Wasting money, burning money
Artificial intelligence (AI) is being hailed as the future of business transformation, but in many organizations it has become an expensive buzzword, benefiting consultants more than the companies paying their fees. The problem isn’t AI itself, it’s the excessive emphasis on flashy strategies and theoretical possibilities while ignoring the foundations of any successful AI initiative: quality data.
The problem of “consultant oversight”
There’s a long-standing criticism of consultants: They ask for your watch to tell you the time. In the world of AI, this adage is more true than ever. Many consultants offer broad strategies like “creating an AI-ready workforce” or “leveraging AI to improve resilience.” These ideas may seem transformative, but they are often nothing more than common sense wrapped in shiny packaging. Worse, they ignore the practical challenges organizations face, including data availability and quality.
The data elephant in the room
Despite all the hype, AI doesn’t work without high-quality, reliable data. The quality of AI models depends on the quality of the data they are trained on. Yet many AI workshops, conferences and consulting engagements ignore this crucial issue. It’s like discussing the potential of a sports car without asking whether it has fuel. According to RANDnonpartisan, nonprofit 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 highlights that data quality issues remain a constant obstacle for organizations hoping to use AI to achieve better business outcomes. It highlights the need for robust, actionable data to support meaningful AI applications. Many companies don’t realize that AI tools are only as effective as the data that powers them; noting that without clear and relevant data, AI initiatives risk failing.
The operational trap
Another challenge is 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 improving customer service using chatbots. Although these applications provide value, they rarely elevate decision-making to the executive level.
The true power of AI lies in its ability to generate higher value-added analytics and predictive insights which can guide strategic decisions. These advanced applications, which analyze trends, predict outcomes and provide actionable insights, have the potential to transform how C-suites operate. Yet most organizations fail to prioritize these initiatives.
This missed opportunity is particularly important for senior executives, 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 companies remain stuck in the weeds of operational AI and fail to explore these broader possibilities.
As the Harvard Business Review points out, companies often turn to operational AI because it’s easier to implement and allows for 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 SeptemberPredictive revenue forecasting 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 always knew that the information collected in these surveys provided factual insights into 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 used very effectively in forecasting public company revenues.
CVS Third Quarter Revenue Forecast
Why organizations continue to fall into the trap
AI consulting has become a multi-billion dollar industry because many organizations feel pressured to show that they are “doing AI.” Leaders pay for workshops, podcasts and sessions to check a box rather than produce real results. Added to this is the misconception that operational AI is a sufficient end point, when in reality it is only the beginning.
A Practical Approach to AI Success
To stop wasting money and start achieving meaningful results from AI, 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 is not glamorous, but it is essential. Without it, AI models will produce unreliable or biased results.
Set specific results-oriented goals. Instead of pursuing vague AI ambitions, focus on specific business challenges or opportunities that AI can solve. For example:
- Forecast customer demand using historical sales data.
- Improve supply chain efficiency with predictive analytics.
- Develop predictive models to anticipate market developments and competitor actions.
Secure leadership buy-in. Management commitment is crucial. AI initiatives require funding, cross-departmental collaboration and a long-term perspective. Leaders need to 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 how much they care about data and results. Avoid those that 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 improve the accuracy of AI models and provide competitive insights.
Go beyond operations. Organizations must challenge themselves to go beyond operational applications of AI and explore its strategic transformation potential. Predictive analytics and other advanced tools can provide leaders 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 trivialities of consultants and focus on concrete results that provide real added value.
By understanding the critical role of data and adopting 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 creating AI initiatives that actually work.