AI is Fueling Change. But Are You Ready for the Relationship?

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Euki Binns

Senior Partner at Logical Approach |  More PostsBio

Euki Binns is a Senior Partner and Practice Lead for M&A and Digital Transformation at Logical Approach. She brings a performance-driven mindset and execution-first philosophy to complex transactions, post-merger integration, and technology modernization from startups to Fortune 500 companies.

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AI is accelerating change across every corner of business, but AI shouldn’t simply plug into your stack. It performs differently depending on how it’s deployed, how well its role is defined, and how clearly it’s aligned to your transformation goals.

When brought in to realign struggling implementations, our experience echoes what BCG quantifies above: the issue usually isn’t the tools. The breakdowns emerge from disconnects in leadership, alignment, and clarity. 

In high-stakes environments like digital transformation, M&A, and other enterprise-wide change, your first question should go beyond features and capability. What matters more is how you and your organization will relate to AI over time.

At Logical Approach, we believe that strategic missteps rarely stem from flawed tools alone. They start with flawed assumptions: unclear evaluation processes, misaligned expectations across teams, and a lack of appropriate oversight for AI-generated outputs.

This article explores what it takes to build a durable relationship with AI that’s capable of supporting real transformation. Because in the end, we believe that AI can’t just perform for you, it has to belong.

When More Options Mean Less Clarity: The Illusion of Choice

One of the most significant challenges companies face when exploring AI is its plethora of optionality. At first blush, it sounds like a great problem to have, but it’s a luxury that often leads to problems during process design and implementation. What results are outcomes that fall short of expectations when weighed against the cost and complexity of adoption.

Let’s be honest: AI is still new. And while Leadership 101 drives us to align technology choices with clearly defined strategic objectives, AI’s performance varies not only in design, but also in how it responds to your prompts, your use cases, and the real-world environments it’s deployed into. That variable-induced unpredictability when not controlled, can trigger errors in even seemingly simple binary outputs, and that makes platform selection feel less like an evaluation, and more like a crapshoot.

In the context of M&A or other large-scale enterprise change, that kind of ambiguity can be costly. Decision-makers are used to evaluating partners, systems, and data strategies based on metrics like fit, integration risk, and long-term alignment. AI should be no different, but as pressures mount and the input variables increase it’s easy to lose sight of your original strategic intent. In doing so, you risk undermining the very reason you invested in the first place. 

You’re Not Just Choosing a Tool: AI as a Change Stakeholder

In transformation work, especially post-merger, every new system is more than a functional add-on. It becomes a participant in how teams work, how decisions get made, and how trust is maintained across departments.

The same is true for AI. Whether it’s surfacing acquisition synergies, analyzing customer data, or generating internal insights to strengthen cross-functional alignment, AI is now a collaborator, and collaborators need to be understood, not just pushed to a live environment.

Think of it as a new hire—a new teammate. AI needs a clearly defined role and the right touchpoints for accountability. Success doesn’t come from expecting it to lead, but from designing an environment in which AI can support the team’s goals. The outcome is about elevating performance.

When our Logical Approach teams work with clients during moments of significant organizational change, this is a consistent theme: transformation isn’t just about systems. It’s about how well people and tools come together under shared expectations, and that’s a hallmark of our biggest client wins.

The Data Integrity Dilemma: Fluency vs. Truth

During internal experimentation, we recently encountered a recurring issue: tools that produced elegant, confident-sounding responses that were fundamentally wrong. In some cases, they omitted key product features. In others, they stated inaccuracies with such fluency that the errors were hard to catch without manual verification.

For low-stakes exploration, these are forgivable mistakes, but in M&A or change initiatives—where facts feed strategy, investment, and stakeholder trust—those kinds of errors are unacceptable. The most “human-like” AI is not always the most helpful. Leaders must be clear: do you want a conversational partner, or a verified research assistant? And in which contexts does each matter?

Don’t Look for a Fantasy Partner: Build a Team

We’re seeing a growing shift in mindset: the most successful AI adopters are building stacks rather than searching for the perfect AI. Here, different tools play different roles, so thinking in terms of stacks allows you to align capabilities with the specific realities of your workflow. It helps you avoid overcommitting to a single platform that may not evolve alongside your business, and it opens up opportunities to integrate AI more effectively into your operations.

Integrating an effective AI capability isn’t just about tool selection and installation. You’ll need to also create the conditions that enhance collaboration in order to hit your targets. Organizational alignment, workforce training, and stakeholder adoption play a role.

If you think your organization isn’t “agile” enough, think again. Monolithic organizations can still unlock significant value from AI alongside their agile counterparts. The key is to begin adopting team-based thinking, even within existing frameworks. That means empowering business units to experiment, identifying targeted use cases, and bringing AI into specific workflows where it can drive measurable gains. So, forget the soulmate—AI works better as a team sport.

Transformation doesn’t require an all-or-nothing approach. Start by targeting a change-space and shaping it into an environment where AI can operate as part of the dynamic. That’s where transformation begins.

A Real-World Parallel: Morgan Stanley’s AI Assistant for Advisors

Morgan Stanley’s 2023 rollout of its “Next Best Action” platform offers a compelling glimpse into what happens when AI is positioned as a collaborator. Designed to support human financial advisors rather than to replace them, the system surfaces real-time insights based on portfolio performance, market shifts, and client behavior. However, it’s the advisor who decides what to act on and when. That deliberate role design ensured the AI was integrated into relationship workflows without disrupting trust-based client conversations.

The result? Higher productivity, increased client engagement, and a team that embraced the technology rather than resisted it. Like any transformation initiative, the key wasn’t the AI itself. Its success came from the clarity that existed around its role, its boundaries, and its partnership with the people who used it.

To learn more about how Morgan Stanley structured this initiative and what it revealed about AI-human collaboration, see the full breakdown in ThinkAdvisor’s feature.

Attractive Tech Won’t Save You: Alignment, Depth, and Fit Will

As with our human relationships, the same holds true with our relationship to technology: surface appeal fades quickly without trust, shared purpose, and meaningful depth. True AI readiness isn’t about understanding neural nets or decoding transformer models, it’s about creating clarity with the goal of extending human capability.

In high-stakes environments where fatigue and cognitive load are real, the right AI can sharpen focus and elevate performance to help drive and sustain momentum across the organization.

At Logical Approach, we encourage leaders to ask:

• What role will AI play in our transformation roadmap?
• How will we validate AI-generated outputs to ensure accuracy and accountability?
• What cultural and operational conditions need to exist for AI to truly belong in our organization?

AI is not a magic wand, but it can be a reliable partner if you define the relationship with intention. After all, our business relationships succeed when we’re clear-eyed about limitations and still choose to collaborate, and AI is no different.

Conclusion: Set the Terms. Shape the Relationship.

In M&A, digital transformation, and every serious change initiative, one principle holds true: clarity is king. The same must apply to AI. Don’t rush to adopt a tool. Take a step back and define the relationship first. Decide how AI will contribute to your goals, how its outputs will be held accountable, and how you’ll preserve trust across every layer of the organization as AI takes a seat at the table.

Like any teammate, AI needs to be brought into the culture with intention, rather than thrown into the workflow with hope. If you’re leading M&A, digital transformation, or post-deal integration, the question isn’t whether to adopt AI, but how to make it perform, align, and belong.

This piece is written for business leaders navigating digital transformation, post-deal integration, and other moments of large-scale change. Need a partner in defining your AI path? Let’s talk.