Embracing the Benefits of AI Revolution with Caution for Gen AI Paradox

AI-Based Enterprise Tech Stack to Unleash Untapped Business Potential

The pace at which AI is transforming organizations in making complex enterprise-wide decision-making is stupendous. In simple words, AI has come a long way from deploying simple chatbots to exhibiting complex decision-making capabilities.

The ongoing artificial intelligence innovations have revolutionized a spectrum of domains like Customer Service, Employee Empowerment, Creative Ideation and Production, Data Analysis & Insights, Code Creation and Optimization, and Cybersecurity Vigilance.

Yes, the modern-day AI models showcase superhuman levels of artificial intelligence. These Models take a few seconds to produce outputs exceeding the capabilities of human experts. But the catch here is the “Gen AI Paradox” meaning “What it creates, it may not understand.”

In a nutshell, the current AI models exhibit superhuman expertise in producing results; they sometimes fail to comprehend what they produce. The challenge here for the organizations embracing AI-based Enterprise Tech Stack is to reap the benefits offered by AI and at the same time understand the responsibilities of adopting AI technologies with utmost importance for Data Governance and Ethics, mitigating the AI Bias and fostering Ethical AI practices.        

This blog sheds light on the best practices of implementing Enterprise Tech Stack with AI technologies and handling the challenges of the Gen AI paradox with paramount responsibility.

The Workflow: AI-Driven Enterprise Tech Stack    

Firstly, let us define what is a Traditional Enterprise Tech Stack. It is a comprehensive pool of technologies, tools, and components that enables organizations to seamless implementation of workflows amongst both internal and external stakeholders. A typical Enterprise Tech stack comprises components like Operating System, Servers, Database Management Systems (DBMS), Back-end Programming Languages, Frameworks, and Front-end Technologies.  

An AI-enabled Enterprise Tech Stack integrates AI technologies like Data Ingestion and Storage, Machine Learning Algorithms, Model Training and Tuning, Infrastructure Management, and Application Interfaces.  

Organizations adopting AI-driven enterprise Tech Stack should focus on the following FOUR key areas to build the futuristic AI Enterprise.

1. Data Empowerment:

Data is the primary ingredient for building AI-based models that exhibit capabilities of deriving actionable insights, producing creative and innovative content, offering cybersecurity vigilance solutions, generating software code, or helping in customer engagement.

Organizations must tap into their organizational Data Lake. Utilize this abundant data (available in the form of text, or multimodal data like audio, video, images, etc.) to build AI models. Data is the quintessence of building AI Models showcasing complex decision-making capabilities. Garbage-in Garbage-out paradox may fail an AI Model. Thus, Data Empowerment is an important step in building AI-based Enterprise Tech Stack.

2. Knowledge Retrieval:

The data alone is insufficient to realize your organizational goal of building an AI-based Enterprise Tech Stack. Extracting meaningful insights and knowledge from the available data is quintessential. Building a resource-rich ‘Knowledge Lake’ through data ingestion and cleansing to pattern recognition and predictive analytics is important. This process enables organizations to make data-driven decisions. It is an important component in building a robust AI enabled Enterprise Tech Stack.

Realizing Knowledge Retrieval is critical and poses abundant challenges to Leaders as they must not only extract meaningful insights from the data to build AI models but also consider future regulation, compliance, and AI sovereignty or control over the output of their AI models.

3. Building AI Models:

As an enterprise, you can feed the ‘Knowledge Lake’ to AI-powered models and develop customized predictive models and algorithms suitable for your organizational needs.

AI Experts recommend that Enterprises build models that suit their unique needs by relying on multiple Large Language Models (LLM). As an organization, you can explore building pre-trained models and use guardrails with Knowledge Lake to produce factual responses. Also, it offers the choice of relying on tested models of AI-native companies that can minimize hallucination and provide better data-driven insights.   

4. Implementing Applications as a Service:

The next step in realizing an AI-enabled Enterprise Tech Stack is to think beyond Data Empowerment, Knowledge Lake, and AI models to build frameworks.

You can build multiple Applications as a Service such as intelligent chatbots, virtual assistants, predictive tools, cybersecurity, and fraud detection systems. With this approach of producing Applications as a Service, your organization can utilize prebuilt AI solutions or develop your in-house applications accelerating time-to-market and reaping better Return On Investment. Moreover, All the applications can be rendered in a constant loop for learning with the Reinforcement Learning with Human Feedback (RLHF) technique. This approach eliminates the need to retrain the model frequently, in fact, you can relax without any need to retrain the model as it learns on its own.

5. Deploying Frontend Applications:

The entire purpose of reimagining AI-enabled Enterprise Tech Stack is to help end users leverage the power of AI tools in the form of Copilots so that the true potential of Human-Machine Augmentation is realized. It also emphasizes that AI tools are not a threat to human workers but it complements them to enhance productivity in the workplace.           

The Challenges of Data Governance and Ethics    

Here we delve into the essential considerations for an organization planning to implement an AI-based Enterprise Tech Stack, with a focus on Ethical AI and Bias Mitigation factors:

1. Ethical AI operates on principles and guidelines to ensure AI technologies line up with human values, avoid harm, and contribute to the welfare of society. The key aspects covered by Ethical AI are Transparency, Fairness, Privacy, Accountability, and Human Rights.

• An organization using AI tools for the hiring process, should be transparent and not favor candidates of a particular demography over others. To accomplish this an Ethical AI system should be continuously audited and the algorithms need to be trained to identify and correct any biases that have crept in.
• Often Deep Learning models fail to interpret and provide reasons for the decisions taken or results presented. Ethical AI researchers should build methodologies explaining “why an AI system made a specific decision.” 
• AI researchers have the responsibility to train AI models so that they do not expose sensitive or confidential information of stakeholders. Differential privacy and federated learning are popular examples of privacy-preserving AI techniques.
• AI for social good comprises AI projects to address societal challenges. Predicting natural disasters, providing healthcare solutions, and addressing climate change are a few such projects of Ethical AI aimed at societal good.

2. Bias mitigation comprises the strategies and techniques devoted to reducing biases in artificial intelligence (AI) models. Such biases creep in from the data used during training models or the algorithms applied. Some bias mitigation methods are:

• Fairness Constraints:  This technique is used by researchers and practitioners in use cases such as building a credit scoring model. They ensure models do not favor or discriminate against certain groups based on race, gender, or other personal attributes.
• Adversarial Debiasing: This technique involves training an additional neural network (the “adversary”) alongside the main model. This technique is used in facial recognition systems to reduce gender or racial biases.
• Optimized Preprocessing: This technique reweighs the training data, and optimizes preprocessing aiming, to balance the impact of different groups. It adjusts the weights of samples to mitigate bias in the resulting model.
• Prejudice Remover Regularizer: This algorithm penalizes the models for making biased predictions early in the training phase.

It is important to note that bias mitigation is an ongoing process, and it demands multidisciplinary collaboration between data scientists, domain experts, and policymakers to create AI systems that work fairly and equitably.

The Technology Considerations to Ponder Upon

Next, let us delve into salient technologies that organizations should consider while building an AI-based Enterprise Tech Stack:

Hybrid Cloud and Edge AI:

Hybrid Cloud:

The hybrid cloud model combines the on-premises infrastructure and public cloud services. By leveraging Hybrid Cloud, enterprises can seamlessly integrate their existing systems with cloud resources, gaining flexibility, scalability, and cost optimization. With strategic placement of workloads in the cloud or at the edge, organizations accomplish the goals of agility and control over sensitive data.

Edge AI:

Real-time decision-making happens at the edge—think IoT devices, edge servers, and local processing. Edge AI helps to minimize latency by processing data closer to the source, enabling faster responses. Enable implementation of robust security measures at the edge to protect against threats and leverage frameworks designed for edge deployment, such as TensorFlow Lite, ONNX Runtime, or NVIDIA Triton.

Quantum Computing and AI:

Though quantum computing is still in its infancy, it has enormous potential. Quantum AI algorithms can revolutionize different domains, importantly:

• Solve complex optimization problems faster than classical computers.
• Quantum-resistant encryption methods can enhance data security.
• Quantum simulations can accelerate drug discovery.

Getting the Human Resource Ready

According to a recent Gartner Inc. survey, 79% of corporate strategists have said that technologies such as analytics, artificial intelligence (AI), and automation will be critical to their success over the next two years. These findings mandate organizations to build a workforce that is AI-ready.

Human-AI Collaboration

A common myth is looking at AI as a competitor to working professionals. But in reality, AI is proving to be a collaborator, not a competitor. Organizations have a bigger responsibility to educate all their stakeholders on this, and the modern workplace functions on the principles of Augmented Intelligence. AI helps human resources in different areas like decision-making, automation of repetitive tasks, and enhancing productivity by collaborating.

Reskilling the workforce is the next biggest challenge for organizations. They must invest in upskilling their teams to be ready for the AI era.  This demands a training roadmap that fosters AI skills amongst employees at all levels.

The Gen AI Paradox:

The Gen AI Paradox arises from its inability to comprehend its own creations like art, software code, or predictions. This puts the leaders working on building AI solutions like Enterprise Tech Stack in a fix as they have to carry out a balancing act of leveraging AI’s power while safeguarding against unintended outcomes. Also, thought leaders must delicately handle Ethical Dilemmas which become prevalent as AI becomes more autonomous.     

Conclusion

In summary, the AI revolution is not just about embracing technologies. It is about reshaping how we work, create, and interact. In this blog, we tried to elucidate the AI-driven enterprise Tech Stack Workflow, The challenges of data governance, and ethical and technology considerations like Hybrid cloud, Edge AI, and Quantum AI, Preparing the workforce for AI challenges like reskilling and upskilling and the implications of the Gen AI paradox while developing an AI-based Enterprise Tech Stack. The Gen AI paradox reminds us that with great power comes great responsibility.