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

1. 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

2. 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

3. 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

4. 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

5. 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

6. Trust Trust of the models that are used, where the quality & source of data is not known
7. 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

Emerging Concerns

• Data Governance

AI output, data publication and usage raise concerns around copyright laws, data infringement, liability, and monitoring

• Regulatory Compliance

EU and UK AI liability and copyright laws require adherence, necessitating AI governance and monitoring standards

In my next blog, let’s look into the governance and regulatory boxes. Until then… 

————————————————————-———————————————————————–

References: 

https://www.recodesolutions.com/why-enterprises-struggle-to-move-gen-ai-led-automation-beyond-pilot-projects-and-into-real-applications/

https://www.linkedin.com/pulse/from-prototype-production-overcoming-ai-deployment-hurdles-xzr1c/

https://www.techtarget.com/searchenterpriseai/feature/Survey-Enterprise-generative-AI-adoption-ramped-up-in-2024

https://fair.rackspace.com/insights/eight-blockers-transitioning-ai-production/

This blog outlines the Artificial Intelligence (AI) governance practices that businesses should adopt to ensure safe and secure AI implementation. As companies increasingly adopt AI-driven platforms to gain a competitive edge and enhance customer engagement, implementing robust governance and safeguards is crucial for successful AI integration and usage.

Risks

The rapid advancement of AI and lower entry barriers heighten risks such as deep-fakes, privacy breaches, and bias. Businesses must take protective measures to mitigate these risks and safeguard employees, customers, and communities.

AI Governance Framework

To address these risks, businesses need a solid governance framework that ensures transparency, fairness and a balance between technological progress and ethical considerations.

AI governance refers to policies, regulations, practices, and ethical guidelines that steer the design, development, deployment, and use of AI systems. This framework provides a structured approach to managing ethical issues, ensuring transparency, accountability, explainability, safety, and security.

Effective AI governance involves multidisciplinary stakeholders from technology, law, ethics, and business and varies based on the organization’s size, AI system complexity, and regulatory environment.

The conversation on AI ethics and governance has evolved notably, with numerous countries and international organizations establishing policies, principles, and frameworks. Many nations, including Australia, Canada, China, the EU, India, Japan, Singapore, South Korea, the UAE, the UK,and the US, have introduced guidelines to oversee AI development and deployment, emphasizing data privacy, security, fairness, and transparency. These guidelines aim to balance ethical considerations with regulatory measures while fostering innovation.

International organizations like OECD and UNESCO have also developed strategies to promote consistent AI governance worldwide, focusing on societal values, human rights, and ethical AI.

The EU pioneered legal frameworks for AI with its Ethics Guidelines for Trustworthy AI which outlines requirements for various AI applications. Similarly, the UK’s National Cyber Security Centre and the U.S. Cybersecurity Infrastructure and Security Agency have released comprehensive set of global guidelines .

Other prominent frameworks include the NIST AI Risk Management Framework, the OECD Principles on Artificial Intelligence, Australia’s AI Ethics Framework , Artificial Intelligence Governance and Auditing (AIGA) Framework , Singapore’s Model AI Governance Framework

The essential parameters to be considered while building an AI governance framework by the businesses, should include:

• Organizational Alignment: Align AI use cases with an organization’s core values, goals, and strategy for ensuring responsible and effective implementation. This alignment helps in maximizing the positive impact of AI while minimizing potential risks or ethical concerns.
• Governance Structure: Establish a cross-functional AI governance committee that includes business, technical, legal, and risk experts. This committee ensures that AI initiatives meet ethical requirements and helps to protect core pieces of the organization, like data and intellectual property.
• Regulation and Compliance: Develop legal frameworks and standards to govern AI activities, ensuring they adhere to societal norms and laws. 
• Privacy Measures: Ensure data is handled securely & “Privacy by Design” is implemented as a principle for design & development.
• Data Management: Establish data management policies and oversight teams. Ensure responsible handling of data that will be used by AI systems, including data collection, storage, processing, and sharing practices.
• Data Compliance: Ensure that the data used to train and fine-tune AI models is accurate and compliant with privacy and regulatory requirements, ensuring minimal hallucinations.
• Transparency: Require AI model to clearly explain their decision-making processes, data inputs, and underlying algorithms.
• Accountability: Establish accountability measures to be followed when AI systems malfunction or cause harm. 
• Robustness and Reliability: Ensure AI models perform consistently across a range of scenarios such as adversarial attacks.
• Mitigate Discrimination: Establish strategies to identify and mitigate biases in AI systems to prevent discrimination and unfair outcomes.
• Risk Management: Identify potential operational, ethical and security risks and create a risk mitigation approach.
• Security Measures: Ensure to protect AI systems and data from unauthorized access, breaches, and cyberattacks by incorporating the proper security measures.
• Comprehensive Testing and Validation: Perform comprehensive validation and testing of AI models to confirm that they perform as expected and meet necessary quality benchmarks.
• Version Control: Track different versions of AI models and their training data to reproduce or scale them when needed.
• Documentation: Maintain Proper documentation of entire AI model life cycle including metrics.
• Continuous Monitoring: Engage in ongoing monitoring of AI system for performance, compliance, and emerging risks, and adaptation. 
• Training: Invest in education and training programs to foster a culture of responsible AI use.
• Regular Audits:  Perform regular audits to assess the AI model’s performance and to identify any gaps or areas of non-compliance and take corrective actions.
• Human Oversight: Integrate human oversight and intervention option in the AI model particularly in critical applications to ensure consistency.
• Governance metrics: Use metrics and key performance indicators (KPIs) to validate whether the organization is adhering to AI governance policies.
• Innovation and Development: Encourage responsible AI innovation while ensuring that governance measures do not stifle technological progress.
• User feedback: Provide mechanisms for end users to provide feedback on AI model behaviour and ensure the technology meets societal needs.

Comparison of some of the governance frameworks across countries that are in use:

Conclusion

This blog covers the essential of AI governance, providing an introduction to its importance and a checklist for the businesses looking to develop a robust governance framework. These foundational steps are crucial for any organization aiming to navigate the complexities of AI responsibly. In the upcoming blogs, lets dive deeper into some of the key components of the AI governance and critical guardrails that ensure AI is used responsibly and ethically. The other perspective to this is the question – are governance & guardrails required to monitor AI, if so from who and to what extent (Food for thought), or in other words, how to monitor ethics, privacy, bias, etc… comments welcome !!!