Agentic AI was one of the tech trends for 2025 presented in my most recent blog post and in Accenture’s Tech Vision 2025 report. Geared to operating autonomously, proactively, and contextually, AI systems of this kind are already on the market and are making very impressive advances. In recent tests focusing on software engineering issues, one leading solution achieved a resolved rate of 49% – up from under 5% in 2023.
But the tech has advantages not only for software engineering; it also holds considerable promise in tech mergers and acquisitions (M&A) – with 64% of M&A executives now believing that GenAI will revolutionize deal processes. Let’s take a deeper dive into agentic AI, its use cases, and some of the benefits it has to offer.
Agentic AI and AI Agents in a Nutshell
While conventional AI models respond to input by following rules, agentic AI has a much higher degree of autonomy. Unlike other forms of AI, it learns proactively and makes autonomous decisions. It refines its understanding by gathering and interpreting data and then uses the findings to anticipate future tasks. In addition, agentic AI has contextual awareness, recognizing environmental nuances and adapting its behavior accordingly.
The AI agents that give the technology its name are specific instances or implementations of agentic AI principles. Examples include virtual assistants like Siri, task-specific agents focusing, for instance, on M&A processes, and customer service bots.
Under the Hood: The Tech Behind Agentic AI
Agentic AI is built on a technological framework comprising three key elements:
RL denotes the process by which agents learn optimal actions through interaction with their environment. This enables them to improve their performance over time – especially in complex, dynamic scenarios like financial modeling or market analysis.
The GUIs in agentic AI are interfaces that incorporate natural language and visual elements to support intuitive, human-like interaction for tasks involving human input, such as validation and prompt engineering.
As discussed in an earlier blog post, multimodal AI integrates text, images, and structured data. In agentic AI, these capabilities allow agents to interpret diverse data formats. This is particularly important in tech due diligence, where varied data types must be analyzed reliably and consistently. (If you’re interested in my take on AI in tech due diligence generally, check out this blog post.)
Some Real-World Use Cases
Current applications of agentic AI focus on day-to-day operations that call for real-time decision-making – for example, supply chain management and optimization of inventory levels.
The tech is also used in software development to autonomously design system architectures and write or debug code. What’s more, agentic AI has a role to play in cybersecurity, where it can monitor network traffic, detect anomalies, and respond to potential threats.
Agentic AI: General Benefits…
Accenture has found that by transforming repetitive, manual know-your-customer tasks into intelligent and adaptive workflows, agentic AI can boost efficiency by up to 50% and reduce costs by as much as 70%.
Demonstrating a strong commitment to AI innovation, the Accenture AI Refinery™ as a Service offering provides organizations with a powerful platform to scale their AI initiatives. Developed in close collaboration with industry leaders like NVIDIA, it delivers tailored industry solutions, seamless data integration, faster deployment, and robust governance.
…and the Benefits for Tech M&A
Uses of agentic AI in the tech M&A space include automating due diligence tasks – for instance, testing hypotheses and reviewing vast datasets, such as financial records, intellectual property, and legal documents. This enables organizations to spot red flags and opportunities extremely quickly and accurately.
Another key tech M&A application is market analysis. Here, the AI analyzes industry trends, competitive landscapes, and market dynamics, providing predictive insights for strategic decision-making. According to an Accenture study, 73% of executives expect generative AI to deliver high or very high value in industry and company research during the pre-deal phase.
And finally, agentic AI offers invaluable support for post-merger integration (PMI), where it can smooth cultural and operational integration by mapping organizational structures, gauging employee sentiment, and proposing optimized workflows.
The Only Limit Is Trust
As with AI solutions generally, it’s essential to ensure that agentic AI is deployed responsibly. Here, trust is a key consideration. In fact, Accenture’s Tech Vision 2025 highlights trust as the only limit for organizations looking to move forward with autonomous AI solutions like AI agents. And it stands to reason that the greater the autonomy and intelligence of these agents, the greater the trust required.
This view is borne out by executives, 77% of whom believe that a foundation of trust is essential for unlocking the true benefits of AI. Against this background, business leaders must build trust not only by deploying AI responsibly, but also by ensuring the accuracy, predictability, consistency, and traceability of its outputs.
Establishing Accountability and Mitigating Bias
Any organization planning to leverage agentic AI must also address the question of accountability – in other words, who's responsible when the tech makes a mistake, and who’s responsible for verifying the output of AI applications.
It’s also essential to effectively mitigate bias. Otherwise, any prejudicial content in the human-created training data used to build the LLM – for example, gender, sexuality, race, religion, socioeconomic status – will spill over into the output. If these issues are addressed successfully, responsible AI holds considerable promise for detecting and neutralizing biased language or tone, for example in chatbots.
The Bottom Line
If you’re considering implementing an agentic AI solution, your focus should be on leveraging the strengths of the tech without losing sight of its limitations and the importance of deploying AI responsibly.
Market experts see great potential here: Gartner predicts that by 2028 agentic AI will be making at least 15% of day-to-day work decisions autonomously. And the tech provides tech M&A teams with the tools they need to navigate the complexities of deal-making with agility and insight.
Any Questions?
Interested in learning more about agentic AI in general and its specific applications in tech M&A? Then, feel free to contact me.
Comments