AI Transformations Done Right
AI has the potential to revolutionize businesses, from supply chains to customer service. For instance, AI-powered demand forecasting has already helped retailers optimize inventory levels, while chatbots have transformed customer support by reducing response times and improving service.
It’s an exciting frontier—one that promises efficiency, intelligence, and a competitive edge. But there’s a hard truth that many companies ignore: Only 4% of AI transformations deliver value, largely due to a lack of strategic clarity, poor data quality, and insufficient talent to execute AI effectively. Many businesses jump into AI without the right foundation, leading to stalled projects and wasted investments.
Before you ask, How do we implement AI? ask, Are we ready for AI? The answer to that question could save your company a fortune.
Why Most AI Efforts Are Failing—and Why That’s Not the Full Story
The single biggest reason AI transformations fail is they don’t have the right foundation. AI isn’t magic; it’s math at scale. Success requires data infrastructure, strategic clarity, and the talent to execute AI effectively. Before jumping in, businesses would do well to focus on four key steps to lay a foundation:
Actionable Data – Invest in data, technology, and talent first.
Efficient Processes – Optimize operations before applying AI.
Align with Business Goals – AI should solve real problems, not just exist for innovation’s sake.
Start Small, Scale Smart – Early successes drive sustainable transformation.
However, waiting for perfect readiness may mean missing opportunities. AI tools today are more accessible than ever, and many prebuilt solutions can deliver quick wins, even with imperfect data. Rather than delaying AI adoption, companies should find the right balance between preparation and action.
1. Actionable Data
To be useful, AI needs a lot of well-structured data that can be predictive. Companies have enterprise financials and some functional data (e.g., revenue, new/repeat buyers, labor costs, etc.). However, when this data is siloed, inconsistent, or disconnected from market data, AI projects struggle.
Hiring AI engineers and data scientists to clean up data and build models is expensive. Top-tier resources can cost well over $200,000 per person annually. However, AI-as-a-service platforms and prebuilt AI models offer cost-effective alternatives. These solutions allow businesses to gain AI-driven insights without needing an army of in-house experts.
Example: A direct-to-consumer (DTC) retailer had historical prices for their goods, including competitor pricing and past promotional strategies. This enabled them to use AI to identify when and where to raise prices or match competitors—driving higher margins without losing customers.
2. Efficient Processes
The goal of any AI project is to change how a process or decision is made. AI won’t fix inefficient processes—it will, however, make inefficiencies even more apparent. Companies need to focus on refining processes before integrating AI to ensure its impact is positive.
Key Considerations:
Standardization: Are your processes well-documented and consistent across teams?
Automation Readiness: Have you eliminated redundant manual steps?
Integration with AI: Can AI insights be seamlessly applied to decision-making?
Yet, AI can also help improve processes while being implemented. Many AI tools today include automation and analytics that assist businesses in optimizing workflows as they scale.
Example: A retailer invested heavily in demand-planning AI to increase inventory turns and cash flow. However, their reordering decisions still required weekly meetings and finance approval, delaying action despite real-time AI recommendations. By changing this process to align with AI insights, they fully unlocked the benefits of the technology.
3. Align AI with Business Goals
Companies should begin by identifying where AI can help them accelerate their progress on top-level business goals. AI can help attract and retain more customers by optimizing marketing efforts, personalizing recommendations, or improving customer service. Alternatively, it can support pricing strategies by identifying where price adjustments could drive higher margins without reducing sales volume.
Once the opportunity is clear, companies should define specific AI initiatives that align with these objectives. This ensures that AI investments directly contribute to business success rather than becoming expensive, unfocused experiments.
However, not all AI implementations need to be small-scale pilots. Some companies benefit from a broader AI strategy that integrates multiple AI solutions at once—especially if they leverage vendor-supported AI tools that are ready to scale.
Example: A company wanted to apply AI across all business functions and democratize access to AI models at every level. While the vision was ambitious, attempting to launch everything at once stalled progress. Instead, they restructured their approach, first implementing AI in marketing and sales before rolling it out to supply chain and finance—allowing them to see results while steadily expanding AI’s reach.
4. Start Small, Scale Smart—But Don’t Think Too Small
A full-scale AI transformation sounds impressive, but it’s rarely the best approach. Rather, the best transformations win early successes to build momentum, refine processes, and justify further investment.
However, starting small doesn’t mean thinking small. Companies that plan for scalability from the outset avoid creating fragmented AI efforts that don’t integrate well later. Cloud-based AI platforms and prebuilt models make it easier than ever to scale AI solutions across an organization once initial success is proven.
Example: A retail company wanted to improve customer engagement through AI. Instead of overhauling their entire customer experience system, they started with a small AI chatbot pilot for handling frequently asked questions. Within months, they saw a 30% reduction in customer service response time and higher customer satisfaction. Encouraged by this success, they expanded AI to personalize product recommendations and optimize inventory forecasting. By using scalable AI tools, they ensured each phase added measurable business value before investing further.
The Digital Maturity Test: Are You Ready for AI?
Before investing in AI, businesses must assess their readiness. The key factors to evaluate include:
Data Readiness: Do you have structured, clean, and accessible data?
Technology Infrastructure: Can your systems support AI deployment at scale?
Talent & Culture: Do you have the right mix of data engineers, AI specialists, and business leaders who understand AI’s role?
Change Management: Are employees ready to adopt AI-driven decision-making?
However, perfection isn’t required to start. AI tools today are more flexible than ever, allowing businesses to implement AI while improving these capabilities in parallel.
Final Thought: AI Success is About Balance
AI transformation isn’t just about AI—it’s about business transformation. Companies must:
Fix foundational gaps – Strengthen data, technology, and talent first.
Justify AI with clear business goals – Solve real problems with measurable impact.
Start smart but plan for scale – Use early successes to drive long-term AI adoption.
The key isn’t delaying AI but adopting the right AI solutions at the right time. By working with AI vendors, leveraging prebuilt models, and integrating scalable AI platforms, companies can unlock the benefits of AI sooner rather than later.
