Introduction
Generative AI’s breakthrough in 2023–2024 sparked an enterprise rush to experiment with AI for content and insights. Surveys indicate the momentum is huge – generative AI adoption roughly doubled in a year to around 65% of companies by 2024. Early adopters have seen promising returns (one estimate found $3.70 in value for every $1 invested) in GenAI initiatives.
Yet many organizations are still in the early stages of realizing AI’s value. In fact, even with heavy investment (global AI spend growing ~37% annually), employees spend only an estimated 0.5–3.5% of their work hours actually using AI tools today. Clearly, there is a gap between awareness and actionable adoption.
Now, a new AI paradigm – agentic AI – is emerging as the next frontier, promising to bridge that gap by moving from passive AI assistance to autonomous, goal-directed agents.
As we enter 2025, enterprises are looking beyond basic generative AI (which produces content or answers on demand) toward agentic AI, which can take initiative, automate complex workflows, and act on behalf of users. This shift builds on the generative AI foundation but adds a proactive, decision-making capability.
Industry experts predict this evolution will accelerate in coming years. Deloitte forecasts that enterprise deployment of AI “agents” will increase sharply – up 25% in 2025 and roughly double by 2027, reaching about 50% of enterprises using AI agents. Similarly, Gartner projects that by 2027 up to 50% of large enterprises will be using tailored generative/agentic AI models, up from virtually none in 2023.
In short, autonomous AI agents are on track to become mainstream business tools over the next 2–3 years. But what exactly is agentic AI, and how does it drive value? In this article, we explore the concept of agentic AI and its top use cases across industries – from financial services and professional services to large corporates – and outline how companies can capitalize on this technology while navigating the maturity curve.
What is Agentic AI?
Agentic AI refers to AI systems (often built on advanced machine learning and large language models) that have agency: they can act autonomously to achieve goals rather than just produce outputs when prompted. In other words, unlike a traditional generative AI which passively responds to a user input, an agentic AI can proactively take actions and make decisions to complete a task with minimal explicit instruction. It’s an AI that not only generates content or predictions, but also plans multi-step workflows, adapts to new information, and executes tasks end-to-end on behalf of the user.
A concise definition from one M&A industry analysis describes agentic AI as "machine-learning technology capable of acting autonomously to achieve specific goals in unpredictable environments."
In practice, an agentic AI might be an "AI agent" or a collection of coordinated agents that a business can deploy for particular objectives. For example, rather than a static chatbot that only answers questions, an agentic system could receive a high-level goal (“onboard a new vendor” or “research and draft a market report”) and then initiate a sequence of actions: gathering data from various sources, processing and analyzing that data, and generating outputs or even initiating transactions – all with minimal human intervention.
As KPMG’s Head of AI aptly noted, these are "goal-driven AI systems that autonomously plan, coordinate, and execute actions under human oversight", effectively working as digital team members alongside humans. This goal-oriented autonomy is the key distinguisher of agentic AI versus traditional AI. Rather than operating in a narrow, pre-defined process (as in classic automation or RPA) or waiting for a prompt (as in gen AI), the agentic AI can dynamically decide what needs to be done next to fulfill its objective.
Crucially, agentic AI doesn’t replace governance or oversight – it augments human teams. The best implementations include mechanisms for human approval at critical junctures and transparency into the agent’s reasoning. In essence, agentic AI marries the creative, reasoning power of advanced AI models with the agency to act, all while remaining aligned to human-defined goals and constraints. This opens up exciting possibilities to streamline complex enterprise operations, as we’ll explore through use cases.
Enterprise Adoption and Maturity Trends (2025–2027)
Even as the vision for agentic AI grows, it’s important to take a realistic look at where companies are today. We are in the early innings of adoption. On one hand, interest is sky-high – more than three-quarters of organizations now report using AI in at least one business function, and the surge of generative AI in 2024 means many firms have pilot projects in place. On the other hand, fully scaling these solutions remains a challenge. KPMG notes that over half of companies have seen no material improvement in performance from their digital transformation efforts to datekpmg.com, implying that simply adding AI proof-of-concepts isn’t yet moving the needle. One barrier is low usage and user adoption internally – as mentioned, only a tiny fraction of work hours involve AI assistance, pointing to change management and skills gaps.
Generative AI vs. Agentic AI maturity
Generative AI (GenAI) tools like chatbots, content generators, and coding assistants are more mature in enterprise use. Many organizations have experimented with these for tasks like copywriting, report generation, or software prototyping. Adoption statistics bear this out: some surveys found that 65%+ of companies had at least piloted GenAI by late 2024.
However, truly agentic AI deployments (AI agents automating decisions and processes) are currently less common – perhaps confined to innovative early adopters and vendors. Deloitte’s 2025 industry predictions call out that enterprise use of AI agents is poised to grow dramatically in the next two years (25% uptick in 2025, doubling by 2027).
Looking toward 2027
By 2027, if these trends hold, we can expect agentic AI to be a common component of enterprise workflows. Gartner envisions at least half of large enterprises will have custom-trained AI models or agents deeply integrated by then. This suggests that the current “experimentation” phase (where companies are trying out use cases in a limited fashion) will shift to a “deployment and scaling” phase over 2025–2026, followed by broad operational use by 2027. We can draw an analogy to the adoption of RPA (Robotic Process Automation) in the 2010s – cautious pilots in year 1–2, followed by rapid scale once value was proven and tools matured. Agentic AI likely follows a similar path, but accelerated by the foundation of cloud and AI infrastructure now in place.
That said, companies today are at varying maturity levels. A few leading organizations (including some profiled below) are already integrating agentic AI into core processes. Many others are still focusing on getting generative AI capabilities right – and as KPMG advises, they must "fix their GenAI adoption problem" before chasing full autonomy.
Common challenges holding back maturity include: data governance and privacy concerns, unresolved questions around IP and compliance (especially for letting AI act autonomously on sensitive data), and the need for new governance structures. Cultural factors play a role too – employees and leaders need to trust AI agents and redesign processes to incorporate them effectively. These aren’t unsolvable problems; they are growing pains as organizations rewire themselves for an AI-first era.
Importantly, there is a strong business case for investing in agentic AI despite the challenges. Early movers are gaining efficiency and competitive advantages that others will struggle to catch up with. A recent industry report calls GenAI a "catalyst for the expansion of AI in the enterprise" and highlights that those who harness it are opening new revenue opportunities.
The enterprise world stands at a turning point – those that lay the groundwork now for agentic AI (in terms of data readiness, talent, and governance) will be poised to leap ahead in the latter half of the decade.
Next, let’s dive into concrete use cases showing how agentic AI is already adding value in different sectors.
Agentic AI Use Cases in Financial Services
01
Private Equity (Deal Origination, Due Diligence & Portfolio Management)
Private equity firms leverage Evalueserve’s agentic AI to amplify deal flow and sharpen investment decisions. Autonomous research agents scan vast market data to identify 3× more potential deals aligned with the firm’s thesis, feeding a richer origination pipeline.
During due diligence, Evalueserve’s AI agents rapidly gather and analyze financials, documents, and open-source intelligence – flagging risks and opportunities that human teams might miss. Post-acquisition, the AI continuously monitors portfolio company KPIs and market signals, alerting fund managers to performance gaps or growth opportunities in real time. Throughout the investment cycle, Evalueserve’s agentic AI stack boosts productivity (by automating data-heavy tasks), accelerates analysis for faster deal evaluations, ensures compliance with due diligence checklists, and supports data-driven decision-making for better returns.
In lending and risk management, Evalueserve’s agentic AI acts as a force-multiplier for credit teams. AI-driven underwriting assistants auto-spread financials, apply risk models, and even draft credit memos – speeding up loan approvals while maintaining rigorous credit standards. These intelligent agents cross-verify borrower data against internal policies and external datasets to ensure compliance (e.g. flagging covenant breaches or KYC issues proactively).
For credit portfolio risk, Evalueserve deploys agents that monitor early warning signals across the portfolio, from macroeconomic shifts to borrower news, triggering alerts and recommended actions for risk officers. By automating routine analyses and monitoring, Evalueserve’s solutions let credit professionals focus on complex judgments, improving both the speed and quality of risk decisions under robust control.
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Credit Operations & Risk
03
Investment Research
Evalueserve helps investment research teams supercharge their insight generation with agentic AI. Research analysts gain AI copilots that can autonomously gather market data, earnings reports, and expert commentary – then distill this raw information into concise, domain-tailored summaries and analyses. For example, an AI agent can comb through thousands of pages of filings and news overnight, presenting analysts with key trends, anomalies, and even draft financial models by morning.
The agentic AI stack is tuned to the finance domain, ensuring outputs meet the rigor required for credible investment recommendations. With mundane data-crunching offloaded to Evalueserve’s AI solutions, research teams achieve higher productivity and produce higher-quality research. This translates to faster delivery of investment ideas, more comprehensive coverage of assets, and well-supported insights that drive smarter portfolio decisions.
Agentic AI Use Cases in Professional Services
Content Creation
In today’s fast-paced professional services environment, Evalueserve’s agentic AI enables rapid, intelligent content creation for marketing and client deliverables. AI content generators – guided by Evalueserve’s domain experts – can draft thought leadership articles, whitepapers, or slide decks by summarizing relevant knowledge bases and data points. These agents produce first drafts of complex content (from market landscapes to case studies) in a fraction of the time, allowing consultants and marketing teams to focus on refining the narrative and adding insight.
Evalueserve’s stack ensures that generated content maintains compliance with brand and legal guidelines, using templates and approved knowledge repositories. The result is a dramatic boost in productivity: what used to take weeks of manual effort can be turned around in days, with consistent quality. By pairing creative professionals with AI co-authors, Evalueserve helps firms deliver compelling content faster without sacrificing accuracy or originality.
Data & Research
For consulting and advisory firms, Evalueserve’s agentic AI serves as a research powerhouse that works 24/7. Instead of analysts spending hours on data gathering and fact-finding, AI agents automatically pull data from trusted sources, databases, and even open web, then synthesize it into ready-to-use insights. Consultants can pose complex questions to a conversational research bot integrated with the firm’s knowledge base, and receive domain-specific answers complete with source citations and trend analysis.
This dramatically shortens research cycles – what once required multiple analysts can now be achieved with a swift AI-assisted query, whether it’s market sizing, competitor profiles, or regulatory updates. Evalueserve’s agentic AI not only accelerates data collection, but also applies reasoning to highlight strategic implications, giving professional service teams a decision-ready edge. Crucially, human experts are in the loop to validate outputs, ensuring that insights remain accurate, context-rich, and client-ready while benefiting from AI-driven speed and breadth.
Internal Productivity Agents
Evalueserve empowers professional services organizations with internal AI agents that streamline operations and amplify productivity. Imagine an AI assistant that can automatically compile project status reports, summarize internal meeting notes, or update forecasting spreadsheets – all without human intervention. Evalueserve’s agentic AI makes this possible: for instance, a reporting chatbot can retrieve and update KPIs from enterprise systems on command, making data accessible to every team member regardless of technical skill.
Routine tasks like time entry reminders, resource scheduling, or expense categorization can be handed off to intelligent agents that execute them consistently and swiftly. By integrating these custom AI agents into internal workflows (e.g. via Slack, Teams, or bespoke dashboards), Evalueserve helps firms eliminate administrative drudgery. The outcome is a more productive workforce that can redirect time to high-value activities – delivering client impact – while the AI ensures nothing falls through the cracks (and maintains logs for compliance and audit as needed).
In short, Evalueserve’s internal productivity agents act as always-on digital team members, driving efficiency and freeing up human talent to focus on innovation and client service.
Knowledge Management & RFP Automation
Evalueserve’s agentic AI stack is a game-changer for knowledge management in professional services, turning knowledge assets into immediate actionable content. Thought Evalueserve’s AI-enabled knowledge platform, firms can automatically reuse and adapt past proposals, case studies, and internal research for new opportunities.
For example, when a new RFP arrives, an AI agent searches the firm’s repository for relevant content and assembles a first draft response document complete with customized inserts – a process that happens in minutes, not weeks. In fact, Evalueserve has deployed this technology to auto-generate tailored RFP responses for clients, drastically cutting down proposal turnaround time.
These AI-driven knowledge agents ensure that the latest approved facts, figures, and win themes are consistently applied, mitigating compliance risks (no more outdated or non-compliant content in proposals). Consultants can then review and polish the AI-prepared draft, spending their time on strategy and persuasion rather than hunting for past materials. By leveraging Evalueserve’s agentic AI for knowledge management, enterprises achieve rapid response capabilities, higher proposal win rates, and a living memory of best practices that is always at their fingertips.
Fund Factsheet Generation
Asset and wealth managers – often served by professional service teams – benefit from Evalueserve’s agentic AI through automated fund factsheet generation. Evalueserve’s solution pulls data from multiple sources (portfolio management systems, market feeds, performance databases) and populates the standard factsheet template with the latest figures, charts, and disclosures. The AI agent applies business rules to ensure every number is accurate and every compliance note is included, resulting in error-free factsheets on each cycle.
What’s typically a frantic end-of-quarter manual process becomes a smooth automated workflow: as soon as the period closes, the AI assembles the factsheet and even notifies the compliance team for review. One global asset manager working with Evalueserve achieved fully accurate factsheets and significantly reduced turnaround time using this approach. The speed is matched by flexibility – if a last-minute data change comes in, the AI can update all related sections instantly, avoiding delays.
By deploying agentic AI for fund reporting, Evalueserve enables firms to publish investor-ready, compliant factsheets faster and with less effort, strengthening transparency and client trust.
Agentic AI Use Cases in Corporates
Customer Service
In corporate customer service, Evalueserve’s agentic AI delivers intelligent, always-on support that elevates the customer experience. Enterprises can deploy AI service agents – trained on company-specific FAQs, product documentation, and support scripts – to handle a wide range of customer inquiries through chat, email, or voice.
These agents autonomously resolve common issues (from troubleshooting product questions to guiding users through account updates) with instant, precise responses, while recognizing when to seamlessly hand off complex cases to human representatives. Evalueserve ensures these customer-facing AI agents are finely tuned to the company’s domain and tone, so interactions feel personalized and on-brand rather than generic.
Clients are seeing great results: customers get 24/7 help with near-zero wait times, and support teams see a reduction in repetitive tickets. By automating the routine and providing human agents with AI-curated suggestions for tougher queries, Evalueserve’s solutions improve service speed and consistency, all under the guardrails of company policy and compliance (the AI will only operate within approved answers and escalate anything uncertain).
The outcome is higher customer satisfaction, cost-effective scalability in support operations, and rich data on customer needs that can inform product and marketing strategies.
Market & Competitive Intelligence
For large corporations keeping tabs on dynamic markets and rivals, Evalueserve’s agentic AI is a force-multiplier in market and competitive intelligence (MCI). Evalueserve – a recognized leader in MCI platforms – leverages agentic AI to gather, analyze, and deliver intelligence far more effectively. Instead of manual research, autonomous agents continuously crawl websites, news feeds, earnings calls, and social media for pertinent developments about competitors, customers, and market trends.
This streamlined data collection and processing means no critical update is missed – the AI extracts signals from noise and compiles concise intelligence briefs for strategy teams. Evalueserve’s stack classifies and tags information by company, topic, and sentiment, and pushes real-time alerts when, say, a competitor launches a new product or regulatory changes loom.
By integrating these AI-driven feeds into dashboards and portals, decision-makers get a 360° view of the competitive landscape in near real time, supported by Evalueserve’s analysts who validate and enrich the insights.
The result is a proactive intelligence capability: companies can anticipate moves and make informed strategic decisions faster, backed by comprehensive, up-to-the-minute data. In short, agentic AI allows corporate strategy and marketing teams to move from periodic reports to a living, breathing intelligence system – one that Evalueserve helps run with precision, scalability, and strict confidentiality/compliance controls.
Commercial Excellence Enablement
Evalueserve’s agentic AI solutions drive commercial excellence by infusing data-driven intelligence into sales and marketing workflows. Using a unique domain-specific AI architecture, Evalueserve supports every stage of the revenue cycle – from pinpointing the right market segments and generating qualified leads to nurturing prospects and closing deals.
For example, AI agents can analyze CRM and market data to prioritize leads with the highest conversion likelihood, or even act as sales development representatives by autonomously engaging initial contacts with personalized, compliant outreach. In marketing, the AI might tailor content and product recommendations to each customer segment, improving campaign relevance and ROI.
Sales teams equipped with Evalueserve’s AI copilots get real-time insights before customer meetings – such as summary briefs on the client’s industry developments, competitor offerings, or even personality insights derived from public information – helping them pitch more effectively.
Pricing and deal optimization can also be agent-assisted: the AI models various pricing scenarios and suggests the optimal strategy based on historical win/loss data and market conditions.
Throughout, Evalueserve ensures that these agentic AI tools are deeply integrated but unobtrusive – they work within existing CRM, marketing platforms, and workflows, so adoption is seamless. The payoff is significant: marketing and sales teams become more agile and informed, conversion rates climb, and revenue grows, all enabled by AI-driven precision and speed in commercial decision-making.
Procurement Intelligence
In procurement and supply chain functions, Evalueserve’s agentic AI delivers end-to-end intelligence and automation that transforms decision-making. Procurement teams face overwhelming volumes of supplier data, market prices, and risk factors – Evalueserve deploys AI agents to tackle this head-on.
These agents consolidate internal spend data with external market intelligence, giving procurement leaders live dashboards that make spend analysis and category strategy development intuitive. They also continuously score and monitor suppliers on key parameters (cost, quality, ESG compliance, etc.), proactively flagging risks such as financial instability or geopolitical exposure, so that there’s always a plan B ready.
When it comes to sourcing, agentic AI can automatically scout and evaluate new suppliers or cost-saving opportunities across the globe, using criteria set by Evalueserve’s domain experts.
One notable capability is cost intelligence: AI models break down the cost structure of products/services and suggest negotiation levers, enabling fact-based discussions with vendors. By integrating with contract management and ERP systems, these agents can also ensure contract terms (renewals, expirations, compliance clauses) are tracked and acted upon without manual oversight.
Evalueserve’s procurement intelligence solutions thus bring unprecedented speed and rigor – negotiations are informed by real-time data, supplier management becomes predictive rather than reactive, and compliance (regulatory and sustainability) is continuously audited by the AI. Companies achieve not just cost savings, but also stronger supplier relationships and resilience in their supply chain, powered by Evalueserve’s blend of AI and procurement expertise.
Account Renewal and Custom Agent Integration
Evalueserve recognizes that every enterprise has unique processes – whether it’s customer account renewals, bespoke compliance checks, or other specialized workflows – and its agentic AI stack can be custom-tailored to these needs.
For example, consider the critical process of account renewals in a B2B context: an AI agent can be integrated to monitor each client’s product usage, satisfaction signals, and contract timelines. Well before renewal dates, the agent proactively alerts account managers about accounts at risk of churn (perhaps usage has dipped or support tickets are up) and even suggests personalized renewal packages or upsell opportunities based on the client’s history.
These recommendations are drawn from analyzing patterns across the company’s entire customer base, a task impossible to do manually at scale. Evalueserve excels at embedding such bespoke AI agents into existing systems – whether as a microservice or a plug-in – ensuring they work seamlessly with CRM, ERP, or other enterprise platforms. Importantly, all custom agents are developed with robust guardrails: they respect data privacy, produce audit logs for any automated action, and can be fine-tuned with domain expert feedback.
Beyond renewals, Evalueserve can integrate agents for virtually any process that can be defined and learned – from automating compliance document reviews to orchestrating complex approval workflows. By doing so, enterprises get the best of both worlds: highly specific AI solutions that address their proprietary challenges, and the reliability of Evalueserve’s proven AI architecture underpinning it all.
The outcome is greater speed and consistency in niche processes, improved outcomes (like higher renewal rates or error reduction), and a scalable way to augment your workforce with custom digital agents that evolve with your business.
The Road Ahead: Building Capabilities and Gaining an Edge
A key question for every executive right now is this:
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How do we move from fragmented AI experimentation to enterprise-scale autonomy?
Agentic AI is not a plug-and-play solution. It’s a capability you build—through the right use cases, data readiness, orchestration, and governance.
The most successful organizations will take a phased approach:
- Start small, with pilots where agents augment human work. Build trust and measure outcomes.
- Invest in clean data and integrated systems—so agents have what they need to operate.
- Design strong governance early: define where autonomy is acceptable, embed oversight, and train teams to work alongside AI.
- Upskill employees to manage, interpret, and guide these intelligent systems—not just use them.
- Scale deliberately, expanding agents across use cases that drive measurable value.
The most forward-looking companies aren’t simply automating—they’re rethinking workflows. Private equity teams are reshaping due diligence with agents that surface insights in hours, not weeks. Credit teams are making faster, more consistent decisions. Consultants and researchers are producing higher-quality outputs with less manual lift.
Corporates are embedding agents across procurement, sales, and service to become more responsive, resilient, and intelligent.
These aren’t distant visions. They are already being built—incrementally, securely, and with purpose—using scalable, agentic architectures designed for enterprise realities.
Agentic AI won’t just reduce overhead. It will change how businesses think, act, and compete. Those who build the muscle now won’t just keep pace.
They’ll set it.
