Raising AI the Right Way: Building a Business Case with Ambition—and Realism

Artificial intelligence has moved from experimental to inevitable. Boards are asking about it. Executives want it embedded in roadmaps. Teams are eager to apply it across functions—from customer support to analytics to content creation. The pressure to “do something with AI” has never been higher.

Yet many AI initiatives struggle not because the technology fails, but because expectations were never grounded in reality.

Building a successful business case for AI requires the same mindset many parents develop over time: setting stretch goals that inspire growth while remaining achievable. Push too little, and progress stalls. Push too hard, and frustration sets in—for everyone involved.

AI, like parenting, is not about unlimited potential. It is about guided development within real constraints.

The Parenting Parallel: Stretch Goals vs. Setting Up for Failure

As parents, we want our kids to grow. We encourage them to tackle harder problems, develop independence, and stretch beyond their comfort zones. But effective parenting also means recognizing developmental limits. Asking a child to perform far beyond their readiness doesn’t accelerate growth—it erodes confidence and trust.

The same dynamic plays out with AI initiatives.

AI is powerful, but it is not magical. It does not automatically understand business context. It does not inherently reason like a human. And it does not replace accountability, governance, or judgment.

A strong AI business case balances aspiration with realism. It clearly articulates what AI can do today—and what it cannot.

The Most Common AI Business Case Mistake: Overestimating Maturity

One of the most common pitfalls in AI programs is assuming the technology is more mature—or more autonomous—than it truly is.

Organizations often expect AI to:

• Deliver consistently accurate outputs without oversight
• Replace entire roles rather than augment them
• Understand complex business rules without structured inputs
• Perform reliably across edge cases and ambiguity

These assumptions are the equivalent of expecting a child to master advanced concepts without foundational skills. The result is disappointment, rework, and skepticism that can stall future innovation.

A credible AI business case starts with an honest understanding of AI’s current limitations:

• AI is probabilistic, not deterministic
• Outputs are only as good as the data and prompts provided
• Models reflect gaps and biases in their training data
• Human review remains essential for critical decisions

Recognizing these realities does not weaken the case for AI—it strengthens it.

Defining Success: What Is an “Age-Appropriate” AI Outcome?

Good parenting adapts expectations to a child’s stage of development. The same approach applies to AI.

Early-stage AI initiatives should focus on:

• Assisting decision-making, not replacing it
• Reducing effort, not eliminating complexity
• Improving speed and consistency, not achieving perfection
• Supporting humans, not operating independently

When building a business case, leaders should ask:

• What process or decision are we augmenting?
• Where does human judgment remain critical?
• What level of improvement is truly meaningful?
• How will success be measured realistically?

Clear, achievable outcomes build trust—in the technology and in the transformation effort itself.

The Hidden Dependency: Data Readiness Still Matters

Just as children need the right environment to thrive, AI depends heavily on its inputs. No AI business case is complete without an honest assessment of data readiness.

Common challenges include:

• Inconsistent or poorly governed data sources
• Unclear data ownership and accountability
• Missing historical context
• Unstructured data without labeling or standards

AI does not “fix” data problems—it amplifies them.

A strong business case explicitly accounts for the effort required to prepare, govern, and maintain data. In many cases, this foundational work delivers value well beyond the AI initiative itself.

Managing Expectations Across the Organization

One of the hardest parts of parenting is managing expectations—not just for children, but for everyone around them. AI initiatives face the same challenge.

Executives may expect immediate ROI. Teams may worry about job displacement. Customers may expect flawless outcomes. A successful business case aligns these perspectives around a shared, realistic narrative.

This includes:

• Clearly stating what AI will not do
• Phasing delivery rather than promising instant transformation
• Communicating trade-offs openly
• Reinforcing that iteration and learning are part of success

When expectations are aligned early, AI initiatives are far more likely to sustain momentum.

Measuring Progress, Not Perfection

Parents don’t measure success by perfection—they look for progress. AI programs should be evaluated the same way.

Effective AI success metrics focus on:

• Time or effort saved
• Improvements in consistency and accuracy over time
• Reduced decision latency
• Adoption and trust by users
• Scalability and repeatability

Expecting flawless performance from day one is unrealistic. Measuring incremental gains reinforces confidence and creates a clear path for continuous improvement.

Governance Is a Safety Net, Not a Constraint

Just as children need boundaries to grow safely, AI initiatives require governance to succeed responsibly.

A mature AI business case addresses:

• Human AI oversight and role clarity
• Ethical and bias considerations
• Data privacy and security
• Accountability for AI-supported decisions

Governance does not slow AI down—it prevents missteps that can undermine trust and derail progress.

Final Thoughts: Raise AI to Succeed

AI offers enormous opportunity, but success depends on how it is introduced, guided, and measured. Like parenting, building a business case for AI is an exercise in balance: ambition paired with realism, innovation grounded in responsibility.

Organizations that approach AI as a developing capability—one that requires structure, achievable goals, and continuous learning—are far more likely to realize lasting value. Those that treat it as a shortcut or silver bullet often learn that unmet expectations can be more damaging than no initiative at all.

The strongest AI business cases don’t ask, “What’s the most impressive thing AI can do?”
They ask, “What meaningful progress can we achieve—right now?”

And just like good parenting, that mindset makes all the difference.

Author

Rebecca Jackson

Director, Business Transformation Solutions

ReJackson@eliassen.com

https://www.linkedin.com/in/rebecca-fritz-jackson/