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GenAI – Reality Check in 2024

Piyush Chowhan, CIO, Panda Retail
Piyush Chowhan, CIO, Panda Retail

As we stride further into the year 2024, there’s a palpable shift in the discourse surrounding Artificial Intelligence (AI) – a discernible sense that the bubble of exaggerated expectations and unfounded promises is primed to burst. The year heralds a sobering reality check, tempering the inflated hype that has surrounded Generative AI in recent times.

Amidst the fervent excitement and soaring projections, the AI landscape is witnessing a recalibration of expectations. The initial exuberance and exaggerated claims about AI’s omnipotence are giving way to a more realistic understanding of its capabilities and limitations. The industry is gradually acknowledging that while AI holds immense potential, its true transformative power requires patience, refinement, and a clearer comprehension of its ethical and societal implications.

Aditya Kaushik, Director ICT, Zakher Marine International Inc.

Generative AI hype has built the right momentum

The hype surrounding Generative AI serves as a catalyst for innovation, fostering excitement and investment. The hype has created awareness and momentum which will continue in 2024. However, it’s crucial to balance expectations with ethical considerations, ensuring responsible development and realistic applications in the evolving landscape of artificial intelligence.

Advice to Enterprises

Implement AI using strategic partners by identifying expertise gaps, collaborating with specialized firms or consultants and leveraging their knowledge. Engage in joint ventures or partnerships to access cutting-edge technology, data or talent fostering successful AI integration within your organization’s framework and objectives.

Create a Governance Framework – Enterprises can establish Generative AI governance by formulating clear policies addressing ethical considerations, bias mitigation and data privacy. Create interdisciplinary teams involving legal, AI experts, and business stakeholders to oversee compliance and risk management. Implement guidelines for model training, validation and monitoring ensuring transparent and accountable AI use. Regular audits, documentation of processes and employee training on ethical AI practices are crucial.

In 2024, there’s a growing emphasis on pragmatic applications and tangible results rather than speculative promises. The focus is shifting from grandiose claims to practical implementations, fostering a more nuanced and grounded approach towards AI development. This shift signals a maturation phase for the AI industry, where substance and genuine value take precedence over inflated hype and unrealistic anticipations. The cost of applications is also being re-calibrated for better ROI and adoption.

“Lack of top management support and cultural resistance within organizations also hamper successful AI implementations.”

Piyush Chowhan, CIO, Panda Retail

While this adjustment may appear as the deflation of AI’s overblown expectations, it heralds a more sustainable and meaningful era for artificial intelligence. As the industry matures, the emphasis on responsible AI deployment, ethical considerations, and transparent practices will undoubtedly contribute to a more robust and purpose-driven AI ecosystem in the years to come.

Jessica Constantinidis, Field Innovation Officer, EMEA, ServiceNow

Middle East is ahead in AI Adoption

Enterprise can look at creating an “Chief AI Officer” which spearheads all AI initiatives across various functions. A CAIO can streamline AI initiatives, align them with business goals, and oversee AI governance, ensuring ethical, responsible AI deployment.

Need for reliable data – Accurate data is crucial as it forms the backbone of AI model training and functionality. Organizations must prioritize data quality assurance, validation, and cleansing processes to ensure that the AI models trained on this data yield reliable, unbiased, and valuable insights or outputs.

Global adoption of AI – The adoption of AI in the US has generally been more aggressive and widespread compared to Europe. In contrast, Europe exhibits a more cautious approach to AI adoption due to stringent privacy regulations (such as GDPR) and concerns about ethics, transparency, and bias in AI systems. Middle East Countries have also shown great openness on adoption of AI in government as well as enterprise by putting in more realistic regulations unlike EU.

Mastering Transition from Conventional AI to Generative AI – A big leap

The evolution from traditional AI models to Generative AI represents a significant leap in artificial intelligence capabilities, marking a transition from pattern recognition to creative content generation.

Conventional AI models primarily focused on recognizing patterns within existing datasets to make predictions or classifications. These models, including machine learning algorithms like regression, decision trees, and neural networks for supervised or unsupervised tasks, excelled at analyzing data and inferring relationships.

However, the advent of Generative AI introduced a paradigm shift by enabling machines not just to understand existing data but also to create entirely new content. Generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) revolutionized the field by allowing AI systems to generate images, text, music, and more based on learned patterns.

This transition represents a move towards AI systems that possess creative capabilities. Generative AI models leverage complex neural architectures to produce outputs that mimic and, in some cases, surpass the quality of human-generated content. This shift unlocks immense potential across industries, fueling innovation in art, design, healthcare, and other fields where creativity and original content are essential.

Sid Bhatia, Regional VP & General Manager for Middle East, Turkey & Africa, Dataiku

Adapting AI with the power of Data

The advancement in AI is pushing organizations to create a robust data platform for AI adoption which involves integrating diverse data sources, ensuring data quality, and implementing scalable infrastructure. It requires designing a unified architecture that facilitates data ingestion, storage and processing at scale and in real-time.

Creating a culture of AI – Enterprises need to Develop MLOps and DataOps which involves integrating machine learning and data operations with agile practices. Implement continuous integration/continuous deployment (CI/CD) pipelines for model development, testing and deployment.

Move towards AutoML – Moving towards AutoML (Automated Machine Learning) involves leveraging automated tools and frameworks to streamline the machine learning pipeline. Establish AI playrooms as sandbox environments for testing AI models by automating tasks like feature engineering, model selection, hyperparameter tuning, and model deployment. AutoML platforms use algorithms to search and optimize the best-performing models, catering to users with varying levels of machine learning expertise.

Enterprise Roadmap for AI Transition

Enterprises need to take a very cautious approach towards GenAI adoption. AI projects often face failure due to various reasons. Poor data quality, inadequate understanding of business needs, and a lack of skilled talent can impede progress. Unrealistic expectations, insufficient planning, and unclear objectives contribute to project failures. Additionally, challenges in integrating AI with existing systems, ethical concerns, and regulatory hurdles can lead to setbacks. Lack of top management support and cultural resistance within organizations also hamper successful AI implementations. Addressing these issues through proper planning, data governance, skilled workforce, and a clear strategy aligned with business goals is crucial to mitigate the failure of AI projects.

Starting a Generative AI project in an enterprise requires strategic planning and a systematic approach to ensure successful implementation.

  1. Identify Objectives: Clearly define the goals and objectives of the Generative AI project aligned with the enterprise’s needs. Determine specific use cases where Generative AI can add value, such as image generation, language processing, or data synthesis.
  2. Assess Resources: Evaluate the existing infrastructure, data availability and skill sets within the organization. Determine if additional resources, such as hardware, software or talent are required for the project.
  3. Data Preparation: Ensure high-quality and diverse datasets relevant to the project objectives. Clean, preprocess and organize the data to feed into the Generative AI model.
  4. Select the Right Model: Choose the appropriate Generative AI model based on the project requirements, considering models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) or Transformer models.
  5. Proof of Concept (PoC): Start with a small-scale Proof of Concept to validate the feasibility and efficacy of the Generative AI model. Test its performance and iterate as needed.
  6. Integration and Deployment: Integrate the Generative AI model into the enterprise infrastructure. Ensure compatibility and establish protocols for seamless deployment, considering security and compliance standards.
  7. Training and Collaboration: Train the team on using and maintaining the Generative AI system. Foster collaboration between data scientists, IT professionals, and business stakeholders for successful implementation and ongoing support.
  8. Continuous Improvement: Monitor the model’s performance, gather feedback, and continuously improve the Generative AI system to adapt to changing business needs and technological advancements.

By following a structured approach that includes meticulous planning, resource assessment, careful model selection and iterative development enterprises can effectively initiate and implement Generative AI projects for transformative outcomes.

Dinesh Varadharajan, Chief Product Officer, Kissflow

GenAI to fly high in 2024

Hype around Generative AI will become stronger 2024 as enterprises and individuals continue to discover and innovate ways to integrate ChatGPT into their workflows and services. We are on the verge of a major disruption across various disciplines, and ChatGPT, along with other GPT systems, is poised to spearhead that revolution.

Generative AI heralds a transformative era in software development, comparable to the revolution brought about by digital photography. Much like how digital photography empowered everyone to capture images, diminishing the exclusive reliance on professional photographers except for specialized tasks like wildlife photography, generative AI enables business users to address their challenges without mastering computer languages. This reduces the dependency on programmers for every business issue, reserving their skills for developing intricate systems.

Identifying right use case for Generative AI

Generative AI’s versatility has sparked innovation across diverse industries, revolutionizing processes and offering novel solutions. Identify use cases for Generative AI by assessing tasks requiring creative content generation or data synthesis. Explore scenarios where AI can augment human creativity, generate novel outputs or streamline design processes. Evaluate areas benefiting from image or text generation, personalized experiences, data augmentation or innovative solutions that leverage AI’s ability to create new and diverse content.

Tread with Caution before you start your Generative AI Journey

Before initiating Generative AI projects, caution and careful consideration are essential due to several reasons:

  1. Ethical Concerns: Generative AI models might generate biased or sensitive content based on the data they were trained on, potentially perpetuating societal biases or creating inappropriate outputs. Consideration of ethical implications and responsible AI deployment is crucial.
  2. Data Quality and Bias: The quality and diversity of the training data directly impact the model’s outputs. Biased or inadequate data could lead to flawed or undesirable results affecting the credibility and reliability of the AI-generated content.
  3. Complexity and Resource Intensiveness: Generative AI models are often complex and resource-intensive, requiring substantial computational power and extensive training on large datasets. Organizations must evaluate their infrastructure and resource capabilities before undertaking such projects.
  4. Legal and Regulatory Considerations: Compliance with data privacy laws and regulations becomes crucial – especially when dealing with sensitive or personal data. Adherence to legal frameworks and data protection standards is essential to prevent legal complications.
  5. Unpredictable Outputs: Generative AI models may produce unpredictable or unexpected outputs. Controlling the output or ensuring it aligns with business objectives might pose challenges, especially in critical applications.
  6. Maintenance and Support: AI models require ongoing maintenance, monitoring, and updates to retain their effectiveness. Organizations should be prepared for continuous support and iterations after deployment.

By acknowledging these challenges and potential risks, organizations can proactively address them, emphasizing the need for careful planning, ethical considerations, robust data governance, and a clear understanding of the project’s implications. This cautious approach ensures that Generative AI projects align with organizational goals while mitigating potential pitfalls and ethical dilemmas.

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