Where to start with AI

Oftentimes, I speak with business leaders who tell me the same thing: "We know AI is important, but we don't know where to start." If that sounds familiar, you're not alone. The challenge isn't whether to adopt AI. It's figuring out the right first step.

The starting problem

The AI landscape can feel overwhelming. Every vendor promises revolutionary results. Every article talks about transformation. But when you sit down to actually begin, the questions pile up: What problem should we solve first? Do we have the right data? How much will this cost? What if we choose wrong?

Here's the truth: there's no universal "right" starting point. But there are proven approaches to finding your right starting point, one that delivers real value while setting you up for long-term success.

Signs your business is ready

Before diving into AI projects, it's worth asking whether your organization is ready. Here are the key indicators:

• You have a specific problem to solve. Vague goals like "use more AI" lead nowhere. But specific challenges like reducing customer wait times, automating invoice processing, and improving demand forecasting give you a target.
• You have relevant data. AI learns from data. If you've been collecting customer interactions, sales records, operational metrics, or other business data, you have raw material to work with.
• You have stakeholder support. AI projects need champions. If leadership understands the potential and is willing to invest time and resources, you can move forward.
• You're willing to start small. The organizations that succeed with AI are those willing to run focused pilots before scaling.

Common starting points

While every business is different, certain AI applications consistently deliver strong returns for first-time adopters:

Customer service enhancement

AI-powered chatbots and virtual assistants can handle routine inquiries, freeing your team for complex issues. This is often an ideal starting point because it's visible, measurable, and improves both efficiency and customer experience.

Document processing

If your team spends hours extracting information from invoices, contracts, or forms, document AI can dramatically reduce that burden. This area often shows 70-90% time savings on routine processing tasks.

Operational efficiency

From predictive maintenance to demand forecasting, AI can optimize operations in ways that directly impact the bottom line. If you have historical data on equipment performance or sales patterns, you may already have what you need to begin.

Knowledge management

Many organizations have valuable information trapped in documents, wikis, and email threads. AI can make this knowledge searchable and accessible, improving decision-making across the organization.

The assessment process

Before committing to any AI project, a structured assessment helps you understand your opportunities and constraints. Here's what that process typically looks like:

1. Identify pain points. Where does your organization lose time, money, or quality? Which processes frustrate your team or customers?
2. Evaluate data readiness. What data do you have? How clean is it? What would you need to collect?
3. Assess technical infrastructure. Can your current systems support AI integration? What upgrades might be needed?
4. Calculate potential ROI. What would success look like in concrete terms? Cost savings? Revenue growth? Time recovered?
5. Prioritize opportunities. Based on impact, feasibility, and strategic alignment, which opportunities should come first?

Building your AI roadmap

A good AI roadmap doesn't try to solve everything at once. Instead, it sequences initiatives to build momentum and capability over time:

Phase 1: Quick Wins. Start with projects that can show results in weeks, not months. These build organizational confidence and provide learning opportunities.

Phase 2: Foundation Building. Use early wins to justify investment in data infrastructure, team training, and process improvements that enable more ambitious projects.

Phase 3: Strategic Initiatives. With proven capability and infrastructure in place, tackle larger transformational projects that can reshape how your business operates.

Common mistakes to avoid

Having guided numerous organizations through their AI journeys, I've seen the same pitfalls repeatedly:

• Starting too big. Ambitious projects fail more often than focused ones. Begin with a defined scope you can execute well.
• Ignoring data quality. AI is only as good as the data it learns from. Invest in cleaning and organizing your data before building models.
• Underestimating change management. Technology is often the easy part. Helping your team adopt new tools and processes requires genuine attention.
• Expecting immediate perfection. AI systems improve over time. Plan for iteration and refinement rather than expecting perfect results on day one.
• Going it alone. Unless AI is your core business, consider working with experienced partners who can accelerate your learning curve.

Taking the first step

The organizations that succeed with AI share a common trait: they start. Not with a perfect plan, but with a willingness to learn by doing. They pick a focused problem, run a pilot, learn from the results, and iterate.

Your AI journey doesn't need to begin with a massive transformation initiative. It can start with a conversation about where you are, where you want to go, and what's possible given your unique situation.

The question isn't whether AI will impact your industry. It will. The question is whether you'll be leading that change or reacting to it.