Empowering M&A dealmakers: the essential role of AI literacy in 2026

The M&A landscape is undergoing a fundamental transformation. Despite $30-40 billion in generative AI investments, a recent MIT study reveals a startling reality: 95% of organizations are seeing zero returns on their AI investments. This disconnect isn't about the technology itself. It's about literacy.

As we move through 2026, the ability to effectively use AI tools has become as essential to dealmaking as financial modeling or negotiation skills. Yet many M&A professionals remain uncertain about how to integrate these powerful capabilities into their workflows.

The learner's permit phase

Think of AI adoption in M&A like learning to drive. Organizations are investing heavily in sophisticated vehicles, but most professionals are still figuring out the basics. They're not ready for the fast lane, and that's perfectly normal.

"People are talking about trying to drive in the fast lane or perhaps even being a Formula One driver going around on a racetrack when they're just learning how to drive," explains Scott Moeller, Professor of Practice at Bayes Business School. "We're at the learner's permit section here."

This perspective reframes the disappointing ROI statistics. The investment isn't wasted. It's necessary groundwork. Just as every driver needs practice time before becoming proficient, organizations need time to develop AI literacy across their teams.

The key is accepting that proficiency takes time, practice, and proper training. The AI systems available in five years will be more sophisticated and easier to use, but only if today's dealmakers invest in building foundational skills now.

Training tomorrow's dealmakers

Leading business schools are embedding AI literacy into every aspect of their curriculum. At Bayes Business School, faculty members are required to integrate AI into their coursework, ensuring graduates enter the workforce with practical experience.

This approach goes beyond theoretical understanding. Students must demonstrate they can use AI tools effectively in real-world scenarios, including exams and case studies. The goal is producing AI-literate graduates who can immediately contribute to their organizations' digital transformation efforts.

Key educational principles include:

• Hands-on experience with multiple AI platforms
• Understanding prompt engineering and query optimization
• Learning to verify AI-generated outputs
• Recognizing credible sources versus unreliable information
• Applying AI tools to specific M&A workflows

These graduates represent a competitive advantage for firms that can effectively harness their skills. They've grown up with AI tools and understand intuitively how to integrate them into professional workflows.

The credibility challenge

One critical skill distinguishes effective AI users from those who struggle: source verification. Generative AI systems produce confident-sounding answers, but not all outputs are equally reliable.

Progressive organizations have established clear standards. Students and junior analysts cannot cite ChatGPT, Claude, or Gemini as sources. They must trace information back to its original source and evaluate its credibility.

"When an analyst comes and says I got this on ChatGPT, she says to them, 'Well, where did ChatGPT get that information?'" Moeller notes, describing practices at major London banks.

This discipline matters because AI systems don't admit ignorance. When they can't find accurate information, they generate plausible-sounding alternatives based on patterns in their training data. Calling this "hallucination" understates the problem. These systems effectively lie by fabricating answers rather than acknowledging limitations.

Developing source verification skills requires:

• Always asking where AI obtained specific information
• Cross-referencing outputs with primary sources
• Evaluating source credibility using established criteria
• Preferring official documents over secondary sources
• Testing AI outputs with multiple queries to identify inconsistencies

Bridging the mid-career gap

Mid-level professionals face a unique challenge. They built their careers without AI tools and may feel uncertain about integrating new technologies into established workflows. The solution requires humility and a willingness to learn from junior colleagues.

"I think they need to have a little bit of humility," Moeller advises. "You are going to learn from some of the junior staff."

This reversal of traditional mentorship dynamics can feel uncomfortable. Senior professionals may hesitate to admit they're using AI tools, fearing it suggests gaps in their expertise. But this mindset prevents organizations from maximizing their AI investments.

Effective strategies for mid-level professionals include:

• Openly discussing AI tool usage with team members
• Asking recent graduates about best practices for prompt engineering
• Experimenting with AI tools on low-stakes projects
• Sharing both successes and failures with colleagues
• Creating peer learning groups focused on AI applications

The technology evolves rapidly. Training programs that work today may be outdated within six months. This reality makes continuous learning essential. Organizations should invest in ongoing training rather than one-time workshops.

Podcasts, vendor-specific training programs, and hands-on experimentation all contribute to skill development. The key is consistent practice with real-world applications rather than purely theoretical learning.

Senior leadership and AI adoption

Many senior dealmakers acknowledge they haven't fully engaged with AI tools. They recognize junior team members use AI for repetitive tasks but struggle to see applications for strategic, non-repetitive work.

This perception underestimates AI's current capabilities and future potential. Even in its present form, generative AI can support senior-level decision-making. As the technology advances over the coming years, these applications will expand significantly.

The barrier isn't capability. It's practice.

"You cannot become a good driver of a car by reading a textbook or by listening to a book," Moeller emphasizes. "You actually have to get out there and get behind the wheel."

Senior professionals must accept initial inefficiency. The first attempts at using AI for complex tasks will likely take longer than familiar methods. Prompts will need refinement. Outputs will require verification. But this investment in learning pays dividends as proficiency develops.

Practical steps for senior dealmakers:

• Start with low-risk applications on back-road projects
• Accept that early attempts will be inefficient
• Gradually increase complexity as skills develop
• Identify specific use cases where AI provides clear advantages
• Recognize some tasks remain better suited to traditional approaches

Not every task benefits from AI. Walking two blocks is faster than driving. Similarly, some dealmaking activities don't warrant AI involvement. Experience helps professionals distinguish between situations where AI adds value and where traditional methods remain superior.

Current and emerging applications

AI's role in M&A continues expanding beyond early use cases. Understanding where the technology excels today and where it's heading helps professionals prioritize their learning efforts.

Established applications:

• Deal sourcing: identifying acquisition targets and potential buyers more comprehensively than traditional methods
• Due diligence: processing large document volumes to extract relevant information quickly
• Financial analysis: analyzing valuation data and pricing information, though modeling applications still require careful oversight

Emerging applications:

• Post-deal integration: managing the human elements of combining organizations
• Negotiation preparation: creating AI-powered avatars of negotiating counterparts for practice sessions
• Scenario planning: modeling multiple integration approaches and their likely outcomes

The negotiation application illustrates AI's evolving sophistication. Imagine preparing for a critical negotiation by practicing with an AI avatar trained on your counterpart's public statements, interviews, and writing. While never perfect, this preparation builds confidence and reveals potential strategies.

These emerging applications focus on areas traditionally considered too human-centric for automation. As AI systems become more sophisticated at understanding context and nuance, they'll provide increasingly valuable support for these complex interpersonal aspects of dealmaking.

Building organizational AI literacy

Creating AI-literate organizations requires more than providing access to tools. It demands cultural change, structured learning opportunities, and knowledge sharing across experience levels.

Effective organizational strategies:

• Leverage recent graduates as internal AI resources and mentors
• Create cross-generational learning partnerships
• Establish clear standards for AI usage and source verification
• Invest in vendor-specific training for key platforms
• Encourage experimentation with appropriate guardrails
• Share lessons learned from both successes and failures

The most valuable learning resources often exist within your organization. Recent hires who grew up using AI tools bring intuitive understanding of effective prompting and verification techniques. Pairing them with experienced dealmakers creates mutual learning opportunities.

External resources also play important roles. Technology vendors like SS&C Intralinks provide specialized training on AI features embedded in their platforms. Academic researchers studying AI applications in M&A offer evidence-based insights. Industry thought leaders share emerging best practices through blogs, podcasts, and conferences.

The key is creating multiple learning pathways that accommodate different learning styles, experience levels, and specific role requirements.

The path forward

AI literacy has become a fundamental competency for M&A professionals at every career stage. The organizations seeing returns on their AI investments are those that recognize technology alone isn't enough. Success requires skilled people who understand how to apply these tools effectively.

The journey from zero returns to meaningful ROI follows a predictable path. It starts with accepting that we're all learners, regardless of seniority. It continues with structured practice, proper training, and willingness to learn from colleagues at all levels. It accelerates as organizations build cultures that encourage experimentation and knowledge sharing.

The AI systems available in 2026 are more capable than ever before, but they're still evolving rapidly. The professionals and organizations that commit to continuous learning will find themselves increasingly advantaged as the technology matures.

The question isn't whether AI will transform M&A. That transformation is already underway. The question is whether individual dealmakers and their organizations will develop the literacy needed to harness these powerful capabilities effectively.

The learner's permit phase won't last forever. Those who invest in building skills now will be ready when it's time to move into the fast lane.