Initiatives for training and development are educational activities carried out within an organization to improve an individual’s or a group’s performance on the job. The majority of these programs focus on improving a worker’s knowledge and skill sets as well as encouraging more drive to improve work performance.

The long-term growth of employees might be the purpose of training programs that are developed independently or with the aid of a learning administration system. Orientations, classroom lectures, case studies, role-playing, simulations, and computer-based training, including e-learning, are all common training techniques.

Most employee training and development initiatives are driven by an organization’s HRD department, often known as human resource development (HRD). These initiatives can be divided into two categories of programs:

Employee Training and Development

Most employee training and development initiatives are driven by an organization’s HRD department, often known as human resource development (HRD). These initiatives can be divided into two categories of programs:

Management Training and Development

The process of developing certain information, skills, and talents through time to develop managers into effective leaders and managers into managers.

Why is training and development important?

Successful companies are aware that developing current staff is more advantageous and cheaper than looking for fresh talent.

The following are the top ten advantages of staff training and development programs:

1. Increased productivity: Employees can boost their overall output by keeping up with new processes and technologies.
2. Reduced micromanagement: Workers tend to need less supervision and work more independently when they feel empowered to complete a task.
3. Train future leaders: For an organization to develop and change over time, it needs a strong pipeline of educated and creative potential leaders.
4. Increased job satisfaction and retention: Employee retention increases as a result of well-trained staff members’ greater confidence and increased job satisfaction.
5. Attract highly skilled employees: Top candidates are attracted to companies with a clear career path built on ongoing training and development.
6. Increased consistency: Tasks are carried out consistently when training is well-organized, leading to strict quality control that end users can rely on.
7. Increased camaraderie: Teamwork and collaboration are promoted by training and growth.
8. Bolstered safety: Employees who receive ongoing training and development are more likely to have the information and abilities needed to do a task correctly.
9. Ability to cross-train: Employees that receive regular training become knowledgeable team members who may support or teach one another as needed.
10. Added innovation: Employees that receive regular training can assist in creating creative ideas and goods, which will improve the business’s bottom line and long-term profitability.

Current trends in training and development

Businesses must be adaptable and agile because the corporate market is always evolving. One of the main forces behind this quick transformation is technology, with automation and artificial intelligence (AI) at the forefront.

The following four major trends will force organizations to reconsider their training and development strategies:

Remote mobile Training

Corporations today have realized that performance is now influenced by when, where, and how development experiences are used rather than just what employees need to know. Companies are depending more on mobile workforces as a result of advances in mobile technologies. Mobile apps offer “just-in-time” advice and information to workers across industries as training migrates to these devices.

AI Training

Artificial intelligence (AI) systems can process unstructured data similarly to humans. These systems are capable of comprehending spoken language as well as auditory, visual, and textual objects. AI-based software may recommend content based on a learner’s past performance, customize how training content is provided to a learner depending on their learning preferences, and forecast what information is most important for them to learn next.

Agile Learning

Employees are encouraged to experiment usually and learn by doing as part of the agile learning process, which promotes organizational change and buy-in. For instance, IBM has unveiled IBM Garage, a platform for carrying out, expanding, and managing a company’s various transformation activities. IBM Garages are being used globally by organizations like Ford Motor Company and Travelport to encourage cultures of open collaboration and ongoing learning.

Remote flexible learning models

Although distance learning has been around for a while, the COVID-19 pandemic has brought attention to how important it is for businesses to have robust, adaptable, and mobile workforce management. Organizations have realized the importance of being productive, engaged, and always learning and improving remote workforces.

Training and Development Challenges

Many corporate training programs may not be very effective, according to recent papers and business surveys. Learners won’t retain most training completely. Using targeted distance learning programs and mobile “just-in-time” training, businesses must foster a culture of continuing, self-directed, and self-motivated learning.

Organizations must also reconsider the bigger picture of the talents that will be required shortly. In the next three years, more than 120 million workers in the world’s twelve top economies may need to undergo retraining due to AI-enabled automation, according to a recent meta-level IBM study (PDF, 916 KB).

A few outcomes from the study are as follows:

• Skilled humans fuel the global economy: Digital literacy is still important, but soft skills are also more significant.
• Skills availability and quality are in jeopardy: Organizations are under pressure to discover strategies to remain ahead of skills relevancy as the half-life of skills continues to shorten and the time it takes to close a skills gap has increased.
• Intelligent automation is an economic game changer: Millions of people will probably need to undergo retraining and pick up new skills, but most businesses and nations are ready for the challenge.
• Organizational cultures are shifting: A new business model, new ways of working, and a flexible culture are now required in the digital age to support the growth of important new skills.

The study comes to the conclusion that alternative techniques and tactics can significantly impact the skills gap closure and that traditional recruiting and training are no longer as successful. Several approaches and techniques include:

• Make it personal: Make career, skills, and learning development opportunities specifically customized for the interests and goals of your staff.
• Improve transparency: Put skills at the center of your training strategy and strive for deep visibility into your organization’s standing on skills.
• Look inside and out: Adopt an open technological architecture and a group of partners that can benefit from the most recent advances.

 

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Today marks a groundbreaking moment for artificial intelligence (AI). After notable progress in the past decade, primarily driven by machine learning (ML) and deep learning techniques, AI has now taken a significant leap forward, fueled by generative AI. This technology has garnered attention, causing excitement and some concerns. Nonetheless, there is widespread agreement that the recent advancements are substantial and present a tremendous opportunity for businesses that act swiftly and strategically.

The key to this leap in AI lies in the use of foundation models. Unlike traditional ML, which requires building specific models for each new use case using labeled data, foundation models are trained on vast amounts of unlabeled data. These models can then be adapted and fine-tuned for new scenarios and business applications. The implementation of foundation models allows for substantial AI scalability, as the initial model-building efforts are amortized and reused across multiple instances. As a result, the return on investment (ROI) increases, and the time to bring AI products to the market significantly shortens. This newfound efficiency in model adaptation opens up promising opportunities for businesses to capitalize on the potential of generative AI.

IBM has been a pioneer in AI advancements for many years, starting with the world’s first checkers playing program and extending to the creation of a cloud-based AI supercomputer. Our dedication to innovation has resulted in a comprehensive range of enterprise AI solutions. The Watson suite, deployed to over 100 million users in 20 different industries, showcases the impact of our AI technologies. Additionally, our teams at IBM Research are continuously pushing the boundaries of AI to unlock even greater potential.

AI is already making a significant impact on businesses, strengthening supply chains, safeguarding critical data from cyber threats, and enhancing customer experiences across various industries. However, the real game-changer lies in the foundation models that power generative AI. These models have the potential to take our achievements thus far and elevate them to a whole new level. Accessibility is key to harnessing the true potential of AI, and at IBM, we believe it’s time to democratize this technology. Our goal is to empower all types of “AI builders,” whether they are data scientists, developers, or even individuals with no coding experience, to leverage the power of AI for transformative outcomes.

IBM’s cutting-edge AI platform, Watsonx, is specifically designed to provide effortless access to reliable and high-quality data, empowering users to collaborate seamlessly on a unified platform. This platform allows them to create and refine both innovative generative AI foundation models and traditional machine learning systems. The initial applications of Watsonx encompass a diverse range of areas, such as digital labor, IT automation, application modernization, security, and sustainability.

Watsonx comprises three key components: watsonx.ai, watsonx.data, and watsonx.governance. These components offer advanced capabilities in machine learning, data management, and generative AI, facilitating the swift training, validation, tuning, and deployment of AI systems across the entire enterprise. This platform ensures speed, reliability, trusted data, and robust governance, covering the entire data and AI lifecycle—from data preparation to model development, deployment, and monitoring. IBM firmly believes that Watsonx possesses the potential to scale and accelerate the transformative impact of advanced AI technologies in every enterprise.

Train, validate, tune, and deploy AI across the business with watsonx.ai

Watsonx.ai represents an AI studio that caters to the needs of present and future businesses. It seamlessly integrates IBM Watson Studio, a platform empowering data scientists, developers, and analysts to construct, execute, and deploy AI solutions based on machine learning, with cutting-edge generative AI capabilities, harnessing the potential of foundation models.

Central to Watsonx is the principle of trust. As AI becomes more pervasive, businesses require assurance that their models maintain reliability and avoid generating inaccurate information or using inappropriate language during customer interactions. Our approach focuses on establishing the right levels of rigor, process, technology, and tools to adapt swiftly to a dynamic legal and regulatory landscape.

Watsonx.ai provides users with access to premium, pre-trained, and proprietary IBM foundation models tailored for enterprises. These models are domain-specific and undergo a rigorous process with a strong emphasis on data acquisition, provenance, and quality. Additionally, IBM offers a diverse selection of open-source models through Watsonx.ai, broadening the range of options available to users.

Trust forms one facet of the equation, while accessibility constitutes the other essential aspect. To ensure that AI can genuinely revolutionize businesses, it should be made accessible to as many people as possible. With this goal in mind, we have carefully designed watsonx.ai to prioritize user-friendliness. This platform is not limited to data scientists and developers; it also extends its reach to business users through an intuitive interface that responds to natural language queries for various tasks.

Through the prompt lab, users can experiment with models by inputting prompts for a wide array of tasks, such as transcript summarization or sentiment analysis on a document. Depending on the task, watsonx.ai empowers users to select a foundation model from a convenient drop-down menu. Developers can seamlessly build workflows in our ModelOps environment, utilizing APIs, SDKs, and libraries, effectively managing machine learning models from development to deployment. For advanced users, our tuning studio enables model customization using labeled data, thus generating new trusted models from pre-trained ones.

At IBM, we believe that the potential of foundation models extends beyond language. We are actively working on models trained on diverse types of business data, including code, time-series data, tabular data, geospatial data, and IT events data. In beta, we will introduce initial foundation models covering language (also known as LLMs), geospatial data, molecules, and code, which will be made available to select clients.

Scale and manage AI with watsonx.data

To achieve truly impactful results across the business, AI must seamlessly integrate into existing workflows and systems, automating critical processes in areas like customer service, supply chain management, and cybersecurity. Enterprises require the ability to effortlessly and securely move AI workloads, which may span across various environments, including cloud, modern software, and legacy hardware systems.

Enter watsonx.data, designed to enable businesses to rapidly connect to their data, obtain reliable insights and lower data warehouse costs. This data store is built on an open lakehouse architecture, functioning both on-premises and across multi-cloud environments.

Tailored for all data, analytics, and AI workloads, watsonx.data combines the flexibility of a data lake with the high performance of a data warehouse, empowering businesses to scale data analytics and AI effortlessly, regardless of where their data resides. Through workload optimization, organizations can reduce data warehouse costs by up to 50% by integrating this solution.

Through a unified point of access, users can seamlessly reach data, benefiting from a shared metadata layer that spans various cloud and on-premises environments. Watsonx.data offers built-in governance, security, and automation features, providing data scientists and developers with the capability to leverage governed enterprise data for training and refining foundation models, all while ensuring compliance and security throughout the data ecosystem.

With watsonx.data, businesses gain the ability to construct reliable AI models and automate AI life cycles within multi-cloud architectures, fully leveraging interoperability with both IBM and third-party services. This empowers enterprises to harness the potential of their data while maintaining robust governance and security measures.

Build trust in your AI lifecycle with watsonx.governance

Trust plays a critical role in AI models, both during their development and tuning process, as well as when they become integrated into your products and workflows.

As AI becomes increasingly ingrained in day-to-day workflows, the need for proactive governance becomes paramount to ensure responsible and ethical decision-making throughout the entire organization.

Watsonx.governance serves as a valuable tool in establishing essential guardrails around AI tools and their applications. This automated data and model lifecycle solution enables the creation of policies, and the allocation of decision-making rights, and ensures organizational accountability for risk and investment decisions. It offers a robust framework to safeguard AI implementation while adhering to ethical principles and guiding responsible AI practices.

Watsonx.governance utilizes advanced software automation to enhance a client’s ability to manage risk, meet regulatory requirements, and address ethical concerns, all without the need for extensive data science platform switching, even when using models developed with third-party tools. This powerful solution empowers businesses to automate and consolidate multiple tools, applications, and platforms, while also ensuring comprehensive documentation of datasets, models, associated metadata, and pipelines.

With its ability to fortify customer privacy, proactively detect model bias and drift, and adhere to ethics standards, watsonx.governance plays a vital role in helping organizations manage risk and safeguard their reputation. By translating regulations into actionable policies and business processes, it ensures compliance and provides customizable reports and dashboards to maintain stakeholder visibility and encourage collaboration. The solution offers a holistic approach to secure AI implementation, addressing both ethical considerations and regulatory compliance while optimizing operational efficiency.

Put AI to work in your business with IBM today

IBM is integrating watsonx.ai foundation models across all its major software solutions and services, incorporating them into core AI and automation products, as well as within their consulting practices. These encompass:

• Watson Assistant and Watson Orchestrate: The NLP foundation model has been integrated into core digital labor products, resulting in boosted employee productivity and improved customer service experiences.
• Watson Code Assistant: Leveraging generative AI, developers can now generate code automatically with simple commands like “Deploy Web Application Stack” or “Install Node.js dependencies.”
• IBM AIOps Insights: AI Operations (AIOps) capabilities are strengthened by integrating foundation models for code and language processing. This enhancement offers enhanced visibility into performance data and dependencies across IT environments, enabling IT operations (ITOps) managers and Site Reliability Engineers (SREs) to resolve incidents more efficiently and cost-effectively.
• Environmental Intelligence Suite: IBM EIS Builder Edition, part of the IBM Environmental Intelligence Suite (EIS) available this year, utilizes the geospatial foundation model. With this powerful tool, organizations can develop customized solutions to identify and manage environmental risks according to their specific objectives and requirements.

Place trust at the core of your AI strategy

As these new AI models have a significant impact on how people interact with technology, the possibilities that were once unimaginable are now becoming everyday realities. This transformation not only changes how businesses operate but also reshapes our entire approach to business.

To unlock the full potential of AI, trust, and transparency must be at its core, and accessibility should be widespread to benefit everyone. IBM identifies five key pillars of trustworthy AI: explainability, fairness, robustness, transparency, and privacy.

With watsonx, IBM has been mindful of these fundamental principles, ensuring trust while making it easily accessible. A future with trustworthy AI holds the promise of increased productivity and enhanced innovation. These times are full of excitement, and together, we can harness the power of AI to improve the world and work more effectively.

 

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What is Quantum Computing?

Quantum computing is an emerging technology that utilizes the principles of quantum mechanics to tackle highly complex problems that classical computers are unable to solve. IBM Quantum is at the forefront of this field, providing access to real quantum hardware that was merely a concept three decades ago. With continuous advancements in software and quantum-classical orchestration, our team of engineers consistently delivers more powerful superconducting quantum processors. These efforts are aimed at achieving the speed and capacity necessary to revolutionize the world.

Distinguished by their stark differences from classical computers that have been in existence for over fifty years, understanding the fundamentals of this transformative technology is essential. Let’s delve into a comprehensive introduction to quantum computing.

Why do we need Quantum Computers?

Sometimes, even supercomputers fail to live up to their “super” status regarding specific challenges.

When faced with intricate problems, scientists and engineers typically rely on supercomputers. These computing giants consist of numerous classical CPU and GPU cores, yet even they encounter difficulties in solving specific problems.

If a supercomputer finds itself perplexed, it’s likely because it was confronted with a highly complex problem. The inability of classical computers to tackle such problems is often attributed to their inherent complexity.

Complex problems are characterized by a multitude of variables that interact in intricate ways. For instance, modeling the behavior of individual atoms within a molecule poses a complex problem due to the intricate interplay among various electrons. Similarly, determining the optimal routes for hundreds of tankers in a global shipping network is also a complex task.

How do Quantum computers work?

Quantum computers possess an inherent elegance, characterized by their compact size and lower energy requirements compared to supercomputers. An IBM Quantum processor, for instance, is a wafer that is not significantly larger than the ones found in laptops. Furthermore, a quantum hardware system, resembling the size of a car, primarily consists of cooling systems designed to maintain the superconducting processor at its ultra-cold operational temperature.

While classical processors utilize bits for their operations, quantum computers operate on a different principle. They employ qubits (pronounced as “cue-bits”) to execute multidimensional quantum algorithms.

Superfluids

In contrast to your typical desktop computer, which relies on a fan to maintain a cool operating temperature, our quantum processors require an extremely low temperature—approximately one-hundredth of a degree above absolute zero. To achieve such frigid conditions, we employ super-cooled superfluids, which enable the creation of superconductors.

Superconductors

At such ultralow temperatures, specific materials utilized in our processors demonstrate a significant quantum mechanical phenomenon: the unrestricted movement of electrons without encountering any resistance. This remarkable property renders them as “superconductors.”

As electrons traverse through superconductors, they align themselves, giving rise to what is known as “Cooper pairs.” These pairs possess the ability to transport electric charge across barriers, such as insulators, through a phenomenon called quantum tunneling. When two superconductors are positioned on opposite sides of an insulator, they form what is called a Josephson junction.

Control

Quantum computers employ Josephson junctions as superconducting qubits. Through the emission of microwave photons directed at these qubits, we gain the ability to manipulate their behavior, enabling us to store, modify, and extract data at the level of individual units of quantum information.

Superpostion

On its own, a single qubit may not offer much utility. However, it possesses a crucial capability – the ability to enter a state of superposition, where it can embody a combination of all conceivable configurations. When groups of qubits exist in superposition, they collectively create intricate and multidimensional computational domains. These spaces enable the representation of complex problems in novel and innovative ways.

Entanglement

Entanglement is a remarkable quantum mechanical phenomenon that establishes a correlation between the behaviors of two distinct entities. When two qubits become entangled, any modifications made to one qubit instantaneously affect the other. Quantum algorithms harness these interconnected relationships to uncover solutions to intricate problems.

Making quantum computers useful

Presently, IBM Quantum stands at the forefront of quantum computing hardware and software, leading the global landscape in this domain. The roadmap encompasses a comprehensive and meticulous strategy to expand the scalability of quantum processors, overcome the challenges of scaling, and construct the essential hardware for achieving quantum advantage.

However, quantum advantage cannot be attained through hardware alone. IBM has dedicated years to advancing the software required for performing practical tasks using quantum computers. One of our notable contributions is the development of Qiskit, a widely-used, open-source, Python-based quantum software development kit (SDK). Additionally, we have pioneered Qiskit Runtime, the most potent quantum programming model worldwide. (For more information on Qiskit and Qiskit Runtime, as well as guidance on getting started, please refer to the next section.)

The quest for quantum advantage necessitates novel approaches in error suppression, speed enhancement, and the orchestration of both quantum and classical resources. The groundwork for these endeavors is presently being laid through the efforts of Qiskit Runtime.

 

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An effective data architecture should be able to facilitate business intelligence and analysis, automation, and AI, all of which can enable organizations to take advantage of market opportunities, enhance customer value, achieve significant efficiencies, and mitigate risks such as supply chain disruptions. Additionally, a well-designed data foundation can play a critical role in managing ESG (environmental, social, and governance) commitments. Fortunately, achieving ESG goals can also benefit businesses, as sustainability initiatives can enhance business value for organizations that are committed and capable of effectively executing them.

Integrating data and using insights to help drive environmental initiatives

Data-driven insights can assist organizations in understanding their performance and measuring progress toward their ESG objectives. Such insights can also be used to drive operational efficiency, and credible environmental reporting necessitates factual data. To achieve this, organizations should implement a modern data architecture and data governance approach. This will enable users to access relevant data quickly and facilitate self-service, regardless of its location, thereby providing a strong foundation for ESG programs and insights.

After determining your data needs, you may have to obtain data from different operational systems and applications and integrate and arrange them for easy access by stakeholders throughout your organization. These stakeholders may include real estate, finance, HR, procurement teams, and the sustainability team. With a unified dataset, everyone can make informed decisions, ranging from goal setting to prioritizing sustainability investments.

Supporting the increasingly important social and governance pillars

The social pillar of ESG includes reporting obligations that intersect with both organizational risk and human impact. As AI increasingly informs HR decisions such as hiring, evaluation, and promotion, organizations must respond to new and expanding regulations while also addressing ESG standards.

Adopting a data architecture that can support AI governance is increasingly crucial for organizations. This means implementing sound data governance practices that ensure access is limited to authorized processes and stakeholders, while also ensuring transparency and explainability in the use and trustworthiness of AI. The approach should also provide sufficient metadata to enable HR decision-makers to identify which decisions and processes are informed by AI while maintaining privacy in the data. Implementing a data fabric architecture can enhance an organization’s governance and oversight capabilities and strengthen its ability to manage various forms of risk.

How a data fabric architecture can support ESG efforts

ESG initiatives can benefit greatly from a data architecture that allows for the collection, integration, and standardization of data from multiple sources and enables broad access to it by different stakeholders. An architectural approach known as data fabric simplifies data access facilitates self-service data consumption and supports the integration of data sources, pipelines, and AI applications. This approach enhances data quality, stewardship, and observability through machine learning-based automation. With the expansive nature of ESG data and its involvement of various departments, partners, and suppliers, a data fabric can provide the necessary governance, integration, and insights at scale.

To report on all three ESG pillars, it’s important to assess your framework and determine what data is necessary for a transparent and credible disclosure report. With a data fabric architecture, accessing and updating the ESG reporting framework is made simpler, enabling teams to deliver reports more efficiently.

Conclusion

Incorporating ESG considerations into business decisions is increasingly critical for companies to meet stakeholder expectations and regulatory requirements. By leveraging data architecture and AI, organizations can effectively gather, integrate, and analyze ESG data to support their initiatives, measure progress, and enhance transparency. This can lead to improved risk management, operational efficiency, and sustainable growth while meeting both business and societal goals.

 

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Enforcement of the new regulations, passed in December 2021, will necessitate organizations that utilize algorithmic HR tools to perform an annual bias audit. Any organization that fails to comply with the new legislation may be subjected to fines ranging from a minimum of USD 500 to a maximum of USD 1500 per infringement. In anticipation of this transition, specific organizations are establishing a yearly assessment, mitigation, and examination procedure. Here’s a recommendation for how this procedure may be implemented.

Step one: Evaluate

For organizations to have their hiring and promotion systems assessed, it’s essential to take an active approach by educating stakeholders on the significance of this procedure. A diverse team responsible for evaluation, comprising HR, Data, IT, and Legal professionals, can be instrumental in navigating the constantly evolving regulatory framework related to AI. This team should be integrated into the organization’s business processes, and its role should be to assess the entire process from sourcing to hiring and scrutinize how the organization sources, screens, and recruits both internal and external candidates.

The evaluation team must scrutinize and record each system, decision point, and vendor based on the population they cater to, including hourly workers, salaried employees, various pay groups, and countries. While some third-party vendor information may be confidential, the evaluation team should still examine these processes and establish protective measures for vendors. It’s vital for any proprietary AI to be transparent, and the team should strive to promote diversity, equity, and inclusion in the hiring process.

Step two: Impact testing

As governments worldwide enforce regulations pertaining to the use of AI and automation, organizations need to assess and revise their processes to ensure compliance with the new mandates. This entails conducting meticulous scrutiny and testing of processes that involve algorithmic AI and automation, taking into account the specific regulations applicable in each state, city, or locality. Given the varying degrees of rules in different jurisdictions, it is crucial for organizations to stay well-informed and adhere to the requirements to mitigate any potential legal or ethical ramifications.

Step three: Bias review

Once the evaluation and impact testing has been concluded, the organization can commence the bias audit, which may be mandated by law and should be performed by an impartial algorithmic institute or a third-party auditor. It is crucial to select an auditor with expertise in HR or Talent, who can be trusted to provide explainable AI and who holds RAII Certification and DAA digital accreditation. Our organization is well-equipped to aid companies in becoming data-driven and achieving compliance. If you require any assistance, please don’t hesitate to contact us.

Data and AI Governance’s Role

Having a suitable technology blend can be critical to ensuring an effective data and AI governance strategy, with a contemporary data architecture like data fabric being a vital element. Policy orchestration is an excellent tool within a data fabric architecture that can simplify the intricate AI audit processes. By incorporating AI audit and associated processes into the governance policies of your data architecture, your organization can gain insights into areas that necessitate ongoing scrutiny.

What will happen next?

IBM Consulting has been assisting clients in establishing an evaluation process for bias and other related areas. The most challenging aspect is setting up the initial evaluation and taking stock of every technology and vendor that the organization engages with for automation or AI. Nevertheless, implementing a data fabric architecture can streamline this process for our HR clients. A data fabric architecture offers clarity into policy orchestration, automation, AI management, and the monitoring of user personas and machine learning models.

Organizations must recognize that this audit is not a one-time or stand-alone event. It is not only about the regulations enacted by a single city or state. These laws are part of a sustained trend where governments are intervening to mitigate bias, establish ethical AI use, safeguard private data, and reduce harm resulting from mishandled data. Therefore, organizations must allocate funds for compliance costs and form a cross-disciplinary evaluation team to develop a regular audit process.

 

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Organizations can rapidly seize market opportunities, improve customer value, achieve significant efficiencies, and mitigate risks such as supply chain disruptions with the help of a well-designed data architecture that supports business intelligence, analysis, automation, and AI. Moreover, a well-designed data foundation can revolutionize the way organizations handle ESG (Environmental, Social, and Governance) commitments. The good news is that the advantages of business benefits and ESG benefits are not contradictory but instead complement each other. By being dedicated and efficient in execution, organizations can increase business value through their sustainability efforts.

Integrating data and using insights to help drive environmental initiatives

Utilizing data that is based on facts can aid organizations in comprehending their performance and assessing their progress toward achieving widespread ESG objectives. The insights produced from this data can help organizations advance their ESG initiatives and enhance operational efficiency. Additionally, trustworthy environmental reporting should be underpinned by accurate data. To accomplish this, organizations need to establish the appropriate foundation, including a modern data governance approach and architecture. By implementing modern data architecture, organizations can provide self-service access to relevant data for their roles, regardless of their location, enabling them to swiftly gain insights.

After identifying the necessary data requirements, it may be necessary to obtain data from various operational systems and applications, integrate them, and arrange them in an easily accessible format for stakeholders throughout the organization. These stakeholders may include teams such as real estate, finance, HR, procurement, and sustainability. By utilizing the same dataset, all stakeholders can make informed decisions, from establishing goals to determining which sustainability investments should be prioritized.

Supporting the increasingly important social and governance pillars

Some reporting obligations connect organizational risk with human impact and are categorized as part of the social component of ESG. As AI becomes increasingly involved in HR decisions, such as recruitment, performance assessment, and promotion, an organization’s obligation to comply with expanding regulations will increasingly overlap with the need to meet ESG standards.

Adopting a data architecture that accommodates AI governance is now imperative for organizations. Such a framework should support appropriate data governance, including limiting access to authorized processes and stakeholders, while also promoting transparency and explainability in the use and reliability of AI. The methodology should be designed to provide sufficient metadata to enable key decision-makers in HR to recognize which processes and decisions are influenced by AI while preserving anonymity and confidentiality in the data. Implementing a data fabric architecture can improve an organization’s ability to manage and oversee key governance areas, while also enhancing its capability to identify and mitigate various types of risks.

How a data fabric architecture can support ESG efforts

An effective data architecture can improve an organization’s ability to implement ESG initiatives by supporting the collection, integration, and standardization of data from diverse sources and providing access to a broad range of stakeholders. A data fabric is an architectural approach that simplifies data access, enabling self-service data consumption at scale. This architecture allows for modeling, integration, and querying of data sources, the building of data pipelines, real-time data integration, and the running of AI-driven applications. Additionally, a data fabric can improve data reliability by providing enhanced data observability and automating data quality tasks across platforms using machine learning. With the vast array of ESG data elements that involve numerous departments within a company and extend to partner and supplier networks, a data fabric can help facilitate governance, integration, and data analysis at scale.

To report on all three pillars of ESG, an organization should start by assessing its framework and identifying the necessary data to support a transparent and credible disclosure report. A data fabric architecture can simplify data access, enabling teams to evaluate and update their ESG reporting framework efficiently and deliver reports more effectively.

Conclusion

A strong foundation of data and AI can help organizations empower their ESG strategy by enabling them to collect and analyze relevant data, identify risks and opportunities, and make informed decisions. With a modern data architecture like data fabric, organizations can achieve better data governance, enhance data observability, and automate data quality tasks. Ultimately, a successful ESG strategy can lead to improved business value and a more sustainable future for all.

 

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An inferencing model refers to a type of model that has been trained to identify patterns of interest in data, with the goal of gaining insights from the data.

Compared to training an artificial intelligence (AI) model, inferencing doesn’t require as much computing power. As a result, it’s feasible and even more energy-efficient to perform inferencing without additional hardware accelerators, like GPUs, and to do so on edge devices. It’s not uncommon for AI inferencing models to run on smartphones and similar devices using just the CPU. In fact, many picture and face filters found in social media phone apps rely on AI inferencing models.

IBM’s Power10 chip

IBM was a trailblazer in incorporating on-processor accelerators for inferencing into its IBM Power10 chip, which it dubbed the Matrix Math Accelerator (MMA) engines. By doing so, the Power10 platform is able to outpace other hardware architectures in terms of speed without requiring the use of additional GPUs, which would consume more energy. This means the Power10 chip can derive insights from data more quickly than any other chip architecture while consuming significantly less energy than GPU-based systems. That’s why it’s an optimal choice for AI applications.

When using IBM Power10 for AI, particularly for inferencing, AI DevOps teams don’t need to exert any additional effort. This is because data science libraries, including openBLAS, libATen, Eigen, and MLAS, among others, have already been optimized to utilize the Matrix Math Accelerator (MMA) engines. Consequently, AI frameworks that leverage these libraries, such as PyTorch, TensorFlow, and ONNX, are already able to take advantage of the on-chip acceleration. These optimized libraries can be accessed through the RocketCE channel on anaconda.org.

IBM Power10 can accelerate inferencing by utilizing reduced-precision data. Rather than using 32-bit floating point data, for instance, the inference model can be fed with 16-bit floating point data, which enables the processor to process twice as much data for inferencing simultaneously. This approach can be effective for some models without compromising the accuracy of the inferred data.

Inferencing is the final phase of the AI DevOps cycle, and the IBM Power10 platform was purposefully designed to be AI-optimized. As a result, clients can extract insights from data in a more cost-effective manner, both in terms of energy efficiency and by reducing the requirement for additional accelerators.

Conclusions

Leveraging the IBM Power10 chip can significantly accelerate AI inferencing while reducing energy consumption and the need for additional accelerators. The Matrix Math Accelerator (MMA) engines built into the chip can enhance the speed and efficiency of inferencing processes without requiring any additional effort from AI DevOps teams. Furthermore, the ability to process reduced-precision data can further enhance the performance of the inferencing model without sacrificing accuracy. All of these factors make the IBM Power10 chip an ideal choice for clients seeking to extract insights from data in a cost-effective manner.

 

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One way to secure supply chains from a chain reaction of cyberattacks is by utilizing IBM Security Supply Chain Cyber Risk Management Services.

The supply chain is an attractive target for cybercriminals because it involves various third-party organizations, vendors, and manufacturers that have access to the same data and systems. A successful cyberattack on a single point in the supply chain can trigger a domino effect of destruction, causing significant operational disruption, financial losses, and reputational damage to the organization and its partners. This highlights the potential for long-term negative consequences on the reputation of the affected organization and the trust of its partners and customers.

Cyberattacks in manufacturing and supply chains

As per the 2023 IBM Security X-Force Threat Intelligence Index, the manufacturing sector witnessed the most extortion cases among all industries, accounting for 30% of such incidents. Over 25% of the total attacks, including ransomware, business email compromise (BEC), and DDoS, were related to extortion. Given the manufacturing industry’s low threshold for downtime and vulnerability to double-extortion tactics, it becomes an alluring target for cybercriminals.

Over 50% of security breaches are linked to supply chain and third-party suppliers, costing an average of USD 4.46 million. Due to the constantly changing and intricate nature of the supply chain, it is challenging for organizations to keep track of the latest cybersecurity risks and identify possible weaknesses. If a cyberattack does take place, it may be difficult to pinpoint the origin of the breach. This confusion can delay the response time, and in the case of a data breach, every moment is important.

As per the IBM Security X-Force Threat Intelligence Index, there has been a minor decrease in ransomware attacks, but the execution time has decreased by 94% in recent years. What previously took months for attackers can now be accomplished in just a few days. This rapid pace of cyberattacks necessitates a proactive and threat-focused cybersecurity strategy for organizations.

Supply chains are highly susceptible to cyberattacks due to the potentially catastrophic consequences of a security breach. Both the organizations within the supply chain and the cybercriminals are aware of this vulnerability.

To protect against cyberattacks, it is essential to comprehend their occurrence and method of operation. When implementing cyber risk management, it is crucial to consider the different types of cybersecurity incidents that can potentially harm the supply chain. These incidents include phishing attacks, malware infections, data breaches, and ransomware attacks.

How to secure your supply chain

In today’s digital environment, securing the supply chain through cyber risk management is critical. Several organizations have an uncoordinated approach to supply chain security, which presents challenges such as identifying and managing risks, assessing third-party software, limited threat intelligence for swift decision-making, and inadequate operational resilience. To enhance their cybersecurity posture, supply chains must adopt a proactive, well-defined, and adaptive approach, utilizing data and AI optimization.

Consider incorporating the following five best practices to develop a cyber risk management plan that safeguards your supply chain:

1. Conduct a risk assessment: Conduct frequent evaluations of cyber risks associated with your supply chain, including the systems and processes utilized by your suppliers. Detect any potential weaknesses and prioritize the most critical ones with significant business consequences for prompt mitigation.
2. Establish security protocols: Establish well-defined security protocols for your suppliers, comprising guidelines for data protection, access control, and incident response. Confirm that your suppliers have implemented adequate security measures such as firewalls, encryption, strong passwords, and multi-factor authentication.
3. Implement continuous monitoring: Maintain continuous surveillance of your supply chain for any security incidents, such as hacking attempts, data breaches, and malware infections. Create an incident response plan in case of a security breach and periodically conduct tabletop or immersive exercises to improve muscle memory for executing the plan.
4. Encourage supplier education: Numerous organizations provide cybersecurity training and education to their workforce to secure company data and assets. If your supplier doesn’t offer structured learning, contemplate extending cybersecurity education and training to your suppliers on best practices and the significance of safeguarding sensitive data, or direct them to available free resources. Motivate them to implement robust security measures and remain attentive to cybersecurity threats.
5. Regularly review and update policies: Consider regularly reviewing and revising your cyber risk management policies to keep them current and applicable. This will assist you in staying ahead of emerging threats and maintaining the security of your supply chain.

 

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Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

For more information, please get in touch with one of our course advisers today or contact us at training@coursemonster.com