Daniel Saks
Chief Executive Officer
The year 2025 is widely seen as a tipping point for agentic AI – artificial intelligence with genuine agency to act autonomously – especially in California’s thriving tech landscape. For the first time, machines are taking part in actual business decisions within organizations, as companies move beyond pilot projects into practical implementations. We’ve entered the era of AI agents, enabled by breakthroughs in AI reasoning and memory that let systems solve problems in new proactive ways(7). Major enterprises and startups alike are embracing this shift. In fact, more than 51% of companies have already deployed AI agents in some capacity, and another 35% plan to do so within two years – meaning 86% adoption by 2027 is expected(5). Executives are bullish on the technology’s value: 62% of companies anticipate over 100% ROI from agentic AI, with an average expected return of 171% on investment(5). These high expectations are grounded in experience – early generative AI deployments delivered ~152% ROI on average, paving the way for even greater impact from autonomous agents(5).
Several converging factors make 2025 a pivotal year for agentic AI in California. Widespread adoption is accelerating across industries: nearly 48% of all new business AI use cases in 2023 came from revenue operations – more than any other department(6) – signaling that AI “agents” are rapidly moving into core business functions. By 2024, over half of the total value from AI was already being realized in operations, sales, and marketing use cases(6). California’s tech community is seizing this momentum. For example, the Berkeley Center for Responsible Decentralized Intelligence is hosting an Agentic AI Summit 2025 with over 1,500 in-person attendees to “shape the future of AI and AI Agents”(2) – a landmark gathering that underscores how significant autonomous AI has become. At the same time, major tech conferences like Microsoft Build 2025 have put a spotlight on AI agents: Microsoft announced that more than 230,000 organizations (including 90% of Fortune 500 companies) have used its Copilot tools to build custom AI agents and automations(7). The vision of an “open agentic web” of interoperable AI agents performing tasks for users is quickly moving from concept to reality(7).
Crucially, California is not just any backdrop for this revolution – it’s the epicenter. The state’s unique blend of world-leading AI talent, enterprise investment, and innovative culture means many breakthroughs are being pioneered in California first. From Silicon Valley corporations rolling out agentic AI features in their products, to Hollywood studios exploring AI-driven creative assistants, to San Francisco retail giants envisioning AI shopper agents, 2025 is the year these once-hyped ideas are being executed on a broad scale. As Deloitte observed, agentic AI is driving a fundamental and rapid shift in how work gets done, one that tech leaders expect to surpass even the cloud or mobile revolutions in speed and impact(10). In short, 2025 in California represents an inflection point where autonomous AI moves from the periphery to center stage in business. The remainder of this article will explore what exactly agentic AI is, why California’s ecosystem is leading the charge, real use cases in go-to-market operations, and how organizations can capitalize on this autonomous business revolution.
What do we mean by agentic AI, and how is it distinct from the traditional AI tools that businesses have used up to now? Agentic AI – sometimes called autonomous AI – refers to AI systems endowed with a level of agency: they can independently design, execute, and optimize workflows without step-by-step human instruction(1). In other words, an agentic AI doesn’t just generate content or predictions when prompted; it can plan actions, make decisions, and carry out tasks in pursuit of a goal. This recent emergence of AI “agents” marks a dramatic shift toward more autonomous decision-making entities capable of complex reasoning and action(1).
This concept differs fundamentally from traditional AI or generative AI tools. In the past few years, businesses became familiar with generative AI (GenAI) models – like GPT-3/4 or DALL·E – which excel at producing content (text, images, etc.) in response to prompts. Those tools are powerful assistants, but they are largely passive: a human must prompt them for each output, and they don’t remember or act beyond that single prompt-response cycle. By contrast, agentic AI is active. It leverages advanced AI (often built on large language models, knowledge graphs, and reinforcement learning) plus integration with data and systems to not only answer questions but to take initiative. As one AI expert describes, it’s about moving from passive content generation to active problem-solving intelligence, using architectural building blocks like retrieval-augmented generation (RAG) to give AI access to real-time data and let it continuously learn(1). An agentic AI can, for example, detect a significant change in business data, decide that it warrants a response, formulate a plan, and then execute that plan by interacting with software or people – all with minimal or no human intervention in that loop.
Another way to understand the difference is to compare the scope of autonomy. A traditional AI tool might automate one step in a process (e.g. automatically categorize an email, or generate a draft report when asked). An agentic AI, in contrast, could manage an entire workflow: for instance, observing incoming customer emails, prioritizing them, crafting personalized responses, sending replies, following up after a delay, and escalating to a human only if unusual conditions are met – all according to goals and rules it has been given. These AI agents are goal-driven rather than just task-driven. They maintain an internal state or memory to handle multi-step operations and use feedback from outcomes to refine their approach over time.
To illustrate, consider the distinction in marketing content creation:
Indeed, agentic AI is often built on top of generative AI components, but adds layers of planning, memory, tool integration, and self-direction. As Prasad Venkatachar (IEEE Silicon Valley chapter) puts it, agentic AI systems are capable of “planning, reasoning, and taking actions in complex environments,” whereas prior AI was mostly confined to producing outputs when asked(1). This autonomy is why many refer to agentic AI as the next evolution beyond the era of chatbots and static machine learning models. Importantly, agentic agents operate under constraints and objectives set by humans – they’re not conscious or truly independent – but within their delegated domain they can function with a remarkable degree of self-sufficiency.
In practical terms, California’s tech industry differentiates agentic AI solutions by their ability to proactively drive outcomes. Traditional AI tools might provide insights (“our model predicts these leads are high-quality”). An agentic AI will go further and act on those insights (“the AI agent automatically contacts those leads, schedules meetings, and updates the CRM”). This autonomy requires robust orchestration capabilities, access to enterprise systems (via APIs, etc.), and guardrails to stay aligned with business rules. We will see next how California’s unique ecosystem has nurtured the development of these sophisticated AI agents.
It’s no coincidence that California is at the forefront of the agentic AI movement. The state boasts a uniquely rich innovation ecosystem that has allowed agentic AI to flourish rapidly in 2025. Several factors contribute to California’s leadership:
In sum, California’s ecosystem provides the perfect storm of talent, capital, infrastructure, and culture for agentic AI to thrive. The state continues to dominate the AI sector in 2025, not only leading in sheer number of top companies and research output(4), but also setting the pace on how AI is adopted in business. As we look next at concrete use cases, it’s clear that many of the most advanced deployments of agentic AI – especially in go-to-market, marketing, and revenue operations – are being spearheaded by California-based firms.
One of the strongest validations of agentic AI’s promise is the explosion of real-world use cases in go-to-market functions – spanning marketing, sales, and revenue operations. In California’s tech-forward companies, autonomous AI agents are already reshaping how organizations attract customers, close deals, and optimize revenue processes. Below, we explore some of the high-impact use cases where agentic systems are transforming GTM, marketing, and RevOps in 2025:
It’s worth noting that these use cases illustrate a broader trend: RevOps and marketing functions are shifting from manual, human-intensive processes to augmented or even autonomous workflows(6). The agentic operating models learn and improve continuously, creating a virtuous cycle of efficiency the longer they run(6). Importantly, the most successful deployments still involve humans in the loop in strategic ways – for oversight, creative input, and handling exceptions – but the heavy lifting of data processing and routine decisions is increasingly offloaded to AI agents.
One California company leading this transformation is Landbase, a San Francisco-based pioneer in agentic AI for go-to-market. Landbase’s flagship platform, GTM-1 Omni, is the world’s first AI action model purpose-built for GTM automation(14). This agentic AI platform deploys specialized GTM agents that work alongside marketing and sales teams to accelerate growth. Trained on an enormous dataset of over 40 million B2B sales and marketing interactions and 24 million companies, GTM-1 Omni can autonomously execute campaigns across email, ads, and other channels(13). It has already been used to automate thousands of marketing campaigns across industries, reducing campaign costs, speeding up execution, and improving conversion rates for Landbase’s clients(13). For example, if a client wants to target a new vertical, Landbase’s AI agents can identify the ideal prospect accounts, craft tailored outreach sequences, run the campaign, and optimize it in real-time – delivering qualified opportunities to the sales team without the typical weeks of manual effort. Landbase (fueled by $12.5M in recent funding) is a prime example of a California startup translating agentic AI theory into tangible business results(14). Their early success showcases how focusing agentic AI on GTM workflows can produce immediate ROI by driving revenue outcomes autonomously.
The above use cases barely scratch the surface – agentic AI is also being applied in areas like customer support (automated Tier-1 agents resolving issues), finance (AI agents optimizing pricing and credit decisions), and even creative fields (AI content agents generating and A/B testing ad creative). But marketing, sales, and RevOps are clearly at the forefront in 2025. It’s telling that RevOps is predicted to be the first function fully taken over by agentic systems in the coming years(6). In the next section, we’ll look at the broader business impact of these deployments and some concrete results organizations are seeing from embracing agentic AI.
Agentic AI is no longer a futuristic concept – it’s delivering real business results for organizations, particularly in California’s tech-savvy market. Companies that have embraced autonomous AI agents in their operations are reporting measurable improvements across key performance indicators. Here, we highlight some of the real-world impact attributed to agentic AI deployments:
Perhaps one of the most compelling pieces of evidence for agentic AI’s impact is how widespread its adoption is becoming among forward-thinking firms. A recent industry survey of IT and business leaders found that 94% of organizations believe they will adopt agentic AI faster than they adopted generative AI in the past, given the lessons learned(5). The learning curve with GenAI has smoothed the path; companies are hitting the ground running with autonomous agents. Furthermore, business leaders recognize the potential for disruption: in that same survey, 44% of executives predicted agentic AI will have an even bigger impact on their operations than generative AIdid(5). (Only 40% felt GenAI’s impact would ultimately be greater, and the rest saw them as roughly equal.) This sentiment indicates that many expect agentic AI to drive the next big leap in business process innovation and competitive advantage.
Real-world outcomes also highlight the importance of execution. Companies seeing the best results treat agentic AI implementation as a strategic initiative – they invest in training the models on quality data, re-engineering workflows to integrate AI, and upskilling their staff to collaborate with AI agents. For example, Landbase’s success with GTM-1 Omni was built on assembling an elite AI team (including a Stanford AI PhD and veterans from Meta, NASA, etc.) in its new Applied AI Lab to continuously refine the model(13). By committing resources to innovation, Landbase ensured its agentic AI delivers real revenue impact, aligning with its mission to “engineer autonomous GTM systems that drive real revenue impact”(13). This kind of result – automating thousands of campaigns and materially lifting conversions – doesn’t happen by accident; it’s the product of both advanced technology and thoughtful deployment.
In summary, the business impact of agentic AI in 2025 is being felt through higher efficiency, better top-line performance, and the agility to innovate new offerings. The technology is proving its value in hard numbers – ROI, percentage improvements, time savings – not just in theory. As these case studies and statistics accumulate, they build a strong business case for why more organizations should seriously consider adopting agentic AI. But reaping these rewards is not without its hurdles. In the next section, we will discuss the key trends shaping agentic AI in late 2025 and what they imply, followed by an honest look at the challenges companies face and strategies to successfully integrate agentic AI into their operations.
As we move into the latter half of 2025, several important trends are shaping the evolution of agentic AI in California and beyond. These trends shed light on where autonomous AI is headed and what businesses can expect in the near future:
1. Mainstream Enterprise Integration: Agentic AI is rapidly becoming a mainstream part of enterprise tech stacks. Gartner projects that by 2025, 70% of organizations will have operationalized AI systems designed for autonomy(9). In California, even traditionally cautious industries (like finance, healthcare, and government) are piloting AI agents for various workflows. We’re seeing agentic capabilities embedded in major enterprise software platforms: e.g., Microsoft’s Copilot Studio now lets companies build custom workflow agents as easily as writing a prompt, and Salesforce’s Einstein AI is evolving from assistive predictions to autonomous action recommendations in CRM. The result is that by late 2025, using AI agents won’t be limited to tech startups or Big Tech products – nearly every mid-to-large organization may have some form of AI agent working alongside employees. This integration also extends to infrastructure: cloud providers are offering “agent orchestration” services, and APIs to allow different agents to communicate (hinting at the “open agentic web” vision(7)). In short, agentic AI is on track to be as commonplace in business operations as cloud computing or mobile apps are today.
2. Multi-Agent Collaboration and Interoperability: A notable trend is the move toward multi-agent ecosystems. Instead of a single AI trying to do everything, companies are deploying collections of specialized agents that can work in concert. For example, an e-commerce operation might have one agent handling inventory optimization, another managing pricing, and another interfacing with customers – and these agents share information or call on each other’s skills as needed. Advances in agent communication protocols and standards (some coming out of California’s AI research hubs) are enabling this interoperability. Google’s AI researchers recently demonstrated scenarios at RSAC 2025 where security agents hand off tasks to each other to handle complex threats. Likewise, in business, we anticipate workflows where one agent triggers or delegates to another – akin to how microservices communicate. This trend is important because it paves the way for scalable, modular AI systems. Each agent can be optimized for a domain, and together they create a network of intelligence within the organization. Industry insiders have dubbed this the emergence of “super-agent ecosystems,” where the collective is greater than the sum of its parts(9). In the latter half of 2025, expect to hear more about frameworks for orchestrating multiple agents (e.g., open-source libraries and standards coming from Silicon Valley startups tackling this problem).
3. Vertical and Domain-Specific Agents: While early AI agents were fairly general-purpose, we’re now seeing a rise in vertical-specific agentic AI solutions. California’s startup scene is buzzing with companies building “AI co-pilots” fine-tuned for particular industries or functions. For example, in legal services there are AI agents trained on case law that assist attorneys by autonomously drafting briefs or doing compliance checks. In real estate, agentic AI is being used to automate property marketing and lead follow-ups. Landbase’s GTM-1 Omni, as we discussed, is purpose-built for B2B go-to-market teams – an agentic model tailored to marketing and sales workflows(13). This vertical focus yields better performance because the AI can be pretrained on domain-specific data (as GTM-1 Omni was, with tens of millions of sales interactions(13)). In H2 2025, we can expect more “pre-trained agent models” for domains like finance (autonomous trading or compliance agents), supply chain (logistics optimization agents), and even creative fields (AI agents for video game level design, for instance). California, with its diverse economy, is often where these domain-AI startups are emerging – from Hollywood-centric AI for media production to Napa Valley wineries exploring autonomous drones (physical agents) for vineyard management. As this trend grows, businesses will have more off-the-shelf agentic solutions to choose from that understand their industry out of the box.
4. Focus on Governance, Ethics, and Regulation: With great power comes great responsibility. A significant trend in late 2025 is the intensifying focus on AI governance and ethical deployment of agentic systems. Businesses have learned from the generative AI wave that issues like bias, transparency, and control can become major risks. When AI agents start making decisions (e.g., deciding who gets a loan or what pricing a customer sees), it raises the stakes for fairness and accountability. Both internal corporate governance and external regulation are evolving to address this. In California, which often leads on tech policy, lawmakers have been discussing updates to privacy laws (like CCPA/CPRA) to cover AI agent interactions and data usage. The state’s proactive stance – for instance, Newsom’s 2023 executive order requiring impact assessments and transparency for GenAI in state agencies(4) – is influencing how private companies formulate their own AI usage policies. Many organizations are establishing AI ethics committees and requiring that agentic AI decisions are auditable. Technically, there’s a trend toward “governance-first” design, meaning tools that allow human managers to set guidelines the agents must follow, and to monitor their decisions in real-time(9). For example, an agent might be constrained to only select from pre-approved marketing messages to avoid off-brand or non-compliant content. In late 2025, expect to see new frameworks (some coming from California’s AI think tanks) for ensuring trustworthy AI, such as standardized reporting on an agent’s decision rationale or kill-switch mechanisms to intervene if an agent behaves undesirably. This focus on governance will be crucial for broad acceptance of agentic AI, especially in regulated industries.
5. Advances in AI Model Capabilities: Underpinning many of the above trends are the rapid advances in AI models themselves. By late 2025, we anticipate the debut of new large language models and multimodal models (think GPT-4.5 or Google’s Gemini) that offer even more robust reasoning, longer context windows, and better integration with tools. These improvements directly enhance agentic AI: longer context windows mean an agent can remember and process more information (leading to more coherent multi-step actions), and improved reasoning reduces errors or nonsensical decisions. Research out of OpenAI’s San Francisco lab and DeepMind’s team has been specifically focusing on making AI reasoning more chain-of-thought oriented – ideal for agents that need to break down tasks and plan. Additionally, memory architectures (like vector databases or episodic memory modules) are maturing, allowing agents to retain knowledge over time instead of being stateless. We’re also seeing progress in reward modeling and simulation environments to train agents safely. All these technical gains will make the second generation of agentic AI in late 2025 more capable, reliable, and specialized. Businesses should keep an eye out for announcements (often made at conferences like NeurIPS or via research blogs in California) about new model capabilities that can be leveraged for even smarter agents.
6. Emergence of Agentic AI Marketplaces and Ecosystems: A final trend worth noting is the start of ecosystems around agentic AI. Similar to how mobile apps had app stores, we’re seeing early versions of “agent stores” – platforms where developers can publish AI agents or skills that others can use. For instance, a startup might create a procurement negotiation agent and list it on an enterprise AI marketplace for companies to plug into their workflows. Big players are encouraging this: Microsoft and OpenAI have hinted at marketplaces for ChatGPT plugins and agents. In California, several venture-backed firms are building hubs for third-party AI agents that can interact (with proper permissions) with a company’s data and systems. This could accelerate innovation by allowing companies to mix-and-match proven agents rather than building from scratch. It also raises new considerations around security (ensuring a downloaded agent doesn’t misuse access) and standards (making sure an agent from one vendor can work with another’s). Nonetheless, this trend indicates a move toward a more open, collaborative agentic AI environment – akin to the early days of cloud, where a plethora of SaaS tools emerged and then consolidated. By end of 2025, we may see the beginnings of consolidation or partnerships, where leading agentic AI platforms in California start to integrate – for example, a sales agent platform integrating an external scheduling agent service to enhance its capabilities.
Together, these trends paint an exciting picture of agentic AI’s trajectory. The technology is maturing, integrating deeply into business operations, and expanding its reach through multi-agent networks and industry-tailored solutions, all while stakeholders place greater emphasis on governing its use responsibly. For organizations, being aware of these trends is vital for planning. Those that ride these waves – adopting the latest proven models, ensuring strong governance, and leveraging ecosystem opportunities – will likely be ahead of the curve.
In the next section, we discuss the challenges that come with adopting agentic AI and strategies to overcome them. Understanding the trends is one side of the coin; preparing for the practical hurdles is the other. By combining foresight about where agentic AI is headed with smart change management, companies can successfully harness this powerful technology.
Adopting agentic AI is a transformative journey, but it’s not without challenges. As many California companies have learned, integrating autonomous AI into existing business processes can pose technical, organizational, and ethical hurdles. Here we outline some key challenges modern organizations face with agentic AI adoption, and strategies to navigate them effectively:
Challenge 1: Organizational Readiness and Change Management – Introducing AI agents often requires a shift in how employees work and how decisions are made. Teams may be wary of ceding control to algorithms, and there can be fear of job displacement or changes in role definitions. For example, marketing managers might wonder if an AI that automates campaigns will make parts of their role obsolete. In practice, companies adopting agentic AI have found that roles do shift – humans move more into oversight, strategy, and creative tasks while AIs handle grunt work. The resistance to change is a real obstacle.
Challenge 2: Process and Integration Complexity – An AI agent is only as good as the systems it connects to and the data it can access. Many organizations find that to effectively deploy an agent, they must first tackle data silos, process inconsistencies, and integration gaps in their operations. For instance, if sales, marketing, and customer support each use different software that don’t talk to each other, an AI agent trying to optimize across the customer journey will struggle. Additionally, legacy systems might not have APIs or be built to accommodate autonomous inputs. Integrating a proactive AI into a process might require significant re-engineering.
Challenge 3: Trust, Oversight, and Ethical Concerns – Handing over autonomy to AI raises valid concerns: Will the AI make choices that align with our strategy and values? How do we know it won’t do something harmful or inappropriate?Trust is hard-won and easily lost with AI. For example, an agent generating customer emails could accidentally produce off-brand or insensitive content if not properly guided. Or an AI managing pricing might, in theory, attempt exploitive price gouging if purely profit-driven without ethical constraints. There’s also regulatory compliance: AI decisions must adhere to laws (like not discriminating in lending or hiring decisions). This challenge boils down to ensuring AI behavior is transparent, controllable, and aligned with human intent.
Challenge 4: Talent and Skill Gaps – Deploying and managing agentic AI requires a mix of skills that many organizations are still developing. There’s high demand for AI engineers, data scientists, and ML operations specialists – talent which is especially concentrated in California. A company might have capable IT staff, but not necessarily people experienced with AI model tuning, prompt engineering for LLMs, or interpreting AI outputs. This skills gap can slow adoption or lead to mistakes in implementation.
Challenge 5: Measuring ROI and Managing Expectations – Like any emerging technology, agentic AI can be susceptible to hype. It’s easy for executives to have inflated expectations of an AI agent “completely running X department” in no time. If not measured properly, an AI project might be deemed a failure simply because it didn’t immediately deliver a dramatic ROI or because initial results were mixed. There’s also the risk of over-investing or under-investing due to misjudging the effort needed. A recent survey showed leaders are split, with about 40% worried their organizations might spend too much on AI, while 35% are conversely worried about spending too little – reflecting uncertainty on investment levels(5). Moreover, 36% of companies admitted not having well-defined ROI expectations during their GenAI efforts, a mistake they aim not to repeat with agentic AI(5).
In confronting these challenges, modern organizations should remember that adopting agentic AI is as much a change management journey as a technological one. The technical feasibility of AI agents is improving by the day, but reaping their benefits requires aligning people, processes, and strategy. Companies that are succeeding with agentic AI typically have strong executive sponsorship (to push past inertia), cross-functional teamwork, and a willingness to learn and adapt quickly. They also choose the right partners and platforms – opting for reliable solutions and expertise (for example, partnering with a proven agentic AI provider like Landbase for GTM automation, rather than building everything in-house from scratch).
California’s leading businesses often serve as a bellwether: as they navigate these adoption hurdles and share their stories, it provides a playbook for others to follow. The key takeaway is that the challenges are surmountable with proper planning and mindset. The next section concludes our discussion and offers a call-to-action, especially for those looking to begin or accelerate their own agentic AI journey.
As we’ve explored throughout this article, Agentic AI in California is not just a buzzword – it’s a genuine revolution in how business gets done. In 2025, autonomous AI agents are transitioning from intriguing demos to deployed solutions delivering real value, especially in go-to-market domains like marketing, sales, and revenue operations. California’s unique convergence of talent, innovation, and investment has made it ground zero for this transformation, setting examples that the rest of the world is watching.
The implications of agentic AI are profound. Companies that harness these autonomous agents stand to gain unprecedented efficiency, agility, and insight – effectively operating with a 24/7 intelligent workforce that scales as needed. We are witnessing the dawn of businesses where repetitive tasks are self-driving, decisions are augmented by tireless AI analysis, and human teams are empowered to focus on creativity, strategy, and relationships. Trends indicate that this is only the beginning: the latter half of 2025 and beyond will bring even smarter multi-agent systems, deeper integration into every industry, and a refined balance of autonomy and governance.
For organizations in California and elsewhere, the message is clear: now is the time to start embracing agentic AI. Waiting on the sidelines risks falling behind more agile competitors who are already reaping the benefits. The good news is that you don’t have to do it alone or start from scratch. The ecosystem of solutions and expertise is rapidly growing. In particular, Landbase has emerged as a leader in this space, offering a proven path to leverage agentic AI for go-to-market success.
Landbase’s GTM-1 Omni platform – as highlighted earlier – is a cutting-edge example of agentic AI in action, purpose-built to autonomously drive marketing and sales workflows. Landbase has poured research and development into making AI agents that truly understand GTM motions, from warming up leads to orchestrating campaigns to optimizing revenue operations. The platform’s impressive track record (automating thousands of campaigns, improving conversion rates, and more(13)) speaks to its capability and maturity. Moreover, Landbase’s commitment to innovation (e.g., establishing the first Applied AI Lab for GTM in Silicon Valley(13)) means clients benefit from continual advancements and support from world-class AI experts.
If you are looking to stay ahead in the autonomous business revolution, now is the moment to act. We invite you to explore how Landbase’s agentic AI solution can empower your organization’s go-to-market strategy. Imagine what your teams could achieve with an intelligent co-pilot handling the heavy lifting of outreach, follow-ups, and optimizations across all channels. Envision a RevOps function where data silos and delays are replaced by a seamless, AI-orchestrated revenue engine. These outcomes are within reach today.
Learn more about the Landbase GTM-1 Omni platform and see firsthand how agentic AI can transform your marketing and RevOps operations. Whether you want to drive higher growth with lower costs, respond to market changes in real-time, or simply free your talent from drudgery to focus on innovation – Landbase’s solution is designed to help you get there. Don’t wait for competitors to set the pace. Take the lead in the Agentic AI era and position your business for unparalleled success in 2025 and beyond.
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