Artificial intelligence (AI) is hailed as the future of business transformation, but in many organizations, it has become an expensive buzzword, benefiting consultants more than the businesses paying their fees. The problem isn’t AI itself—it’s the overemphasis on flashy strategies and theoretical possibilities while ignoring the bedrock of any successful AI initiative: quality data.
The “Consultant’s Watch” Problem
There’s a long-standing critique of consultants: they ask for your watch to tell you the time. In the AI world, this adage rings truer than ever. Many consultants offer broad-stroke strategies like “building an AI-ready workforce” or “leveraging AI to enhance resilience.” These insights may sound transformative, but they are often little more than common sense wrapped in a shiny package. Worse, they ignore the practical challenges organizations face—chiefly, the availability and quality of data.
The Data Elephant in the Room
Despite the hype, AI doesn’t work without reliable, high-quality data. AI models are only as good as the data they’re trained on. Yet, many AI workshops, conferences, and consulting engagements gloss over this critical issue. It’s akin to discussing the potential of a sports car without addressing whether it has fuel. According to RAND, nonprofit, nonpartisan research organization, the industry’s collective failure to prioritize data organization and preparation is one reason studies show that up to 80% of AI initiatives fail.
A recent Forbes article emphasizes that data quality issues remain a consistent roadblock for organizations hoping to use AI to drive better business outcomes. It highlights the need for robust, actionable data to support meaningful AI applications. Many businesses fail to recognize that AI tools are only as effective as the data fueling them; noting that without clean and relevant data, AI initiatives are likely to fall short.
The Operational Trap
Another challenge lies in the limited scope of most commercial AI initiatives. The majority of AI applications in businesses today focus on operational improvements such as automating repetitive tasks, optimizing logistics, or enhancing customer service through chatbots. While these applications provide value, they rarely elevate decision-making at the executive level.
The true power of AI lies in its ability to generate higher-value analytics and predictive insights that can guide strategic decisions. These advanced applications, which analyze trends, forecast outcomes, and provide actionable intelligence, have the potential to transform how the C-suite operates. Yet, most organizations fail to prioritize these initiatives.
This missed opportunity is particularly significant for the C-suite, where predictive analytics could revolutionize decision-making. By using AI to forecast revenue, anticipate market disruptions, and analyze competitive landscapes, executives could make more informed strategic choices. Unfortunately, many firms remain stuck in the weeds of operational AI and fail to explore these broader possibilities.
As Harvard Business Review highlights, firms often gravitate toward operational AI because it is easier to implement and demonstrates quick wins. However, this approach leaves untapped opportunities for AI to drive broader, long-term value by improving revenue forecasting, market strategy, and competitive analysis.
As covered in this column in September, revenue forecasting predictive models incorporating survey-based consumer insights outperform traditional time-series models in terms of accuracy. Dr. Demirhan Yenigun, Chief Strategy Officer at Ereteam explains, “We have always known that the information collected in these surveys provides factual insights on current consumer behaviors as well as their future intentions and expectations. It is very exciting to see the significant predictive power of this information being utilized very effectively in public company revenue forecasting.”
Why Organizations Keep Falling Into the Trap
AI consulting has become a multi-billion-dollar industry because many organizations feel pressured to show they are “doing AI.” Leaders pay for workshops, podcasts, and sessions to check a box rather than produce real results. This is compounded by the misconception that operational AI is a sufficient endpoint, when in reality, it is only the beginning.
A Practical Approach to AI Success
To stop wasting money and start achieving meaningful AI outcomes, businesses need a practical, data-driven approach:
Start with the Basics: Data Quality and Organization. Invest in cleaning, organizing, and enriching your data. This foundational work isn’t glamorous, but it’s essential. Without it, AI models will produce unreliable or biased results.
Define Specific, Outcome-Oriented Goals. Instead of chasing vague AI ambitions, focus on specific business challenges or opportunities that AI can address. For example:
- Forecasting customer demand using historical sales data.
- Improving supply chain efficiency through predictive analytics.
- Developing predictive models to anticipate market shifts and competitor actions.
Ensure Executive Buy-In. Management commitment is crucial. AI initiatives require funding, cross-departmental collaboration, and a long-term perspective. C-suite leaders must understand that AI can be a strategic tool, not just an operational convenience.
Evaluate Consultants Critically. Before hiring an AI consultant, ask tough questions about their focus on data and outcomes. Avoid those who emphasize grand strategies without addressing practical execution.
Leverage High-Quality Data Sources. Many organizations overlook the importance of external data sources. Partnering with trusted data providers can enhance the accuracy of AI models and provide competitive insights.
Move Beyond Operations. Organizations must challenge themselves to go beyond operational applications of AI and explore its potential for strategic transformation. Predictive analytics and other advanced tools can provide the C-suite with insights to improve decision-making and drive growth.
Focus on Action, Not Hype
The allure of AI can make it easy to focus on futuristic ideas and ignore the fundamentals. However, organizations that succeed with AI are those that prioritize data as the foundation of their efforts. They reject the consultant-driven fluff and focus on actionable outcomes that deliver real value.
By understanding the critical role of data and taking a disciplined approach, businesses can avoid becoming just another statistic in the AI failure rate. It’s time to stop paying for people to “tell you the time” and start building AI initiatives that actually work.