revenue per employee. Relying only on SaaS apps with AI in them leads to the AI Tool Zoo. It's not bad but it won't bring transformation. Speaking of which, stop penny pinching and buy your team Gemini or Copilot or ChatGPT Enterprise or AITP's EnterpriseGPT. The cost is irrelevant next to the daily efficiencies. For every $3 spent on the technology, invest $7 more for training, enablement, and support or you’ll waste the license spend. Even once your entire company is operational on SaaS AI tools in their office suite (and their other 65 apps), it’s not enough - none of that will drive real ROI. Real AI is not an IT project or SaaS AI rollout, it's a business transformation, therefore it must be led by the business. The only way to drive real ROI is through transformation and process re-engineering. That requires having a private AI platform that you control and that does not lock you into any foundation model vendors. There are only a small set of companies with the team, rigor, discipline, and knowledge to build their own private platform. It’s an $8M-$9M 3 yr investment with a high likelihood of failure. Like in prior waves, the only way to navigate this chaos is with a partner who can handle the velocity of change of the underlying stacks and systems. Even if you find the right partner and build your platform that can let your team build hundreds or thousands of assistants and agents, it will be tough. Production platforms and agents are 1000x harder than POC ones. We'll unpack each of these in the coming weeks. These are just off the top of my head, there are many more things to share and discuss. For those of you out there building, what else would you add?

This is absolutely spot on. 👏 AI isn’t another hyped up tech cycle. Okay, there is a lot of hype, but it’s a fundamental rewiring of how orgs create value. A structural shift in how work happens, not just how software runs or where data is stored. The view that real transformation starts inside the business, not in IT, nails it. The biggest risk isn’t lack of ambition, it’s leaders jumping into AI without grounding it in strategic intent of the organization. Every AI initiative should start with a hard look at business strategy and objectives: 👉 What is the org actually trying to achieve? 👉 How does this help the org compete differently? 👉 What processes are still around because “that’s how we’ve always done it”? Without clarity on alignment to objectives, the best models and tools will become expensive toys (no joke: a CEO avatar that delivers company updates). But when AI is used to rethink the work itself and challenge legacy workflows, decision loops, and operating norms, the compounding ROI kicks in. AI isn’t about replacing people; it’s about reimagining the systems they work within. The organizations that thrive will be the ones disciplined enough to connect AI R&D to strategy, and brave enough to rebuild what no longer works. Couldn’t agree more with Troy's call to arms. It’s time to move from playing with tools to changing how business gets done. What would I add? A process mindset that forces alignment between AI use cases and business objectives. Because if it’s not advancing the org's strategy, it’s just noise before the defeat. #AIBusinessStrategy #MarketingModernization #AILeadership #FutureOfWork #StrategyExecution

Agentic AI is officially here - but does that mean SaaS is dead? 𝐍𝐨𝐩𝐞; 𝘣𝘶𝘵 𝘵𝘩𝘦 𝘳𝘦𝘢𝘴𝘰𝘯 𝘪𝘴 𝘮𝘶𝘤𝘩 𝘮𝘰𝘳𝘦 𝘴𝘪𝘯𝘪𝘴𝘵𝘦𝘳 𝘵𝘩𝘢𝘯 𝘺𝘰𝘶 𝘮𝘪𝘨𝘩𝘵 𝘵𝘩𝘪𝘯𝘬... Agentic AI is brewing a perfect storm that has SaaS giants rubbing their hands with glee. This isn't about innovation. It's about 𝐭𝐡𝐞 𝐢𝐧𝐞𝐯𝐢𝐭𝐚𝐛𝐥𝐞 𝐜𝐨𝐥𝐥𝐢𝐬𝐢𝐨𝐧 𝐨𝐟 𝐟𝐢𝐯𝐞 𝐟𝐨𝐫𝐜𝐞𝐬 that could lock enterprises into their legacy stacks for another decade. 𝐅𝐨𝐫𝐜𝐞 𝐎𝐧𝐞: 𝐅𝐢𝐧𝐢𝐭𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐂𝐚𝐩𝐚𝐜𝐢𝐭𝐲 Organizations can only transform so much simultaneously - 40% of dev resources already go to maintenance (𝘔𝘤𝘒𝘪𝘯𝘴𝘦𝘺). Even the best organised teams have to make tradeoffs - 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘴𝘢𝘵𝘪𝘰𝘯 𝘪𝘴 𝘰𝘧𝘵𝘦𝘯 𝘧𝘪𝘯𝘢𝘯𝘤𝘪𝘢𝘭. 𝐅𝐨𝐫𝐜𝐞 𝐓𝐰𝐨: 𝐓𝐡𝐞 𝐑𝐎𝐈 𝐂𝐡𝐚𝐬𝐦 AI delivers 333% ROI with <6 month payback; legacy modernization sees 70-95% failure rates with up to 200% cost overruns. Your board will choose AI rollout over architectural cleanup 𝘦𝘷𝘦𝘳𝘺 𝘴𝘪𝘯𝘨𝘭𝘦 𝘲𝘶𝘢𝘳𝘵𝘦𝘳. 𝐅𝐨𝐫𝐜𝐞 𝐓𝐡𝐫𝐞𝐞: 𝐀𝐠𝐞𝐧𝐭𝐬 𝐚𝐬 𝐔𝐬𝐞𝐫𝐬, 𝐍𝐨𝐭 𝐑𝐞𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭𝐬 The fastest path to AI value isn't agents replacing legacy SaaS systems; it's making agents 𝘶𝘴𝘦𝘳𝘴 of them. As agents paper themselves across your SaaS stack, the underlying systems become invisible, locking you into dependencies 𝘺𝘰𝘶 𝘤𝘢𝘯 𝘯𝘰 𝘭𝘰𝘯𝘨𝘦𝘳 𝘴𝘦𝘦, 𝘮𝘢𝘯𝘢𝘨𝘦, 𝘰𝘳 𝘯𝘦𝘨𝘰𝘵𝘪𝘢𝘵𝘦. 𝐅𝐨𝐫𝐜𝐞 𝐅𝐨𝐮𝐫: 𝐄𝐱𝐢𝐬𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐏𝐫𝐞𝐬𝐬𝐮𝐫𝐞 Early adopters are already capturing 30-40% productivity advantages. When your competitors deploy effective agentic strategies, responding might not simply be strategic - 𝘪𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘴𝘶𝘳𝘷𝘪𝘷𝘢𝘭 𝘢𝘵 𝘢𝘭𝘭 𝘤𝘰𝘴𝘵𝘴. 𝐅𝐨𝐫𝐜𝐞 𝐅𝐢𝐯𝐞: 𝐓𝐡𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐓𝐢𝐦𝐞 𝐁𝐨𝐦𝐛 Gartner predicts 40%+ of agentic AI projects will fail by 2027 - but the ones that succeed will run for years, workflows evolving at machine speed, without human oversight. By 2028-2029, you could face 𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘤𝘪𝘦𝘴 𝘯𝘰 𝘰𝘯𝘦 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵𝘦𝘥 𝘢𝘯𝘥 𝘳𝘪𝘴𝘬𝘴 𝘯𝘰 𝘰𝘯𝘦 𝘤𝘢𝘯 𝘲𝘶𝘢𝘯𝘵𝘪𝘧𝘺. --- 𝐀𝐧𝐝 𝐭𝐡𝐞 𝐒𝐚𝐚𝐒 𝐠𝐢𝐚𝐧𝐭𝐬 𝐤𝐧𝐨𝐰 𝐭𝐡𝐢𝐬. Forrester calls it "the end of discounting era" - vendors are using AI integration to eliminate competitive pricing and deepen customer lock-in. That's right; they're betting on your architectural paralysis. 𝐈𝐭'𝐬 𝐮𝐩 𝐭𝐨 𝐮𝐬 𝐭𝐨 𝐩𝐫𝐨𝐯𝐞 𝐭𝐡𝐞𝐦 𝐰𝐫𝐨𝐧𝐠. I honestly feel we need to look closer at this. The research I've been doing - academic studies, CIO surveys, historical precedents - is painting a troubling picture, but also revealing what the smart architects are doing 𝘯𝘰𝘸 to avoid the trap. More next week. Where are you already seeing this unfold? Which system in your stack has become mysteriously "untouchable"? #EnterpriseArchitecture #AgenticAI #TechnicalDebt #DigitalTransformation

Salesforce stock down 26% while S&P rose 13%. So they're hedging with OpenAI and Anthropic partnerships. Is this strategy or survival? Even they don't know which AI will win enterprise. They just hedged their AI bets. At Dreamforce 2025, Salesforce announced expanded partnerships with both OpenAI and Anthropic, embedding GPT-5 and Claude directly into their new Agentforce 360 platform. You can now query Salesforce data, build Tableau visualizations, and even sell products through ChatGPT's interface, all while keeping your customer data under Salesforce's control. The OpenAI integration taps into ChatGPT's 800 million weekly users, letting companies access Salesforce records directly through the world's most popular AI assistant. The Anthropic partnership targets regulated industries like financial services and healthcare, where Claude's enterprise-focused safety approach matters more than raw performance. 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Salesforce stock is down 26% this year while the S&P 500 rose 13%. Analysts are calling Agentforce revenue "modest" despite the company claiming 12,000 customers. And they're facing intense competition from Google's Gemini Enterprise at $30/user and Microsoft's massive OpenAI investment. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Salesforce perhaps isn't picking winners- they're covering their bases. By integrating both OpenAI and Anthropic, they're essentially saying "we don't know which AI will dominate enterprise, so we're betting on both." This multi-model strategy mirrors what we're seeing across the industry. Microsoft diversified beyond OpenAI to include Anthropic. IBM chose Claude for development tools. Nobody wants vendor lock-in when the AI landscape is changing this fast. CEO Marc Benioff's vision of "agentic enterprises" where AI elevates rather than replaces workers sounds great. But the stock performance and modest revenue numbers suggest the market isn't convinced yet. Enterprise customers are testing AI agents, not deploying them at scale. At Sales Crusader, we're watching this enterprise AI convergence closely because it validates what we've been saying: the future isn't one AI dominating everything. It's multiple specialized models working together. Whether it's AI voice notes, AI video messages, or AI reply automation, we're building with that multi-model reality in mind from day one. The question isn't which AI will win enterprise. It's whether any single AI can win when the smartest companies are refusing to pick just one. 🎯 Stay Amplified. -RM #Salesforce #OpenAI #Anthropic #EnterpriseAI #AI #Agentforce #SalesCrusader