Why starting small with AI pilots before scaling up is a mistake

Many organizations start small with AI pilot projects before scaling up. But what if this cautious approach is slowing you down?

Starting small may seem safe, but it can prevent you from reaching your bigger goals quickly and effectively. This is not the best approach to proving the value of AI to senior leaders in your organization.

Ultimately, AI pilots lead to disconnected efforts and fail to meet the pressing ROI demands faced by many marketers.

The problem with AI drivers

Too slow for the pace of business

AI pilot projects may seem like a less risky way to learn about new technologies, but they often have a downside: things move too slowly. AI pilots are like dipping your toes in the water when in reality you have to jump in if you want to swim with the big players.

• Delays in return on investment: AI pilots often fail to generate significant ROI quickly. Slow or no value creation can frustrate stakeholders who expect quick wins. Quarterly results motivate many leaders. They expect AI to drive efficiency and immediately create measurable value. However, many small-scale experiments take too long to prove their value or don’t solve the business-critical problems that make things happen.
• Missed opportunities: Taking too long to move beyond experimentation can mean missed opportunities to capitalize on market developments and opportunities. While your team cautiously works on a pilot project, competitors can expand AI across their operations and capture market share.

Lack of integration with broader marketing strategy

Another downside to starting small with AI pilots is that it can lead to a lack of integration with your broader marketing strategy and wider organization. AI pilots often find themselves isolated from the rest of the organization, reducing their impact.

• Isolated projects: When AI is only tested in small pilot projects, it tends to become a siled project. This makes it difficult to see how AI can fit into a broader marketing organization. These projects can work well independently, but without integration, their sum is no greater than the sum of their parts.
• Risk of fragmentation: Isolated pilot projects can lead to fragmented efforts. Instead of developing a cohesive, results-driven marketing strategy, you end up with scattered initiatives that don’t contribute meaningfully to your overall goals. To truly benefit from AI, it must be integrated into the fabric of your marketing strategy from the start.

Dig Deeper: 5 Ways to Jumpstart AI Adoption

Align your AI marketing strategy with your business goals

If starting small isn’t the answer, what is? The key is to be ambitious and create an AI marketing strategy aligned with your business goals from the start.

This means thinking more broadly about how AI can advance your business and collaborating with other departments to make an impact across the organization.

Start with business goals

Start by understanding the overarching goals of your business. Whether your goal is revenue growth, customer acquisition, improved customer satisfaction, or operational efficiency, these goals should inform your AI marketing efforts.

Identify high-impact AI marketing use cases

Where can AI help your marketing have the most significant impact on your business goals? Prioritize use cases based on their impact and feasibility. Start with projects that have high impact potential but are also feasible, given your current resources and capabilities. These projects will help you quickly generate value while laying the foundation for further AI expansion.

Cross-functional collaboration

AI does not exist in a vacuum. Ensure alignment between different departments: Marketing, IT and sales must collaborate to help AI initiatives have the most significant long-term impact.

Dig Deeper: A Marketing Guide to Implementing Generative AI

Deliver tangible results quickly

Marketers are under immense pressure to deliver results – and quickly. An ambitious AI strategy aims to quickly deliver tangible results to meet these expectations and prove the value of AI to your organization.

Identify key indicators of success

To know if your AI initiatives are working, set measurable goals such as key performance indicators (KPIs) and objectives and key results (OKRs). These metrics will help track progress, measure success, and justify new AI investments to stakeholders.

Quick wins, then scaling

Quick wins are essential, but they must be part of a scaling process that demonstrates early successes while laying the foundation for larger achievements. For example, you might focus on automating a critical marketing workflow to demonstrate immediate time savings while establishing process guidelines to automate all marketing workflows.

Evolve by iteration

You may not achieve your business goals the first time. Iteration must be expected and planned. Iterate quickly, seeking to improve with each iteration. Where possible, break larger initiatives into manageable phases. This approach reduces the risk of each phase while ensuring that each phase of your AI marketing strategy is aligned with the larger vision.

Case Studies: Successful Examples of Bold AI Adoption

The best way to understand the power of rapid AI development is to look at companies that have done it successfully. Here are some examples:

Tomorrow.io

Dan Slagen and his team at Tomorrow.io AI integrated into various marketing functionsincluding content, video, events, public relations, lead generation, product marketing and sales enablement. This increased productivity by over 30%, lead generation by 50% and made the ROI of their marketing efforts positive.

Point correction

Point correction AI scaled successfully by integrating data science and AI into its core operations. The company uses AI to personalize clothing recommendations for customers, combining algorithms with human stylists to deliver a unique data-driven experience. This holistic approach to AI has enabled them to achieve measurable results in customer satisfaction and operational efficiency.

Bayer

In early 2022, Bayer’s Australian team launched a project that combined Google Trends data with weather and climate information to predict cold and flu season trends in different regions of Australia. By combining this data with state-specific search trends, they tailored ads to reach the right audiences at the optimal times. This approach led to an 85% year-over-year increase in click-through rates and a 33% decrease in cost per click compared to the previous year.

Dig Deeper: A user-friendly approach to adopting AI in marketing

Go big or lag: ignore AI drivers to see real results

Starting small with AI pilots may seem like a wise approach. But this could prevent your organization from reaching its full potential. By thinking big, you can align your AI efforts with your core business objectives from the start, creating real value and driving growth.

It’s time to rethink AI adoption strategies. Instead of cautious experimentation, embrace an evolutionary, integrated, and iterative approach to drive quick wins and long-term success. A bold strategy can transform AI from a small experiment into a significant driver of business outcomes.