14 - Voice of Industry Experts - AI in Action: Tackling Key Challenges

 AI in Action: Tackling Key Challenges

 

Across industries, organizations are increasingly investing in AI-driven initiatives to tackle complex business problems and unlock value from their data. Initially, these efforts spark enthusiasm—with expectations of accelerated innovation, efficiency, and competitive advantage. Yet, after the initial phase, many management teams find themselves facing a perplexing reality: the promised results are falling short.

This raises a critical question—what’s going wrong?

AI implementation, much like any software development project, carries inherent uncertainty. However, the return on investment (ROI) in AI is often more nuanced and harder to quantify. Unlike traditional systems, AI demands a deeply integrated approach involving continuous learning, experimentation, and cross-functional collaboration. It's not just a technical journey; it's an organizational transformation.

 

In this article, I aim to delve into these organizational challenges more deeply—capturing lessons learned, sharing practical solutions, and offering reflections based on my diverse experience with AI transformations. By unpacking what often goes wrong and how it can go right, I hope to provide a roadmap for organizations aspiring to make AI a true engine of growth.

 

From below simple diagram lets understand these in details

Key Challenges in Driving AI Initiatives in Organizations

Organizations face a range of obstacles when launching and scaling AI initiatives:

1. Lack of Clear Strategy & Business Alignment: 

    Many AI projects fail to deliver value due to an absence of a well-defined strategy that aligns with real business goals, well defining problem statement is the key initial stage.

2. Talent Shortage & Skills Gaps: 

    AI projects require highly specialized skills in fields like machine learning and data engineering, but many organizations struggle to recruit or develop this talent. Key roles like data scientists, business analyst (SME), Machine learning engineer, AI architect, UI developer , DevOps / MLOps Engineer.

3. Data Quality & Availability: 

    Poor, fragmented, or inaccessible data undermines AI models, leading to inaccurate results and failed projects.

Gartner estimates that 85% of AI models fail due to poor data quality.

As a solution first identify data maturity in the organization, quality of data, availability of the data which require for AI projects

Case Example:  - Retail Demand Forecasting

Context: A national retail chain wanted to implement an AI-driven system to forecast product demand across regions to optimize inventory and reduce waste.

Challenge Encountered:

• The historical sales data was incomplete—some stores hadn’t been logging transactions consistently.
• Product codes varied across branches due to legacy systems, leading to confusion and duplication.
• External datasets (weather, local events) were sporadically updated and missing crucial timestamps.
• Customer data was poorly structured with inconsistent formats across platforms.

Impact on the AI Model:

• The predictive model showed erratic results, with high variance in accuracy across store locations.
• Stockouts and overstocking persisted because the AI couldn’t learn reliable patterns.
• Business stakeholders lost trust in the system due to its inconsistencies.

Remediation Strategy:

• A data audit was conducted to identify gaps, inconsistencies, and duplications.
• A centralized data pipeline was introduced with automated quality checks (e.g., missing values, schema validation).
• A master data management system (MDM) was deployed to unify product and customer information.
• External data sources were integrated with strict API protocols to ensure reliability.

Outcome:

• Forecasting accuracy improved by over 30% in the first quarter.
• Inventory turnover increased, while markdown losses decreased significantly.
• The AI platform became a trusted tool for supply chain decisions across departments.

4. Cultural Resistance & Change Management: 

    Employees may fear AI or see it as a threat, leading to low adoption, reliance on old workflows, and resistance to new systems.

    The change management is require , AI projects should be nicely collaborations with business team like partnership.

5. High Implementation Costs & Uncertain ROI: 

    AI projects often require significant upfront investment with delayed or unclear returns, making it hard for organizations to commit resources.
Would wide the success rate from AI projects is low , between 70-85% of current AI initiatives fail to meet their expected outcomes. 
With strategic planning, adequate resources, high-quality data, and the right technological approach, organizations can significantly enhance the success rate of their AI initiatives.
Case Example: AI-Powered Radiology Diagnostics in a Hospital Network

Context: A large private hospital chain aimed to use AI-based diagnostic tools to assist radiologists in detecting anomalies like tumors and fractures more efficiently.

Implementation Costs:

• Partnered with a leading AI vendor—licensing fees exceeded ₹3 crore annually.
• High-end GPU servers and storage upgrades added another ₹1.5 crore.
• Staff training and reskilling programs cost ₹50 lakh.
• Integration with hospital’s legacy IT systems demanded months of consulting and development work.

Expectation:

• Faster diagnosis turnaround time.
• Reduced workload for radiologists.
• Higher accuracy in identifying early-stage diseases.
• Better patient outcomes, driving loyalty and revenue.

Reality Check:

• Low adoption rates by medical staff due to unfamiliarity and mistrust of AI decisions.
• Frequent false positives led to rechecks, frustrating staff and lowering patient throughput.
• Inconsistent performance across different imaging machines and formats.
• After a year, cost savings were marginal and diagnostic accuracy improvements were inconclusive.

Outcome:

• Project was paused and re-evaluated.
• ROI remained uncertain; benefits were too long-term or indirect to quantify immediately.
• The hospital shifted focus toward AI tools with clearer operational impact, like patient scheduling optimization.

6. Ethical, Regulatory, & Governance Issues: 

    Increasing concerns around data privacy, AI ethics, and regulatory compliance require strong governance frameworks to manage risks and build trust.

Also, Many AI projects remain stuck in proof-of-concept or pilot phases and fail to scale across the enterprise. Proper scalable AI/ML algorithms ,integrate with any MLOps or cloud deployment and user friendly user interface will make confidence and to management and business users.

Finally , to summarise on Align AI initiatives with clear business goals and develop a strategic roadmap for implementation. ,Invest in high-quality data, robust governance, and ensure data accessibility across the organization, Upskill employees, foster cross-functional collaboration, and promote a culture that embraces innovation. And Start with high-impact pilot projects, measure value quickly, and iterate to scale successful solutions.

 

Successful AI projects can create a ripple effect of transformation—both within organizations and across industries

 

Let’s ignite our AI journey with renewed energy and a bold, focused vision for the future !

 

Pankaj Kokate - AI Transformation Leader