Holistic asset management for utility network companies

Addressing challenges of the energy transition with grid asset management

The energy transition is gearing up to full speed as renewable energy sources replace fossil-based systems of energy production. The grid itself must green to operate within the environmental, social and governance (ESG) objectives and become carbon neutral by 2050. This shift requires energy utility companies to plan their grid asset management holistically as they find a new balance between strategic objectives.

Sustainable asset performance has become one of the key drivers in decision-making for asset planning and grid modernization business processes. New emerging technology enables AI-powered digital twins to operate the smart grid. However, operators must balance intermittent renewable energy intake to produce a controlled, stable output.

A balanced transition between old and new systems

The demand to fulfill existing long-term contracts and an abundance of new demands for industrial electrification pose new challenges to grid management. Finding the right balance requires load forecasting and simulation to prevent net congestion. Economical optimization must factor in new market dynamics and ensure reliable operation.

Existing network assets are aging, and more intelligent asset management strategies must emerge to maintain and replace the grid within tightening budgets. Asset investment planning must find a balance between these systems while minimizing risk and carbon footprint.

To manage the grid of the future, utility companies must shift from traditional asset management to a holistic approach. This shift will broaden insights so these companies can take strategic, tactical steps to optimize operational network development and operation decisions.

Asset lifecycle management

Holistic grid asset management adopts a lifecycle view across the whole asset lifespan to obtain a safe, secure, reliable and affordable network. Utility companies must break down the internal departmental walls between the silos of grid planning, construction, operation, maintenance and replacement to allow end-to-end visibility. They must connect underlying technology systems to create a single pane of glass for all operations. A shared data model across operating systems serves as the basis for integration, simulation, prediction and optimization by using generative AI models to drive next-level business value.

The goal of asset management is to optimize capital expenditures (CapEx) and operating expenses (OpEx) in a seamless transition between the timescale of the planning horizons. The following figure demonstrates the complex planning and optimization objectives required for a holistic view of the asset management lifecycle:

A top-down strategic approach for whole-life planning of asset investment portfolio matching future ESG goals needs to connect with a bottom-up maintenance and replacement strategy for existing assets. Asset investment planning (AIP) results in project portfolio management and product lifecycle management to plan, prioritize and run asset expansion and replacement projects within the boundaries of available budget and resource capacity.

Real-time operational data provides an asset health view that drives condition-based maintenance and replacement planning. This is the domain of enterprise asset management (EAM) for maintenance execution and asset performance management (APM) for strategy optimization. Traditionally, a disconnect at the tactical level has separated these planning and optimization methodologies. At the same time, operational risk management requires respecting health, safety and environment (HSE) management and process safety management to manage potentially hazardous operations. The maintenance repair and overhaul (MRO) spare parts strategy must align with the asset strategy in terms of criticality and optimal stock value.

Acknowledgment of the complexity of planning with multidimensional objectives on different timescales is the starting point for adopting a holistic view of asset management.

Source: ibm.com

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The path to embedded sustainability

Businesses seeking to accelerate sustainability initiatives must take an integrated approach that brings together all business and technology functions. Sustainability is no longer the responsibility of only the chief sustainability officer (CSO). It is not managed by a single department in a silo. Driving true sustainable impact, at scale, takes place when an enterprise is fully aligned to that transformation. To scale progress in combating climate change, this alignment and collaboration must happen across value chain partners, ecosystems, and industries.

Sustainability and ESG: An opportunity for synergy

Sustainability and ESG are not synonymous. While ESG seeks to provide standard methods and approaches to measuring across environmental, social and governance KPIs, and holds organizations accountable for that performance, sustainability is far broader. ESG can serve as a vehicle to progress sustainability but it can also distract from the urgent need of combating climate change and working toward the 17 UN SDGs.

As we have seen with any sort of external reporting liabilities, this type of accountability does drive action. It’s our responsibility to ensure we don’t just do ESG reporting for the sake of reporting, and that it doesn’t impede actual progress in sustainability. We must ensure ESG progress and sustainability are driving towards a common goal. The reality is companies might be ready to fund ESG initiatives, but not as ready to fund ‘sustainability’ initiatives.

If designed intentionally, these do not have to be separate initiatives. When something is ‘regulatory,’ ‘mandatory,’ or ‘involuntary,’ companies have no choice but to find a way. A pre-existing sustainability office may find resources or funds shifted to ESG, or a reprioritization of targets based on ESG measurements. However, to capture both the business value behind ESG compliance as well as its ability to drive impact, it requires a holistic approach that strategically captures these synergies.  

We are helping our clients maximize those investments, leveraging the requirements of ESG to drive compliance as well as sustainability. Our clients are improving their ability to measure and track progress against ESG metrics, while concurrently operationalizing sustainability transformation.

Maximizing value with a holistic strategy

The first step in maximizing that dual value is upfront due diligence. It is necessary to assess the current state of reporting readiness, the alignment between ESG requirements and voluntary sustainability initiatives, and any consideration on how to drive acceleration with future-proofed solutions. Questions might include:

• Where is the organization relative to its required and voluntary sustainability goals?
• Have the sustainability goals evolved in response to recent regulation or market shifts? 
• How aligned is the sustainability strategy to the business strategy? 
• Is ownership of delivering sustainability goals distributed throughout the organization or is every leader aware of how they are expected to contribute?
• How is sustainability managed—as an annual measuring exercise or an ongoing effort that supports business transformation?
• What regulations are owned by specific functional areas that may contribute to a broader ESG roadmap if viewed holistically?
• Are there in flight business or technology initiatives where I can embed these requirements?

Up until recently, sustainability was most likely handled by one central team. Now, functional areas across the organization are recognizing their role in measuring ESG progress as well as their opportunities to help make their company more sustainable.  

Similar to a company executing any corporate strategy, progress is made when the organization understands it, and employees are aware of how they play a role in bringing it to life. All leaders must enable teams and departments to understand how sustainability is part of the corporate strategy. They must provide the enablement and tools so these teams can integrate the overarching sustainability purpose and objectives within the corporate strategy into their respective roles in accelerating sustainable outcomes.

I see a clear shift in companies becoming more aware that they must work across departments to drive sustainability. A company cannot report on scope 3 category 7 of employee commute without employee data from HR or facilities management data, or without the technology platform and data governance to have an auditable view of that data. Businesses cannot prove there is no forced labor in their supply chain without working with procurement to understand their supplier base, where they are located, and what might be high risk, and then solution to embed proactive risk management in vendor onboarding. 

Embedding sustainability in practice

Accountability is where an enterprise can ensure that sustainability is embedded and activated. The idea of embedding is integrating it into the day-to-day role. It’s enabling employees to make informed decisions and understanding the climate impact based on that decision. Any business or investment decision has a profit lever, a cost lever, and sometimes a performance lever, such as an Service Level Agreement (SLA). Now, sustainability can be a lever to truly embed impact into everyday operations. Employees can make more sustainable decisions knowing the tradeoff and impact.

A recent study from the IBM Institute for Business Value surveyed 5,000 global C-suite executives across 22 industries to find out why sustainability isn’t generating more impact for organizations. The study found companies were just “doing sustainability,” or approaching sustainability as a compliance task or accounting exercise rather than a business transformation accelerator.

Executives recognize the importance of data to achieve sustainability objectives; 82% of the study’s respondents agree that high-quality data and transparency are necessary to succeed. However, a consistent challenge they encounter in driving both ESG reporting and sustainable transformation is the shared reality is that companies cannot manage what they cannot measure.

Data not only provides the quantitative requirements for ESG metrics, it also provides the visibility to manage the performance of those metrics. If the employees of a company don’t have the data, they cannot publish financial grade reporting, identify opportunities for decarbonization, or validate progress towards becoming a more sustainable company.

One point addressed in our study surrounds the data specific challenges that can come with sustainability. Findings revealed that “despite recognizing the link between data and sustainability success, only 4 in 10 organizations can automatically source sustainability data from core systems such as ERP, enterprise asset management, CRM, energy management, and facilities management.”

When clients embed the right processes and organizational accountability across ESG reporting and sustainability, they can make sure they are getting the right information and data into the hands of the right people, often system owners. Those ‘right people’ can now make more informed decisions in their respective roles and scale transformation from one team to the entire organization while also incorporating these needs of ESG data capture, collection, and ingestion for the sake of both reporting and operationalizing.

The study found organizations that successfully embedded sustainability approached the data usability challenge through a firmer data foundation and better data governance. The criticality of a clear data strategy and foundation brings us to our final topic: how generative AI can further accelerate sustainability.

Utilizing generative AI to embed sustainability

There are many different applications for generative AI when it comes to embedding sustainability, especially when it comes to filling in data gaps. The data needed for ESG and sustainability reporting is immense and complex. Oftentimes, companies don’t have it available or have the correct protocols to align their data and sustainability strategies.

Most clients, regardless of the size of the company, have sustainability teams that are stretched, trying to manually chase data instead of focusing on what the data is saying. Generative AI can unlock productivity potential, accelerating data collection and ingestion reconciliation. As an example, instead of sustainability teams manually collecting and reviewing paper fuel receipts, technology can help translate receipt images into the necessary data elements for fuel-related metrics. This allows these teams to spend more time on how to optimize fuel use for decarbonization, using time for data insights instead of time chasing the data.

By spending all your time on reconciling invoices or collecting physical fuel receipts, how are you or others in your organization going to have the time to understand the data and in turn make changes to drive sustainability? If time is spent collecting data and then pulling together reports, there is little time left to garner actionable insights from that data and enact change. Systems and processes must be in place so that an organization can drive sustainability performance, while meeting ESG reporting requirements, and not use all of its resources and funding on data management that provides eventual visibility without the capacity to use it for impact.

As mentioned in the study, generative AI can be a “game changer for data-driven sustainability, enabling organizations to turn trade-offs into win-wins, identify improvement opportunities, and drive innovation at speed and scale.” It is little wonder why 73% of surveyed executives say they plan to increase their investment in generative AI for sustainability.

To truly leverage the power of generative AI tomorrow, companies must first understand their data readiness today. Then, we can prioritize how generative AI can improve existing data for visibility and use that data for performance insights.

Companies can identify immediate opportunities for generative AI to help them move faster, while concurrently ensuring that the core data collection and management is established to support current and future reporting needs. We want our clients to focus on leveraging ESG reporting to have a return on investment (ROI) financially, as well as in driving sustainable impact. While external mandatory requirements will be a driver for where an organization’s budget is allocated, organizations can intentionally embed sustainability as a part of those initiatives to capture the full value of their transformation efforts.

Source: ibm.com

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The “hidden figures” of AI: Women shaping a new era of ethical innovation

The end of March marks the conclusion of Women’s History Month. And despite the increased focus on women’s issues and contributions to society throughout the month, the conversation would be incomplete without recognizing how indispensable the success of women—past and present—has been in the tech industry. In particular, women are leading the way every day toward a new era of unprecedented global innovation in the field of generative AI.

However, a New York Times piece that came out a few months ago failed on its list of people with the biggest contribution in the current AI landscape. The piece rightly received criticism for reflecting a broader narrative that has long minimized the contributions of women in technology. That narrative says the contributions of women in AI and technology are peripheral; but we know this isn’t true. In fact, they are central to the innovation and continued development of this field.

Women have been challenging the outdated notion that AI development solely belongs to those who code and construct algorithms—a field that, while shifting, remains significantly male-dominated—for years. Many have been doing this by leading the charge on responsible AI innovation, centered on ethics and transparency, throughout their entire careers.

Women like Kay Firth Butterfield, the world’s first Chief AI Ethics Officer; Elham Tabassi from NIST, spearheading initiatives on ethical AI standards; Miriam Vogel from EqualAI and NAIAC, championing AI equality; Paula Goldman from Salesforce; and Navrina Singh from Credo, advocating for responsible AI use are just a few of the many examples of women leading the way in this space.

Other prominent women figures in tech include Fei-Fei Li from Stanford’s Human-Centered AI Institute, renowned for her contributions to AI image recognition and her advocacy for inclusive and ethical AI development; Joy Buolamwini, the founder of Algorithmic Justice League, highlighting and mitigating biases within AI systems; Lila Ibrahim from DeepMind, responsible for operational strategy behind one of the world’s leading AI research organizations; and Francesca Rossi, leading Global AI Ethics at IBM®, who stands at the forefront of addressing critical AI governance, ethics, responsibility and responsible innovation matters.

These are just a few of the many, many examples of women leading in this field. Leaving women out of the conversation and coverage not only overlooks the diverse perspectives necessary for responsible innovation, but also fails to recognize the vital role of ethics, governance, and consideration of societal implications in the development of AI. It is time for a critical reevaluation, one that acknowledges innovation is as much about its impact as it is about invention.

In a study conducted by the IBM Institute for Business Value, Debra D’Agostino, Managing Director of Thought Leadership at Oxford Economics, reinforces the importance of diverse leadership in AI’s evolution. She highlights how women don’t need to be IT experts to lead AI innovation. The study revealed that women are already more likely than men to have used AI to generate, edit and summarize content; and 40 percent say using generative AI has resulted in a greater than 10 percent increase in productivity. Understanding and anticipating how AI can best augment the unique needs and capabilities of a business or team is as crucial as working with the right people in IT to make it happen, D’ Agostino said.

As Women’s History Month comes to an end, it’s important to acknowledge how the contributions of women in AI are not just paving the way for more equitable technology, but are also crucial in realizing the possibility, and confronting and mitigating the immediate and long-term risks that AI poses to our society. Their work is setting the standards for how we, as a global community, approach the integration of AI into our lives.

The future of AI is being written today and women are not just supporting roles in that narrative—they are leading characters in the story. As we forge ahead, it’s important to remember that the true measure of AI’s advancement goes beyond its technical capabilities. It’s about how we harness this technology to reflect our collective values, address our shared challenges and create a world where innovation benefits all of society, not just the privileged few.

Source: ibm.com

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Ahead of the curve: How generative AI is revolutionizing the content supply chain

The global adoption of generative AI is upon us, and it’s essential for marketing organizations to understand and play in this space to stay competitive. With content demands expected to grow in the next few years, organizations need to create more content at a faster pace to meet customer expectations and business needs. Knowing how to manifest these improvements is not always clear: Enter generative AI and the content supply chain.

A content supply chain brings together people, processes, and technology to effectively plan, create, produce, launch, measure, and manage content. It encompasses an end-to-end content journey—a journey that can create faster time to value. We know that infusing generative AI into the content supply chain will enable companies to produce more personalized content faster and more efficiently. So, what is stopping companies from delivering content with generative AI across their content supply chain?

Leadership and the content supply chain

The advent of generative AI is raising several questions and concerns for leaders. In recent years organizations have been concerned with their ability to create and deliver content fast enough to meet customer expectations, and now that generative AI could address those issues, another question comes to the fore: Can we trust AI tools and technology to augment employees. Leaders across the world are experiencing a mix of emotions when it comes to implementing and embracing generative AI. There is excitement, curiosity, and a bit of angst—sometimes felt simultaneously. Most of us are familiar with the term “FOMO”, or fear of missing out. But people are also feeling “FOGI”, fear of getting in, with generative AI.

The FOMO these organizations face relates to not being able to create content fast enough to keep up with expectations or wasting money on tools that may not turn out to be as efficient as once thought. The FOGI concern revolves around trust, and whether they can trust the AI tools and technology to augment employees. Will the outputs deliver content that will resonate with their customers? Can they trust the AI will operate in a secure way? Can they trust that the AI will reward the initial individual creator? Can they trust that what’s created isn’t going to break any brand guidelines?

This blog and the IBM Institute for Business Value study The Revolutionary Content Supply Chain aim to answer these questions to help  executives and their employees to better understand the changing landscape in content creation and embrace the power of generative AI models when it comes to optimizing their content supply chains.

A new way to create and manage content

Any new concept or major change comes with some hesitancy and push back. Change isn’t linear; it requires strategic change management to deal with the transition. Employees and executives alike struggle to take on a new way of thinking or working when they’ve been operating the same way for years. Modernizing a workflow to introduce a content supply chain means disruption and uncertainty. But it also means creating an end-to-end content journey that is fast and accurate and, ultimately, meets customers at the level of their expectations. 

Change management is a crucial part of adopting a new content supply chain and trusting the process. These new technologies can garner a lot of power and a level of uncertainty. However, the adoption of generative AI and a content supply chain can be a massive opportunity for your organization.

Respondents to the study are “keenly aware” of where their content processes need improvement. 88% said they need an easier way to access approved assets for activation across applications and 79% want to experiment with content, audience, and experience variation to drive customer engagement and the customer experience.

As described in the IBM Institute for Business Value study, an ad hoc “Frankenstein” like system that engages a variety of platforms and tools, can turn to a consolidated system and operating model to meet the increasing demand for more and more data integration, content generation, and intelligent automation.

Separately, the findings from the study show that while most respondents are already engaging with generative AI, a very small number—just 2%—are optimizing the technology. Organizations are seeking out new approaches to managing their content supply chain and generative AI embedded in platforms, such as Adobe Firefly, could be the most impactful. 

Understanding the potential of generative AI

Generative AI isn’t just for one area of a business. Instead, it can help content creators across many functions, such as marketing, customer support, product development, operations, and more. The study found 95% of respondents agree that generative AI will be a game changer. And nearly all CMOs surveyed believe that generative AI will free up marketing teams from mundane tasks so they can focus on more creative endeavors.

Content supply chains and generative AI are still very much in the early days, but it’s important to power your ecosystem prior to engagement. For these new technologies to be successful, it means bringing together different business units and stakeholders to align on a shared vision. More than 80% respondents report already engaging with generative AI. Additionally, almost three in four (74%) report that they’re still in a pilot mode, while just a quarter have gone beyond pilots to start implementation.

In addition to internal ecosystems, it’s also important to power your external ecosystem so that external parties—such as Adobe, IBM and AWS—can work together to enable generative AI to supercharge a content supply chain. Specifically, the study points out, many organizations are taking a hybrid approach to AI by blending their proprietary models with best-in-class SaaS platforms infused with AI and public and open-source models. It’s no surprise that adoption of AI has been so popular given its wide swathe of activities across the full content supply chain journey.

To deliver the most value from generative AI, taking the time to set a solid foundation is key. It is clear there is still a lot of work to be done, with only 5% of respondents saying they have an organization-wide approach for generative AI best practices and governance, and half of organizations still in the process of establishing these measures.

Setting a solid foundation for generative AI

We’ve established the benefits that generative AI has to offer and the potential it can bring to transform the content supply chain. But with major transformations such as these come potential risks and any organization interested in generative AI should be taking steps to mitigate said risks.

The IBM Institute for Business Value study found 43% of survey respondents confess their organizations have not set up an AI ethics council. Beyond ethics risks, the study points out, there are also cost risks to consider. Organizations must weigh the impact that a content supply chain expansion fueled by generative AI will have on their back-end technologies. If organizations are aiming to produce more content, then more high-performance computing is required and could in turn increase on-premises computing costs.

These risks must be assessed in the context of benefits and trusting the generative AI tool your organization chooses to implement. The end-to-end makeup of an enterprise content supply chain is one of its biggest advantages, but is also one of its biggest challenges, with ownership being one of the main areas of contention. The respondents’ answers varied widely when it came to who was the primary owner of their content supply chain.

Therefore, it’s no surprise that many respondents surveyed said they are worried about the potential for organizational silos, complex stakeholders and competing agendas. The lack of change management strategy for new processes and tools is apparent across organizations and needs to be addressed in order content supply chain to set up the content supply chain for success. Instead of moving quickly to demonstrate positive outcomes and ultimately shortchanging this long-game effort, organizations need to take preliminary action at the requirements-gathering stage. By doing so, it enables trust from employees who then help to navigate the transformation within their teams and across the organization.

Revolutionizing the content supply chain

The disruptive nature of generative AI can feel overwhelming, but through long-term change management and trust, organizations can transform their content supply chain and be the catalyst for a needed organizational culture shift.

The study highlights the advantages of content supply chain. It provides readers and clients a better understanding of how generative AI can enhance outcomes and overcome some of the operational challenges suppressing progress. 

Content supply chain transformation touches many functions and requires cooperation across executives. The study provides a detailed breakdown of practical actions for key C-suite executives, including CMOs, CTOs, and CFOs, to help prepare them for content supply chain enhancements.

Generative AI is changing the world and now is the time to establish your organization as a leader in your industry. Get started by embracing the technology and ensuring your organization has the right internal and external ecosystems to manage the transformation. Breaking down silos is not easy and won’t be fast, but organizations taking the more calculated route will lay the groundwork for innovation that can keep up with the pace of change brought to bear by generative AI. This is only the beginning.  

Source: ibm.com

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Driving quality assurance through the IBM Ignite Quality Platform

Quality Assurance (QA) is a critical component of the software development lifecycle, aiming to ensure that software products meet specified quality standards before release. QA encompasses a systematic and strategic approach to identifying, preventing and resolving issues throughout the development process.

However, various challenges arise in the QA domain that affect test case inventory, test case automation and defect volume. Managing test case inventory can become problematic due to the sheer volume of cases, which lead to inefficiencies and resource constraints. Test case automation, while beneficial, can pose challenges in terms of selecting appropriate cases, safeguarding proper maintenance and achieving comprehensive coverage. Defect volume is a perpetual concern, impacting software quality and release timelines.

Overcoming these challenges demands a thoughtful and proactive approach to streamline test cases, optimize automation effectiveness and minimize the volume of defects in the QA process. Balancing these aspects is crucial for delivering high-quality software products that meet user expectations and industry standards.

How IBM helps

To reduce test case volume, it’s essential to focus on test case optimization. This process involves identifying redundant or overlapping test cases and consolidating them to cover multiple scenarios. Prioritizing test cases based on critical functionalities and potential risks to streamline the testing effort is also important. Additionally, leveraging risk-based testing allows teams to allocate resources where they are most needed, optimizing coverage without compromising quality. Test case automation effectiveness can be enhanced through careful planning and continuous maintenance.

Another way is to choose the test cases wisely for automation, focusing on repetitive, time-consuming and critical scenarios. It is also necessary to regularly update automated test scripts to adapt to changes in the application, making sure they remain relevant and reliable. A proactive approach for defects involves implementing robust testing methodologies, such as shift-left testing, where testing activities are initiated earlier in the development process. Conducting thorough code reviews, employing static analysis tools and emphasizing collaboration between development and testing teams to catch and address defects early.

IBM® brings in all this through The IBM IGNITE Quality Platform (IQP), which is a DevOps-enabled single sign-on platform that leverages AI capabilities and patented methods to optimize tests. The platform brings in shift left methodologies that promote faster automation with healing capabilities and predict and prevent defects, which in turn drive high-quality delivery that supports the end to end testing lifecycle of an organization.

It consists of the following pillars:

Administer:

Supported through an Integrated Platform that manages multiple tenants, users, applications, projects and all necessary functional and technical configurations needed across the testing journey, centrally at one place. Likewise, it supports quality plan journey that aims to reduce defects. It is also integrated with quality recommendations that flow in from other components and multiple third-party integrations, which include leading git-based repositories, test and defect tools and cloud-based web and mobile testing tools

Optimize:

Aimed towards creating the optimal set of testcases with 100% coverage and bring in a shift left in surfacing defects early.

1. Requirement analytics (RA): NLP-based tool for analysis of requirements to identify ambiguity, drive in shift left and determine complexity. It also aids semi-automatic identification of key attributes for the optimization journey.

2. Search tag & model (STAM): Text-based analytics tool for quick analysis of a huge number of existing tests to identify redundancy and identify key attributes for the optimization journey.

3. Optimization (TO): Combinatorial Test Design Methodology-based tool that enables building an optimized test plan with maximum coverage from existing requirements, existing tests, YAML and even relational data. Also includes reusability via attribute pool and functional context modelling concepts.

Automate:

Aimed to quickly generate and automate and execute multiple tests unattended on various data, environments and platforms.

1. Test Generation (TG): Helps generate both TO model-based and nonmodel-based tests, ready for both manual and automated testing. It also supports custom BDD generation for client-based frameworks, automatic BDD script generation through recording mechanism and quick conversion of custom selenium-based frameworks to IQP specific automation.

2. Optimized Test Flow Automation (OTFA): Cucumber-based scriptless test automation framework supporting automation of Web, Mobile, REST, SOAP based applications, with a built-in test healing capability and integrated Jmeter-based performance testing and visual testing.

Analyze:

Trained in understanding a client’s defect patterns—cognitive test components drive quicker resolution, provides insight, and makes predictions around defects, which in turn gives preventive recommendations across Agile and traditional engagements. It also supports in better planning and reduced test cycles using defect prediction capability.

1. Defect classify (IDC): Plug-in solution for on-the-go classification and automatic assignment of defects to aid faster defect analysis and resolution.

2. Defect Analytics (IDA): Designed using defect reduction methodology that understands the semantics of the defects and provides prevention recommendations to reduce them further.

3. Defect Predict (IDP): Assesses and predicts defect trend in a test cycle aiding better planning and test management.

Our differentiated automation approaches

Prioritizing optimization over automation: This is our strategy to mitigate waste snowball effect by adopting multiple shift-left methodologies. We leverage a modern framework that is Behaviour-Driven Development (BDD) enabled and incorporates low-code practices. Our approach extends to comprehensive automation covering Web, Mobile, API and SOAP-based applications, seamlessly integrated with performance testing.

Embracing a philosophy of continuous testing, our strategy is to intricately weave all functions into the DevOps pipeline, promoting a cohesive and efficient development lifecycle. Beyond this, our commitment extends to cloud deployment and Software as a Service (SaaS) offerings, driving scalability, flexibility and accessibility in a rapidly evolving technological landscape.

Evidence of success of usage of IGNITE Quality and Test

Our primary focus is on driving tangible value to our clients through a strategic approach that involves reducing testing efforts while concurrently instilling confidence in our clients. Our proficiency extends across multiple technologies, which puts in place a comprehensive and adaptable solution that aligns with the diverse needs of our clients. By consistently delivering results and earning the trust of our clients, we have established ourselves as leaders in the industry, dedicated to providing solutions that make a meaningful impact.

Source: ibm.com

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Building for operational resilience in the age of AI and hybrid cloud

Each year we see the challenges that enterprises face become more complex as they strive to keep up with the latest technologies, such as generative AI, and increasing customer expectations.

For highly regulated industries, these challenges take on an entirely new level of expectation as they navigate evolving regulatory landscape and manage requirements for privacy, resiliency, cybersecurity, data sovereignty and more. Organizations in the financial services, healthcare and other regulated sectors must place an even greater focus on managing risk—not only to meet compliance requirements, but also to maintain customer confidence and trust.

To do this, it’s crucial that enterprises place an emphasis on operational resilience with the aim of maintaining stability, preserving market integrity and protecting confidential data for themselves and their customers.

Prioritizing operational resiliency

In our view, the essence of operational resilience is an assumption that disruption is inevitable, and organizations must have measures in place to be able to absorb and adapt to any shocks. This includes cyber incidents, technology failures, natural disasters and more. With more dependency on technology and third and fourth parties, expectations are increasing for organizations to continue delivering critical business services through a major disruption in a safe and secure manner. This means actively minimizing downtime and closing gaps in the supply chain to remain competitive.

This is different from the long-standing industry practice of disaster recovery where, traditionally, companies would return to normal operations in the several days after an event with defined recovery point objectives and recovery time objectives. Although still an important practice, appetite for conventional disaster recovery approaches is diminishing across industries and especially with regulators. This is evident from emerging regulatory requirements and expectations in UK (Bank of England’s Critical Third-Party regime), Europe (Digital Operational Resilience Act), Australia (APRA CPS-230 Operational Risk Management) and Canada (OSFI – Operational Resilience and Operational Risk Management), etc. Similarly, in the U.S. the Office of the Comptroller of Currency (OCC) also indicated that the Federal Banking Agencies are considering updates to operational resilience frameworks and approaches for critical business services and for third-party services providers. 

As hybrid cloud and generative AI adoption increases, data and applications are everywhere—across multiple clouds and vendors (SaaS/Fintech), on premises and even at the edge. For this reason, it’s more important than ever for enterprises to ensure their cybersecurity and resiliency strategy incorporates their entire IT estate, no matter where it resides.

To do this, enterprises must first prioritize the most critical business services and develop a workload and data placement strategy to determine which applications and data should reside in a certain environment based on its specific security, resiliency and data sovereignty needs. 

According to the 2024 IBM X-Force Threat Intelligence Index, attackers are increasingly shifting from ransomware to malware that is designed to steal information, which reinforces the importance of leveraging technology and approach that provides holistic view and end-to-end protection across your entire IT estate, including your partners.

While partnerships are essential for businesses to remain competitive and tap into new entry points, enterprises must make sure third parties are thinking about security, resiliency and controls in the same way they and their regulators are.

It’s clear trust and security must be at the foundation of decisions about where workloads and data reside—regardless of the industry. But how can an enterprise ensure these priorities remain front and center, especially when working with third and fourth parties?

Taking an industry-specific approach to accelerating digital transformation

Hybrid cloud is now the dominant architecture adopted by enterprises, according to an IBM Study, but critical to hybrid cloud strategy is an industry cloud approach. Over the past few years, IBM Cloud® has continued to innovate on, and made significant enhancements to our enterprise cloud platform designed for regulated industries. This purpose-built approach has enabled clients to take advantage of cloud services, SaaS providers and Fintechs at a consistent level of security, resiliency and compliance to build and deliver world-class solutions for their customers, while managing third- and fourth-party risk. 

Several years ago, we took a strategic step to address the needs of our clients in regulated industries with the first industry-specific cloud platform designed to meet the needs of financial services sector. This includes the highest set of operational, resiliency, cybersecurity and regulatory standards with built-in controls informed by the industry. By meeting the stringent standards for financial services, it can be seamlessly leveraged across other industries including insurance, government, healthcare, manufacturing and telecommunications, allowing for continuous and central management of security and risk management. 

To support clients in their transformation journey, we are continuing our work with key industry organizations to further address risk and allow organizations to leverage the cloud with confidence. One of our premier industry forums is the IBM Financial Services Cloud Council, which now consists of a network of more than 160 CIOs, CTOs, CISOs and Risk and Compliance officers from over 90 financial institutions working together to develop safe, secure and compliant adoption of cloud and Gen AI.

Moreover, we are collaborating with industry leading organizations such as the Cloud Security Alliance to advance hybrid cloud security and Gen AI adoption for enterprises. On-going engagement with regulators around the globe and private-public sector collaboration through organizations such as the U.S. Financial Services Sector Coordinating Council (FSSCC) and engagements with the Financial Stability Board Third-Party Risk group are also important in developing practical and consistent industry-wide approach to common challenges.

Shared understanding and ownership

As enterprises continue to balance the complexities of innovation, risk and resilience, we believe the path forward will be working towards a common, risk-based understanding of the core principles that underpin effective operational resiliency. It’s essential for enterprises to take ownership of their operations and prioritize their actions and investments based on the impact to themselves, their customers and market stability, but this can’t happen in a vacuum. 

At IBM, we are committed to helping clients on this journey. We believe it takes all of us—enterprises, trade organizations, policy makers, regulatory authorities and cloud providers— to work in unison to accomplish the same critical mission: accelerating digital experiences that move the world in a secure, resilient and compliant manner. 

Source: ibm.com

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6 ways the recruitment process is boosted by AI

Nobody likes paperwork. And as important as talent acquisition is for any organization, it involves a lot of it: sifting through resumes, posting job descriptions, onboarding new employees. These tasks aren’t all tedium, and in fact, they often require human-level discernment. However, many components of these tasks can now be automated or augmented by AI, allowing hiring managers to focus on providing smarter, higher-level engagement with candidates. The organization that learns to leverage the latest in AI tools is able to free up employee time, so they can put a little more “humanity” into their human resources operations.

The typical goal of the talent selection process is simple: target the highest qualified candidates and persuade them to apply to vacancies and sign contracts at the most favorable rates to the organization. But there are many ways where this seemingly simple process can break down. A poorly written job description, for example, can result in a deficit of applications—or an abundance of applications from candidates who might not have the right skills, resulting in wasted effort and lost time in either case. Optimizing the process with AI tools can help recruiting teams zero in on right candidates, an essential capability in increasingly competitive employment markets.

Below are some ways that AI is enhancing the recruitment process across its workflow, from discovering hiring needs to attracting, courting, onboarding and retaining top talent.

Predictive analytics

Before a new job listing is even written or an open position has been identified, AI algorithms can help analyze various data sources like historical hiring trends, employee turnover rates, business growth projections and workforce demographics. By processing this data, AI identifies patterns and correlations, providing insights into future hiring needs based on past trends and organizational goals. AI can help predict demand trends for specific competencies, and help hiring teams develop recruitment strategies to plan for skills gaps that might not have even presented themselves as problematic yet. AI can also analyze external data, scraping job postings and public salary information, then model various scenarios and generate reports that might help an employer make hiring decisions about, for example, whether to fill a position with an internal recruitment, fill a gap with a contractor relationship or spring for a new hire. Such tools can also help organizations develop recruitment plans for achieving diversity, equity and inclusion (DEI) goals, identifying areas where hiring policies and trends might be adjusted to align with the organization’s broader DEI strategy.

Job posting

Once a comprehensive hiring strategy is developed, AI can get to work contributing to the creation of job descriptions. Generative AI tools can quickly develop descriptions based on short prompts. Then, once these have been posted on job boards, AI can conduct A/B testing on different versions of job descriptions to evaluate their effectiveness in attracting candidates. By analyzing metrics such as click-through rates, job application conversion rates and time-to-fill, AI helps organizations identify the most successful iterations and refine their approach accordingly. Employment-based social media companies like LinkedIn use AI to help organizations A/B test ads on their platform.

AI contributes to the creation of more inclusive and broadly enticing job descriptions. Language biases and unintentional exclusions can deter diverse candidates from applying. AI algorithms, armed with insights derived from a vast array of data, can craft job descriptions that are not only gender-neutral and culturally sensitive but also optimized to attract a wider pool of candidates. By fostering inclusivity, organizations can tap into a more diverse talent pool, bringing in fresh perspectives and skills that contribute to a vibrant and innovative company culture.

Resume screening

Reviewing resumes is probably the first thing that many HR professionals imagine when they think of the rote work they wish they could automate. And fortunately, AI-based screening technologies are getting smarter all the time, so there’s less chance of accidentally screening out a great potential hire.

With traditional methods, recruiters grappled with a deluge of resumes and cover letters, sometimes thousands for a single role. How could HR professionals expect to pick the needle out of the haystack in a timely fashion? AI, on the other hand, can swiftly analyze vast volumes of resumes, extracting relevant information and highlighting the best candidates whose qualifications most align with the job specifications. This ensures a more objective and consistent screening process, reducing the risk of overlooking qualified candidates. AI tools can deliver a shortlist to hiring managers, enabling them to spend less time sifting through huge piles of resumes, and more time both enhancing the candidate experience and delivering value to their organization.

Initial interviews

AI recruitment software can also come in handy during this phase to schedule interviews by coordinating available time slots between the candidate and the recruiter. This reduces the administrative burden on recruiters and streamlines the interview process.

Some job openings require many rounds of interviews. Conducting interviews, especially when high-level managers are involved, can become quite expensive. The point of initial interview questions is to give the candidate and the organization basic information about one another. This “first impression” does not necessarily need to involve a human agent on the organization’s side. Chatbots can engage candidates in a conversation to gather basic information about their preferences, availability and eligibility for a role. This can serve as an additional filter on top of the resume screening phase. Meanwhile, chatbots can answer frequently asked questions (FAQs) and distribute documentation about the organization to potential candidates.

This exchange of information can make subsequent interviews more useful to both parties, and help save both parties time if the candidate lacks necessary skills that the resume screening, for whatever reason, didn’t catch. On the flip side, a chatbot-led interview might also indicate to the interviewee that the position is not what they thought, obviating the need for subsequent interviews.

Chatbots can also administer quizzes or skills assessments to evaluate a candidate’s knowledge, skills or problem-solving capabilities. Virtual assistants can use the latest Natural language processing (NLP) capabilities to field open-ended answers in plain language, and help determine if those answers predict whether or not an employee is likely to be a good “culture fit.” If a candidate fails to meet certain performance criteria during this phase, the organization can move on with more suitable candidates without engaging HR staff. AI can also help job seekers more seamlessly provide information for background checks.

Contract negotiation

After selecting candidates and building a job offer, the organization can rely on AI for the negotiation process. AI is increasingly good at parsing information in offer letters and contracts to ensure compliance with relevant laws, regulations and industry standards. By flagging potential legal issues or discrepancies, AI helps ensure that contracts adhere to legal requirements, reducing the risk of disputes or litigation. By evaluating factors such as termination clauses, non-compete agreements and intellectual property rights, AI helps negotiators assess the potential impact of contract terms and negotiate accordingly.

AI can analyze clauses within employment contracts and compare them to industry benchmarks or standard templates. By identifying deviations or unusual provisions, AI helps negotiators understand the implications of each clause and negotiate more effectively.

AI can provide recommendations to the organization for negotiation strategies based on historical data, industry norms and the specific context of the negotiation. By analyzing past negotiation outcomes and success factors, AI helps negotiators develop informed strategies to achieve their objectives.

AI can automate the redlining and drafting of contract amendments or revisions based on negotiators’ input. New job title? No problem. NLP technology can make quick updates that don’t need to involve manual edits. By generating proposed changes and alternatives, AI streamlines the negotiation process and accelerates the exchange of contract drafts between parties.

Onboarding and retention

The onboarding process is a fantastic arena for AI to prove itself useful, from providing new hires with relevant information, answering their queries to guiding them through the initial steps, ensuring a smoother transition for new employees. AI-powered chatbots or virtual assistants can provide immediate support to new hires by answering frequently asked questions about company policies, benefits, IT setup and other onboarding-related queries. This reduces the burden on HR staff and empowers new employees to find information quickly and independently.

AI systems can automate the creation and processing of onboarding documentation. By streamlining administrative tasks, AI frees up HR personnel to focus on high-touch aspects of the onboarding process, while ensuring compliance with regulatory requirements.

As an extension of the overall employee experience, AI can also help to ensure that employees stay satisfied throughout their tenure with the organization. AI can recommend relevant training and development opportunities for employees based on their performance, skills and career goals, contributing to ongoing professional development. By offering tailored training programs and career paths aligned with individual goals, AI helps employees feel valued and invested in their professional growth, increasing their likelihood of staying with the company.

Algorithms can analyze employee workloads, productivity levels and stress indicators to identify individuals at risk of burnout. By recommending workload adjustments, time management strategies or wellness initiatives, AI helps prevent burnout and promote work-life balance, leading to higher retention rates. AI algorithms can analyze employee profiles, skills and interests to match new team members with peers and mentors. By connecting new employees with experienced colleagues who can provide guidance and support, AI accelerates the integration process and promotes knowledge sharing within the organization.

Bringing automation to your recruitment process

Looking for ways to develop a more effective recruitment process? Your search would be missing something if it didn’t include AI. IBM watsonx Orchestrate automates repetitive HR tasks with a conversational interface to manage and simplify multiple application workflows in HR. It includes robust recruiting automation capabilities. Built to automate repetitive tasks in your recruitment process, watsonx Orchestrate integrates with the top tools you already use every day to save you time and effort across your recruitment workflow.

Source: ibm.com

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How IBM helps clients accelerate app modernization and control costs

A large US-based healthcare company recently engaged with IBM to accelerate their cloud adoption with consistent and predictable outcomes. This collaboration enhanced their confidence to navigate app modernization across various applications and landing zones for both hybrid cloud and platform-native modernization.

As a healthcare company, this client had an obligation to provide safe, reliable, time-sensitive, high-quality services to its customers. Ultimately, they needed best-in-class application modernization tooling to help deliver on that obligation.

When a client is not able to properly visualize all applications and their underlying dependencies properly, they risk experiencing diminished reliability. Managing large internal and vendor teams to maintain multiple applications can be extremely difficult. To accelerate the hybrid cloud journey and control costs, it is critical to prioritize specific execution and transformation steps. 

How IBM helped improve the app modernization strategy

IBM Consulting® worked with the client’s cloud team and technical experts to deploy IBM Consulting Cloud Accelerator, which helped them understand the business logic surrounding their many applications. The solutions deployed included IBM® Application Discovery and Delivery Intelligence (ADDI), Cloud Transformation Insights (CTI), Analysis and Renovation Catalyst (ARC), Mainframe Application Modernizer (MAM) and Candidate Microservice Advisor (CMA).

IBM Consulting Cloud Accelerator understands complex program interactions in the current state of applications and microservices and can perform a what-if analysis of possible target states. It provides flexibility to choose what applications to move and when to move them based on business imperatives. It also helps to enable consistency and provides an accelerated modernization process end-to-end, from rapid discovery to solutioning and low-touch delivery.

Hybrid cloud migration with predictable outcomes and lower risk

The client was able to leverage a repository that provides a collaborative environment where multiple resources can search, modify and test the business rules by ingesting extracted business rules.

The IBM Consulting Cloud Accelerator also provided a detailed discovery of mainframe applications. These improvements are further underscored by the solution’s automated discovery of dead code or unreachable code and its ability to identify microservices in potential target states.

IBM Consulting Cloud Accelerator integrates and orchestrates a wide range of migration tools across IBM’s assets and products, as well as open source, and third-party tools. IBM Consulting Cloud Accelerator offers these capabilities:

• Cloud transformation planning: Create a wave plan for migration and modernization workloads to the cloud.
• Application and workload analysis: Collect infrastructure, app data and target state preferences. Produce cloud modernization paths that are optimized for time, cost and business benefits.
• Migrate, modernize and build capabilities: Migrate workloads to the cloud or develop new applications natively on the cloud with our automation-first, journey-based approach with low-touch factory squads to deliver predictable outcomes.
• Application and platform design: Create technical blueprints to help guide the implementation of consistent experiences across multiple cloud platforms and services.
• Cloud services configuration: Automate the build-out and configuration of the cloud platform and the required cloud services for application workloads.
• Day 2 operations: Drive consistency in cloud operations in a vendor agnostic manner, regardless of choices in cloud providers or landing zones.
• Co-creation with IBM Garage™: Ideate, build, measure, iterate and scale solutions seamlessly with our end-to-end framework of design thinking, agile and DevOps practices. Achieve speed-to-value and adopt breakthrough technologies through the partnerships between your team and a diverse set of IBM technology, business and design experts.

IBM Consulting Cloud Accelerator enabled the healthcare company by deploying IBM hybrid-cloud capabilities across the client’s locations in a cost-effective manner. This allowed them to deliver higher-quality health outcomes to their customers. The company is now expanding to additional use cases as a result of their collaboration and innovation with the IBM Consulting team.

We believe that this engagement reflects the IBM values of client dedication and innovation that matters. It is our honor and privilege to provide value to our clients. IBM’s mission is to harness the power of data and hybrid cloud to drive real-time, predictive business insights that help clients make intelligent business decisions.

Source: ibm.com

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Cash versus digital payments: How to achieve financial inclusion

One of the more complex challenges banks must solve is to make payments more efficient. Recent news headlines show significant shifts from physical identification and physical forms of payments to digital forms in many jurisdictions. Europe recently announced a mandate that real-time payments be available from any provider who currently offers batch euro payments (such as SEPA credit transfers) at a price of no more than the cost of batch transfers.

This, combined with the increased use of digital identity in Europe and many other countries, is good news for consumers and businesses. These changes, along with other payments modernization efforts, may eliminate friction in financial ecosystems and the economy in general. But as some countries move forward with the advancement of digital identities and payments, other countries fail to realize the potential of these solutions.

In Malaysia, Alipay advances digital payments and digital identity

Malaysia, along with many southeast Asian countries, is moving to align and integrate their digital payment systems with other networks to make cross-border payments easier. Malaysia’s payments network, PayNet, is collaborating with Ant Group (the parent company of Alipay) to allow Alipay+ wallet holders from seven countries to pay via QR code using PayNet’s DuitNow QR system. The service launch means that if a bank or wallet participates in Alipay+, customers can make real-time payments simply by scanning the QR code using DuitNow in Malaysia.

The cross-border advantage to this system allows customers in China, Hong Kong SAR, Philippines, Mongolia, Macau SAR, South Korea and Thailand to make payments with a single Alipay supported wallet. AliPay also introduced its “Smile to Pay” facial recognition application on mobile devices in 2017, which allows customers to make purchases by posing in front of point-of-sale machines. Mastercard also announced its pilot of biometric recognition just under two years ago. It is likely that this form of digital identity for payments will continue to expand.

A missed opportunity: The US prioritizes cash over digital advancements

In contrast to recent digital payment advancements, the Washington D.C. Council recently banned cashless businesses. Cash as a physical commodity is a costly means of payment, given the security issues, risks and handling costs to every stakeholder in the value chain handling the cash. Increasing the use of cash does not reduce cost or friction in the economy.

The reasoning for the D.C. ban is that many people don’t have a bank debit or credit card, so they must use cash to make payments. In the United States, approximately 7% of the population are unbanked, according to Global Finance. That 7% may not seem like much, but it represents around 23 million people who rely on cash or other non-bank forms of payments.

The D.C. ban makes access to retailers more equitable for the unbanked, but it doesn’t address the root cause of being unbanked in the first place. One explanation is the lack of access to government-issued ID, for reasons such as having no fixed address. A digital identity that is established based on some attribute of the person themselves, as opposed to where they live or if they can drive, makes the problem of economic access easier to solve.

Achieving financial inclusion through a digital solution

For a good case study, look at what has happened in India since the introduction of Aadhaar, the digital identity system established by the country’s federal government. As a result of this system, financial inclusion for millions was possible. People could qualify for a bank account or a digital wallet with their digital identity, wherein they could store funds obtained from government or other sources. Those who formerly had no chance to participate in the economy, except through cash and the generosity of others, can now make payments at a merchant using India’s UPI digital real-time payments system.

The link between digital identity and financial inclusion is clear: with this system, India’s poverty rate declined by around 10% or nearly 135 million people in 5 years. Furthermore, the Indian economy is benefiting from this financial inclusion. Real GDP growth was 6.9% in FY 2022-2023 and is expected to be 6.3% in 2023-2024. The reduction in the use of cash is a contributing factor. It stands to reason that the US and other countries should consider accelerating the move away from cash to digital payments to achieve financial inclusion and economic growth.

There is an immediate opportunity to accelerate the adoption of digital alternatives to cash. Combined with the adoption of digital identity, the economies of countries who pursue this path will grow and be more competitive globally than those who don’t.  Moreover, businesses and consumers will be more satisfied with the ability to conduct business as the economies in which we live become more efficient.

Source: ibm.com

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Maximizing business outcomes and scaling AI adoption with a Hybrid by design approach

For established businesses, the debate is settled: a hybrid cloud approach is the right strategic choice.

However, while embracing hybrid cloud might be intrinsic, clients continually seek to derive business value and higher return on investment (ROI) from their investments. According to a study conducted by HFS Research in partnership with IBM Consulting, only 25% of surveyed enterprises have reported solid ROI on business outcomes from their cloud transformation efforts.

The lack of ROI progress can be attributed to several factors, including slow adoption, unrealized use cases and unaddressed cloud sprawl. This is exacerbated by the increasing challenges of platform scaling, skilling, cybersecurity and the explosion of data volumes.

 

Businesses aiming to derive value from business outcomes through their cloud investments and harness the potential of artificial intelligence (AI) must adopt an intentional approach.

At IBM, we provide clear guidance for navigating these challenges and achieving better business outcomes. Adopting a hybrid by design strategy allows organizations to align architectural choices with business priorities across technology, platforms, processes and people, resulting in significantly enhanced outcomes and a higher ROI.

The latest paper from IBM, titled “Maximize the value of hybrid cloud in the generative AI era,” offers clarity for businesses seeking to optimize their hybrid cloud architecture and amplify the impact of generative AI (gen AI).

What does a Hybrid by design approach involve?

Many organizations have adopted cloud in isolated areas, seeking immediate benefits, but inconsistently applying the technology throughout their operations. This complexity and inconsistency increase costs and hinder businesses from meeting demands, goals and outcomes. We view this as a default approach, which characterizes most companies today.

Businesses must recalibrate transformation programs to align more closely with business imperatives and outcomes. They must also recognize how gen AI can amplify the value of hybrid cloud and accelerate digital transformation.

A hybrid by design approach intentionally structures your hybrid, multicloud IT estate to achieve key business priorities and maximize ROI. Organizations can shift from technology dictating business limitations to purposefully built architectures ready for evolving technology landscapes, new business and customer demands, and emerging processes and skill requirements. Driven by an organization’s key business objectives, intentional and consistent architectural decisions enable organizations to fully use hybrid cloud and gen AI to drive business outcomes. 

IBM built a codified framework to help clients strategically focus on decisions that drive the most outcomes for their business, promoting a hybrid by design approach. This framework facilitates the translation of business priorities into key architectural decisions.

We implement this framework by aligning business objectives with a set of decision points across 3 domains: product native by design, technology by design and integration by design. These domains also encompass various design points. Each enterprise situation requires different levels of capabilities to address and achieve its business objectives. This value framework aids in capturing these decision points.

In an AI-centric world, why does this matter for enterprises? 

Gen AI plays a critical role in shaping the digital enterprise, helping to maximize business returns on IT investments. However, wide-scale adoption and deployment of gen AI present challenges. Enterprises must manage vast volumes of data, significant computing power, advanced security architecture across distributed environments and ensure rapid scalability.

An intentional and consistent hybrid architecture design helps enterprises to overcome these challenges, facilitating the successful adoption and scalability of gen AI capabilities to meet business needs.

Current pilots and deployments prove that a hybrid by design approach is required to scale the adoption of gen AI to meet business needs. Gen AI accelerates the execution and value of hybrid cloud by providing enhanced automation with observability, improved cost optimization with automated insights, and heightened security with compliance across the hybrid platform. 

Applying intentional architecture decisions based on business needs amplifies the value of hybrid cloud and AI, driving business outcomes.

Source: ibm.com

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Application performance optimization: Elevate performance and reduce costs

Application performance is not just a simple concern for most organizations; it’s a critical factor in their business’s success. Driving optimal application performance while minimizing costs has become paramount as organizations strive for positive user experiences. These experiences can make or break a business, that’s why prioritizing high performance among applications is non-negotiable. What’s needed is a solution that not only safeguards the performance of your mission-critical applications, but also goes above and beyond through reduced cost, time efficiency and monetary savings.

The significance of optimizing application performance

Optimizing application development and performance is a must in a world where a user’s experience can control a business’ trajectory. Poor application performance can negatively impact an organization and their users may become frustrated and distrustful, causing them to abandon ship and even switch to a competitor.  An array of problems, anywhere from application bottlenecks to network latency to bugs can cause app performance issues. Striving to optimize application performance means taking a strategic approach to boosting baseline functionality and overall user experience. 

A positive shift in application performance optimization  

To optimize performance organizations will need to not only address their app performance concerns but also tackle the critical aspect of cost reduction. This approach paves the way for substantial savings, aligning resource allocation precisely with the demands of the application. There are three essential components that organizations need to master to achieve optimal application performance in the most cost-effective manner. 

1. Dynamic resource allocation 

Traditional static resource allocation methods often lead to inefficiencies, either resulting in underutilized resources, over provisioned resources, and conversely, performance bottlenecks. Instead, organizations need a solution that makes sure resources are allocated precisely where and when they are needed, optimizing performance without unnecessary resource expenditure.

2. Continuous application performance 

Traditional monitoring tools fall short in the face of dynamic workloads, unable to keep pace with the evolving demands of applications. To truly ensure continuous application performance, organizations must adopt a solution that automatically analyzes application workloads and makes real-time adjustments. An in-depth, proactive and automatic approach is important as it mitigates the risk of performance issues providing a seamless and reliable end-user experience. 

3. Real-time observability 

Unraveling the intricacies of applications and infrastructure is made seamless with advanced observability capabilities. This aspect is crucial for app performance optimization, as it provides real-time insights through high-fidelity data. But a traditional observability framework is not needed. Instead, organizations need to leverage an approach that gives users a deep understanding of their applications and allows for automatic incident remediation.

By dynamically allocating resources precisely where and when they are needed, organizations can optimize performance without unnecessary expenditure, while continuous application performance promotes reliability in the face of ever evolving demands. Meanwhile, real-time observability provides deep insights into an applications performance, enabling proactive identification and resolution of issues before they impact users. IBM® Turbonomic® is a key enabler of success in optimizing application performance. And when users integrate Turbonomic with IBM Instana®, organizations unlock a comprehensive solution that transcends traditional boundaries and ordinary performance monitoring tools.

Optimize performance through automation

Turbonomic revolutionizes application performance optimization by leveraging AI and machine learning algorithms to analyze real-time performance data and give insight into application response time and transaction time. Turbonomic integrates via API with all the elements in an organization’s technology stack no matter their provider and generates actions that solve performance issues. Costs and resource utilization are also optimized simultaneously as performance improvements are made.

Whether an applications resources are underutilized or over provisioned, Turbonomic dynamically allocates those resources exactly where they are needed as demand changes, optimizing performance while keeping costs down. Turbonomic helps engineers, architects and cloud infrastructure operators to proactively optimize their applications CPU, memory, storage and network resources through automation. By continuously analyzing application workloads and making real-time adjustments, Turbonomic establishes continuous application performance that mitigates performance issues, optimizes costs and provides a seamless end-user experience.  

Real-time observability and incident remediation

Instana complements Turbonomic’s capabilities by providing advanced real-time observability into application performance. Instana’s AI-powered monitoring capabilities enable organizations to gain deep insights into their applications, which identify potential issues before they impact performance. Instana’s easy-to-use dashboards allow organizations visibility into performance metrics and takes into account application dependencies. With automatic incident remediation and proactive monitoring, Instana secures continuous application performance from an APM standpoint and maintains a seamless end-user experience.

Enhancing application performance with IBM Turbonomic and IBM Instana

Combining the capabilities of IBM Turbonomic and IBM Instana creates a seamless user experience and optimizes application performance and resource usage while effectively managing costs. Turbonomic’s dynamic IT resource management capabilities make sure that IT resources are continuously optimized in real-time, aligning with application demand without over or under provisioning. By intelligently automating critical actions and optimizing resources across hybrid and multicloud environments Turbonomic maximizes application performance while minimizing costs.

Meanwhile, Instana’s fully automated real-time observability platform provides continuous high-fidelity data and end-to-end traces, empowering DevOps, SRE, platform engineers, IT Ops and development to access the data they need with contextual insights. This real-time visibility enables proactive identification and resolution of performance issues, which drive a smooth and reliable end-user experience. With Turbonomic and Instana, there is no more troubleshooting or end-users with a low bandwidth as they provide organizations with a comprehensive solution that solves the root cause of their applications performance issues. These solutions create an even stronger solution together, optimizing performance, streamlining operations and driving cost efficiencies, ultimately enabling businesses to achieve their goals.

Source: ibm.com

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5G use cases that are transforming the world

In the tech world and beyond, new 5G applications are being discovered every day. From driverless cars to smarter cities, farms, and even shopping experiences, the latest standard in wireless networks is poised to transform the way we interact with information, devices and each other. What better time to take a closer look at how humans are putting 5G to use to transform their world.

What is 5G?

5G (fifth-generation mobile technology) is the newest standard for cellular networks. Like its predecessors, 3G, 4G and 4G LTE, 5G technology uses radio waves for data transmission. However, due to significant improvements in latency, throughput and bandwidth, 5G is capable of much faster download and upload speeds than previous networks.

How is 5G different from other wireless networks?

Since its release in 2019, 5G broadband technology has been hailed as a breakthrough technology with big implications for both consumers and businesses. Primarily, this is due to its ability to handle large volumes of data generated by complex devices using its networks.

As mobile technology has expanded over the years, the amount of data users generate every day has increased exponentially. Currently, other transformational technologies like artificial intelligence (AI), the Internet of Things (IoT) and machine learning (ML) require much faster speeds to function than 3G and 4G networks offer. Enter 5G, with its lightning-fast data transfer capabilities that allow newer technologies to function in the way they were designed to.

Here are some of the biggest differences between 5G and previous wireless networks.

• Physical footprint: The transmitters used in 5G technology are smaller than in predecessors’ networks, allowing for discrete placement in out-of-the-way places. Furthermore, “cells”—geographical areas that all wireless networks require for connectivity—in 5G networks are smaller and require less power to run than in previous generations.
• Error rates: 5G’s adaptive Modulation and Coding Scheme (MCS), a schematic that WiFi devices use to transmit data, is more powerful than ones in 3G and 4G networks. This makes 5G’s Block Error Rate (BER)—a metric of error frequency—much lower.
• Bandwidth: By utilizing a broader spectrum of radio frequencies than previous wireless networks, 5G networks can transmit on a much wider range of bandwidths. This increases the number of devices they can support at any given time.
• Lower latency: 5G’s low latency, a measurement of the time it takes data to travel from one location to another, is a significant upgrade over previous generations. This means routine activities like downloading a file or working in the cloud is going to be much faster with a 5G connection than a connection on a different network.

How does 5G work?

Like all wireless networks, 5G networks are separated into geographical areas known as cells. Within each cell, wireless devices—such as smartphones, PCs and IoT devices—connect to the internet via radio waves transmitted between an antenna and a base station. The technology that underpins 5G is essentially the same as in 3G and 4G networks, but due to its lower latency, 5G networks are capable of delivering much faster download speeds—in some cases as high as 10 gigabits per second (Gbps).

As more and more devices are built for 5G speeds, demand for 5G connectivity is growing. Today, many popular Internet Service Providers (ISPs), such as Verizon, Google and AT&T, offer 5G networks to homes and businesses. According to Statista, more than 200 million homes and businesses have already purchased it with that number expected to at least double by 2028 (link resides outside ibm.com).

Let’s take a look at three areas of technological improvement that have made 5G so unique.

New telecom specifications

The 5G NR (New Radio) standard for cellular networks defines a new radio access technology (RAT) specification for all 5G mobile networks. The 5G rollout began in 2018 with a global initiative known as the 3rd Generation Partnership Project (3FPP) that defined a new set of standards to steer the design of devices and applications for use on 5G networks.

The initiative was a success, and 5G networks began to grow swiftly in the ensuing years. Today, 45% of networks worldwide are 5G compatible, with that number forecasted to rise to 85% by the end of the decade according to a recent report by Ericsson (link resides outside ibm.com).

Independent virtual networks (network slicing)

On 5G networks, network operators can offer multiple independent virtual networks (in addition to public ones) on the same infrastructure. Unlike previous wireless networks, this new capability allows users to do more things remotely with greater security than ever before. For example, on a 5G network, enterprises can create use cases or business models and assign them their own independent virtual network, dramatically improving the user experience for their employees by adding greater customizability and security.

Private networks

In addition to network slicing, creating a 5G private network can also enhance personalization and security features over those available on previous generations of wireless networks. Global businesses seeking more control and mobility for their employees increasingly turn to private 5G network architectures rather than public networks they’ve used in the past.

5G use cases

Now that we better understand how 5G technology works, let’s take a closer look at some of the exciting applications it’s enabling.

Autonomous vehicles

From taxi cabs to drones and beyond, 5G technology underpins most of the next-generation capabilities in autonomous vehicles. Until the 5G cellular standard came along, fully autonomous vehicles were a bit of a pipe dream due to the data transmission limitations of 3G and 4G technology. Now, 5G’s lightning-fast connection speeds have made transport systems for cars, trains and more much faster than previous generations, transforming the way systems and devices connect, communicate and collaborate.

Smart factories

5G, along with AI and ML, is poised to help factories become not only smarter but more automated, efficient and resilient. Today, many mundane but necessary tasks associated with equipment repair and optimization are being turned over to machines thanks to 5G connectivity paired with AI and ML capabilities. This is one area where 5G is expected to be highly disruptive, impacting everything from fuel economy to the design of equipment lifecycles and how goods arrive at our homes.

For example, on a busy factory floor, drones and cameras connected to smart devices utilizing the IoT can help locate and transport something more efficiently than in the past and prevent theft. Not only is this better for the environment and consumers, but it also frees up employees to dedicate their time and energy to tasks that are more suited to their skill sets.

Smart cities

The idea of a hyper-connected urban environment that uses 5G network speeds to spur innovation in areas like law enforcement, waste disposal and disaster mitigation is fast becoming a reality. Some cities already use 5G-enabled sensors to track traffic patterns in real time and adjust signals, helping guide the flow of traffic, minimize congestion and improve air quality.

In another example, 5G power grids monitor supply and demand across heavily populated areas and deploy AI and ML applications to “learn” what times energy is in high or low demand. This process has been shown to significantly impact energy conservation and waste, potentially reducing carbon emissions and helping cities reach sustainability goals.

Smart healthcare

Hospitals, doctors and the healthcare industry as a whole already benefit from the speed and reliability of 5G networks every day. One example is the area of remote surgery that uses robotics and a high-definition live stream connected to the internet via a 5G network. Another is the field of mobile health, where 5G gives medical workers in the field quick access to patient data and medical history, enabling them to make smarter decisions, faster, and potentially save lives.

Lastly, as we saw during the pandemic, contact tracing and the mapping of outbreaks are critical to keeping populations safe. 5G’s ability to deliver of volumes of data swiftly and securely allows experts to make more informed decisions that have ramifications for everyone.

Better employee experiences

5G paired with new technological capabilities won’t just result in the automation of employee tasks, it will dramatically improve them and the overall employee experience. Take virtual reality (VR) and augmented reality (AR), for example. VR (digital environments that shut out the real world) and AR (digital content that augments the real world) are already used by stockroom employees, transportation drivers and many others. These employees rely on wearables connected to a 5G network capable of high-speed data transfer rates that improve several key capabilities, including the following:

• Live views: 5G connectivity provides live, real-time views of equipment, events and even people. One way in which this feature is being used in professional sports is to allow broadcasters to remotely call a sporting event from outside the stadium where the event is taking place.
• Digital overlays: IoT applications in a warehouse or industrial setting allow workers equipped with smart glasses (or even just a smartphone) to obtain real-time insights from an application, including repair instructions or the name and location of a spare part.
• Drone inspections: Right now, one of the leading causes of employee injury is inspection of equipment or project sites in remote and potentially dangerous areas. Drones, connected via 5G networks, can safely monitor equipment and project sites and even take readings from hard-to-reach gauges.

Edge computing

Edge computing, a computing framework that allows computations to be done closer to data sources, is fast becoming the standard for enterprises. According to this Gartner white paper (link resides outside ibm.com), by 2025, 75% of enterprise data will be processed at the edge (compared to only 10% today). This shift saves businesses time and money and enables better control over large volumes of data. It would be impossible without the new speed standards generated by 5G technology. 

Ultra-reliable edge computing and 5G enable the enterprise to achieve faster transmission speeds, increased control and greater security over massive volumes of data. Together, these twin technologies will help reduce latency while increasing speed, reliability and bandwidth, resulting in faster, more comprehensive data analysis and insights for businesses everywhere.

5G solutions with IBM Cloud Satellite

5G presents big opportunities for the enterprise, but first, you need a platform that can handle its speed. IBM Cloud Satellite lets you deploy and run apps consistently across on-premises, edge computing and public cloud environments on a 5G network. And it’s all enabled by secure and auditable communications within the IBM Cloud.

Source: ibm.com

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