CXO

Navigating ESG Excellence: Brief Outlook for Large Enterprises in 2024

kush — Fri, 26 Jul 2024 12:08:07 +0000

In the fast-evolving digital business landscape, the focus on Environmental, Social, and Governance (ESG) principles has become paramount. As we enter 2024, the imperatives for large enterprises have shifted, demanding a nuanced and strategic approach to ESG.

Credible reporting on ESG data is becoming a must-have for all companies. From the board room to investors, enterprises need to navigate through the overwhelming and complex world of frameworks, rankings, and regulations. Regulators worldwide are steadily ramping up reporting requirements, and while most of these requirements apply to financial institutions and large or listed companies, they can impact smaller organizations in their supply chains. As requirements evolve, SMEs will also, increasingly come into scope for ESG-related disclosures.

This brief outlook aims to guide large enterprises through the key trends, challenges, and opportunities in the ESG landscape for the year ahead.

Embracing ESG Integration: Large enterprises are urged to move beyond mere compliance and embrace ESG as a strategic driver. Integration into core business operations is not just a trend; it’s a fundamental shift towards sustainable and resilient business models.

Strategic ESG Frameworks: Adopt comprehensive frameworks that align ESG goals with business objectives, ensuring that sustainability becomes ingrained in decision-making processes.
Stakeholder-Centric Approach: Prioritize stakeholder engagement, understanding that customers, employees, investors, and communities play pivotal roles in shaping and validating ESG initiatives.

Technological Innovations and ESG: Technology continues to be a game-changer in the ESG landscape. In 2024, large enterprises are encouraged to leverage technological innovations for both sustainable practices and enhanced ESG reporting.

Data-Driven ESG Metrics: Embrace advanced data analytics & management and gen artificial intelligence to enhance the accuracy and transparency of ESG reporting, demonstrating a commitment to measurable outcomes.
Blockchain for Transparency: Explore blockchain technology to enhance transparency in supply chains, allowing for immutable records of sustainability practices and ensuring the credibility of reported data.
Digital technology is a key component: Digital technologies can be applied to support environmental issues, including the use of robotics, the Internet of Things, and drones to improve efficiency, reduce waste, and provide less carbon-intensive means of environmental management.

Social Impact and Employee Well-being: ESG strategies are no longer confined to environmental considerations; social impact and employee well-being take center stage in 2024.

Diversity, Equity, and Inclusion (DEI): Prioritize DEI initiatives to foster an inclusive workplace, acknowledging that diversity is a key driver of innovation and long-term business success.
Employee Well-being Programs: Implement comprehensive well-being programs that address physical and mental health, showcasing a commitment to the welfare of the workforce.

Climate Resilience and Circular Economy: With climate change at the forefront of global concerns, large enterprises must proactively address climate risks and transition towards circular economy principles.

Carbon Neutrality Commitments: Set ambitious carbon neutrality goals and implement strategies to achieve them, recognizing the importance of mitigating climate-related risks.
Circular Supply Chains: Embrace circular economy principles by reevaluating supply chain processes, reducing waste, and reusing materials to minimize environmental impact.

As large enterprises move into 2024, the integration of ESG principles are not just a corporate responsibility but a strategic imperative for long-term success. By embracing technological innovations, focusing on social impact, and championing environmental sustainability, organizations can position themselves as leaders in a world that increasingly values purpose-driven business.

By taking a focused and strategic outlook on ESG, large enterprises can not only meet the expectations of stakeholders but also contribute to a sustainable and equitable future.

Cybersecurity Requires Strategic Leadership

kush — Fri, 26 Jul 2024 12:02:57 +0000

In today’s digital age, enterprises are accelerating their digital journeys as this empowers them to become more efficient and adapt to evolving market dynamics driving growth and competitiveness. However, this also exposes businesses to cybersecurity challenges that need to be addressed urgently to ensure digital transformation is indeed sustainable long term.

As cyber threats are growing in scale and sophistication, leading to financial and reputational damages besides having regulatory consequences, enterprises and their boards are looking at cybersecurity very seriously and strategically for every business decision.

There is clearly a realization that the attack vectors that are going to come into agents that are exposed to the outside world are not known and in what shape-size will they hit upon the business.

Emerging technologies like Generative AI is making phishing attacks more convincing, and large language models in particular have created a massively exposed attack surface.

Companies across sectors are now scrambling to not only understand emerging generative AI–enabled attacks and build new defense tools but deal with fast-moving challenges regarding internal usage of these tools, policy, and compliance.

The Chief Information Security Officer (CISO) is playing a critical and evolving role in organizations, especially in the context of rapidly advancing emerging technology and the increasing threat landscape.

As the digital landscape evolves and cyber threats become more sophisticated, the CISO takes the center stage and plays a crucial leadership role in safeguarding the organization’s information assets, ensuring their confidentiality, integrity, and availability.

Cybersecurity today requires Strategic Leadership

Strategic Planning: CISOs are involved in developing and implementing the organization’s cybersecurity strategy aligned with overall business objectives.

Leadership: They provide leadership and guidance to the cybersecurity as well as business team, fostering a culture of security awareness throughout the organization.

Risk Management

Risk Assessment: CISOs assess and prioritize cybersecurity risks, considering both internal and external threats.

Compliance: They ensure that the organization complies with relevant regulations and standards and stays ahead of evolving compliance requirements.

Incident Response and Crisis Management

Preparedness: CISOs develop and maintain incident response plans to effectively respond to and mitigate cybersecurity incidents.

Coordination: They work closely with internal teams and external partners to coordinate responses during security incidents.

Technological Advancements and Emerging Technologies

Adoption of New Technologies: CISOs stay updated on emerging technologies and evaluate their potential impact on the organization’s security posture.

Integration: They ensure that security measures are integrated into new technologies and business processes from the outset.

Security Awareness and Training

Employee Training: CISOs promote a culture of security awareness by conducting regular training sessions for employees.

Communication: They communicate security policies and best practices to all levels of the organization.

Third-Party and Supply Chain Security

Vendor Management: CISOs assess and manage the security risks associated with third-party vendors and the supply chain.

Contractual Agreements: They ensure that security requirements are included in contracts with third-party vendors.

Continuous Monitoring and Threat Intelligence

Monitoring Systems: CISOs implement continuous monitoring systems to detect and respond to security threats in real-time.

Threat Intelligence: They leverage threat intelligence to stay informed about the latest cyber threats and vulnerabilities.

Collaboration and Communication

Board and Executive Communication: CISOs communicate effectively with the board and executive leadership, translating technical issues into business risks.

Cross-Functional Collaboration: They collaborate with other departments such as IT, legal, and risk management to align security efforts with overall business goals.

Adaptability and Learning

Continuous Learning: Given the rapidly changing nature of cybersecurity, CISOs need to engage in continuous learning to stay abreast of new threats and technologies.

Adaptability: They must be adaptable and able to adjust strategies and tactics in response to evolving threats.

Measuring and Demonstrating Security Effectiveness

Metrics: CISOs establish and track key performance indicators (KPIs) to measure the effectiveness of security programs.

Reporting: They provide regular reports to executive leadership on the state of cybersecurity within the organization.

The CISO role is dynamic and requires a combination of technical expertise, leadership skills, and a deep understanding of business operations. As the cybersecurity landscape continues to evolve, the CISOs role will remain critical in safeguarding organizations against cyber threats.

However, increasingly CISOs are now being held personally liable regarding their handling of attacks on their companies, arguably there could be a shortage of CISOs in the future.

Lastly, given there are growing risks of being a CISO, speculatively this the role could also, split into two—one more operational role, and one that’s more governance-oriented.

Generative AI: Enterprise-Grade LLMs

kush — Fri, 26 Jul 2024 11:52:43 +0000

In the realm of generative AI, building enterprise-grade large language models (LLMs) requires expertise collecting high-quality data, setting up the accelerated infrastructure, and optimizing the models.

“Enterprise-grade generative artificial intelligence” typically refers to advanced AI systems designed for large-scale business applications and operations within an organization. These systems can generate human-like responses, content, or solutions based on their training data.

Recent breakthroughs in the field, such as GPT and Midjourney, have significantly advanced the capabilities of GenAI These advancements have opened new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research.

Generative Models are algorithms or architectures designed to generate new data samples. Common types include:

Generative Adversarial Networks (GANs): GANs consist of two neural networks – a generator and a discriminator – which are trained simultaneously. The generator creates data, and the discriminator evaluates whether it’s real or generated. This adversarial training process leads to the generation of realistic data.
Variational Autoencoders (VAEs): VAEs are a type of neural network that learns to encode and decode data. They work by learning the underlying distribution of the training data and can generate new samples by sampling from this distribution.
Autoregressive Models: These models generate data sequentially, with each step conditioned on the previous ones. Examples include the Transformer-based models like GPT (Generative Pre-trained Transformer)

Enterprises today are leveraging generative AI, while most firms may not feel the need to build their models, most large enterprises are expected to build or optimize one or more generative AI models specific to their business requirements within the next few years.

The widespread adoption of large language models (LLMs) has improved the ability to process human language. However, their generic training often results in suboptimal performance for specific tasks. To overcome this limitation, fine-tuning methods are employed to tailor LLMs to the unique requirements of different application areas.

Finetuning can enable businesses to achieve these goals:

Achieve higher accuracy by customizing model output in detail for their own domain.
Cost Saving. Customizable models with licenses permitting commercial use have been measured to be almost as accurate as proprietary models at significantly lower cost.
Reduce attack surface for their confidential data.

Large enterprises who have large data sets can generate world-class performance by building their own generative AI tools leveraging internal data or follow an LLM-agnostic approach and leverages multiple LLMs.

Enterprises need for finetuned LLMs

Enterprises may have a strong need for a fine-tuned Large Language Model (LLM) for various reasons, depending on their specific requirements, industry, and objectives and especially when dealing with complex business processes and diverse communication requirements. Here are some key considerations:

Industry-Specific Terminology: Enterprises often operate within specialized industries with unique terminologies. A fine-tuned LLM can be customized to understand and generate content specific to the industry, improving communication and ensuring accuracy in domain-specific language.
Legal and Compliance Documents: Industries such as finance, healthcare, and legal require precise and compliant language in their documentation. A fine-tuned LLM can assist in drafting and reviewing legal documents, financial reports, and compliance-related content, reducing the risk of errors and ensuring adherence to regulations.
Customer Support and Interaction: For businesses with large customer bases, a fine-tuned LLM can enhance customer support processes. It can be trained on historical customer interactions to understand and generate responses in a way that aligns with the company’s brand voice and provides relevant information to customers.
Content Generation for Marketing: A fine-tuned LLM can be invaluable in content marketing efforts. It can help generate engaging and persuasive marketing copy, product descriptions, and other promotional content tailored to the enterprise’s target audience.
Internal Knowledge Sharing: Large enterprises often deal with vast amounts of internal documentation and knowledge sharing. A fine-tuned LLM can assist in summarizing, generating insights, and facilitating the retrieval of relevant information from extensive internal databases, improving knowledge management within the organization.
Customization for Policies and Procedures: Enterprises have unique policies, procedures, and guidelines. A fine-tuned LLM can be trained to understand and generate content that aligns with these internal rules, making it a valuable tool for creating standardized and consistent documentation.
Multilingual Capabilities: For global enterprises, a fine-tuned LLM can be trained to handle multiple languages effectively, facilitating communication across diverse teams and markets.
Sensitive Data Handling: Enterprises often deal with sensitive information. A fine-tuned LLM can be customized to handle and generate content in a way that ensures data privacy and compliance with security protocols.
Integration with Enterprise Systems: A fine-tuned LLM can be integrated into existing enterprise systems, making it easier to incorporate the model into various workflows and applications, enhancing overall operational efficiency.
Brand Consistency: Maintaining a consistent brand voice is crucial for enterprises. A fine-tuned LLM can be customized to align with the organization’s brand guidelines, ensuring coherence in external communications and marketing efforts.

LLM builders and LLMOps platforms provide services for LLM finetuning. Given the nascent nature of the market, there is a large variation in pricing between different providers.

Their services could include not just finetuning pre-trained models which is already available but also, training models from scratch using internal data.

While leveraging a fine-tuned LLM offers these benefits, it’s essential for CIOs to consider ethical considerations, ensure responsible AI practices, and regularly update and monitor the model to maintain its effectiveness in evolving business contexts.

Neuromorphic Computing: The Next Wave of AI Capabilities

kush — Fri, 09 Dec 2022 02:15:31 +0000

In the ever-evolving AI landscape, new computational challenges surface each day as the technology continues to learn and adapt. Neuromorphic computing emerges as a potential game-changer, offering the prospect of future-proofing AI research against the unpredictable demands of tomorrow.

Neuromorphic computing, in simple terms, is a type of computer engineering where the components of a computer are designed to mimic the human brain and nervous system. This term encompasses the creation of both hardware and software components. Neuromorphic computing is also known as neuromorphic engineering.

It is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons (neuroscientists consider neurons the fundamental units of the brain) to do computations.

Neuromorphic computing is much better candidate for next-gen computation. The term was first conceived by Professor Carver Mead back in 80s, describing computation mimicking human brain.

What is the difference between AI and neuromorphic computing?

Artificial Intelligence (AI) is a vast domain that includes diverse techniques and technologies aimed at emulating human-like intelligence in machines. Neuromorphic computing represents a distinct subset of computing inspired by the architecture and processes of the human brain.

Machine learning algorithms are the core of neuromorphic computing, enabling its function and flexibility. Spiking neural networks (SNNs) lead the way, utilizing synaptic plasticity and spike-timing-dependent plasticity (STDP) to their advantage.

Although neuromorphic chips, which power neuromorphic computers, may not supplant traditional computational chips like CPUs, GPUs, or application-specific ICs, they can enhance existing computers that perform deep learning tasks for artificial intelligence.

Neurons and Synapses

Neurons: Fundamental units that process and transmit information in the brain.
Synapses: Connections between neurons through which signals are transmitted.

Spiking Neural Networks (SNNs)

SNNs are models of neural networks that incorporate the concept of time into their operating model. Unlike traditional neural networks, which process information in a static manner, SNNs use spikes (discrete events) to transmit information.

Event-Driven Processing

Neuromorphic systems operate in an event-driven manner, where computation occurs only when there are events (spikes), leading to potential energy savings.

Analog and Digital Mixed-Mode Circuits

Neuromorphic hardware often uses a combination of analog and digital circuits to mimic the continuous and discrete nature of biological neural processes.

While there are multiple future directions for this technology, one of the key aspects is that of developing new algorithms that can fully exploit the potential of neuromorphic hardware that will be crucial for future advancements.

Multiple organizations are leading the research in neuromorphic computing, including Intel, IBM, Qualcomm, and DARPA.

Intel Labs is researching to advance neuromorphic computing, which aims to empower the next generation of intelligent devices and autonomous systems. This technology holds the potential to revolutionize computing and is currently employed in diverse fields such as sensing, robotics, healthcare, and large-scale AI applications. The Loihi 2 neuromorphic processors utilize principles of brain-like computing, featuring asynchronous, event-driven spiking neural networks (SNNs), combined memory and computation, and dynamic, sparse connectivity.

Neuromorphic computing holds the promise of revolutionizing AI by providing more efficient, adaptable, and powerful systems. As research and development continue, we can expect to see neuromorphic technology playing a critical role in the next wave of AI advancements, enabling new applications and improving existing ones across various domains.

The effort to produce artificial general intelligence (AGI) also, is driving neuromorphic research.