A successful solution succumbed to its practical utility.
Despite the brilliance of AI innovations, they frequently fail to reach their transformative potential. This failure in building AI solutions stems from a mismatch between the AI solutions developed and the actual needs of the users anticipated to adopt these technologies. This gap often leads to resistance and underutilization, as users may find the technology too complex, unintuitive, and misaligned with their daily workflows. As a result, the integration of these advanced tools into practical applications is significantly hindered.
Innovation: A Case Study
A CPG company with a strong dealer network and a significantly large sales team was facing challenges in optimizing its sales performance due to three main issues:
(1) Frequent loss of sales caused by insufficient inventory levels at local warehouses.
(2) Superficial analysis for setting sales targets resulted in missed targets and demotivated sales teams.
(3) Unsustainable promotional strategies characterized by deep discounting without assessing the long-term impact on profitability and revenue.
Solution: Building AI for Success
To overcome these challenges, the company partnered with our data science team to implement a comprehensive strategy that included a data-driven inventory management system, a dynamic sales target model, and a balanced promotional strategy that optimizes both sales volume and profitability. Machine learning was used to predict crucial business variables, to not only improve sales performance but also ensure sustainable growth and market competitiveness.
Successful AI model, great insights — Less impact
The project advanced with a strong determination to leverage the predictive power of AI. It took into account critical internal data, such as historical sales, complaints, and visits, as well as external factors like market dynamics, demography, and seasonality.
A potent solution was developed to reduce the loss of sales by 50% by optimizing the inventory, increasing revenue by 19% by analyzing the sales potential of each sales area and contributing to an additional 10% sales growth by prioritizing effective promotions.
The pursuit of a “great” model led to overengineering complex models for practical use and we failed despite technical superiority
The overlooked aspect of end-user adoption:
Machine learning models can generate vast amounts of insights. However, if these insights are not presented in an actionable and easily digestible manner, they can overwhelm rather than empower users.
1. Maximized AI potential, misaligned with business goals
This innovative solution was developed under the oversight of the top executives, generating a plethora of insights taking into account the AI capabilities. Having a limited understanding of the business team’s needs and the fact that they would require time to transition from the traditional approaches.
Developed under the oversight of executives, the predictive models aimed to maximize business impact by leveraging AI’s full potential. However, they failed to align with the business team’s needs and overlooked the transition process from traditional methods, leading to their non-adoption.
“At least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value, according to Gartner, Inc.”
2. Success is not in building the best models
A moderate-quality model with better adoption can create significant value while a great model with limited adoption is a failure. We must strive to build great models and ensure adoption to achieve sustainable business outcomes.
Advanced model + Limited adoption = Missed Opportunity
Functional model + Basic adoption = Modest Improvement
Effective model + Widespread adoption = Transformational Success
Lessons Learned:
1. Incremental approach (Aup tsoop strategy)
Take baby steps right from the grassroots level from proper education, and change management strategies to ensure practical use of insights. Rollout features in phases, don’t overwhelm end-user.
Start with foundational education and strategic change management. Introduce features gradually to prevent overwhelming users, ensuring smoother integration and greater acceptance.
2. Use a bottom-up approach
Involve the end user in the process to understand their daily routines, challenges, and needs. This guides the development process to ensure the solution serves its intended purpose. Integrate feedback loops into the model development process to avoid a black-box approach.
Engage end users early to tailor solutions to their real-world needs. Incorporate continuous feedback loops during development to maintain transparency and adaptability, fostering better engagement and successful adoption.
To ensure best adoption practices for advanced technologies, e
3. Don’t overwhelm, only must have
Develop a robust insights delivery model that prioritizes relevant and timely insights for maximum impact, while developing confidence among business teams. Insights provided by AI systems should be clear, concise, and directly actionable. Focus on user experience and consider the end-user’s skillset to create better usability and therefore adoption for better results.
4. If it requires a lot of training, don’t do it
We tend to give our best in every project and we also conducted training programs for specialized business teams to soon realize the impracticality of learning a new skill while having demanding work schedules.
Conclusion
This AI project’s failure at the last mile underscores the importance of a collective effort needed in designing and implementing technological solutions. While the technological aspects of AI projects are critical, their success ultimately hinges on how well they are received and utilized by those on the ground. Future projects can learn from this story by prioritizing the end-user experience as much as the sophistication of the underlying algorithms.
In our first failure story, we aimed to revolutionize oil well management by predicting bottom hole pressure using AI. Initial success was undone by neglected maintenance, leaving the model outdated and unusable.
Our second failure story AI failed to optimize blast furnace efficiency due to a lack of domain expertise. Even strong data science couldn’t compensate for the missing real-world insights.
These stories highlight a key lesson: AI cannot succeed without business-relevant expertise and internal capabilities. To prevent failure, companies must blend advanced technology with practical solutions, ensuring ongoing collaboration and continuous improvement to unlock AI’s full potential. Ultimately, “we solved an AI problem, not a business problem.” Ensuring future success requires balancing AI innovation with real-world business needs.
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