Every day, we’re seeing more businesses launch innovative AI-powered services, leading many others to jump headfirst into the hype. While I believe that all businesses should be planning for the AI revolution, premature implementation is the biggest mistake I see many brands make, often leading to a loss in capital, disappointment from stakeholders or, in the worst-case scenario, unhappy customers. A thoughtful strategy and well-developed plan for AI implementation are necessary to manage expectations and achieve tangible success.
Many clients ask me where they should start with AI and in this piece, I’ll share actionable tips and advice for building out and implementing an AI strategy. While AI isn’t a quick fix that will solve all challenges, it can help drive employee empowerment and competitive advantage when used thoughtfully and in alignment with business goals.
Assess Your Unique Business Needs
The first step toward implementing AI into your workflow comes down to analyzing your business needs and goals: What functions could be made more efficient? Where are employees struggling to perform? Will AI help you better engage your customers?
These questions will help you identify key priorities and pinpoint where AI can fit in to support your business outcomes. Whether you’re improving customer service, predicting churn or converting casual visitors into loyal customers, you’ll want to identify the specific problem for AI to solve. A targeted approach is essential to determine whether AI is the right solution for your business challenges.
Most AI use cases fit into one of the following areas:
- Boosting internal productivity, which includes using AI tools like co-pilots or agents to improve efficiency or processes;
- Empowering decision-makers by deploying AI for quick access to insights to help employees make better decisions;
- Enhancing customer experiences by applying AI to personalize consumer interactions, such as recommending relevant offers, products or complementary services; and
- Transforming business models by using AI to drive innovation.
Educate Yourself On AI Capabilities
Once you have proper context on your business needs, the next step is familiarizing yourself with the different capabilities and requirements of AI programs. While they belong to the same family, predictive AI, generative AI and machine learning all have distinct capabilities that complement a variety of business needs. The technology that creates customer service chatbots may require different resourcing than technology that organizes and analyzes internal data.
Consider how each type of AI model might integrate into your existing company data or processes, and whether you already have the tools to power them. Evaluating your employee skill set will also help you assess various AI solutions and determine if additional training and education are necessary.
Identify Goals & Gaps
After defining your desired outcomes and understanding the available solutions, the next step is to assess your current capabilities and identify any gaps that need to be addressed. However, the way you prioritize based on potential business value and effort may differ for small businesses compared to larger companies.
Small businesses should seek to implement AI that delivers value quickly and cost-effectively, while larger companies may have the resources to invest in more experimental processes or outcomes. For small businesses, focusing on improving employee efficiency with tools like automated data reporting, an AI-powered meeting scheduler or automated resource management can be a great starting point.
Identifying your goals will help you create a clear plan for stakeholders, outline how/when each outcome will be tackled and provide clear benchmarks for success and optimization. Be sure to communicate transparently about your AI intentions and policies, as some employees and stakeholders may be skeptical or resistant to AI. Working with external experts can further streamline this process, helping secure executive support for your AI initiatives while providing the necessary education and justification for success.
Prioritize Data Readiness
In addition to strategy questions, the next biggest question we get is, “How can I prepare for AI, while I am working on my strategy?” The starting point for leveraging any type of AI is clear: Data. Data is your most valuable asset, and the success of your AI efforts will depend heavily on your data’s quality and readiness. To get started, your data needs to be in a state where AI can effectively use it.
Here is what to consider when starting the journey:
Centralize Your Data
Your data must be consolidated in one place, ideally a privacy-compliant cloud data warehouse data — that’s scattered across silos is useless.
Prepare It For AI
Cleanse, enrich and govern your data, ensuring it is standardized. Take additional steps to ensure your data is free of bias, helping uphold your business’s ethical standards.
Add Metadata
Enriched data should include metadata tags and rules, both essential for large language models (LLMs), which are key to AI functionality.
Document Business Rules
Clearly define and standardize your business practices, market regulations and brand values. These need to be connected to the data used for training AI models.
Continuously Learn From Your AI Efforts
Before implementing AI across your organization, be sure to define KPIs and other metrics you’d like to achieve. Your AI solution will not be perfect from the start — it will require consistent monitoring and adjustments. Building AI observability into your roadmap will ensure optimal performance and a successful program.
Embarking on an AI journey requires more than just jumping on the latest trends; it demands a clear and strategic approach. By identifying business needs, educating yourself and stakeholders and preparing data, you can create a robust AI roadmap to ensure calculated success that moves from AI hype to reality for your organization.
As SVP of Technology Strategy, Courtney Trudeau supports Merkle’s marketing technology consulting team, focusing on solution strategy and technology enablement. She is an accomplished marketing technology leader with more than 22 years of agency and in-house experience and expertise guiding digital experience strategy, B2B and E-commerce website development and driving marketing technology strategy and implementation.