Agentic AI is the next frontier in artificial intelligence.
Where generative AI (genAI) requires prompting to generate information, text, images, etc., agentic AI is designed to solve complex tasks and make decisions autonomously. If used properly, agentic AI could have a transformative impact across industries, including M&A.
In this guide, we break down how agentic AI works, the specific ways in which it’s impacting M&A, and how Grata, Datasite, and Blueflame AI are blazing the trail.
Key Takeaways
- The vast majority of dealmakers cite increasing efficiency and time savings as their primary goals for adopting AI.
- Agentic AI systems are designed to autonomously manage and execute complex tasks.
- Dealmakers can use agentic AI to surface high-quality leads before their competitors.
- AI agents can continuously monitor market conditions and identify emerging trends in real time, allowing for more thorough market analysis.
- Agentic AI tools can dramatically reduce the time and resources that deal makers devote to the due diligence process.
Where Modern Dealmakers Stand on AI Adoption
At this point, AI in M&A workflows is no longer a foreign concept to dealmakers. Almost half of dealmakers (49%) use AI tools nearly every day, according to a recent survey conducted by Sourcescrub.
So how are M&A professionals putting those tools to use? The overwhelming majority of respondents (76%) said that their primary goal for adopting AI technology is increasing efficiency and time savings.
Seventy-one percent of respondents reported using generative AI tools like Claude or ChatGPT to draft emails, analyze data, and more.
Source: Sourcescrub, AI Agents: Exploring Dealmakers’ New Frontier
But agentic AI is designed to go beyond streamlining basic, menial tasks. From sourcing to due diligence to post-merger integration, the tech’s decision-making capabilities are set to boost productivity and efficiency across the entire dealmaking process.
What Is Agentic AI?
Agentic AI systems are designed to complete complex tasks. Once a goal is set, agentic AI tools run independently to identify and analyze the data they need, make an action plan, and execute the necessary steps. Agentic AI systems also evaluate outcomes and adjust their actions accordingly.
AI agents are much more dynamic than genAI tools, which create new material (e.g., data, text, images, or code) based on patterns they’ve learned from large sets of training data. GenAI requires prompting by a human user to execute a task, and it delivers a single output at a time. Each new task requires a new prompt from the user.
Source: Grata
Note, however, that agentic AI and genAI work together. GenAI handles the creative content, like presentations, emails, or proposals, while agentic AI takes care of the strategic planning and execution (e.g., deciding when to send an email, who to target, and how to follow up).
Agentic systems can also orchestrate several generative models. For example, an AI agent could request specific content from a genAI at a specific moment in the sales process and use the output as part of a larger strategy.
In this way, agentic AI and genAI create an end-to-end digital assistant that produces compelling content and manages the whole workflow.
How Agentic AI Works
Source: Grata
There are four key phases in an agentic AI’s task-execution process:
- Perception – During this phase, the AI agent identifies data from various sources. These could include databases, sensors, or digital interfaces.
- Reasoning – Next, a large language model (LLM) analyzes the data to suss out which pieces are relevant to the task at hand. It then devises a plan of action.
- Action – The AI agent interacts with other tools and software applications to carry out the plan.
- Learning & Adapting – The agentic AI leverages its data flywheel, or feedback loop, to assess the results of its actions and adapt its strategies accordingly so that it improves over time.
How Agentic AI Is Transforming M&A Workflows
In the M&A world, agentic AI tools like Blueflame AI — paired with platforms like Grata and Datasite — are streamlining every step of the dealmaking process. Dealmakers are using the tech to find deeper, more precise insights, scale their teams, and make smarter decisions faster.
Business Development
AI agents can sift through huge amounts of data, both structured and unstructured. Firms can provide their AI agents with access to market reports, financial statements, news sources, proprietary data, contracts, and more. The agent will use that data and its problem-solving methods to help teams surface and evaluate high-quality leads before their competitors.
As the tech advances, dealmakers will be able to use agentic AI to build dynamic valuation models that automatically update as new data becomes available. Agentic systems can simulate different market, regulatory, and competitive scenarios, test assumptions, and produce a range of possible outcomes instead of a single number. These systems will also analyze various data sources to evaluate intangible assets, like brand reputation and intellectual property, and factor that into their valuation models.
Because these tools can synthesize complex data and provide recommendations tailored to each specific deal, private market investors will be able to come to negotiations with more confidence.
Market Analysis
Part of what makes agentic AI so powerful is its ability to monitor multiple channels of data and update its predictions automatically as new information becomes available. In the M&A world, that means AI agents can continuously monitor market conditions and identify emerging trends in real time.
For example, they could flag new regulatory changes that might open up new opportunities or risks as they move through the approval process. AI agents can also “understand” why given factors matter for their firm’s specific strategy and make recommendations based on that context.
Due Diligence
The due diligence process is notoriously time- and labor-intensive. Agentic AI is changing that. The tech can dramatically cut the time that dealmakers spend reviewing documents by autonomously scanning and synthesizing them. AI agents flag inconsistencies and potential risks in minutes instead of hours.
The agents can even identify undisclosed liabilities that humans might miss. They can cross-reference information from legal, financial, and HR documents to call out non-standard clauses and compliance risks.
From there, AI agents can assign follow-up tasks to key players and set up automated reminders to keep the process running smoothly.
Post-Merger Integration
The post-merger integration stage is another high-value area where dealmakers can tap in agentic AI. Agents can analyze both companies’ processes to pinpoint inefficiencies and areas or redundancy.
These systems can also streamline tracking key milestones, identifying potential blockers, and automating administrative processes like data migration.
Additionally, AI agents can analyze internal documents, communications, and employee feedback to evaluate cultural compatibility and guide integration processes accordingly.
Finally, after the deal closes, AI agents can continue to monitor operational and performance data and make tailored recommendations. This can help mitigate potential issues like employee turnover.
The Future of the Firm
Agentic AI is evolving rapidly. As the tech becomes more fine-tuned and gains more capabilities, it will be able to augment nearly every part of the M&A firm. Human team members will be able to leverage their AI counterparts to work more efficiently and make smarter decisions.
Remember, however, that the success of any AI tool is strictly tied to the quality of its training data. No matter what role an AI agent is intended to augment, it must be trained on specific, accurate, and meticulously annotated data.
Challenges and Key Considerations for Agentic AI
Of course, as with any technology, agentic AI is not without challenges and potential risks. As mentioned in the previous section, high-quality training data is absolutely necessary for high-quality outputs. Relying on agentic AI for high-stakes decision making could put deals — or even the firm — in jeopardy if the model was trained on incomplete or inconsistent data.
Dealmakers should also conduct careful due diligence when considering a partnership with an AI provider to ensure that all security bases are covered. Agentic AI systems handle highly sensitive data, so strict privacy policy compliance is crucial.
Finally, firms must work to establish a culture of AI enablement. Bringing AI into day-to-day workflows represents a significant paradigm shift. Those who embrace it fully will see the greatest success. Hire and develop people who possess deep domain expertise, critical thinking skills, strong ethical judgment, and an eagerness to integrate AI tools into their daily work lives.
Grata, Blueflame AI, and Datasite Are Blazing the Trail
Grata, Blueflame AI, and Datasite are building the M&A tech stack of the future. We’re combining forces to eliminate workflow disruptions and provide seamless access to investment-grade data for 19M+ companies across the private market with game-changing AI and investment-grade data.
Find better opportunities, screen deals with precision, and connect with the right decision-makers faster.
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