AI Adoption from PoC to Production

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by Abhijit Bangalore

AI Adoption from PoC to Production – Overcoming Impediments

Overcoming Impediments

In continuation of the AI blog series, this blog gives viewpoints on the impediments for AI adoption from PoC to production. When we take a look at the current AI adoption, we see that the chip makers are way ahead of their software counterparts, which in my view is a first we have seen over the past few decades!

What’s holding the adoption back? Is it proper use case fitment or non-availability of base data or vulnerability of current security frameworks to DDOS (Distributed Denial of Service) or the CSOs thinking that current systems or processes can’t handle protection policy (data, network)? Well, one can’t be sure! It can be a combination of all these points! And, this concoction is the reason why adoption of AI use cases from PoC to Production is pegged at a maximum of a measly 28% conversion rate.

Diving into the primary obstacles-

Primary Obstacles

Data Quality

Inadequate base data quality hinder AI model effectiveness.
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. According to the IBM Big Data & Analytics Hub, poor data costs the US economy $3.1 trillion every year. Research from Cognilytica points out that over 80% of the time in most AI and Machine Learning projects is spent on Data preparation and engineering tasks. This emphasises the need for high quality data obtained by leveraging the solutions and best practices in data preparation, management and engineering for implementing effective AI algorithms

Risk Controls

Inadequate risk management frameworks and controls.

According to a recent survey report by Deloitte, tracking generative AI adoption and challenges, risk and governance are the among the top 4 major challenges companies have in implementing generative AI applications and tools. Only 23% of the companies were highly prepared for ri
k and governance

Infrastructure Costs:

Prohibitive costs of infrastructure development and maintenance.
In a recent Gartner Data & Analytics Summit in Sydney, an analyst cited the cost of projects as a big pressure on generative ai deployment, with upfront investments ranging from 5 million to 20 million

Uncertain ROI:

Difficulty quantifying financial benefits of AI use cases.

As AI applications vary widely across industries, it is difficult to establish standardized benchmarks for expected ROI. Typically, around 90% of AI initiatives do not meet their ROI targets, primarily due to challenges in deploying models efficiently and aligning outcomes with business goals

Technical Expertise

Shortage of skilled professionals to create and maintain AI models.

41% of businesses are struggling to find employees to support their generative AI initiatives, according to Enterprise Strategy Group’s recent survey

Trust Trust of the models that are used, where the quality & source of data is not known
Model Scalability and Performance:

According to a 2022 study by MLOps Community, 62% data scientists face issues with model performance when moving to production. PoCs are typically conducted in a controlled environment with limited resources. The AI model needs to be scaled to be moved into production. A robust deployment strategy needs to be in place to scale and serve concurrent users without degradation in performance

That being said, there are areas where AI adoption is seeing greater traction, there are a few use cases that are into production successfully

Successful AI Adoption Use Cases

Productivity Measurement

Retail, hospitality and customer service industries leverage AI to measure process and employee productivity. According to the Forbes Advisor survey, 53% of businesses apply AI to improve production processes, while 51% adopt AI for process automation and 52% utilize it for search engine optimization tasks such as keyword research

Data Analysis

AI-driven data segmentation and ROI calculations are gaining traction.

Automation Agents

Self-sustaining routines automate tasks efficiently. 75% of users want to leverage AI to automate workplace tasks

Code assistance

Aid in software development. An Enterprise Strategy Group survey of application developers found that 63% used generative AI in production, citing faster code creation and improved customer support as top benefits. Business executives observe 55% developer productivity improvement due to generative AI adoption

Challenges

To bridge the gap between PoC and production, it’s crucial to address the following challenges:

Data Quality Improvement

Ensuring reliable, high-quality data by defining a data governance framework to ensure accuracy, completeness, consistency, and timeliness of the data used in AI models

Risk Management Frameworks

Developing and implementing robust risk controls

Cost-Effective Infrastructure

Exploring cloud-based, scalable solutions. Platforms like AWS, Google Cloud Platform (GCP), and Microsoft Azure offer specialized AI and machine learning services that allow businesses to scale resources up or down based on demand. Applications that require real time processing such as Self-driving cars, wearable devices, and smart home appliances can leverage edge AI which enables models to be deployed closer to where the data is generated, reducing data transfer costs and reliance on centralized cloud processing

Clear ROI Definitio

Establishing measurable financial benefits. A study from ESI ThoughtLab and Deloitte found that organizations with mature AI strategies generally see higher ROI as these companies focus on robust data management, effective tracking, Privacy, security, ethics and strong alignment with strategic goals from the onset

Skill Development

Investing in AI talent acquisition and training. Businesses need to focus on upskilling, knowledge sharing, and fostering a culture of continuous learning among existing employees by investing in the right AI training programs for data engineers, developers and analysts

Embracing AI governance after tackling these impediments, organizations can unlock the full potential of AI, driving successful adoption from proof of concept to production. However, there are concerns emerging as AI adoption is set to increase multifold