Where is the value of AI in M&A

Multi-agent systems which are viewed as connected multiple specialized AI agents collaborating toward goals, promise to transform how organizations analyze information and automate processes. But to unlock its full potential, it must be built on a strong data foundation. Modern data architecture (cloud-based, scalable and well-governed) provides that backbone, ensuring AI agents have the right information at the right time. It’s a symbiotic pairing, advanced AI drives insights from data and a robust data architecture feeds and enables smarter AI. Industry trends underscore this convergence, Gartner projects 75% of large enterprises will adopt multi-agent systems by 2026 and BCG estimates these systems could generate $53 billion in business revenue by 2030 (up from $5.7 billion in 2024). [1] [2] Forward-thinking firms are taking note, as in Deloitte’s latest survey, many executives see deeply embedding AI into business processes as the #1 way to drive value from the technology. [3]

This article explores what multi-agent AI systems are and why they matter, how modern data architecture underpins their success and real-world applications in the finance and M&A domains. Throughout, we will highlight practical examples and best practices from algorithmic trading desks to due diligence teams, illustrating how combining these technologies can enable smarter, faster decisions. The goal is to provide a clear, executive-level understanding of this emerging paradigm, positioning you to lead in an era where intelligent agents and agile data infrastructures together redefine what’s possible. Considering all mentioned cases throughout the paper, the "human + AI" synergy appears a winning strategy formula in finance, combining computational power with domain expertise.

What are Multi‑Agent AI systems (MAS)

At its core, a multi-agent AI system is a collection of autonomous but cooperating AI agents, each with specific roles or skills, that work together to achieve a common objective. Instead of relying on a single monolithic AI, tasks are divided among multiple agents that can communicate, coordinate and even negotiate with one another much like an effective human team or an orchestra of specialists. This approach significantly amplifies the power of AI by orchestrating collaboration as individual AI agents might handle distinct subtasks (data retrieval, analysis, validation, etc.) and a supervising logic coordinates their efforts. The result is a system that can tackle complex, multi-step workflows beyond the scope of any single model. As Deloitte report notes, multi-agent AI systems can understand requests, plan workflows, delegate responsibilities, streamline actions, collaborate with humans and ultimately validate and improve outputs. [1] Essentially, they turn a solitary AI assistant into a dynamic team that can solve richer problems.

Multi-agent systems have been studied for years, but recent advances (especially large language models and better integration tools) have brought them to the forefront. They differ from a typical AI or chatbot in significant ways. A standalone AI (like a basic chatbot) might answer questions, but a multi-agent system can plan and execute entire processes. For example, a single language model may struggle with multi- step requests or reasoning over sequences, whereas an AI agent system can break a complex goal into steps, distribute those to specialized agents and adjust actions based on feedback. Agents can also incorporate tools and external data,  one agent might fetch financial data via an API, another generates a report, while a third agent cross-checks the output for accuracy. This leads to higher-quality results. In fact, multi-agent AI significantly enhances the quality and complexity of work compared to single agents. 

Some of the key benefits include:

• Greater capability: Agents collectively access multiple skills and tools far beyond what one AI could handle. They can autonomously call APIs, databases, or models needed to perform tasks.
• Productivity & speed: By working in parallel and planning together, agents execute complex  workflows from a single prompt, accomplishing in minutes what might take humans days. 
• Adaptability: Agents reason and adjust strategies in real-time. They share short- and long-term memory, learning from each interaction and each other to improve over time. 
• Accuracy & resilience: Some agents can serve as validators or error-checkers for others, catching mistakes and refining outputs to increase reliability. The system can thus self-correct and provide explanations by showing how different agents arrived at a result .
• Collective intelligence: Perhaps most importantly, the whole is greater than the sum of parts. When agents collaborate, emergent intelligence can arise novel solutions and insights that wouldn’t surface in isolation. It mirrors how a well-coordinated team can solve problems more creatively and effectively than any individual alone.

A simple analogy is to imagine a busy trading floor or an M&A project team. In which a multi-agent AI system, each agent is like a specialist, one scours market data, another assesses risk, another prepares a summary report and together they respond to a mission orchestrated by a coordinating logic. For instance, JPMorgan Chase’s DeepX multi-agent system exemplifies this in practice as it deploys multiple AI agents where each agent analyzes different market indicators (macroeconomics, sector trends, company data) and then combines them to deliver more comprehensive and nuanced investment recommendations. [5] By dividing the analytical workload, DeepX aims to give traders and investors a richer, 360-degree view than any single model could generate. Thus, thoughful aim for companies seems to prioritize building AI agents to enhance employees capabilities, as our most successful implementations use AI to augment human experts, not replace them. A recent Deloitte analysis observes that forward-thinking businesses and governments are implementing AI agents and multiagent systems across a range of use cases and urges executive leaders to prepare for this next era of intelligent automation. [6] 

Modern data architecture as the digital backbone

While AI agents are the analytical brains, data is their lifeblood. Modern enterprises have decades of accumulated databases, countless SaaS applications, real-time feeds and big data streams. Harnessing multi-agent AI effectively requires that all this data can be accessed, integrated and trusted by the AI agents. This is where modern data architecture comes in it's the foundational design that makes data available and usable across the organization. In simple terms, modern data architecture means moving away from siloed legacy databases and rigid pipelines and embracing a more flexible, scalable and governed approach to data management. Key characteristics include cloud-based data lakes or lakehouses, unified data warehouses, data integration platforms and increasingly data products (self-contained, business-focused datasets with clear ownership and APIs) that can be easily consumed by applications and AI alike. It also incorporates strong data governance, security and compliance measures that are critical for regulated industries like finance.

In the finance sector, adopting a modern data architecture has become crucial to addressing the changing regulatory and reporting requirements. With ever-stricter rules (e.g. Basel IV, GDPR, DORA) and rising cyber risks, banks and insurers must ensure data is well-governed and auditable. A modern data platform helps by enforcing data privacy and access controls, while also providing self-service analytics capabilities to business users. In practice, that means authorized users and AI agents alike can quickly find and query a "single source of truth" data repository without jumping through IT hoops. Modern architectures achieve this by making real-time data ingestion and integration far easier. They can pull in varied data sources in real time structured transactions, unstructured documents, even streaming events into one analytical environment. The payoff is faster insights and more informed decision-making. For example, a modern data stack often leverages automation to move data from source systems to a cloud data warehouse and transform it consistently. One telling case was Autodesk Construction Services after a string of acquisitions as they unified data across the acquired companies using a modern cloud stack (Fivetran for pipelines, Snowflake for storage, dbt for transformation) and created a trusted, organization-wide single source of truth. [7] This eliminated duplicate data pipelines and saved hundreds of hours in maintenance, accelerating their post-merger integration. In short, modern data architecture not only consolidatesdata but also streamlines how it's cleaned and combined which is especially valuable in M&A scenarios where disparate systems need to become one.

Equally important, a modern architecture is scalable and future-proof. It can handle growth in data volume and users without major overhauls and it's flexible enough to incorporate new data analytics tools (like AI/ML services or real-time dashboards) as needs evolve. This scalability is essential in finance, where trading volumes can spike or new compliance requirements can demand quick reporting changes. It's telling that many organizations now see modernizing data architecture as vital to staying resilient. 

In summary, modern data architecture provides the high-performance plumbing and guardrails that let data flow to those who need it, while keeping that data reliable and secure. Without this, even the smartest AI agents would be starved of timely, quality information or, worse, might feed on incorrect data a recipe for poor outcomes. Thus, an investment in data architecture is really an investment in a strong foundation for any advanced AI initiative.

The synergy of agents and architecture

Bringing together multi-agent AI and modern data architecture creates a whole that is even more powerful than its parts. On one side, AI agents need data often lots of it from many sources to function intelligently. On the other, organizations have invested in making vast data accessible through modern architectures, but they need intelligent agents to truly unlock actionable value from that data. When combined, the modern data platform becomes the playground (and the fuel) for AI agents, while the agents become the savvy players that can navigate and act on the data. This synergy addresses a long-standing enterprise challenge as the persistent gap between data availability and actionable business outcomes. Even companies with sophisticated data warehouses often struggle to translate all those data points into rapid decisions. As Deloitte observes, autonomous agentic AI working atop robust data products offers a pathway to bridge this activation gap, turning raw data into real-time insights and automated processes that drive business value. [8] In other words, modern data architecture makes sure you have all the data and multi-agent AI makes sure you use it to full effect.

For multi-agent systems specifically, a rich data architecture dramatically expands what they can do. Because agents are often domain-specific, they thrive when they can easily pull exactly the data they need. A well-designed architecture might allow an AI agent to query a customer transactions database, another to retrieve market prices from a data lake and another to fetch a document from a content repository, all through standardized interfaces. This dynamic data flow is so important that it's cited as a key design principle as effective agent frameworks enable data to flow in two patterns, data to the agent and agent to the data, meaning agents can either be fed relevant data streams or proactively seek out data as needed. Modern data platforms (with APIs, streaming pipelines and unified data catalogs) make such flexibility possible. They also allow agents to work with real-time information instead of static, stale datasets. For example, a trading agent can subscribe to live market feeds in a streaming architecture, or a compliance agent can automatically get alerts from a transaction monitoring system enabling near real-time responses to events rather than after-the-fact analysis.

Conversely, AI agents enhance the value of a modern data environment by serving as intelligent intermediaries between the data and decision-makers or processes. Rather than just generating reports or dashboards for humans to interpret, agents can interpret the data and take actions directly. This is transformative. Imagine a scenario in due diligence part of M&A process where the data room might be loaded into a modern repository (structured and searchable) and then an army of AI agents can comb through it, summarizing contract clauses, flagging anomalies and even answering investors’ questions by synthesizing across documents. That turns weeks of manual data sifting into an immediate insight-generating process. As M&A technology experts noted,the trust in agentic AI grows, use cases will move from insights to autonomous actions in dealmaking. [9]  We're already seeing glimmers of that future. For instance, Google Cloud's Agentspace platform is exploring agent-assisted deal sourcing through architecture was the canvas an agentic layer that can scan internal and external data to suggest a pipeline of possible acquisition targets and even advise on M&A strategy. [10] Those agents sit on top of rich data pools (financials, market data, CRM info, etc.) and turn raw data into proactive strategic options.

Even more striking is how some firms are layering agentic AI onto legacy systems to accelerate integration. This means AI agents could access multiple disparate systems, extract and consolidate information on the fly, sparing the immediate need for a massive data migration. It's a novel workaround when a true modern data platform is still in progress. Over the long term, of course, integrating those systems into a unified architecture yields benefits but agents can deliver quick wins in the interim by bridging silos virtually. Some private equity firms are already experimenting with advanced data analytics tools that give an integrated overview of all assets in a portfolio, even if each company has different internal systems. These tools (often powered by AI) sit atop various data sources to present one coherent picture to decision-makers a clear example of agentic intelligence leveraging underlying data plumbing.

Real-world success stories illustrate the potent combination of architecture and agents. Take BlackRock, the world's largest asset manager which recently deployed an AI agent platform integrated with its famed Aladdin data system. Aladdin has long been BlackRock's modern data-driven platform for portfolio management and risk analytics. Now, BlackRock added an Aladdin Copilot essentially a multi-agent AI assistant on top of it. This agent system is embedded across 100+ front-end applications used by portfolio managers and it leverages a federated plugin architecture to tap into various domain-specific data and tools. [11] The result, when a user poses a complex query  "What's my exposure to aerospace in Portfolio X and how might a certain acquisition affect it?", the AI orchestrator pulls contextual data (holdings, market data, portfolio metrics) and spins up the appropriate agents to answer. One agent might retrieve exposure analytics, another scans news or filings about the aerospace sector, another perhaps models a scenario all coordinated by an orchestration layer (built with tools like LangChain/LangGraph). [12] BlackRock reports this has enabled proactive, personalized and secure insights for their investors and operations teams. Importantly, this was only feasible because Aladdin already provided a unified, high-quality data environment. The modern data architecture was the canvas, and multi-agent AI became the paintbrush that could rapidly draw meaningful patterns on it. This synergy is yielding very tangible benefits in productivity (automating routine analyses), in alpha generation (surfacing investment ideas faster) and in user experience (personalized assistance. It's a leading example of how finance firms can marry data and AI to reinvent processes.

In summary, multi-agent AI systems and modern data architectures reinforce each other. A quote from Deloitte Luxembourg captures it well with "The integration of autonomous AI and robust data products is revolutionizing enterprise data utilization, enabling real-time insights and automated processes". [13] But this integration also requires careful design, balancing autonomy with accountability, as we will discuss to truly deliver the promised leap in decision-making capabilities. Firms that manage this balance will not only handle information better, they'll gain a competitive edge by making decisions faster and with more intelligence than peers.

Applications in M&A

The world of mergers and acquisitions is inherently complex and information-intensive  making it ripe for transformation by agentic AI and modern data architectures. From the early strategy phase through due diligence to post-merger integration, there are numerous high-value opportunities to apply these technologies:

• Deal sourcing and strategic scanning: Identifying the right acquisition or merger targets is a bit like finding needles in haystacks. Traditionally, M&A teams rely on industry knowledge, networks and manual or web-scraped research to source deals. Now, AI agents can dramatically expand that funnel by continuously scanning data for potential fits. A multi-agent system can monitor news feeds, financial databases, patent filings, startup ecosystems, etc., for signals that a company might be a good target or is seeking a buyer. For instance, an agent could flag a small tech firm with a unique capability that complements your portfolio and has recent rapid growth (perhaps indicating it's primed for acquisition). Another agent might simultaneously analyze market consolidation trends or competitor moves. Google's Agentspace, as referenced earlier, is one example where agents offer a pipeline of possible targets to M&A professionals. By plugging into a modern data architecture (which could include internal CRM data, market data and third-party databases like CapIQ or PitchBook), these agents ensure no rock is left unturned. They can also score or rank targets based on criteria set by corporate strategy essentially automating the first pass of target screening. Beyond just listing targets, agents can assist in strategy formulation. For example, aggregating data on adjacent markets to suggest where acquisition could drive growth, or identifying gaps in the company's product lineup and then finding targets to fill them. This frees human dealmakers to focus more on evaluating and relationship-building, rather than raw research. In practice, a corporate development team might receive a periodically updated "long list" of potential targets generated by AI with key metrics attached, ensuring they stay proactive rather than reactive in deal pursuit. For now, agents serve as tireless analysts scanning the horizon 24/7 for M&A opportunities.
• Due diligence and document analysis: The due diligence phase of M&A can be grueling. Buyers often must review thousands of documents financial statements, contracts, customer data, intellectual property, HR information under tight timeframes. Modern data platforms have enabled virtual data rooms (VDRs) where all this information is stored digitally, but shifting and interpreting it is still largely manual. Enter multi-agent AI. Agents can vastly accelerate due diligence by automating much of the information processing. For example, one agent can categorize content in the data room (cluster documents by type or issue area), another can summarize documents (producing an executive summary of a 100-page contract or a set of employment agreements) and another can extract key data points (like all change-of-control clauses, or all instances of a certain liability). Together, these agents can map out the data room's contents and even answer specific questions. In fact, deal practitioners have been using AI for targeted tasks like data extraction for a few years, but multi-agent systems take it further by combining tasks into an end-to-end workflow. Imagine two AI agents swapping standard documents and clarifications, highlighting areas of concern (such as anomalies in financials or pending lawsuits) and only escalating to humans when a truly non-standard issue arises. That could compress deal timelines significantly. Even today, an AI agent can be tasked with checking that all necessary documents are present and correctly filed reducing the nightmare scenario of a critical document being misplaced or overlooked. And importantly, this gives deal teams more time to focus on interpreting findings and negotiating terms, rather than combing through paperwork.
• Valuation and financial modeling: Determining the value of a target company and modeling different deal scenarios is another area where multi-agent systems shine. Financial modeling typically requires pulling data from various sources: historical financials, market benchmarks, projections, etc. and running scenarios (best case, worst case, synergy models). Agents can automate much of this heavy lifting. One agent can continuously update a financial model with the latest actuals and compare against projections, another agent can search for comparable transactions or trading multiples in the market and feed that data in and another might test different synergy assumptions (e.g., varying cost savings if two IT systems are consolidated). Thanks to a modern data backbone, these agents can access the requisite data (financial databases, internal ERP data, industry KPI datasets) without manual collation. They effectively become a virtual team of financial analysts that work overnight and over weekends. For example, an agent could instantly tell you how a 10% increase in the price offering would affect deal accretion/dilution to earnings, because it can modify the model and compute the result on the fly. MAS can also help identify discrepancies or risks in the target's numbers, acting as a due diligence check on financial integrity. If something doesn't add up (say revenue recognition issues or unusual cash flow patterns), agents can flag it for deeper human review. Some forward-leaning private equity firms use AI-driven models that recompute valuations dynamically as new data comes in (e.g., target company updates or market shifts), enabling them to adjust their bids more finely. [14] While the human deal team still sets the strategy (what synergies to aim for, what the walk-away price is), AI agents provide a continuous, data-driven second opinion that can prevent mispricing or highlight extra value. Notably, this too depends on having integrated data linking the target's financial data, the acquirer's data (for synergy modeling) and market data in a unified system that agents can draw from.
• Post-merger integration and operations: The work doesn't stop once the deal is signed as in many ways, the hardest part is integrating two organizations. Here, multi-agent AI and solid data architecture can be a game-changer in capturing deal value. During integration, companies must consolidate systems, processes and data from both sides. A modern data architecture can provide a unified layer where data from both merger partners is combined (even if underlying systems remain separate initially). On top of that, AI agents can be deployed to monitor and drive integration tasks. For instance, an agent could monitor the combined company's data for operational KPIs (say, tracking combined sales pipeline or supply chain performance) and alert integration managers to any drop-offs. Another agent could continuously reconcile data between legacy systems ensuring that, for example, customer records or product catalogs from two companies are properly matched and merged, highlighting any conflicts. Agents can also help in process integration. If the two companies have different invoice approval workflows, an AI agent might route tasks between systems or suggest an optimized unified workflow by analyzing both. One of the trickiest aspects of integration is IT migration moving data from one system to another without disrupting business. Agents can assist by acting as go-betweens. As mentioned earlier, experts suggest an agentic layer can sit atop legacy systems, meaning users and decision-makers interact with a unified AI-driven interface, while the agents fetch and push data to each old system behind the scenes 30. This can buy time and allow gradual migration rather than a risky big-bang switch. A concrete examplet some PEs using a data platform to get a single view of KPIs across all portfolio companies. Each portfolio company might have its own ERP/CRM, but a central AI agent aggregates the financial and operational data into one dashboard for the PE firm. If a new company is acquired, the agent is simply given access to that company's systems and it starts incorporating the data no immediate re-platforming needed. Additionally, multi-agent AI can identify synergy opportunities by analyzing cross-company data. Suppose Company A and Company B (just merged) have overlapping procurement vendors an AI agent could analyze spend data from both (through the data lake) and recommend where the new entity can negotiate volume discounts, thereby achieving a key cost synergy. Another agent might compare product customer lists and find cross-selling opportunities, effectively pointing the sales team to "low-hanging fruit" revenue synergies. By doing this systematically and early, agentic AI helps realize deal value faster. And when integration hits snags (it always does), agents can assist in troubleshooting for instance, detecting data mismatches when consolidating customer databases and either auto-correcting or highlighting them. The end result is a smoother integration process withfewer costly delays or missed synergies.

From sourcing to integration, the message is that agentic AI systems supercharged by robust data architecture can speed up and improve the M&A lifecycle at every stage. They act as force-multipliers for deal teams: mundane, repetitive tasks (data entry, basic analysis, document prep) get automated, while complex analytical tasks get augmented with insights that would be hard for humans alone to generate in time. Importantly, M&A professionals remain in control the AI is an assistant, handling the grunt work and surfacing insights, but human judgment calls the shots on negotiating deal terms, handling sensitive communications and making final decisions. Caution and trust are still factors as many dealmakers are (rightly) cautious about fully autonomous deal-making, given concerns around confidentiality and the high stakes involved . The current approach is to introduce AI in assistive roles. We’re likely heading toward an era where a large portion of the M&A process is intelligently automated dealmakers will orchestrate a symphony of AI agents and data systems that execute much of the heavy lifting, leaving humans to focus on strategy, relationships and nuanced decision-making. Those who embrace these tools early could complete deals faster with better outcomes and outmaneuver slower competitors.

Implementation considerations and best practices

Implementing multi-agent AI systems on top of a modern data architecture is not a trivial endeavor, it’s a strategic initiative that touches technology, people and processes. Below are key considerations and best practices for organizations aiming to leverage this paradigm shift:

• Start with a strong data foundation: As mentioned in the beggining, your AI is only as good as your data. Before unleashing a swarm of AI agents, ensure your data architecture is solid. This means having centralized or well-virtualized data stores, clear data governance (data quality checks, metadata catalogs, access controls) and scalable infrastructure (often cloud-based). Banks and firms that have embraced data lakes, warehouses, or data mesh concepts are a step ahead here. Data integration should be largely automated. For example, using pipelines or ELT tools to continuously feed data from transactional systems into analytic stores . If you’re dealing with an M&A situation, prioritizing the data integration of the new acquisition is crucial (successful mergers often share modern data integration as a core factor). A clean, unified data environment will make it much easier to deploy AI agents that can roam freely and securely to fetch what they need. It’s also vital for compliance as any AI initiative in finance must respect privacy and regulations, so your data architecture should have built-in compliance features (encryption, audit trails, role-based access). Those capabilities are non-negotiable in regulated environments. Get your data house in order first as it will pay dividends when the AI arrives. 
• Adopt a composable, modular design: Multi-agent systems should be designed in a composable architecture, meaning you build them out of reusable components and services. This aligns perfectly with modern microservices architecture on the data side. The idea is to avoid one big black-box AI. Instead, develop multiple smaller agents, each specializing in a task or having a specific “skill, and a way for them to communicate (or be orchestrated). Internally at Deloitte the recommendable aproach is a composable design which bring best-of-breed components together in a microservices architecture when constructing AI agent ecosystems. In practice, this could mean using containerized services for each agent and APIs or message queues for their interactions. BlackRock’s approach provides a blueprint as they created a plugin registry where different engineering teams can plug in their domain-specific agents (for trading, compliance, etc.) without needing to know the whole system. Your organization can mirror this by empowering each domain (finance, HR, sales, etc.) to build agents relevant to their data and workflows, while a central AI platform coordinates them. Modular design also eases scaling.  You can add more agents as new needs arise, or update one without overhauling everything. It’s akin to adding new services to a cloud architecture. The orchestrator (which could be a workflow engine or an AI planning module) is key as it decides which agents to call and when. Ensure your orchestrator can handle flexible workflows and conflict resolution if agents have overlapping duties. Sometimes, a hierarchical orchestration (one master agent delegating) is simplest, but more complex tasks might benefit from decentralized negotiation between agents. Choose the model that fits the use case and be ready to hybridize. The bottom line is to design your agent system like a well-architected software system, not a magical one-off AI. This will also help with explainability. When each agent’s role is clear and its actions can be logged, the overall system is easier to understand and trust. 
• Define roles and domains clearly: Clarity in what each AI agent does is crucial. Aim for a domain-driven approach as our research at Deloitte shows most AI agents should be sourced and/or designed based on specific domain requirements. [15] In other words, identify the distinct domains or problem areas in your business process and tailor agents to those. If you’re building an M&A due diligence agent suite, you might have one agent for legal docs, one for financial analysis, one for compliance checks. If it’s a trading MAS, you might have separate agents for different asset classes or strategies. Defining roles prevents agents from stepping on each other’s toes and limits the scope each needs to handle (which makes it easier to train or configure them). It also ties into the principle of role-based design as agents should be designed to perform roles rather than specific tasks. Meaning design them as flexible specialists. For example, an Earnings Summary Agent can generally summarize earnings reports (role), rather than an agent hardcoded just for summarizing Company X’s report (specific task).  This way the agent can be reused for any similar input. Proper role definition also helps with assigning the right tools and data to each agent. A best practice is the principle of controlled access to data, skills & tools. Give each agent only the data and tools necessary for its role. This not only improves efficiency (they won’t waste cycles on irrelevant info) but is good for security (an agent responsible for HR analytics might not need to touch payment card data, for instance). Essentially, partition the problem space and match it with a partitioned agent space.
• Keep humans in the loop (at least for now): No matter how autonomous your AI agents are, human oversight and collaboration remain vital, especially in finance and M&A where stakes are high. Implement a “human-in-the-loop” model for critical decisions or whenever the AI’s confidence is low. Multi-agent systems can be set up to seek human approval at predefined checkpoints. For example, an agent drafts an investment recommendation but a human portfolio manager must approve execution, or an agent flags a contract issue but a human lawyer must validate it. This practice guards against AI errors and builds trust. Deloitte explicitly lists “Human in the loop: knowledgeable humans are essential to safeguard against system errors & biases” as a key principle in multi-agent system design. [16] Early in adoption, it’s wise to lead on the side of more human oversight. M&A experts echo this sentiment to treat agentic AI as an assistant, not a replacement . Encourage users to challenge the AI’s results, add their judgment and provide feedback. This practice not only catches mistakes but also feeds valuable feedback into improving the AI (many systems can retrain or adjust based on user corrections, thereby learning). Over time, as confidence in certain agents grows, you might dial up autonomy, but always with a mechanism for human override. And importantly, don’t remove humans from the loop in areas that involve ethical judgments, sensitive negotiations, or regulatory compliance sign-offs. Those should remain under human purview with AI as decision support.
• Upskill and prepare your workforce: Introducing multi-agent AI will change how teams work. Some tasks will be automated as roles will shift toward oversight, interpretation and strategic analysis rather than rote processing. This requires a cultural shift and likely a skill shift. Employees may need training to work effectively with AI agents. For instance, learning how to formulate good prompts/ questions for the AI and how to interpret AI outputs critically. It’s wise to involve the end-users (bankers, analysts, managers) early in the design process so that the AI tools truly augment their workflow rather than feel imposed. Change management is key to communicate clearly that the goal is augmentation, not replacement. Emphasize how automating grunt work lets staff focus on higher-value activities like client interaction, creative problem-solving and decision-making. In many organizations, there can be resistance.  Whether from mid-level managers who feel loss of control or junior staff worried about skill development. Address these by highlighting new opportunities as junior professionals can actually learn faster by handling a broader scope of analysis with AI doing data gathering. In fact, tasks that traditionally served as training grounds (like assembling data or initial modeling) might be done by AI, so rethink how young professionals will gain experience. Perhaps by focusing more on interpreting AI findings and making recommendations, under mentorship. A professor interviewed on the future of dealmaking noted that as agents take over 10x more work, new roles will emerge and companies should optimize collaborative interaction between humans and agents, not cling to old task bundles. So, redesign processes and roles to make the most of human-AI collaboration. Finally, cultivate a data-driven culture. Encourage employees to trust insights that are backed by data (and AI analysis of that data), but also to maintain healthy skepticism,  the mindset should be trust, but verify. Over time, success stories will build confidence. Celebrate wins where AI agents helped close a deal faster or caught a risk that saved the company money as this reinforces positive adoption.

Implementing these systems is indeed a journey. Early on, identify pilot projects that are high-impact but manageable in scope. Perhaps start with automating a particular workflow (e.g., loan document review, or internal financial reporting consolidation) using a couple of agents and a cleaned dataset. Learn from that, then iterate and expand. As you mature, you’ll accumulate reusable components, a library of agents and data APIs  that can be recombined for new problems. It’s also smart to watch industry benchmarks and possibly partner with technology firms or consultancies that specialize in multi-agent frameworks and data architecture. They can bring templates and accelerators so you’re not reinventing the wheel. The competitive landscape is moving quickly while many dealmakers and finance executives are still cautious, the momentum is there. As one of my collegue at Deloitte said, AI adoption is inevitable in this field and those who have robust data foundations and a strategic approach to AI will be in the best position to capitalize on it.

Conclusion

The convergence of multi-agent AI systems with modern data architectures heralds a new era for the finance and M&A industries where data-driven intelligence and automation reach unprecedented levels. By orchestrating specialized AI agents on top of unified, well-governed data platforms, organizations can achieve feats that once seemed out of reach, the near-instant analysis of complex datasets, workflows that execute in minutes instead of weeks and decisions informed by a 360-degree view of information. This is not futuristic hype as it’s already happening in pockets today. Financial institutions are using collaborative AI to manage portfolios and risk in real time and dealmakers are exploring agentic tools to streamline everything from target scouting to integration planning. The results so far point to significant realying on faster speed to insight, higher productivity and the ability to handle complexity with more confidence. No bank or company wants to be left behind if a competitor can close deals 30% faster or respond to market shifts more nimbly thanks to these technologies.

Yet, realizing this vision requires more than plugging in an AI tool. It calls for a strategic alignment of technology and people by building the right data infrastructure, ensuring governance and ethical guardrails and fostering a workforce that embraces collaboration with AI. Those that get it right will find that multi-agent AI doesn’t replace human expertise, but rather amplifies it. Freed from drudgery, bankers, analysts and executives can focus on strategy, creativity and client relationships armed with richer insights at their fingertips. Business processes will evolve as routine tasks become automated, new higher-level roles and tasks will emerge for humans. 

In the coming years, we can expect the currently reluctant adopters to turn into enthusiasts as success stories multiply. The trajectory likely mirrors past tech shifts (from spreadsheets to ERP systems, from paper data rooms to virtual ones) as initial caution followed by widespread adoption once value is proven. Leadership should therefore start preparing now. That means investing in data capabilities, piloting agentic AI in targeted areas and crafting a roadmap for broader integration. The window for gaining early mover benefits is open.