Information Week

As IT leaders, we’re facing increasing pressure to prove that our generative AI investments translate into measurable and meaningful business outcomes. It’s not enough to adopt the latest cutting-edge technology; we have a responsibility to show that AI delivers tangible results that directly support our business objectives.   To truly maximize ROI from GenAI, IT leaders need to take a strategic approach — one that seamlessly integrates AI into business operations, aligns with organizational goals, and generates quantifiable outcomes. Let’s explore advanced strategies for overcoming GenAI implementation challenges, integrating AI with existing systems, and measuring ROI effectively.  Key Challenges in Implementing GenAI  Integrating GenAI into enterprise systems isn’t always straightforward. There are several hurdles IT leaders face, especially surrounding data and system complexity.   Data governance and infrastructure. AI is only as good as the data it’s trained on. Strong data governance enforces better accuracy and compliance, especially when AI models are trained on vast, unstructured data sets. Building AI-friendly infrastructure that can handle both the scale and complexity of AI data pipelines is another challenge, as these systems must be resilient and adaptable.  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report Model accuracy and “hallucinations.” GenAI models can produce non-deterministic results, sometimes generating content that is inaccurate or entirely fabricated. Unlike traditional software with clear input-output relationships that can be unit-tested, GenAI models require a different approach to validation. This issue introduces risks that must be carefully managed through model testing, fine-tuning, and human-in-the-loop feedback.  Security, privacy, and legal concerns. The widespread use of publicly and privately sourced data in training GenAI models raises critical security and legal questions. Enterprises must navigate evolving legal landscapes. Data privacy and security concerns must also be addressed to avoid potential breaches or legal issues, especially when dealing with heavily regulated industries like finance or healthcare.  Strategies for Measuring and Maximizing AI ROI  Adopting a comprehensive, metrics-driven approach to AI implementation is necessary for assessing your investment’s business impact. To ensure GenAI delivers meaningful business results, here are some effective strategies:  Define high-impact use cases and objectives: Start with clear, measurable objectives that align with core business priorities. Whether it’s improving operational efficiency or streamlining customer support, identifying use cases with direct business relevance ensures AI projects are focused and impactful.  Quantify both tangible and intangible benefits: Beyond immediate cost savings, GenAI drives value through intangible benefits like improved decision-making or customer satisfaction. Quantifying these benefits gives a fuller picture of the overall ROI.  Focus on getting the use case right, before optimizing costs: LLMs are still evolving. It is recommended that you first use the best model (likely most expensive), prove that the LLM can achieve the end goal, and then identify ways to reduce cost to serve that use case. This will make sure that the business need is not left unmet.  Run pilot programs before full rollout: Test AI in controlled environments first to validate use cases and refine your ROI model. Pilot programs allow organizations to learn, iterate, and de-risk before full-scale deployment, as well as pinpoint areas where AI delivers the greatest value, learn, iterate, and de-risk before full-scale deployment.  Track and optimize costs throughout the lifecycle: One of the most overlooked elements of AI ROI is the hidden costs of data preparation, integration, and maintenance that can spiral if left unchecked. IT leaders should continuously monitor expenses related to infrastructure, data management, training, and human resources.   Continuous monitoring and feedback: AI performance should be tracked continuously against KPIs and adjusted based on real-world data. Regular feedback loops allow for continuous fine-tuning, ensuring your investment aligns with evolving business needs and delivers sustained value.   Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations Overcoming GenAI Implementation Roadblocks  Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations Successful GenAI implementations depend on more than adopting the right technology—they require an approach that maximizes value while minimizing risk. For most IT leaders, success depends on addressing challenges like data quality, model reliability, and organizational alignment. Here’s how to overcome common implementation hurdles:   Align AI with high-impact business goals. GenAI projects should directly support business objectives and deliver sustainable value like streamlining operations, cutting costs, or generating new revenue streams. Define priorities based on their impact and feasibility.  Prioritize data integrity. Poor data quality prevents effective AI. Take time to establish data governance protocols from the start to manage privacy, compliance, and integrity while minimizing risk tied to faulty data.  Start with pilot projects. Pilot projects allow you to test and iterate real-world impact before committing to large-scale rollouts. They offer valuable insights and mitigate risk.  Monitor and measure continuously. Ongoing performance tracking ensures AI remains aligned with evolving business goals. Continuous adjustments are key for maximizing long-term value.  source

“The AI-Driven Security Operations Platform for the Modern SOC E-Book“ Don’t Imagine the Future. Deploy It. Unleash ML intelligence on your SOC. Cybersecurity has a threat remediation problem. The proliferation of applications, workloads, microservices and users is quickly expanding the digital attack surface. It’s generating vast amounts of data faster than you can detect and protect. As such, the cybersecurity industry needs to continually innovate to stay ahead of evolving challenges. Cortex® XSIAM™ embraces an AI-driven architecture in profound ways. It’s transforming SecOps by leaning into AI in areas where machine learning can best augment teams. XSIAM is the realization of our vision to create the autonomous security platform of the future. It enables dramatically better security with near-real-time detection and response. It allows the SOC team to be proactive instead of reactive. And it frees analysts to focus on the critical issues, like unusual behavior and anomalies. Don’t get bogged down in outdated methods. Download this e-book for a detailed look under the hood of XSIAM. Offered Free by: Palo Alto Networks See All Resources from: Palo Alto Networks Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. source

A core problem with artificial intelligence is that it’s, well, artificial. Generative AI systems and large language models (LLMs) rely on statistical methods rather than intrinsic knowledge to predict text outcomes. As a result, they sometimes spin up lies, errors and hallucinations.  This lack of real-world knowledge has repercussions that extend across domains and industries. The problems can be particularly painful in areas such as finance, healthcare, law, and customer service. Bad results can lead to bad business decisions, irate customers, and wasted money.  As a result, organizations are turning to retrieval-augmented generation (RAG). According to a Deloitte report, upwards of 70% of enterprises are now deploying the framework to augment LLMs. “It is essential for realizing the full benefits of AI and managing costs,” says Jatin Dave, managing director of AI and data at Deloitte.  RAG’s appeal is that it supports faster and more reliable decision-making. It also dials up transparency and energy savings. As the competitive business landscape intensifies and AI becomes a tool that differentiates organizations, RAG is emerging as an important tool in the AI arsenal.   Says Scott Likens, US and Global Chief AI Engineering Officer at PwC: “RAG is revolutionizing AI by combining the precision of retrieval models with the creativity of generative models.”  Related:IT Pros Love, Fear, and Revere AI: The 2024 State of AI Report RAG Matters  What makes RAG so powerful is that it combines a trained generative AI system with real-time information, typically from a separate database. “This synergy enhances everything from customer support to content personalization, providing more accurate and context-aware interactions,” Likens explains.  RAG increases the odds that results are accurate and up to date by checking external sources before serving up a response to a query. It also introduces greater transparency to models by generating links that a human can check for accuracy. Then there’s the fact that RAG can trim the time required to obtain information, reduce compute overhead and conserve energy.  “RAG enables searches through a very large number of documents without the need to connect to the LLM during the search process,” Dave points out. “A RAG search is also faster than an LLM processing tokens. This leads to faster response times from the AI system.”  This makes RAG particularly valuable for handling diverse types of data from different sources, including product catalogs, technical images, call transcripts, policy documents, marketing data, and legal contracts. What’s more, the technology is evolving rapidly, Dave says. RAG is increasingly equipped to manage larger datasets and operate within complex cloud frameworks.   Related:Keynote Sneak Peek: Forrester Analyst Details Align by Design and AI Explainability For example, RAG can combine generalized medical or epidemiological data held in an LLM with specific patient information to deliver more accurate and targeted recommendations. It can connect a customer using a chatbot with an inventory system or third-party logistics and delivery data to provide an immediate update about a delayed shipment. RAG can also personalize marketing and product recommendations, based on past clicks or purchases.  The result is a higher level of personalization and contextualization. “RAG can tailor language model outputs to specific enterprise knowledge and enhance the LLMs core capabilities,” Likens says. Yet all of this doesn’t come without a string attached. “RAG adds complexity to knowledge management. It requires dealing with data lineage, multiple versions of the same source, and the spread of data across different business units and applications, “he adds.  Beyond the Chatbot  Designing an effective RAG framework can prove challenging. Likens says that on the technology side, several components are foundational. This includes vector databases, orchestration, a document processing tool, and a scaled data processing pipelines.”  Related:Sidney Madison Prescott Discusses GenAI’s Potential to Transform Enterprise Operations It’s also important to adopt tools that streamline RAG development and improve the accuracy of information, Likens says. These include hybrid retrieval solutions, experiment tracking and data annotation tooling. More advanced tools, such as LLMs, vector databases, data pipeline and compute workflow tools are typically available through hyperscalers and SaaS providers  “There is not a one-size-fits-all RAG pipeline, so there will always be a need to tailor the technology to the specific use case,” Likens says.  Equally important is mapping out a data and information pipeline. Chunking — breaking data into smaller strings that an LLM can process — is essential. There’s also a need to fine-tune the language model so that it can contextualize the RAG data, and it’s important to adapt a model’s weights during post-training processes.  “People typically focus on the LLM model, but it’s the database that often causes the most problems because, unlike humans, LLMs aren’t good with domain knowledge,” explains Ben Elliot, a research vice president at Gartner. “A person reads something and knows it makes sense without understanding every detail.”  Elliott says that a focus on metadata and keeping humans in the loop is critical. Typically, this involves tasks like rank ordering and grounding that anchor a system in the real world — and increase the odds that AI outputs are meaningful and contextually relevant. Although there’s no way to hit 100% accuracy with RAG, the right mix of technology and processes — including using footnoting so that humans can review output — boosts the odds that an LLM will deliver value.  Designs on Data  There’s no single way to approach RAG. It’s important to experiment because a system might not initially generate the right information or response for an appropriate reason, Likens says. It’s also wise to pay close attention to data biases and ethical considerations, including data privacy. Unstructured data magnifies the risks. “It may contain personally identifiable information (PII) or other sensitive information,” he notes.  Organizations that get the equation right take LLMs to a more functional and viable level. They’re able to achieve more with fewer resources. This translates into a more agile and flexible Gen AI framework with less fine tuning. “RAG equals the playing field between ultra-large language models that exceed 100 billion parameter and more compact models of 8

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The concept of open banking, the ability for customers to share their financial information easily with third parties, is gaining momentum in the United States though in a piecemeal way. The Consumer Financial Protection Bureau recently finalized rules for financial institutions to offer open banking securely. It is one of the latest steps to further define how banks, credit card issuers, and other financial institutions should proceed forward in this space. Open banking already has footing in Europe. Meanwhile countries such as Canada, Japan, and Singapore have yet to formally adopt it, though their policymakers are exploring open banking frameworks. Though there is no single cohesive regulatory policy in the US yet, securing financial information will be paramount as open banking is made available. What is the balance of making financial information available to authorized parties versus keeping financial data secure? For this episode of DOS Won’t Hunt, Ben Shorten (upper left in video), Accenture’s finance, risk and compliance lead for banking and capital markets in North America; Adam Preis (lower right), director of product and solution marketing with Ping Identity; and Fernando Luege (upper right), CTO with Fresh Consulting, came together to discuss security hurdles and the way ahead for open banking. Related:2024 Cyber Resilience Strategy Report: CISOs Battle Attacks, Disasters, AI … and Dust Listen to the full podcast here. source

Six Case Studies and the Journey Toward More Efficient and Secure Operations source

“Talking About a Revolution: Making the Unsolvable Solvable in the SOC Infographic“ A Look into the Past in Order to Move Forward with a Machine-Led, Humna-Empowered Security Platform. Elevate your SOC with Automation and AI Capabilities Designed for the Modern Threat Landscape. In the last few years, the needs of the ­ security operations center (SOC) have changed, but the designs of the SIEM and SOC have not. The security information and event management (SIEM) category has served security operations for years with significant manual overhead and slow incremental improvement in security outcomes. Most other key pieces of the security architecture have been modernized: The endpoint moved from antivirus (AV) to endpoint detection and response (EDR) to extended detection and response (XDR); the network moved from a “hard shell” perimeter to Zero Trust and SASE; runtime moved from the data center to the cloud. In contrast, the SOC still operates on a SIEM model designed 20 years ago.  Explore the Future of Cybersecurity with Cortex XSIAM® – Palo Alto Networks’ AI-Driven Security Operations Platform. Discover how this innovative approach leverages AI to enhance, not replace, your security teams.Dive into our informative infographic to learn more. Offered Free by: Palo Alto Networks See All Resources from: Palo Alto Networks Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. Thank you This download should complete shortly. If the resource doesn’t automatically download, please, click here. source

Cyberattacks in the first half of 2024 have been relentless, with organizations facing a surge in ransomware and data breaches aimed at theft and extortion. Unlike previous years, 2024 has seen major disruptions across industries, with consumers feeling the burn.   Unless you’ve been living under a rock, you already know that today’s ransomware operators are highly sophisticated and target businesses of all sizes across different industries. You’ve likely already deployed technology aimed at protecting against and recovering from a ransomware attack.   However, even with these technologies in place, many organizations find themselves unprepared when an actual attack happens.   Wargaming, a strategic military tool, has found its place in the world of cybersecurity through tabletop exercises designed to simulate these high stakes cyberattacks, such as ransomware. Cyber wargames equip corporate leaders with the skills needed to make swift, informed decisions in the critical first 24-48 hours of a crisis. Beyond backups, these exercises stress-test incident response plans, offering an essential, hands-on approach to disaster recovery.  Here’s what you need to know and how to approach.  What Is a Tabletop Exercise and Why Does It Matter?  A ransomware tabletop exercise is a simulation of a ransomware attack aimed at identifying vulnerabilities in your ransomware protection and recovery plan. Conducting a tabletop exercise is one of the best ways to increase your organization’s cyber resilience and prepare for recovery scenarios you have not yet encountered in the wild.   Ransomware tabletop exercises have other benefits, too. For example, a tabletop exercise could identify areas where you are out of compliance with security frameworks and/or demonstrate to regulators that you have taken steps to address these issues. Exercises can also help shape employee training efforts and technology investments.   There’s no “right” way to conduct a tabletop exercise. However, many exercises include some or all of the following:  A realistic scenario. All tabletop exercises should start with a realistic scenario, designed to challenge both technical and non-technical aspects of the organization’s incident response plan.  Key stakeholders. Key personnel from IT, cybersecurity, legal, communications, and executive teams should be involved to ensure all critical functions are covered.  Well-defined responsibilities. Stakeholders should be assigned a specific role that mirrors their real-world responsibilities during an actual ransomware incident (e.g., IT, executives, public relations).  Ransomware response testing. Technical and non-technical response activities should be tested. This might include IT activities like detection, containment, eradication, and disaster recovery operations. Internal and external communications should be tested as well. We’ll look at testing in more detail below.  A post-incident report. A review of the gaps, successes, and areas for improvement in the organization’s response strategy is critical. This review should be properly documented, both for future reference and to satisfy any regulatory or compliance requirements.  Ransomware Response Testing Food for Thought  Obviously, all aspects of your security stack should be considered in your IT testing. Preventing an attack before it happens is the goal, so testing should be designed to identify gaps in access controls, vulnerability management, employee security awareness training, and more.  Since attacks have the potential to cause prolonged IT downtime, a tabletop exercise should also reveal how long it could take to restore normal business operations following an attack. The exercise should account for the wide variety of restore scenarios IT might face (e.g., restoring a few desktops vs. a server hosting numerous virtual machines) and the recovery time associated with each.  Legal, HR, PR, and executive teams may have important responsibilities during and immediately following a ransomware attack. For example, do customers and/or vendors need to be notified? What about law enforcement? Who is responsible for these communications? Who is responsible for filing a cyber insurance claim? What specifically is required to file a claim?   Tabletop exercises require a good deal of coordination and can be time-consuming. However, they are highly effective and should be considered an essential piece for your security and disaster recovery efforts.  Conclusion  Ransomware tabletop exercises are invaluable for organizations looking to strengthen their defenses against one of the most serious cyber threats today. These exercises help businesses identify vulnerabilities, improve response strategies, and build long-term cyber resilience.   By involving leadership, focusing on realistic scenarios, and emphasizing secure recovery methods, ransomware tabletop exercises offer a practical and insightful way to ensure that your organization is prepared to handle a real ransomware attack.   source

America is speeding towards an unusually high-stakes election. The winner in the clash of polar opposite views will oversee drastically different government policies. Here’s a look at what either a Republican or Democratic outcome is likely to mean in terms of tech directives affecting networks. First, it’s important to acknowledge that tensions are likely to spill over after the election and potentially change the policies touted by either candidate in pre-election pledges. Public pressure, both for and against any given policy, would likely have some impact on the final version.   Meanwhile, the unease across the nation is palpable and nearly universal, while fear and anger creep insidiously into several segments of the population. Those tensions are unlikely to dissipate entirely after the election. Indeed, they are expected to become further entrenched, no matter which candidate wins. Come election day or sometime thereafter, one or the other of the top two candidates will be declared the victor. There’s been lots of ink, pixels, and TV and video streams dedicated to what either outcome means in terms of the most obvious social issues and abrasive policy contentions. But few have dug deep to see beyond these to other policy differences that can also impact America in important ways. “Telecom infrastructure remains a crucial yet under-discussed policy issue, and Trump and Harris are proposing distinct approaches,” says Richard Brandon, Vice President of Strategy at RtBrick, a provider of multi-service edge routing software for telcos. Let’s take a look at how networks are likely to be impacted by each candidate should they be the final winner in this election. Overall Candidate Tech Positions “In sum, I think we can expect divergent approaches to regulations, broadband funding, blockchain innovation, and digital assets based on recent policies and public statements made by each candidate,” says Dr. Tonya M. Evans, ESQ., professor of Law at Penn State Dickinson Law and Digital Money Expert at Penn State Dickinson Law. As to the specific points made or eluded to in the candidates’ public statements and other research, Deltek’s key technology findings to apprise government contractors of the two candidates’ positions are as follows: Kamala Harris: Prioritizes winning the global competition in space, AI, quantum computing, and emerging technologies. Advocates continued export controls to prevent Chinese companies from acquiring advanced semiconductor and computing technologies. Supports enforcement of the Biden AI executive order, requiring cross-agency collaboration, procurement decisions tied to risk and performance management, and investment in AI data centers. Promotes investment in Regional Technology and Innovation Hubs and the National Artificial Intelligence Research Resource to integrate AI and machine learning into healthcare technology. A strong proponent of telehealth and further cloud computing modernization. Donald Trump: Plans to invest in AI to compete with China while scaling back elements of the Biden AI executive order that he views as restrictive. Supports continued export controls similar to Harris but would pull back on regulations concerning data bias. Advocates expanding U.S. federal cyber capabilities, including offensive cybersecurity operations and workforce development. Likely to continue supporting cybersecurity investments while revisiting antitrust actions targeting large U.S. tech companies. What these positions will lead to in the way of policy actions remains to be seen. Even so, a recent EY report finds that the outcome of the US election will most impact the following areas of regulation: cybersecurity/data protections, artificial intelligence and machine learning, and user data and content oversight. The report found that 74% of tech leaders believe the results of the upcoming US election will have a major impact on the US tech sector’s ability to stay ahead of global competitors. Overall Candidate Positions on Broadband, Networks “All the presidential candidates have come up with encouraging policies regarding broadband access, but they can be implemented in different ways,” says Chris Dukich, the owner of a SaaS company that provides digital signage to screens called Display Now. As Dukich alludes to, the devil is in the details, so here are a few of the key takeaways for each of the leading candidates. Kamala Harris Harris’s platform “emphasizes her Opportunity Economy approach, which seeks to address the digital divide and expand broadband infrastructure as a means of economic empowerment for marginalized communities. This mirrors previous Democratic-led initiatives like the Infrastructure Investment and Jobs Act, which allocated $65 billion for broadband expansion under the Biden administration,” says Dr. Evans. And how will that likely play out in terms of government funding? “The future of this approach will probably be more government grants and public-private partnerships which will enable moving towards the direction of having high-speed internet access to all the communities within the country,” Dukich said. Other industry players and watchers tend to agree. “A Harris administration would continue investing in cost-efficient network expansion, extending Biden’s initiative of increasing broadband access and bringing digital equity to rural areas through programs like BEAD. Billions of dollars are already earmarked and distributed to states to support the development of next-generation telecom networks,” says Brandon. As to the impact on regulations, Dukich expects Harris to continue Biden’s “push for more restraints over tech corporations with a strong emphasis on data privacy, rivalry, and consumer welfare.” Merrill agrees and points to the FTC efforts currently underway as continuing under Harris. “Future regulations in a Harris administration will be the same in support of net neutrality and increase oversight against ISPs,” Merrill adds. But not everyone agrees with the assessment that Harris will follow in Biden’s footsteps. “Harris has signaled a shift from the strict regulatory stance of the Biden administration toward a more innovation-friendly framework. However, her policies are likely to continue emphasizing net neutrality and consumer protections, requiring network managers and architects to adapt to stricter compliance and reporting standards, which could increase opportunities for firms involved in public contracts and broadband development,” says Evans. Cloud infrastructure and distributed networking will also see significant policy impact. Harris has “indicated a focus on cloud security and resilience, integrating support for cloud computing technologies within her broader economic agenda. By

As organizations strive to meet their goals, integrating digital technology into analytics, artificial intelligence and machine learning, and cloud migration has become essential. The end game is to transform businesses’ operations, share information, and deliver customer value.   While digital transformation promises increased efficiency, productivity, and reduced costs, its success fundamentally depends on people. Neglecting the human aspect of transformation is a recipe for failure from the outset.  A BCG study on digital transformation found that 90% of companies focusing on culture during their transformation journey experienced solid financial performance, compared to 17% that didn’t. Despite projections that global spending on digital transformation will reach $3.4 trillion by 2026, there’s a high failure rate — around 70%, according to McKinsey. Much of this failure can be attributed to organizational culture shock, where employees react negatively to sudden changes.  In 1955, Sverre Lysgaard developed a model describing how individuals adapt to a new culture, beginning with a honeymoon phase, followed by culture shock, then adjustment, and finally adaptation. This process mirrors what happens to employees during digital transformation. Companies must invest in addressing culture shock to ensure the success of their digital initiatives.  Related:2024 InformationWeek US IT Salary Report: Profits, Layoffs, and the Continued Rise of AI We recently embarked on a significant digital transformation with the introduction of our solution enablement platform. This platform unites various data and analytic assets built for risk management, marketing, and fraud prevention into one unified environment. This transformation enhances our ability to provide a more accurate picture of consumers across various use cases. From my experience rolling out this platform, I’ve identified five key strategies companies can use to navigate digital transformation successfully and avoid employee culture shock.  1. Foundation setting  It’s essential to communicate your vision and strategy. A well-defined roadmap that outlines the steps to achieve transformation goals is crucial. McKinsey reports that organizations with a clear change management strategy are six times more likely to succeed. Personalizing the vision for each employee ensures they believe in the transformation and actively participate in it.  2. Employee training and education  Training is vital for engaging employees and advancing their careers. Yet only 56% of organizations report expanding training on digital tools and new processes, according to PwC. At our company, we incentivize employees to complete training programs that enhance their skills, which leads to a more engaged workforce. Employees are encouraged to think about the skills they want to develop for their future, ensuring that our digital transformation also benefits their personal career growth.  Related:Curtail Cloud Spend With These Strategies A significant focus of our training has been on our solution enablement platform. We’ve curated specific training for employees, including certifications, across the organization. This approach encourages long-term career development while promoting a deeper understanding of new technologies.  3. Be transparent and share progress  Frequent updates on successes and challenges foster trust and authenticity. Organizations should openly communicate any changes to the roadmap or strategy. At my company, we hold regular meetings where we showcase both the progress and the hurdles we face during our technology evolution. Integration is a crucial theme; we highlight how different teams benefit from the work.  4. Embrace learning and failures  Encouraging a culture that views failure as a learning opportunity fosters innovation. Open lines of communication allow employees to share issues and contribute to continuous improvement. This helps employees feel secure enough to try new things and become active participants in the transformation.  Related:Forrester Speaker Sneak Peek: Analyst Jayesh Chaurasia to Talk AI Data Readiness At our company, we conduct regular retrospectives of our planned releases. When things don’t go as expected, we focus on what can be learned, not the failure itself. This feedback loop is shared transparently, providing valuable insights for the entire team and fostering a culture of continuous improvement.  5. Find champions  Too often, change management is reduced to sending out emails or presentations. While these methods are helpful, true transformation requires more personal involvement. Identifying champions within the organization can significantly boost morale and support. These champions don’t need to be formal leaders but are individuals who believe in transformation and help guide their peers through the process.  Recently, our enterprise capabilities marketing and investor relations teams met with our engineers to better understand the benefits of our solution enablement platform. They became champions of the transformation and shared their enthusiasm with key stakeholders, which in turn had a positive impact on investors.  Conclusion  Digital transformation offers tremendous potential, but it comes with inherent challenges. To succeed, organizations must place people at the heart of the process through training, transparent communication, and fostering a culture that embraces learning from failures. Companies can mitigate culture shock and achieve their transformation goals by following these five strategies.  source