Enterprises worldwide have been exploring AIs potential to redefine business efficiency and value, aiming to gain a competitive edge. AI innovations helps enhance customer interactions, optimize business processes and unlock growth opportunities through actionable insights, predictive analytics and personalized experiences.

Business leaders are quickly realizing that merely having a high number of AI PoCs dosen’t provide real value unless they transition to production. A Gartner survey* reveals that despite 17% to 25% of organizations planning AI deployment annually from 2019 to 2024, actual production deployment growth remained low at only 2% to 5% per year. This highlights the gap that exists between PoCs and the real-world value from AI, raising the pertinent question: what factors are hindering all these organizations from advancing beyond the PoC stage?

Let’s explore the five fundamental reasons and discuss what you can do to ensure most PoCs move to Production.

Data Maturity in Disarray:

Leaders often feel their organizations’ data maturity is high, but that is not always the case. Organizations need to align the readiness of their data infrastructure with their ambitions to leverage cutting-edge AI technology.

For example, while working with one of our large European customers, we discovered that the data maturity level of their Marketing division was high, whereas the data maturity level of their Finance division was low. Their finance division lacked essential data governance practices such as role-based access controls on their applications, indicating a deficiency in data management protocols. Our Generative AI PoC worked really well, demonstrating the potential value of AI applications within the organization. But the lack of data governance at a cross-functional level posed a significant barrier in productionizing the application.

Data Engineering Infrastructure:

While AI and Gen AI get all the attention, the data Infrastructure that makes AI possible often gets neglected. Data engineering is essential for developing AI applications, as it forms the infrastructure for gathering, storing, processing, and managing the large volumes of data crucial for training and running AI models. Insufficient data engineering capabilities and infrastructure can lead to data silos, inconsistent data quality, limited scalability, inadequate data governance mechanisms and more.

A large proportion of our customers realized that they need to invest in data engineering to make AI use cases possible. Many of them are reallocating resources to their data engineering capabilities, adopting modern data management practices, and developing newer data engineering talent. We have seen lack of data engineering expertise leading to extended development cycles and reduced competitive agility. 

The Achilles’ Heel of Progress: Strategic Misalignment

While this may sound straight out of Management Consulting lexicon, the fact is that many business divisions are eager to pursue AI, but the lack of alignment and support at an organizational level often leads to delays and, in many cases, derails AI roadmaps.  

To state an example, working with a manufacturing client who was eager to leverage AI solutions, it took two quarters for our team to get their Global IT convinced that our AI application will not compromise their data integrity. This was despite leveraging latest encryption methods, access controls, & following stringent protocols and best practices to safeguard sensitive data. With bureaucratic hoops and internal debates, we lost vital time in just getting a PoC started. 

Inexperienced AI teams:

In most AI teams, it’s observed that over half of the system integrators lack substantial experience in the crucial process of productionizing AI models. While some AI partners may demonstrate proficiency in executing PoCs, given the already dismal PoC to production ratio, very few proofs translate from their preliminary success into robust, scalable production systems. For AI models, it’s important for teams to effectively manage MLOps for taking models to production.

We advocate focused efforts on upskilling, knowledge sharing, and fostering a culture of continuous learning. This involves investing in the right AI training programs, mentorship initiatives, and technology platforms.

Poor Data Quality:

Data is the oil that keeps the AI engine running. Just as contaminated oil cannot run an engine and causes damage, poor quality data causes models to fail in production. When dealing with low-quality data, AI models run the risk of making flawed assumptions or reaching inaccurate conclusions, resulting in less-than-optimal performance and potentially significant financial repercussions. Production grade models must have high quality annotated data which is the correct representation of the real-world scenario that the model is trying to recreate. 

While navigating AI’s potential, organizations face a challenge bridging the gap between PoCs and production deployment. It’s crucial for businesses to address the five fundamental reasons behind this gap to unlock AI’s true value. For comprehensive guidance on successfully implementing an AI project, access our ‘Accelerate Your AI Product Development Journey’ guide.

References:

https://www.gartner.com/en/information-technology/topics/ai-readiness