#GenAI operating systems

[procurement-insights]

"I Had To Jon, There Was No Money Or Management"

Are we entering the era of ProcureTech Apps?

Is another Solution Map Star falling? The following is a message I wrote to someone in our industry for whom I have great respect. I will not provide specific details because of respect for this individual and the solution provider they worked with. But I will say that this was a needless occurrence brought on by the fear of getting left behind in the GenAI “Hype Cycle.” A cycle in which…

[kalikalahansa]

On generative AI

I've had 2 asks about this lately so I feel like it's time to post a clarifying statement.

I will not provide assistance related to using "generative artificial intelligence" ("genAI") [1] applications such as ChatGPT. This is because, for ethical and functional reasons, I am opposed to genAI.

I am opposed to genAI because its operators steal the work of people who create, including me. This complaint is usually associated with text-to-image (T2I) models, like Midjourney or Stable Diffusion, which generate "AI art". However, large language models (LLMs) do the same thing, just with text. ChatGPT was trained on a large research dataset known as the Common Crawl (Brown et al, 2020). For an unknown period ending at latest 29 August 2023, Tumblr did not discourage Common Crawl crawlers from scraping the website (Changes on Tumblr, 2023). Since I started writing on this blog circa 2014–2015 and have continued fairly consistently in the interim, that means the Common Crawl incorporates a significant quantity of my work. If it were being used for academic research, I wouldn't mind. If it were being used by another actual human being, I wouldn't mind, and if they cited me, I definitely wouldn't mind. But it's being ground into mush and extruded without credit by large corporations run by people like Sam Altman (see Hoskins, 2025) and Elon Musk (see Ingram, 2024) and the guy who ruined Google (Zitron, 2024), so I mind a great deal.

I am also opposed to genAI because of its excessive energy consumption and the lengths to which its operators go to ensure that energy is supplied. Individual cases include the off-grid power station which is currently poisoning Black people in Memphis, Tennessee (Kerr, 2025), so that Twitter's genAI application Grok can rant incoherently about "white genocide" (Steedman, 2025). More generally, as someone who would prefer to avoid getting killed for my food and water in a few decades' time, I am unpleasantly reminded of the study that found that bitcoin mining emissions alone could make runaway climate change impossible to prevent (Mora et al, 2018). GenAI is rapidly scaling up to produce similar amounts of emissions, with the same consequences, for the same reasons (Luccioni, 2024). [2]

It is theoretically possible to create genAI which doesn't steal and which doesn't destroy the planet. Nobody's going to do it, and if they do do it, no significant share of the userbase will migrate to it in the foreseeable future — same story as, again, bitcoin — but it's theoretically possible. However, I also advise against genAI for any application which requires facts, because it can't intentionally tell the truth. It can't intentionally do anything; it is a system for using a sophisticated algorithm to assemble words in plausibly coherent ways. Try asking it about the lore of a media property you're really into and see how long it takes to start spouting absolute crap. It also can't take correction; it literally cannot, it is unable — the way the neural network is trained means that simply inserting a factual correction, even with administrator access, is impossible even in principle.

GenAI can never "ascend" to intelligence; it's not a petri dish in which an artificial mind can grow; it doesn't contain any more of the stuff of consciousness than a spreadsheet. The fact that it seems like it really must know what it's saying means nothing. To its contemporaries, ELIZA seemed like that too (Weizenbaum, 1966).

The stuff which is my focus on this blog — untraining and more broadly AB/DL in general — is not inherently dangerous or sensitive, but it overlaps with stuff which, despite being possible to access and use in a safe manner, has the potential for great danger. This is heightened quite a bit given genAI's weaknesses around the truth. If you ask ChatGPT whether it's safe to down a whole bottle of castor oil, as long as you use the right words, even unintentionally, it will happily tell you to go ahead. If I endorse or recommend genAI applications for this kind of stuff, or assist with their use, I am encouraging my readers toward something I know to be unsafe. I will not be doing that. Future asks on the topic will go unanswered.

Notes

I use quote marks here because as far as I am concerned, both "generative artificial intelligence" and "genAI" are misleading labels adopted for branding purposes; in short, lies. GenAI programs aren't artificial intelligences because they don't think, and because they don't emulate thinking or incorporate human thinking; they're just a program for associating words in a mathematically sophisticated but deterministic way. "GenAI" is also a lie because it's intended to associate generative AI applications with artificial general intelligence (AGI), i.e., artificial beings that actually think, or pretend to as well as a human does. However, there is no alternative term at the moment, and I understand I look weird if I use quote marks throughout the piece, so I dispense with them after this point.

As a mid-to-low-income PC user I am also pissed off that GPUs are going to get worse and more expensive again, but that kind of pales in comparison to everything else.

References

Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., ... & Amodei, D. (2020, July 22). Language models are few-shot learners [version 4]. arXiv. doi: 10.48660/arXiv.2005.14165. Retrieved 25 May 2025.

Changes on Tumblr (2023, August 29). Tuesday, August 29th, 2023 [Text post]. Tumblr. Retrieved 25 May 2025.

Hoskins, P. (2025, January 8). ChatGPT creator denies sister's childhood rape claim. BBC News. Retrieved 25 May 2025.

Ingram, D. (2024, June 13). Elon Musk and SpaceX sued by former employees alleging sexual harassment and retaliation. NBC News. Retrieved 25 May 2025.

Kerr, D. (2025, April 25). Elon Musk's xAI accused of pollution over Memphis supercomputer. The Guardian. Retrieved 25 May 2025.

Luccioni, S. (2024, December 18). Generative AI and climate change are on a collision course. Wired. Retrieved 25 May 2025.

Mora, C., Rollins, R.L., Taladay, K., Kantar, M.B., Chock, M.K., ... & Franklin, E.C. (2018, October 29). Bitcoin emissions alone could push global warming above 2°C. Nature Climate Change, 8, 931–933. doi: 10.1038/s41558-018-0321-8. Retrieved 25 May 2025.

Steedman, E. (2025, May 25). For hours, chatbot Grok wanted to talk about a 'white genocide'. It gave a window into the pitfalls of AI. ABC News (Australian Broadcasting Corporation). Retrieved 25 May 2025.

Weizenbaum, J. (1966, January). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. doi: 10.1145/365153.365168. Retrieved 25 May 2025.

Zitron, E. (2024, April 23). The man who killed Google Search. Where's Your Ed At. Retrieved 25 May 2025.

[jcmarchi]

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

New Post has been published on https://thedigitalinsider.com/deepfake-misuse-deepfake-detection-before-its-too-late-cybertalk/

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

Micki Boland is a global cyber security warrior and evangelist with Check Point’s Office of the CTO. Micki has over 20 years in ICT, cyber security, emerging technology, and innovation. Micki’s focus is helping customers, system integrators, and service providers reduce risk through the adoption of emerging cyber security technologies. Micki is an ISC2 CISSP and holds a Master of Science in Technology Commercialization from the University of Texas at Austin, and an MBA with a global security concentration from East Carolina University.

In this dynamic and insightful interview, Check Point expert Micki Boland discusses how deepfakes are evolving, why that matters for organizations, and how organizations can take action to protect themselves. Discover on-point analyses that could reshape your decisions, improving cyber security and business outcomes. Don’t miss this.

Can you explain how deepfake technology works? 

Deepfakes involve simulated video, audio, and images to be delivered as content via online news, mobile applications, and through social media platforms. Deepfake videos are created with Generative Adversarial Networks (GAN), a type of Artificial Neural Network that uses Deep Learning to create synthetic content.

GANs sound cool, but technical. Could you break down how they operate?

GAN are a class of machine learning systems that have two neural network models; a generator and discriminator which game each other. Training data in the form of video, still images, and audio is fed to the generator, which then seeks to recreate it. The discriminator then tries to discern the training data from the recreated data produced by the generator.

The two artificial intelligence engines repeatedly game each other, getting iteratively better. The result is convincing, high quality synthetic video, images, or audio. A good example of GAN at work is NVIDIA GAN. Navigate to the website https://thispersondoesnotexist.com/ and you will see a composite image of a human face that was created by the NVIDIA GAN using faces on the internet. Refreshing the internet browser yields a new synthetic image of a human that does not exist.

What are some notable examples of deepfake tech’s misuse?

Most people are not even aware of deepfake technologies, although these have now been infamously utilized to conduct major financial fraud. Politicians have also used the technology against their political adversaries. Early in the war between Russia and Ukraine, Russia created and disseminated a deepfake video of Ukrainian President Volodymyr Zelenskyy advising Ukrainian soldiers to “lay down their arms” and surrender to Russia.

How was the crisis involving the Zelenskyy deepfake video managed?

The deepfake quality was poor and it was immediately identified as a deepfake video attributable to Russia. However, the technology is becoming so convincing and so real that soon it will be impossible for the regular human being to discern GenAI at work. And detection technologies, while have a tremendous amount of funding and support by big technology corporations, are lagging way behind.

What are some lesser-known uses of deepfake technology and what risks do they pose to organizations, if any?

Hollywood is using deepfake technologies in motion picture creation to recreate actor personas. One such example is Bruce Willis, who sold his persona to be used in movies without his acting due to his debilitating health issues. Voicefake technology (another type of deepfake) enabled an autistic college valedictorian to address her class at her graduation.

Yet, deepfakes pose a significant threat. Deepfakes are used to lure people to “click bait” for launching malware (bots, ransomware, malware), and to conduct financial fraud through CEO and CFO impersonation. More recently, deepfakes have been used by nation-state adversaries to infiltrate organizations via impersonation or fake jobs interviews over Zoom.

How are law enforcement agencies addressing the challenges posed by deepfake technology?

Europol has really been a leader in identifying GenAI and deepfake as a major issue. Europol supports the global law enforcement community in the Europol Innovation Lab, which aims to develop innovative solutions for EU Member States’ operational work. Already in Europe, there are laws against deepfake usage for non-consensual pornography and cyber criminal gangs’ use of deepfakes in financial fraud.

What should organizations consider when adopting Generative AI technologies, as these technologies have such incredible power and potential?

Every organization is seeking to adopt GenAI to help improve customer satisfaction, deliver new and innovative services, reduce administrative overhead and costs, scale rapidly, do more with less and do it more efficiently. In consideration of adopting GenAI, organizations should first understand the risks, rewards, and tradeoffs associated with adopting this technology. Additionally, organizations must be concerned with privacy and data protection, as well as potential copyright challenges.

What role do frameworks and guidelines, such as those from NIST and OWASP, play in the responsible adoption of AI technologies?

On January 26th, 2023, NIST released its forty-two page Artificial Intelligence Risk Management Framework (AI RMF 1.0) and AI Risk Management Playbook (NIST 2023). For any organization, this is a good place to start.

The primary goal of the NIST AI Risk Management Framework is to help organizations create AI-focused risk management programs, leading to the responsible development and adoption of AI platforms and systems.

The NIST AI Risk Management Framework will help any organization align organizational goals for and use cases for AI. Most importantly, this risk management framework is human centered. It includes social responsibility information, sustainability information and helps organizations closely focus on the potential or unintended consequences and impact of AI use.

Another immense help for organizations that wish to further understand risk associated with GenAI Large Language Model adoption is the OWASP Top 10 LLM Risks list. OWASP released version 1.1 on October 16th, 2023. Through this list, organizations can better understand risks such as inject and data poisoning. These risks are especially critical to know about when bringing an LLM in house.

As organizations adopt GenAI, they need a solid framework through which to assess, monitor, and identify GenAI-centric attacks. MITRE has recently introduced ATLAS, a robust framework developed specifically for artificial intelligence and aligned to the MITRE ATT&CK framework.

For more of Check Point expert Micki Boland’s insights into deepfakes, please see CyberTalk.org’s past coverage. Lastly, to receive cyber security thought leadership articles, groundbreaking research and emerging threat analyses each week, subscribe to the CyberTalk.org newsletter.

[insightcatalyster]

Applications of GenAI in Healthcare

Generative AI in Medical Imaging revolutionizes healthcare practices by streamlining various processes. In diagnostics, Generative AI enhances the accuracy of disease identification through detailed image analysis, leading to quicker treatment decisions and improved patient outcomes.

Moreover, this technology facilitates personalized treatment plans tailored to individual patients based on comprehensive imaging data. Beyond diagnostics, Generative AI assists in drug discovery by simulating molecular structures, accelerating research efforts to develop new treatments. Its applications extend to improving operational efficiency in healthcare settings, optimizing resource allocation, and workflow management.

By integrating Generative AI into healthcare systems, providers can enhance patient care, drive innovation, and pave the way for transformative advancements in medical practices.

For more in-depth insights on the transformative applications of Generative AI in healthcare, explore:

[getoncrm-salesforce-insights]

 Smart Workflows in Modern Healthcare Using Salesforce

In the evolving landscape of patient care, healthcare providers are under growing pressure to improve efficiency, maintain compliance, and offer personalized care at scale. One of the most effective ways to meet these demands is by implementing smart healthcare workflows using Salesforce Health Cloud. This approach connects clinical teams, automates operational processes, and empowers healthcare institutions to deliver more reliable, real-time care.

As hospitals face challenges related to high patient volumes and staffing shortages, smart automation becomes critical. With the help of platforms like Salesforce for healthcare automation, providers are reducing manual errors, accelerating workflows, and improving care coordination. These advancements not only support operational agility but also improve patient outcomes.

Automating the Care Journey with Salesforce Health Cloud

The foundation of any efficient healthcare system lies in connected, intelligent workflows. By using Salesforce Health Cloud services, hospitals can create an integrated view of each patient—from intake to discharge. This centralization allows for seamless communication, faster decisions, and improved care planning.

With Salesforce healthcare CRM systems, providers gain access to:

Automated patient data intake and registration workflows

AI-powered healthcare workflows for triage and follow-ups

Claims and billing automation in healthcare systems

Real-time clinical insights and alerts

Compliance-ready health data management using Salesforce

These tools help eliminate manual handoffs and allow care teams to work more efficiently across departments.

Real Results Through GetOnCRM Implementation

At GetOnCRM, we specialize in implementing Salesforce Health Cloud for hospitals and clinics. One of our recent projects involved working with a multi-location healthcare network to digitize its workflows and automate its patient engagement strategy.

Our team of Salesforce healthcare consultants deployed solutions like Salesforce Sales Cloud for healthcare lead management and Marketing Cloud for personalized engagement. These smart workflows helped the client achieve a 30% increase in appointment confirmations and a 25% boost in patient response times. Additionally, administrative tasks like intake form processing and appointment reminders were completely automated, reducing manual workload by over 40%.

Salesforce, GenAI, and the Rise of Intelligent Healthcare

The use of GenAI in healthcare automation is another area where hospitals are seeing transformational gains. With the integration of AI-powered tools in Salesforce Health Cloud, care teams can access features like

AI scribes for automatic clinical note generation

Conversational AI for appointment scheduling and patient queries

Predictive analytics for risk scoring and decision support

IoT-based health monitoring integrated with Salesforce Health Cloud

These innovations represent a new era of smart workflow automation in healthcare departments, where decisions are data-driven and actions are instant.

Scaling Automation Across Healthcare Departments

Smart workflows are not limited to one department. With Salesforce workflow automation tools, every unit within a healthcare organization can benefit:

Clinical workflows become faster with AI-driven alerts and digital triage

Pharmacy departments automate inventory and e-prescription management

Administration and HR teams benefit from RPA in hospital operations

Chronic care management improves with behavior-based patient tracking

This department-wide transformation is essential for building truly connected healthcare ecosystems using Salesforce.

The Future Is Smart, Seamless, and Salesforce-Powered

As we move toward 2025 and beyond, smart healthcare workflow automation will define the future of care delivery. With tools like Salesforce Health Cloud for patient management, hospitals can achieve agility, compliance, and personalized care—all in one ecosystem.

At GetOnCRM, we help healthcare providers design, deploy, and scale Salesforce-based smart workflows that adapt to real-world demands. From Salesforce CRM for healthcare operations to AI-driven automation strategies, we offer end-to-end solutions that power the next generation of care.

[krutikabhosale]

Evolution of Agentic and Generative AI in 2025

Introduction

The year 2025 marks a pivotal moment in the evolution of artificial intelligence, with the Agentic AI course in Mumbai gaining traction as a key area of focus for AI practitioners. Agentic AI, which involves goal-driven software entities capable of planning, adapting, and acting autonomously, is transforming industries from logistics to healthcare. Meanwhile, the Generative AI course in Mumbai with placements continues to push boundaries in content creation and data analysis, leveraging large language models and generative adversarial networks. As AI practitioners, software architects, and technology decision-makers, understanding the latest strategies for deploying these technologies is crucial for staying ahead in the market. This article delves into the evolution of Agentic and Generative AI, explores the latest tools and deployment strategies, and discusses best practices for successful implementation and scaling, highlighting the importance of AI training in Mumbai.

Evolution of Agentic and Generative AI in Software

Agentic AI represents a paradigm shift in AI capabilities, moving from rule-based systems to goal-oriented ones that can adapt and evolve over time. This evolution is driven by advancements in machine learning and the increasing availability of high-quality, structured data. For those interested in the Agentic AI course in Mumbai, understanding these shifts is essential. Generative AI, on the other hand, has seen rapid progress in areas like natural language processing and image generation, thanks to large language models (LLMs) and generative adversarial networks (GANs). Courses like the Generative AI course in Mumbai with placements are helping professionals leverage these technologies effectively.

Agentic AI: From Reactive to Proactive Systems

Agentic AI systems are designed to be proactive rather than reactive. They can set goals, plan actions, and execute tasks autonomously, making them ideal for complex, dynamic environments. For instance, in logistics, autonomous AI can optimize routes and schedules in real-time, improving efficiency and reducing costs. As of 2025, 25% of GenAI adopters are piloting agentic AI, with this number expected to rise to 50% by 2027. This growth highlights the need for comprehensive AI training in Mumbai to support the development of such systems.

Generative AI: Revolutionizing Content Creation

Generative AI has transformed content creation by enabling the automated generation of high-quality text, images, and videos. This technology is being used in various applications, from customer service chatbots to product design. However, the challenge lies in ensuring that these models are reliable, secure, and compliant with ethical standards. Professionals enrolled in the Generative AI course in Mumbai with placements are well-positioned to address these challenges.

Latest Frameworks, Tools, and Deployment Strategies

LLM Orchestration: Large Language Models (LLMs) are at the heart of many Generative AI applications. Orchestration of these models involves integrating them into workflows that can handle complex tasks, such as content generation and data analysis. Tools like LLaMA and PaLM have shown significant promise in this area. Recent advancements include the integration of Explainable AI (XAI) to enhance model transparency and trustworthiness. For those interested in the Agentic AI course in Mumbai, understanding the role of LLMs in AI is crucial.

Autonomous Agents: Autonomous agents are key components of Agentic AI systems. They operate across different systems and decision flows without manual intervention, requiring robust data governance and cross-system orchestration. Syncari's Agentic MDM is an example of a unified data foundation that supports such operations. This highlights the importance of comprehensive AI training in Mumbai for managing complex AI systems.

MLOps for Generative Models: MLOps (Machine Learning Operations) is crucial for managing the lifecycle of AI models, ensuring they are scalable, reliable, and maintainable. For Generative AI, MLOps involves monitoring model performance, updating training data, and ensuring compliance with ethical standards. Courses like the Generative AI course in Mumbai with placements emphasize these practices.

Advanced Tactics for Scalable, Reliable AI Systems

Unified Data Foundation

A unified data foundation is essential for Agentic AI, providing structured, real-time data that supports autonomous decision-making. This involves integrating data from various sources and ensuring it is accurate, reusable, and auditable. Implementing data governance policies is critical to prevent issues like hallucinations and inefficiencies. For professionals enrolled in the Agentic AI course in Mumbai, understanding data governance is vital.

Policy-Based Governance

Policy-based governance ensures that AI systems operate within defined boundaries, adhering to ethical and regulatory standards. This includes setting clear goals for AI agents and monitoring their actions to prevent unintended consequences. AI training in Mumbai programs often focus on these governance aspects.

Cross-System Orchestration

Cross-system orchestration allows AI agents to interact seamlessly across different platforms and systems. This is critical for achieving end-to-end automation and maximizing efficiency. For those pursuing the Generative AI course in Mumbai with placements, mastering cross-system orchestration is essential.

Ethical Considerations and Challenges

The deployment of AI systems raises several ethical challenges, including bias in AI models, privacy concerns, and regulatory compliance. Ensuring transparency through Explainable AI (XAI) and implementing robust data privacy measures are essential steps in addressing these challenges. Additionally, AI systems must be designed with ethical considerations in mind, such as fairness and accountability. AI training in Mumbai should emphasize these ethical dimensions.

The Role of Software Engineering Best Practices

Software engineering best practices are vital for ensuring the reliability, security, and compliance of AI systems. This includes:

Modular Design: Breaking down complex systems into modular components facilitates easier maintenance and updates.

Continuous Integration/Continuous Deployment (CI/CD): Automating testing and deployment processes ensures that AI systems are scalable and reliable.

Security by Design: Incorporating security measures from the outset helps protect against potential vulnerabilities. Courses like the Agentic AI course in Mumbai often cover these practices.

Cross-Functional Collaboration for AI Success

Cross-functional collaboration between data scientists, engineers, and business stakeholders is essential for successful AI deployments. This collaboration ensures that AI systems are aligned with business goals and that technical challenges are addressed promptly. For those involved in the Generative AI course in Mumbai with placements, this collaboration is key to overcoming implementation hurdles.

Data Scientists

Data scientists play a crucial role in developing and training AI models. They must work closely with engineers to ensure that models are deployable and maintainable. AI training in Mumbai programs often emphasize this collaboration.

Engineers

Engineers are responsible for integrating AI models into existing systems and ensuring they operate reliably. Their collaboration with data scientists is key to overcoming technical hurdles.

Business Stakeholders

Business stakeholders provide critical insights into business needs and goals, helping to align AI deployments with strategic objectives. For those pursuing the Agentic AI course in Mumbai, understanding these business perspectives is vital.

Measuring Success: Analytics and Monitoring

Measuring the success of AI deployments involves tracking key performance indicators (KPIs) such as efficiency gains, cost savings, and customer satisfaction. Continuous monitoring and analytics help identify areas for improvement and ensure that AI systems remain aligned with business objectives. AI training in Mumbai should include strategies for monitoring AI performance.

Case Studies

Logistics Case Study

A logistics company recently implemented an Agentic AI system to optimize its delivery routes. The company faced challenges in managing a large fleet across multiple regions, with manual route planning being inefficient and prone to errors. By implementing a unified data foundation and cross-system orchestration, the company enabled AI agents to access and act on data from various sources. This led to significant improvements in delivery efficiency and customer satisfaction, with routes optimized in real-time, reducing fuel consumption and lowering emissions. For those interested in the Agentic AI course in Mumbai, this case study highlights the practical applications of Agentic AI.

Healthcare Case Study

In healthcare, Generative AI is being used to generate synthetic patient data for training AI models, improving model accuracy and reducing privacy concerns. This approach also helps in addressing data scarcity issues, particularly in rare disease research. Courses like the Generative AI course in Mumbai with placements often explore such applications.

Actionable Tips and Lessons Learned

Prioritize Data Governance: Ensure that your AI systems have access to high-quality, structured data. This is crucial for autonomous decision-making and avoiding potential pitfalls like hallucinations or inefficiencies. For those pursuing the Agentic AI course in Mumbai, prioritizing data governance is essential.

Foster Cross-Functional Collaboration: Encourage collaboration between data scientists, engineers, and business stakeholders to ensure that AI deployments align with business goals and address technical challenges effectively. AI training in Mumbai emphasizes this collaboration.

Monitor and Adapt: Continuously monitor AI system performance and adapt strategies as needed. This involves tracking KPIs and making adjustments to ensure that AI systems remain aligned with strategic objectives. For those enrolled in the Generative AI course in Mumbai with placements, this adaptability is crucial.

Conclusion

Mastering autonomous AI control in 2025 requires a deep understanding of Agentic AI, Generative AI, and the latest deployment strategies. By focusing on unified data foundations, policy-based governance, and cross-functional collaboration, organizations can unlock the full potential of these technologies. As AI continues to evolve, it's crucial to stay informed about the latest trends and best practices to remain competitive in the market. Whether you're an AI practitioner, software architect, or technology decision-maker, embracing emerging strategies and pursuing AI training in Mumbai will be key to driving innovation and success in the autonomous AI era. For those interested in specialized courses, the Agentic AI course in Mumbai and Generative AI course in Mumbai with placements are excellent options for advancing your career.

[quantuminnovationit]

Why Your Business Needs a GenAI Consultant in 2025

Artificial Intelligence is no longer a futuristic concept—it’s today’s competitive advantage. As businesses race to embrace AI, one particular role is gaining traction in boardrooms and tech departments alike: the GenAI consultant.

From creating content and automating workflows to improving customer experience and streamlining decision-making, Generative AI (GenAI) is transforming how organizations operate. But unlocking its full potential requires the expertise of a skilled GenAI consultant.

Who Is a GenAI Consultant?

A GenAI consultant is an AI expert who helps businesses implement, integrate, and optimize generative AI tools like ChatGPT, DALL·E, and Claude into their operations. Their job goes beyond just installing an AI chatbot—they assess your business goals, design tailored AI solutions, and ensure ethical and effective use of AI technologies.

Why Hire a GenAI Consultant?

1. Tailored AI Strategy

Every business is different. A GenAI consultant develops custom AI strategies aligned with your specific needs—whether that’s automating customer support, generating marketing content, or analyzing massive data sets.

2. Seamless Integration

Integrating AI with your existing systems can be complex. A GenAI consultant ensures smooth API integrations, scalable architecture, and minimal disruptions to your operations.

3. Cost & Time Efficiency

By automating repetitive tasks and speeding up content creation, a GenAI consultant helps you reduce operational costs while increasing productivity.

4. Staying Ahead of the Competition

AI is evolving rapidly. A GenAI consultant keeps you updated with the latest advancements and ensures your business stays ahead of the curve.

5. Risk Mitigation

With growing concerns around data privacy and AI misuse, working with a GenAI consultant ensures responsible, compliant, and ethical implementation of generative AI systems.

Key Areas a GenAI Consultant Can Help With

Marketing: Auto-generating blog posts, social media content, and ad copy

Customer Support: Implementing intelligent AI chatbots for 24/7 service

Sales: AI-driven lead scoring and personalized outreach

HR: Automating onboarding documents and training materials

Productivity: AI meeting summarization, email drafting, and internal tools

With a GenAI consultant, these solutions are not just theoretical—they become operational realities.

Choosing the Right GenAI Consultant

When selecting a GenAI consultant, look for:

Proven experience with LLMs and AI models (like OpenAI, Anthropic, Meta)

Strong understanding of your industry

Portfolio of successful AI projects

A focus on data security and compliance

Ability to offer long-term AI strategy and support

Final Thoughts

As businesses strive to scale smarter in 2025, the demand for personalized, intelligent automation is surging. But implementing AI without the right guidance can lead to wasted investment and missed opportunities.

That’s why hiring a professional GenAI consultant isn’t a luxury—it’s a strategic move. With the right consultant by your side, you can unlock the full value of generative AI and transform the way you work, sell, and grow.

Ready to supercharge your business with AI? Partner with an expert GenAI consultant and start building your intelligent future today.

[procurement-insights]

The GenAI Metaprise (Orchestration) and Operating System (Intake) Priority

How well would your PC work without an operating system?

EDITOR’S NOTE: The following is an insightful exchange I had with AOP’s Philip Ideson’s comment on my recent post, DPW Remote Dispatch: To Build Or Not To Build In-House. PHILIP IDESON COMMENT: Jon its a good question. I moderated a main stage panel with two leading CPOs where this discussion came up. The issue at hand is AI governance and risk – and so at least the CPOs I talked to are…

[centizen]

AI Investment Strategies: Deciding to Build or Buy Cloud-Based Generative AI Solutions

The decision between building a custom cloud-based generative AI (genAI) system and purchasing a prebuilt one is critical for businesses venturing into the AI realm. This choice can define not only the trajectory of your company’s AI capabilities but also its overall technological agility.

Building In-house: Complete customization meets complexity

Advantages:

Complete customization and control: Building your own genAI system from the ground up allows for total control over its features, enabling a precise fit for your organization’s unique needs.

Creativity and technical control: In-house development fosters creativity and complete control over the technical process, especially regarding compliance and specific functionalities.

Independence: A self-built system ensures independence from external vendors, avoiding risks associated with their changing policies or potential discontinuation.

Disadvantages:

Talent acquisition challenges: Finding the right expertise for building a genAI system is challenging and might require innovative recruitment strategies.

Complexity and expense: The development of a custom solution is fraught with complexities and high costs, which can lead to increased project timelines and budgets.

Ongoing support and maintenance: The responsibility for continuous updates, security, and maintenance lies entirely with your team, requiring significant ongoing investment.

Buying a prebuilt system: Efficiency and reliability

Advantages:

Rapid deployment and immediate value: Purchasing a genAI system facilitates quick implementation, providing immediate benefits and a faster route to market.

Risk and expertise transfer: Buying transfers the burden of expertise and associated risks to the vendor, often ensuring professional support and updates.

Cost-benefit efficiency: Off-the-shelf solutions often offer a more practical and cost-effective alternative, especially for businesses that do not require highly customized solutions.

Disadvantages:

Dependency and operational risk: Relying on a vendor’s platform can create risks, particularly if the platform changes direction or becomes unavailable.

Limited customization: Prebuilt solutions may not align perfectly with every unique business requirement, limiting customization capabilities.

Potential for poor decision-making: There’s a risk of making a poor decision if a custom solution would have been more appropriate for the business’s unique needs.

A strategic decision: Balancing needs and resources

The decision to build or buy should emerge from a thorough analysis of your business’s specific needs, resources, and strategic objectives. Consider the following:

Long-term value vs. Immediate needs: Weigh the long-term value of a custom-built solution against the immediate benefits and lower upfront costs of a prebuilt system.

Risk assessment: Assess the risks associated with both dependency on a vendor and the challenges of building and maintaining a system in-house.

Strategic alignment: Ensure that your choice aligns with your overall business strategy, considering factors like scalability, adaptability, and competitive advantage.

Choosing whether to build or buy a cloud-based generative AI system is a complex decision requiring a delicate balance of strategic planning, resource allocation, and risk management. By carefully considering these factors, businesses can make an informed decision that aligns with their long-term objectives and paves the way for successful AI integration. For tailored guidance, Centizen AI consulting services offer expert insights and customized solutions to help navigate this crucial decision effectively.

[techit-rp]

How GenAI Is Revolutionizing Investment Banking: What Aspiring Bankers Need to Know

The finance world is undergoing a massive shift. From spreadsheets to smart assistants, the tools of investment banking are evolving at lightning speed—and at the center of this transformation is Generative AI (GenAI). Whether it’s drafting pitchbooks, automating financial models, or analyzing deal trends, GenAI is redefining what it means to be a modern-day investment banker.

For aspiring professionals looking to learn investment banking in Hyderabad, there has never been a better time to understand how GenAI is impacting the industry—and how you can leverage it to build a future-ready career.

What is GenAI?

Generative AI refers to AI systems that can create original content, such as text, code, or even images, based on prompts. Tools like ChatGPT, Claude, and Google Gemini are already being used in industries like marketing, education, and tech—but finance is catching up fast.

In investment banking, GenAI can:

Draft company profiles and pitchbooks

Summarize financial statements and annual reports

Build financial models with automation

Conduct deal sourcing using market sentiment analysis

Generate insights from massive unstructured datasets

GenAI in Action: Real-World Use Cases in Investment Banking

1. Automated Pitchbook Creation

Creating pitchbooks is a labor-intensive task that junior analysts often spend hours on. With GenAI, banks are automating the drafting of slides, inserting deal comps, and summarizing company overviews—all within minutes.

2. Smarter Financial Modeling

AI tools can now assist in building three-statement models, DCF analyses, and even scenario forecasts by parsing raw data and integrating formulas. While human oversight is still critical, the speed and accuracy of these models are improving rapidly.

3. Faster Due Diligence

GenAI can digest thousands of pages of documents—10-Ks, investor presentations, earnings calls—and summarize the most relevant insights. This dramatically cuts down the time required for deal research and valuation.

4. M&A Opportunity Identification

By analyzing news articles, earnings calls, and market signals, AI can flag potential M&A opportunities or distressed assets—giving investment banks a data-driven edge in identifying leads.

Why GenAI Skills Are Essential for Investment Banking Careers

As automation becomes standard, investment banks are looking for talent that combines financial acumen with tech fluency. This doesn’t mean you need to be a coder—but understanding how AI tools function, and where to apply them, will make you far more competitive.

If you're planning to learn investment banking in Hyderabad, choose a course that includes:

GenAI tools for finance

Real-world modeling assignments using AI

Industry use cases involving AI-driven M&A

Exposure to tools like ChatGPT, Microsoft Copilot, and Tableau with LLMs

Hyderabad: A Rising Hub for Tech-Enabled Finance Talent

Known as India’s Cyber City, Hyderabad is rapidly emerging as a finance-tech powerhouse. Global banks like HSBC, Wells Fargo, and JPMorgan have large operations in the city, blending technology, operations, and analytics.

When you learn investment banking in Hyderabad, you're not just entering the finance world—you’re stepping into a talent pool where AI, data, and finance intersect. Many leading institutes in the city are already integrating AI modules in their investment banking programs, helping students stay ahead of industry expectations.

Learning how to integrate these tools into your workflow will not only save time but also elevate the quality of your work—especially as a junior analyst or associate.

Key Skills for the GenAI-Powered Banker

Here’s what aspiring bankers must master in this new era:

Financial Modeling + AI Assistance: Know the fundamentals, but learn how GenAI can speed up repetitive tasks.

Prompt Engineering: Learn how to give precise instructions to tools like ChatGPT to get actionable outputs.

Data Interpretation: Combine AI insights with critical thinking to make investment decisions.

Communication: Use AI-generated drafts but polish them with personal judgment and storytelling.

Final Thoughts: Don’t Compete With AI, Collaborate With It

GenAI won’t replace investment bankers—but those who understand how to use it will replace those who don’t. As the financial industry evolves, the combination of traditional IB knowledge and next-gen AI fluency will define the most sought-after professionals.

If you're planning to learn investment banking in Hyderabad, now’s the time to future-proof your career. Look for a course that goes beyond textbooks and Excel sheets—choose one that prepares you for the tech-enabled world of banking.

[educationalmafia]

Why Now Is the Time to Get Certified in Generative AI for Leaders

The AI revolution is no longer coming; it's already here. Leaders across industries are now expected to adapt swiftly to this transformative wave. That’s why the Certified Generative AI for Leaders program by GSDC is creating buzz. If you’re looking to lead with confidence and clarity in the age of intelligent systems, this certification might be your next big move.

So, why is the generative ai for leaders certification making headlines in boardrooms and LinkedIn feeds?

Let’s break it down.

What Is It?

The Certified Generative AI for Leaders course is a globally recognized program designed for decision-makers, C-suite executives, and strategic thinkers who want to harness AI responsibly and effectively. It offers both foundational understanding and real-world application strategies of generative AI tailored for leadership roles.

Why It’s Trending

Unlike tech-heavy AI courses meant for engineers, AI for leaders certification focuses on practical leadership use cases, how AI can improve decision-making, business modeling, innovation, and even team management. It helps leaders integrate AI ethically and strategically in operations.

Key Benefits of the Certification:

Understand the core of generative AI without getting lost in technical jargon

Learn how to leverage GenAI for innovation and competitive advantage

Master frameworks for ethical AI integration

Gain confidence in AI-related decision-making

Earn a respected generative ai for leaders certificate that sets you apart

Who Should Enroll?

This certification is ideal for:

Executives and department heads

Product managers and innovation leads

Strategy consultants

Entrepreneurs and startup founders

Whether you're trying to align your team with AI-driven goals or steering a digital transformation, enrolling in gen ai leadership courses will ensure you’re not left behind in the AI wave.

Final Thoughts

As AI transforms industries and job roles, leadership needs to evolve too. The certified generative ai for leaders program empowers professionals to guide organizations through this change, not just follow it.

#GenerativeAI #AILeadership #GSDCCertification #FutureOfLeadership #AIForLeaders #ExecutiveEducation #GenAI #DigitalTransformation #LeadershipDevelopment #CertifiedAILeader

For more details : https://www.gsdcouncil.org/certified-generative-ai-for-leaders   

Contact no :  +41 41444851189

[cntechinsights]

Top Business Concerns When Implementing AI Technologies

It won’t be wrong to say that AI has engulfed our lives for all good reasons. In fact, this revolutionary technology is impacting how we work, make decisions, and engage with the immediate environment. Sounds fascinating? Yes, it is. Because of the manifold advantages this ground-breaking technology offers, AI has come to be associated with convenience. What are these benefits? Increased productivity, better decision-making, enhanced customer experiences, improved efficiency, and more. 

New AI tools are being released frequently, and companies have all eyes on them. These systems are helping businesses to automate many of their laborious and time-consuming tasks so that organizational leaders and C-level executives can focus more on innovation. According to a study, GenAI (a subset of AI) will drastically change industries over the next five years, and it's expected to add between $2.6 and $4.4 trillion in value annually.

Despite the promising scenario regarding AI adoption in business functions, there are also a few bottlenecks that organizations need to address. More often, these challenges arise during AI implementation. Whether you own a startup or are a CTO of a large organization, the problems remain the same, more or less.

Go through this blog to understand the business concerns with AI adoption and their respective solutions.

What are the Common Challenges of AI Integration and Their Fixes?

Every progressive company wants to use AI to boost output while maintaining quality criteria. However, willingness is one thing, and implementation is a whole different genre. While implementing AI, organizations face many obstacles, and they need to create appropriate strategies to address these challenges. So, what are these bottlenecks, and what are their solutions? Read on to know: 

1. Missing AI-First Culture

For a business to stay adaptable, innovative, and competitive in this fast-paced world, building an AI-first culture isn’t a luxury but a necessity. Unfortunately, most organizations fail to do so despite promising big. If it’s the case, companies will face multiple obstacles, such as slow innovation, failing to implement cutting-edge technologies, missed opportunities, and reduced efficiency.

Solution: Businesses have to change their strategy if they are to foster an AI-first culture. When it comes to incorporating artificial intelligence into organizational operations, business leaders should have a strategic vision in the first place. Companies also have to invest in AI training, so their staff members have the required knowledge and skills. 

2. Lack of Skill and Knowledge

Standing in 2025, AI isn’t a new concept anymore. It’s revolutionizing industries in more ways than one due to its immense potential. Though most companies want to utilize AI for their processes, they are unable to do so. Lack of specialized knowledge and skill sets is one of the key factors explaining this reality. Programming, statistics, domain knowledge, machine learning, deep learning, and data science are some of the sought-after skills for AI integration.

Also, many companies view AI as just “another tool” to accomplish their purpose. This thinking has to be changed. They neglect the training and support needed in an AI integration project.

Solution: Every problem has a solution, and this isn’t an exception. Being a business leader, you can invest in training, coordinate with professionals, or hire employees with advanced skills and AI knowledge. Besides this aspect, it’s advisable to start with pilot projects and implement user-friendly AI tools so that your employees become accustomed to this technology.

3. Not Having a Clear Idea About Where to Implement AI Technologies

Most business owners and top-level executives don’t have a concrete idea of where to implement AI. For instance, they may say, “Let’s stuff our blog page with AI-generated content” or “Let’s integrate that chatbot into our website for customer inquiries.” In most cases, these decisions backfire and don’t contribute to any real value. After all, the customers matter for your business, and AI is a technology that elevates their experiences. So, if you use AI in the wrong fashion because of your unawareness, things won’t work.

Solution: You need to identify tasks where AI can support employees. To be precise, consider AI as an add-on to achieve your business goals and not as a replacement for humans. For example, you can use AI to accomplish time-consuming and repetitive tasks within a short period, and, more importantly, without any errors. What does it imply in the broader context? By doing this, you will lessen the workload on employees and free them up to concentrate on other crucial tasks.

4. Poor Quality of Data

The digital world is driven by data. If you think this statement is an exaggeration, you are wrong. The AI models depend heavily on data, and based on data quality, these tools deliver the output. If the data quality isn’t up to the mark, it’s very obvious that the results won’t be accurate. Many organizations don’t have access to the necessary data, or even if they have, the data is of poor quality. What’s the outcome? Incorrect conclusions and misguided strategies.

Solution: A proper data management strategy is required to address the above problem. This approach should encompass data collection and centralization, data cleaning, data enrichment, and investing in data governance.

5. Unintentional Biases

Similar to humans, AI models can also give biased results at times. Yes, you heard it right. But why? The answer lies in the data we use to teach machines how to learn and identify various patterns. Chances are always there for that data to be incomplete or not wholly representative. If this is the case, the results are likely to be biased.

Solution: If you want these models to generate accurate results and be free from all sorts of biases, focus on the quality of the training data. You must ensure that this data is diverse and representative. However, the solution doesn’t revolve around data since there are other aspects. You must monitor and audit these AI models while implementing fairness-aware techniques during their development.

6. AI Models can be Delusional

You may not know that most AI models are probabilistic or stochastic. What does it mean? Machine learning algorithms, predictive analytics, deep learning, and other technologies work together to scrutinize data and, thereafter, generate the most likely response in each scenario. In other words, they suggest the best guess based on your prompt. Hence, they aren’t 100% accurate.

Solution: To deal with the probabilistic nature of AI models, organizations should adopt requisite measures to improve data quality, utilize hybrid models, and add human intervention in decision-making processes.

7. Absence of Updated Infrastructure

A lack of proper infrastructure prevents organizations from implementing AI technologies into their operations. Companies that still rely on outdated tools, systems, and applications won’t be able to integrate AI into their processes.

Solution: It’s necessary for businesses to set up an updated infrastructure with superior processing capabilities. Such an infrastructure can process huge volumes of data within a short period.

8. Integration Issues with Legacy Systems

There is a high chance that legacy systems will be incompatible with AI technology. If you try to integrate, it will consume a lot of time, and the process is also complex. Moreover, you may not get any results despite your efforts.

Solution: You need to know that for tapping the potential of AI, modernizing legacy systems isn’t a prerequisite. What you can do is use custom APIs and middleware strategically to integrate your existing legacy system with AI technology.

9. Determining Intellectual Property Ownership

This is another major business risk when implementing AI technologies. It’s very hard to identify the ownership and inventorship of AI-assisted outputs these days. This is even more prevalent when several human and machine agents are involved. 

Solution: Before utilizing AI technologies, businesses must define ownership rights and responsibilities in contracts. A good approach is to use traceable AI models for proper documentation. Apart from this, organizations should implement licensing agreements that clearly highlight how the outputs will be used, shared, and sold.

10. Regulatory and Ethical Issues

AI models raise a number of ethical and legal issues. Mostly, these issues revolve around data privacy and transparency. Organizations must abide by the data usage and privacy guidelines; otherwise, legal issues and harm to their reputation are inevitable.  

Solution: Regulations on AI technologies are continuously evolving, and hence, it’s necessary for companies to stay up to date. At the same time, businesses should practice ethical and responsible data utilization to reduce the concerns.

Conclusion

Whatever the industry the organization is in and regardless of its size, they are eager to adopt AI. It’s mainly because of the positive impact of AI on business operations. However, there are multiple business concerns with AI implementation as mentioned above. Businesses must identify these bottlenecks and come up with solutions to overcome AI implementation challenges.

[generativeinai]

AIOps Platform Development Trends to Watch in 2025

As IT environments grow in complexity and scale, organizations are increasingly turning to AIOps (Artificial Intelligence for IT Operations) platforms to manage, monitor, and optimize their digital operations. With the rapid advancement of artificial intelligence, machine learning, and automation, AIOps platforms are evolving fast—and 2025 is poised to be a transformative year.

In this blog, we’ll explore the top AIOps platform development trends that IT leaders, DevOps teams, and platform engineers should keep a close eye on in 2025.

1. Hyperautomation Across the IT Stack

In 2025, AIOps will go beyond simple automation to achieve hyperautomation—the orchestration of multiple tools and technologies to automate entire IT processes end-to-end. This trend will be driven by:

Seamless integration with ITSM and DevOps pipelines

Intelligent remediation using AI-based decisioning

Workflow automation across hybrid and multi-cloud environments

By reducing manual intervention, hyperautomation will not only accelerate incident response times but also enhance reliability and scalability across enterprise IT.

2. Edge AIOps for Distributed Infrastructure

The rise of edge computing is pushing data processing closer to where it's generated, creating new challenges for monitoring and management. In 2025, AIOps platforms will evolve to support edge-native environments by:

Deploying lightweight agents or AI models at the edge

Aggregating and analyzing telemetry data in real-time

Providing anomaly detection and predictive insights without reliance on central data centers

This decentralization is essential for use cases like smart factories, autonomous vehicles, and IoT networks.

3. Explainable and Transparent AI Models

AIOps platforms have long been criticized as “black boxes,” making it hard for IT teams to understand how decisions are made. In 2025, explainability and transparency will become core design principles. Look for:

Integration of Explainable AI (XAI) frameworks

Visual traceability for root cause analysis

Model validation and fairness reporting

Organizations will demand greater trust in AI-driven recommendations, especially in regulated industries like finance, healthcare, and critical infrastructure.

4. Unified Observability Meets AIOps

The lines between observability and AIOps are blurring. In 2025, we’ll see a convergence where AIOps platforms offer:

Unified telemetry ingestion (logs, metrics, traces, events)

AI-driven noise reduction and correlation

Full-stack visibility from application to infrastructure

This merger will empower IT teams with faster root cause identification, reduced alert fatigue, and improved mean time to resolution (MTTR).

5. Self-Healing Systems Powered by Generative AI

With the maturing of generative AI, AIOps will shift from reactive problem-solving to proactive, self-healing systems. Expect to see:

GenAI models generating remediation scripts on the fly

Autonomous rollback and recovery mechanisms

Intelligent runbooks that evolve over time

These capabilities will reduce downtime and free up human operators to focus on innovation rather than firefighting.

6. Vertical-Specific AIOps Solutions

Generic AIOps solutions will give way to industry-specific platforms tailored to vertical needs. In 2025, we’ll see a rise in AIOps platforms built for:

Telcos needing low-latency incident detection

Banks with strict compliance and audit requirements

Healthcare systems managing sensitive patient data

These tailored solutions will offer pre-trained models, domain-specific KPIs, and compliance-ready toolchains.

7. Data-Centric AIOps Development

As model performance is increasingly tied to data quality, 2025 will see a pivot toward data-centric AI in AIOps development. This involves:

Enhanced data governance and lineage tracking

Automated data labeling and cleansing pipelines

Feedback loops from operators to continuously improve AI accuracy

Well-curated, high-quality data will be a competitive differentiator for AIOps vendors and adopters alike.

8. AI-Augmented Collaboration for DevSecOps

AIOps will increasingly act as a collaborative intelligence layer across development, security, and operations. Platforms will support:

Shared dashboards with contextual insights

AI-driven alerts tailored to team roles (Dev, Sec, Ops)

Secure collaboration workflows across toolchains

This shift toward cross-functional enablement will align with the growing popularity of platform engineering and GitOps practices.

Final Thoughts

The AIOps landscape in 2025 will be defined by more intelligent, agile, and domain-aware platforms. As the pressure mounts to deliver seamless digital experiences while managing increasing complexity, organizations will need to adopt AIOps platform Development strategies that prioritize automation, trust, and observability.

Forward-thinking enterprises that invest early in these trends will position themselves for operational resilience, cost optimization, and continuous innovation in an increasingly dynamic IT world.

[procurement-insights]

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