In an artificial intelligence (AI)-accelerated world, a significant risk is moving quickly against the wrong problem, with data priorities driven by familiar solutions instead of decision clarity.
Business leaders often bring real pain points and urgency to analytics teams, but how those teams approach these requests can introduce challenges, such as, defaulting to what worked in the past or what they expect will help, without fully understanding what they are trying to solve. When teams take this approach, they unintentionally increase the risk of moving in the wrong direction, often leading to rework, frustration, and missed opportunities.
AI raises the stakes because it can accelerate flawed assumptions. Moving faster in the wrong direction is rarely a good thing. However, those same tools can also be used earlier in the process to help teams explore the situation more fully and ask better questions, so the approach is aligned from the start.
What Drives This Pattern
A few patterns tend to drive this tendency to act before fully understanding what’s driving the need for action:
- Narrow focus: Attention centers on immediate pain, pulling focus toward what is most visible or seemingly urgent.
- Perceived clarity: The situation can feel obvious, reducing the perceived need to step back and validate what is driving it.
- Pressure to act: The need to show progress quickly pushes teams toward familiar actions, known metrics, or new capabilities.
- Familiarity and past success: What has worked before shapes expectations of what will work again, even when the context has changed.
- Framing and tool bias: How a request is framed, along with the tools available, shapes how teams respond, often leading them toward a specific approach.
- Data overload: The volume of available data makes prioritization more difficult, reinforcing attention on disconnected signals.
Together, these patterns create a familiar trap: confident movement without sufficient clarity on the decision, behavior, or outcome that should guide the work.
When Effort Is Applied in the Wrong Direction
Even the most well-known businesses are not immune to this.
In the mid-1980s, a leading beverage company faced a very real competitive threat: a rival product was outperforming it in blind taste tests, particularly with younger consumers. The conclusion seemed obvious: the problem was taste. Extensive testing followed, and the company replaced its flagship product with a new formula optimized for flavor.
Instead, consumers were outraged. Not because of the taste, but because of their emotional attachment to the original product. Customers were not choosing the brand for superior flavor; they were choosing it for identity, habit, and nostalgia, which are factors a taste test could not capture. Within months, the company reintroduced the original product, and the new version was eventually discontinued.
The company had data, resources, and good intentions. What it missed was alignment on the real problem. It solved what was easiest to measure, not what mattered most.
How This Shows Up in Modern Requests
This pattern continues in modern requests:
| What Outcome They Wanted | What They Thought They Needed | Why That Seemed to Make Sense | What They Actually Needed |
|---|---|---|---|
| Increase revenue | Build a dashboard to track sales performance | Sales performance was already being tracked across multiple dimensions, and increasing visibility into that data felt like a logical next step. | Understand what actions drive revenue and how teams should respond to changes in performance. |
| Increase user adoption | Improve performance of existing solutions | Disconnected global filters and multiple similar report versions contributed directly to performance issues, making performance appear to be the primary barrier to adoption. | Simplify the user experience and align content to the decisions users need to make. |
| Identify meaningful patterns | Five years of datetime data | There was a belief that more detail would provide deeper insight into trends and behavior. | Provide clearer summarization and trend visibility to make patterns easier to interpret. |
| Anticipate future performance | Build a predictive model | Historical trends were already available, but inconsistent use of those insights led to the assumption that better prediction would improve planning. | Define how insights should inform decisions and create consistency in how they are used. |
| Improve efficiency and reduce manual effort | Automate decisions using AI | The process involved multiple conditional steps and repeated actions that sometimes required manual judgment, making automation appear to be a natural way to improve efficiency. | Understand decision complexity and determine where automation adds value versus where judgment is needed. |
In each case, the desired outcome reflected a legitimate business priority; however, addressing it effectively requires first focusing on the underlying question and the factors driving it.
Start With These Four Questions
Before acting, clarify what is driving the need to act.
A simple way to test for clarity is to ask four questions:
- What decision is being made?
- What action should this lead to?
- What outcome defines success?
- What needs to change?
If the answers are unclear, it is too early to act.
These fundamentals matter more as data, tools, and expectations continue to evolve.
How Advanced Analytics Strengthens Early Decision Making
Advanced analytics are often used as the solution, but they can also play an important role earlier in the decision process.
| Advanced Analytics Technique | What It Does | What It Enables |
|---|---|---|
| Pattern detection | Identifies relationships and trends not visible in standard reporting. | Surfaces potential drivers worth investigating. |
| Behavioral modeling | Analyzes patterns in user or process behavior to identify likely outcomes. | Highlights friction points and areas where change would have the greatest impact. |
| Anomaly detection | Identifies unusual patterns or deviations in data that may indicate issues or opportunities. | Helps teams focus attention on signals that matter. |
| Scenario modeling | Tests potential outcomes under different assumptions using models or simulations. | Helps evaluate options and validate decisions before committing. |
| Signal vs. noise reduction | Helps distinguish meaningful signals from less relevant data. | Reduces distraction and helps teams focus on what matters most. |
Used well, these AI-enabled techniques can scale more effectively, surface deeper patterns, and help teams focus where action matters most.
Effective Planning Enables Confident Action
Most organizations know the direction they want to go; the challenge is focusing effort on the decisions, behaviors, and outcomes that will make a difference. When teams take the time to step back, understand what is driving the need for action, and align on what needs to change, it helps everything that follows become more effective. That discipline turns analytics from a response to a request into a sharper way to prioritize investments, build confidence, and move faster in the right direction.
How Forvis Mazars Can Help
Analyzing organizational data can be overwhelming, but it doesn’t have to be. Forvis Mazars helps clients reframe the business question, prioritize the right data, and apply advanced analytics and AI with discipline. Our professionals can unlock the power of your data to help you enhance marketing strategies, develop competitive advantages, open up new revenue opportunities, reduce risk, and improve operational efficiency. If you have any questions or need assistance, please reach out to a professional.