Agentic AI is automating complex business processes
Most people still think of AI as a chatbot answering questions. That view is outdated. Agentic AI takes this much further. We’re not talking about just giving answers, we’re talking about autonomous systems that execute full workflows across departments, data systems, and tools. These agents understand what needs to be done and act without waiting for a human to guide every step.
We’re seeing significant uptake. Enterprises aren’t hesitating. In Cloudera’s survey of 1,500 senior IT leaders from 14 countries, 57% already use AI agents. Another 96% plan to expand usage within 12 months. In another study by SnapLogic, 50% of IT decision-makers were already deploying agents. Notably, 79% intend to spend more than $1 million on these systems over the next year. That kind of spending tells you one thing, this technology has moved out of the lab and into operational strategy.
Executives need to understand what’s really happening here. Agentic AI connects multiple models and tools to act on tasks, not just respond to prompts. That might mean processing thousands of documents across global networks, coordinating between CRM, ERP, and external APIs, without waiting for a single button click. With orchestration frameworks and new protocols enabling real-time task execution, it’s not just more automation. It’s fundamentally new operational infrastructure.
This isn’t experimental anymore. Gartner calls agentic AI the top strategic tech trend for 2025. According to their projections, 80% of routine customer service tasks will be handled autonomously by 2029. One-third of enterprise apps will embed agentic functionality by 2028. That changes the playing field across industries, from logistics to finance, legal to product development.
Sid Nag from Gartner said it best: “It’s certainly not just marketing hype. It’s something of very high importance for automating many tasks in many environments.” That’s the shift. It’s not about chatbots. It’s about delegating real work to intelligent systems that run with minimal oversight, and it’s happening fast.
Agentic AI is transforming software engineering
This is where we see the strongest signal of disruption. AI in software development was already making waves. Agentic AI just kicked it into high gear. Developers aren’t just asking AI for code snippets anymore. These systems now plan entire projects, write code, test it, fix bugs, and push updates, end to end.
There’s a reason adoption is exploding. GitHub’s 2024 survey across Brazil, Germany, India, and the US reported that 97% of developers are already using AI coding tools. A separate HackerRank study spanning 13,000+ developers in 102 countries showed that AI is now generating 29% of all code. Let that sink in, nearly one-third of code written globally is by machines. You don’t ignore a trend that big.
With agentic AI, the role of coding assistants has shifted. Systems like Cursor, Devin by Cognition Labs, Windsurf, and open-source options like Cline go beyond suggestion. They modify large codebases using context from previous commits. GitHub Copilot, Amazon’s CLI agent, Google’s Firebase Studio, and Microsoft’s VS Code all offer agentic modes designed to integrate directly into developer workflows.
Dan Shiebler, Head of Machine Learning at Abnormal AI, said that between 50% and 75% of their 350 engineers are already relying on these tools day-to-day. Platforms like Cursor and v0 are helping teams implement full applications without touching raw code. According to him, efficiency gains are so high that older copy-and-paste methods feel outdated by comparison.
And if that sounds like specialization, it’s not. OpenAI is finalizing its own agentic software engineer, A-SWE, while companies like Lovable and Bolt are pushing boundaries in automated UI and infrastructure provisioning. This ecosystem is fast-evolving and diverse.
Gartner expects these changes to shift developer roles entirely. By 2027, 80% of developers will need to upskill or adapt. This isn’t optional. If engineering teams aren’t investing now, they’ll fall behind. Kevin Merlini, VP of Product and CoCounsel at Thomson Reuters, put it plainly: “If they’re not, I don’t know why they’re not doing that.” His team uses a multi-model approach to stay flexible, which is smart. Binding to a single solution limits access to new innovations in a space evolving every quarter.
Bottom line: Agentic AI in software engineering is no longer experimental, it’s operational. Companies making tech should already be using it. Companies not using it need to ask why.
Agentic AI improves research and document analysis
This is a major upgrade from basic chatbots. Enterprises rely on research and document analysis across legal, tax, audit, and compliance workstreams. Until recently, even the best AI interfaces in this space were limited to searching one folder or document set at a time. You typed a prompt, got an answer, and that was it.
Agentic AI elevates this process. These agents can move across multiple repositories, determine which to access, and coordinate steps automatically across various systems. For example, a single question that requires data from contracts, regulatory databases, internal memos, and external APIs can now be answered with minimal input from the user, and maximum context built in. That’s something conventional tools don’t provide.
At Thomson Reuters, this is no longer just internal innovation. Their CoCounsel genAI platform is now in use by over 240,000 professionals in the legal, tax, audit, and accounting sectors. Kevin Merlini, VP of Product and CoCounsel, explained that agentic tech is not just enhancing functionality, it’s opening entirely new categories of software. With agentic AI, researchers don’t just get faster results, they get more accurate ones across complex datasets, stitched together through automated task inference.
There’s a strategic takeaway here. C-suite executives need to understand that agentic systems reshape how expert knowledge is accessed and used. Legal reviews, audit investigations, and due diligence reports can all be accelerated and scaled without expanding headcount. The efficiency benefits are clear, but the real value is in creating competitive advantage through speed and higher operational intelligence.
Companies that depend on document-heavy processes should already be testing these tools. The cost of waiting is seeing competitors move faster, and with better data.
Agentic AI supports customer service
Customer service doesn’t tolerate failure. Deploying AI that interacts with customers directly, especially AI that can take autonomous action, is risky. A system issuing incorrect refunds, rescheduling deliveries improperly, or misrepresenting company policy damages trust fast. That’s why most enterprises aren’t going fully autonomous yet, at least not in front-facing roles.
What leading companies are doing is smart: using agentic AI behind the scenes to support human agents. At Bosch Power Tools, Victor Nguyen, Project Lead for GenAI in Business Operations, explained their approach. The company uses Cognigy.AI, integrated with OpenAI’s GPT-4 and Google Gemini models. These agents read emails, interpret tickets, translate documents, and surface suggested actions, but everything still goes through a human before reaching the customer.
This offers real operational leverage. Tasks that took minutes now take seconds. Agents are better informed, respond faster, and handle more cases with fewer errors. Internally, Bosch is coordinating with its central IT unit to scale this approach across the company, starting with 1 of 23 customer service centers happening this quarter.
There’s another friction point to address: inconsistency. Different regions use different systems and processes. Nguyen highlighted that standardization, in data, processes, and UI, is a bigger challenge than the AI itself. Enterprises aiming to scale agentic AI must solve this foundational problem first. Uniform input structures and streamlined backend systems aren’t optional; they’re critical for agentic AI to perform correctly.
For C-suite leaders, the path forward is straightforward. Don’t delegate your customer’s experience to autonomous agents without first building a foundation of structured, high-quality data and standardized procedures. AI won’t fix a broken system. But when the system is ready, agentic tools can scale that capability globally, without adding headcount or sacrificing consistency.
Agentic AI streamlines document processing workflows
Document processing has always been a time-consuming part of business, filled with redundant review cycles, formatting, and manual data extraction. Traditional automation helped with templated tasks, but agentic AI now offers a different approach. These systems connect multiple inputs, generate output with clear structure, and refine the final product without micromanagement.
At Route Three Digital, a marketing firm, this shift is already in action. Sharmilla Singh, Chief Marketing and Operations Officer, shared how they built an AI agent using Google’s Vertex platform with Gemini models for one of their clients. What originally took seven days, gathering information, drafting and cleaning up a proposal, now takes only a few hours. The system captures key input, generates a draft, refines the content for clarity, and presents a high-quality result ready for quick human review.
This is a working example of how agentic systems transform mid-volume, critical document workflows. There’s still human involvement for tailoring final content, but the bulk of the manual effort is offloaded.
For senior executives, this isn’t just about productivity, this is about speed to execution. The faster a project moves from input to output, the more deals you close, the more campaigns you launch, the more value you extract from the same workforce. Singh also points out that marketing is a low-risk environment to experiment, meaning companies can pilot safely and observe measurable ROI early.
There’s strong upside here. Any function that handles proposals, reporting, or client communications can benefit fast. Corporations waiting for full maturity before deploying agentic AI will lose momentum while others gain operational edge.
Democratization platforms are making it easier for businesses to build, deploy, and scale agentic AI solutions.
Until now, building agentic AI required in-house machine learning teams, integrations with multiple vendors, and significant infrastructure investment. That barrier is dropping quickly. Platform giants are simplifying agent creation and orchestration, giving enterprises drag-and-drop access to capabilities that once required core development.
Google’s recent upgrades to Vertex AI and its Agentspace platform now include no-code builders, pre-built research tools, and an agent marketplace. Businesses can start immediately and scale as needed. Over 130 third-party agents are already listed, offered by firms like Deloitte, Amdocs, VMware, and Palo Alto Networks.
But the scale here is best seen in Microsoft’s numbers. Charles Lamanna, Corporate VP of Business and Industry Copilot, stated in March that over 160,000 organizations are already using Microsoft’s Copilot Studio. Over 400,000 custom agents were created in the previous quarter alone. These are not pilot tests, these are live deployments transformed into enterprise systems with low overhead.
For the C-suite, this means time-to-value is shorter and experimentation is dramatically cheaper. You don’t need a full data science department to explore how agents could streamline procurement, research, or support tasks. Services like AWS Bedrock Agents, Salesforce’s AI tools, and SAP’s integrations allow quick adaptation across verticals.
But with ease of access comes responsibility. While platform tools simplify the deployment, the burden of outcome still rests with leadership. These systems must be tested in real workflows, and their performance monitored continuously. The technology is becoming more accessible. That doesn’t make strategy optional.
The window is open for competitive rollout, especially in areas like IT, sourcing, finance, and operations. It’s not about trying to catch the wave later. It’s about starting now, testing quickly, and scaling what actually works.
Key takeaways for leaders
- Agentic AI adoption is accelerating fast: Over 50% of enterprises are already using AI agents, with 96% planning to expand in the next year. Leaders should prioritize early integration to build competitive operational infrastructure and avoid falling behind.
- Software development is being redefined: Agentic AI now autonomously writes, tests, and iterates code, with 97% of developers already using AI tools. CTOs should invest in these platforms and commit to workforce upskilling by 2027 to stay relevant.
- Research and document workflows are becoming autonomous: Complex research tasks and data aggregation across diverse systems are being streamlined by agentic AI, as seen with Thomson Reuters’ CoCounsel platform. Executives should explore agentic tools in data-heavy functions to boost turnaround time and decision accuracy.
- Customer service improves with AI-human partnership: Companies like Bosch use agentic AI to assist, not replace, human agents, enhancing speed while maintaining oversight. Leaders should begin by deploying supportive agents internally while standardizing systems for long-term scalability.
- Document processing time is collapsing: Marketing and business teams are cutting multi-day proposal tasks to a few hours using agentic AI. Chief operations officers should examine low-risk departments for deployment to gain immediate efficiency without high implementation risk.
- AI agent platforms are lowering the barrier to entry: Platforms like Microsoft Copilot Studio and Google Agentspace now support no-code AI agent creation, enabling faster deployment at scale. Decision-makers should empower teams to prototype with these tools to identify high-impact use cases quickly.
