What Three Questions Should You Ask About AI?

Between 2020 and 2026, artificial intelligence went from science fiction to something most people encounter before their morning coffee. ChatGPT launched in late 2022 and within months, every company had an ai strategy conversation happening somewhere in their organization. By 2023, enterprise rollouts accelerated. By 2025, the hype started facing hard financial scrutiny.

Now, in 2026, the conversation has shifted. CIOs face pressure to prove ROI. Pilots that seemed promising two years ago have been quietly shelved. And yet, new ai tool announcements arrive weekly, each promising to revolutionize how you work, think, and create.

Here’s what I’ve noticed: asking “What’s your AI strategy?” or “Which tools should we use?” leads nowhere useful. It’s not just about asking questions—it's also about carefully evaluating the answer you get from AI, since these outputs are not always reliable and should be scrutinized. These questions are too vague. They spawn pilots that burn money and erode trust. The future belongs to professionals who can ask sharper questions.

This article gives you three questions any manager, founder, or individual contributor should ask before committing time, money, or reputation to AI. By the end, you’ll know how to decide when to use ai, how to use it responsibly, and when to walk away.

Introduction to Artificial Intelligence

Artificial intelligence (AI) is no longer just a buzzword or the stuff of science fiction—it’s a transformative technology shaping the world around us. From the moment you ask a virtual assistant for the weather to the algorithms recommending your next favorite show, AI tools powered by machine learning are woven into daily life and business. These AI systems can analyze massive amounts of data, recognize patterns, and even generate new content, making them essential drivers of innovation across industries.

As generative AI and other advanced technologies continue to evolve, the future belongs to those who can harness their potential thoughtfully. AI is not just about automating tasks; it’s about reimagining what’s possible, solving complex problems, and unlocking new opportunities for growth. But with this power comes responsibility. Understanding the basics of artificial intelligence—what it is, how it works, and where it can be applied—is crucial for anyone looking to stay ahead in a rapidly changing world.

AI’s benefits are clear: increased efficiency, smarter decision-making, and the ability to tackle challenges that once seemed impossible. Yet, it also brings new challenges, from ethical considerations to the need for robust data and careful oversight. As we move forward, it’s essential to approach AI not as a distant future, but as a present-day reality—one that demands both curiosity and caution. By building a solid foundation in AI, you’ll be better prepared to leverage new technology for meaningful, lasting impact.

Question 1: What Problem Am I Really Trying to Solve With AI?

Most failed ai systems between 2018 and 2025 started the wrong way. Someone said “We need AI” instead of “We need to fix X.” A sales team chased a churn prediction project for eighteen months—never shipped. A marketing department pursued generative ai for content creation without defining what success meant. The result? Frustration, wasted budgets, and abandoned efforts.

The difference between failure and success often comes down to one thing: specificity. When considering AI, it's essential to focus on concrete business outcomes and prioritize impactful projects that address real organizational needs. Here are examples of well-formed problem statements:

• Customer support (2026): Reduce average first-response time from 4 hours to under 1 hour by Q3, without adding headcount
• Clinical research: Cut protocol deviation documentation errors by 40% before regulatory audit in November
• HR recruiting: Screen 500 weekly applications to a shortlist of 50 qualified candidates, saving recruiters 15 hours weekly
• Finance: Generate weekly cash flow forecasts with error rates below 5%, replacing manual spreadsheet work

Notice what these have in common: they define the business case in measurable terms. They don’t start with “use GPT-4.1.” They start with pain.

AI services and solutions are most effective when they are created to address specific business needs, ensuring that the AI initiatives make sense within the context of the organization's operations. For example, AI has the potential to fundamentally reshape how companies discover, develop, and deliver new therapies, demonstrating its transformative impact when tied to real business problems.

There’s a crucial distinction between tool problems and business problems. “I want to try the latest machine learning model” is a tool problem. “Our sales team loses 10 hours weekly to manual data entry” is a business problem. Solve the second, and the first becomes obvious—or irrelevant. AI should be tied to specific business problems to demystify its capabilities and clarify its application.

Think about whether you’re facing an efficiency problem (save time on repetitive tasks), an effectiveness problem (improve decision quality), or an innovation opportunity (do something entirely new). AI tends to excel at efficiency for knowledge workers. It’s shakier on innovation without foundational data readiness. When assessing where to start with AI, companies should prioritize solving the biggest problems that matter.

How to Turn AI Curiosity Into a Concrete Use Case

Here’s a simple framework to refine your thinking:

Step 1: Identify the core task. What specific work are you hoping AI could assist with? Be precise—not “marketing” but “writing first drafts of product descriptions.”

Step 2: Quantify the pain. How much time, money, or errors does this task currently cost? For example: 15 hours weekly on meeting note summarization, costing roughly $750 in labor.

Step 3: List current tools. What do you use now? Manual editing in Word? An outdated template? Understanding the baseline matters.

Step 4: Reality-check the AI solution. Could AI realistically cut this pain by 50% within 90 days? If not, reconsider.

Ask yourself: “What would success look like 90 days from now?” Define it as a deliverable—20 summarized reports with 95% accuracy approved by clients—not a multi-year digital transformation.

This keeps you focused on quick wins you can scale, not vague visions that never materialize.

Question 2: Do I Have the Right Data, Processes, and People to Use AI Responsibly?

By 2024, many organizations had acquired licenses for ai tools that sat idle. Why? Unclean data. Unstable workflows. Untrained staff. Tools went unused or produced outputs nobody trusted. Another key reason is the lack of attention to human language, communication, and the nuanced ways people use words when implementing AI tools—especially in environments where clear communication is critical.

I’ve seen a company try to build a contract-review assistant on inconsistent PDF scans—the outputs were unreliable. A hospital deployed AI to summarize medical notes without clinician review; errors slipped through. AI amplifies both strengths and weaknesses. If your data is messy and your processes unclear, AI won’t fix that. It will make the problem worse, faster.

Before adopting any ai project, audit three things: data quality, process clarity, and human expertise. Developing an AI mindset also means understanding that AI is fundamentally a data-driven topic.

Data: Is What I’m Feeding AI Complete, Current, and Legal to Use?

Your data needs to be accurate, up to date, and legally usable. That last point matters more than ever in 2026.

Consider these examples:

• A sales team using CRM data last cleaned in 2021—pre-pandemic customer behavior patterns may be wrong
• A hospital using EHR data with missing fields, risking biased clinical insights
• A startup using scraped web data with unclear intellectual property rights

Regulations have teeth now. GDPR and CCPA remain in force. The EU AI Act’s high-risk system requirements activate in August 2026, demanding documentation and control. The Cyber Resilience Act adds vulnerability reporting requirements in September. If you don’t know where your data comes from, you can’t speak to its compliance.

Practical check: Can you trace every data source feeding your AI? Is it post-2021? Does legal know about it?

Processes: Where Exactly Does AI Plug Into the Workflow?

Many 2024-2025 pilots failed because AI was bolted on at the edges. Nobody changed the underlying workflow. The AI output sat in a folder nobody opened.

Map your process explicitly. For example, drafting a client proposal might look like drawing a clear line of steps:

Research (human) → Outline (AI-generated, human-checked) → Draft (human-edited) → Review (team approval)

Where exactly does AI plug in? Who checks the output before it moves forward? How fast would you detect an error? These questions matter more than which model you use.

People: Who Owns the Decision, and Who Reviews the AI?

Every AI-assisted decision needs a human owner. In medical diagnosis support, clinicians must override AI suggestions when necessary. In automated credit scoring, quarterly bias audits are essential. It's easy to forget your own biases or assumptions over time, much like wearing tinted glasses for so long that their influence becomes second nature and goes unnoticed when working with AI.

Don’t assume everyone knows how to use ai effectively. Basic AI literacy training—through free resources like Microsoft Learn or OpenAI Academy—should be plan for 2026. Prompt engineering isn’t intuitive to most people.

Good practice looks like this:

• A marketing lead approves all AI-generated copy before publication
• A data scientist monitors model drift quarterly
• A compliance officer signs off on new AI workflows

AI is an assistant, not a decision-maker. Final accountability remains with humans—in your own hands. The most successful users of AI are those who know when to use AI and when to rely on their own judgment.

Question 3: How Will I Measure Value and Manage the Risks?

From 2020–2024, too many AI initiatives reported fuzzy outcomes. “Efficiency gains.” “Improved productivity.” No baselines. No cost analysis. Trust eroded.

Forrester’s 2025 State of AI Survey found that while 73% of organizations had basic data-use policies, few covered training or risk guidance. Security remained a top concern for 40% of respondents even as deployments accelerated.

Before deploying AI, define both success metrics and guardrails. Write them down. Get agreement in advance.

Defining “Success” in Numbers, Not Vague Promises

Pick 2-3 primary metrics per use case:

• Hours saved per employee weekly: Track before and after
• Error rate: Incorrect outputs ÷ total outputs
• Revenue uplift: Percentage increase attributable to AI-assisted work
• Satisfaction scores: Customer or employee feedback

Before finalizing these metrics, ensure there is structured talk among stakeholders to clarify AI goals and align on what success looks like. Meaningful discussions help set clear, actionable metrics and foster agreement on priorities.

Worked example: A support team deploys an AI chatbot in mid-2026. They track:

That’s proof beyond hype. But it requires baseline measurement—capture pre-AI performance for 2-4 weeks minimum.

Planning for Mistakes, Bias, and Over-Dependence

Risk isn’t just catastrophic failure. Between 2022 and 2025, we saw hallucinated facts in language models, biased recommendations in hiring tools, and overconfident financial forecasts. These are subtle erosions, not explosions. Many professionals have learned the importance of critical thinking the hard way when working with AI, realizing that effort and perseverance are needed to spot these issues.

Create a pre-rollout checklist:

1. What could go wrong? (Hallucination triggers bad customer advice)
2. Who would notice? (Support lead reviews weekly sample)
3. How fast? (Within 24 hours)
4. What’s the fallback? (Manual escalation to senior rep)

When using this checklist, it’s important to consider AI’s perspective—AI systems have inherent biases based on their training data and cultural context, so their outputs reflect just one worldview among many. Asking critical questions about AI outputs can help uncover hidden biases and assumptions in AI-generated information. It is also crucial to ask what AI could predict, how it could optimize performance, and what it could automate within existing workflows.

Consider cognitive risks too: staff losing skills by over-outsourcing thinking, or teams accepting outputs without scrutiny because AI “sounds confident.” The key to developing strong critical thinking skills is making skepticism engaging and enjoyable. Practical mitigations include mandatory human review in high-stakes contexts, weekly spot-checks, and occasional “AI-off days” to keep human creativity and judgment sharp.

Understanding AI Tools

AI tools are specialized software applications that use artificial intelligence and machine learning to tackle specific challenges—whether that’s analyzing customer data, translating languages, or recognizing images. These tools are rapidly becoming indispensable in the modern business landscape, helping organizations streamline operations, improve accuracy, and unlock new sources of value.

But before jumping into the latest new AI tool, it’s crucial to pause and ask the right questions. What problem are you actually trying to solve? How will this tool support your business case and fit into your overall AI strategy? Too often, companies rush into digital transformation without a clear plan, only to find that the technology doesn’t deliver the expected results.

A thoughtful approach starts with understanding both the capabilities and limitations of AI tools. Not every solution is a fit for every problem, and successful adoption depends on aligning technology with your organization’s unique needs and goals. This means developing a strategy that includes not just the selection of tools, but also training and support for your team, clear processes for implementation, and ongoing evaluation to ensure quality and fairness.

It’s also important to recognize the potential risks—like bias in AI decision-making or the impact on jobs—and to have a plan in place to address them. By involving your team, providing the right support, and keeping humans in the loop, you can use AI to enhance creativity, speed, and quality, rather than simply replacing human effort.

Ultimately, AI tools are most powerful when they’re used to complement human intelligence, not replace it. By putting careful thought into your AI strategy and keeping your own hands on the wheel, you’ll be better positioned to drive innovation and create real, measurable value for your business. In a world where technology is constantly evolving, that kind of smart, strategic thinking is more crucial than ever.

Putting the Three Questions Together: A Simple 10-Minute Checklist

Before any new ai project, run through this:

Question 1: What specific problem am I solving, and what does success look like in 90 days?

Question 2: Is my data clean and compliant? Is the workflow mapped with clear handoffs? Does someone own the decision?

Question 3: What 2-3 metrics will prove value? What risks exist, and who catches them?

Example: A SaaS product lead in 2026 evaluates AI for user onboarding. She confirms the problem: 20% drop-off due to manual personalization taking 8 hours per cohort. She audits: user data is clean and EU AI Act compliant, processes are mapped with designer handoffs, and her team has completed training. She sets metrics: 15% uplift in completion rates. Risk: biased suggestions flagged by A/B tests weekly.

Ten minutes. Documented. Repeatable.

Adapt these three questions into a one-page template. Use it for every AI idea that crosses your desk.

Conclusion: Your Role in Shaping AI, Not Just Surviving It

AI is a general-purpose technology like electricity or the internet. Access isn’t the differentiator anymore—discernment is.

If you consistently ask what problem you’re solving, whether you have the right data, processes, and people, and how you’ll measure value and manage risk, you’ll make better decisions than most organizations realized they needed between 2020 and 2025.

In other cases—such as healthcare, hospital environments, or highly regulated industries—AI adoption may face unique challenges related to user acceptance, workflow disruption, and data privacy. The three questions remain relevant for navigating those complexities and ensuring successful implementation.

The world doesn’t need more AI enthusiasm or more AI fear. It needs thoughtful adoption. The professionals who learn to ask disciplined questions—of AI and of themselves—will set the standards the rest follow through the late 2020s and beyond.

Your Friend,

Wade