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Anu Shukla is a serial entrepreneur with over 20 years of high-tech industry experience…
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Anu Shukla reposted thisAnu Shukla reposted thisWhat happens when AI systems become capable of making decisions we can no longer fully predict or control? Sandeep Shilawat, Partner & Hybrid Cloud CTO at IBM, joins the Apex Perspective Podcast to explore why technical expertise alone cannot guarantee business success, how AI is fundamentally changing the nature of work, and why trust may become one of the biggest challenges in the age of autonomous intelligence. Drawing from experience across NASA, federal government programs, and IBM, Sandeep shares insights into connecting technology with business outcomes, leading complex initiatives, and navigating the risks of increasingly autonomous AI systems. In this episode, we cover: 1. Why technical expertise alone is not enough, and how bridging the communication gap between business and engineering teams drives meaningful outcomes. 2. Why effective communication and expectation management are essential for technical leaders handling complex projects and challenging stakeholders. 3. Why enterprise projects can fail despite excellent technical execution, especially when business expectations, project scope, and delivered solutions become misaligned. 4. How leaders identify the right problems to solve, and why understanding business priorities and desired outcomes matters more than technical sophistication. 5. Why AI represents a shift toward outsourcing intelligence itself, and what this could mean for the future of business operations and decision-making. 6. How AI could transform the workforce, reducing routine work while increasing demand for advanced skills and entirely new roles. 7. Why autonomous AI systems introduce new risks when agents pursue objectives through unexpected methods that may violate intended boundaries. 8. Why AI trustworthiness cannot simply be assumed, and the growing importance of alignment, continuous monitoring, and maintaining control over autonomous systems. #IBM #ArtificialIntelligence #AgenticAI #AITrust #ResponsibleAI #Cybersecurity #EnterpriseAI #TechnicalLeadership #BusinessStrategy #DigitalTransformation #Podcast
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Anu Shukla reposted thisAnu Shukla reposted thisManash Majhi, Principal Data Architect at Microsoft, drawing from experience across software engineering, large-scale data systems, cloud technologies, and agentic AI, this conversation explores how technical leaders bridge the gap between engineering and business value, avoid overengineering, and prepare for a future where AI changes not just how software is developed, but how entire businesses operate. **In this episode, we cover:** 1. Why technically impressive solutions can fail when they do not match the customer's actual business needs, including how a sophisticated web crawler created unexpected operational overhead because the team had misunderstood how the customer wanted to use it. 2. How simpler solutions can create significantly more business value than complex engineering projects, including an example where a script built in less than half a day automated an existing process and saved approximately $100,000 per month. 3. Why understanding customer problems requires experiencing their workflows firsthand, and how becoming your own customer helps uncover usability issues, integration challenges, and operational friction that technical requirements alone may overlook. 4. How data architecture should move beyond simply delivering information toward data democratization, enabling business users to access insights, ask their own questions, and interact with information in ways that support their actual decisions. 5. Why successful technical solutions require alignment between three essential components: business strategy, the people using the technology, and the technology itself, while avoiding unnecessary complexity and building through rapid iteration and customer feedback. 6. What currently limits the effectiveness of AI agents, including the supervision, repeated iterations, and refinement required to make them consistently perform the intended tasks, and why organizations must look beyond individual efficiency improvements when evaluating AI's potential. 7. How AI could fundamentally reshape business processes rather than simply automate existing tasks, creating opportunities for new business models and potentially smaller engineering teams capable of building products with the assistance of specialized AI agents. 8. Why the future of software engineering may place greater value on business understanding, curiosity, problem identification, and strategic thinking, as engineers increasingly need to define product direction, coordinate AI agents, evaluate their work, and understand the business outcomes their systems are supposed to create. #Microsoft #PrincipalDataArchitect #DataArchitecture #ArtificialIntelligence #AI #AgenticAI #SoftwareEngineering #EngineeringLeadership #DataEngineering #BusinessStrategy #DigitalTransformation #TechnicalLeadership #Podcast
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Anu Shukla reposted thisAnu Shukla reposted thisWhat separates engineers who build impressive technology from leaders who solve problems that actually move the business forward? Ajay Tripathi, Senior Software Development Manager at Amazon, joins the Apex Perspective Podcast to explore how engineering leaders identify valuable customer problems, measure business impact, and build the trust and ownership needed to turn technical solutions into successful products. Drawing from experience across Samsung Electronics, entrepreneurship, Bank of America, and Amazon, Ajay shares how his journey from software development to building a business transformed his understanding of customers, leadership, and what creates meaningful results. In this episode, we cover: 1. Why technology is only an enabler, and why successful engineering starts with a clearly defined customer problem and measurable business outcome. Ajay explains why even technically successful projects can miss the mark when teams lack the right metrics to determine whether customers are actually benefiting. 2. Why what customers initially request may differ from what they actually need, and how customer research, prototypes, and A/B testing help distinguish promising assumptions from solutions that genuinely improve the customer experience. 3. How engineering leaders identify the right problems to prioritize by balancing three critical factors: the severity of the customer problem, its potential business impact, and the engineering team's ability to solve it. We explore why a valuable problem is not automatically the right problem to pursue. 4. Why customer behavior varies across markets, and how differences in shopping preferences, promotions, and trust influence the technical solutions businesses need. 5. How entrepreneurship changes an engineer's perspective on business, including Ajay's experience moving from technical development into sales and business development to understand customers firsthand and discover which problems were actually worth solving. 6. Why ownership and trust can matter more than resources. Ajay shares how building his own company changed his understanding of accountability, why external dependencies cannot become excuses for unfinished work, and how earning customer trust can create opportunities even when competing against better-funded businesses. 7. Why effective engineering leadership is not defined by job titles, and how moving between individual contributor and management roles can reveal different ways to create value for an organization. 8. How psychological safety strengthens engineering teams, and why inclusive communication, respectful disagreement, and shared accountability are essential for encouraging innovation, particularly when managing people from different cultures and backgrounds. #Amazon #EngineeringLeadership #SoftwareEngineering #TechnicalLeadership #CustomerCentricity #ProductStrategy #BusinessStrategy #Entrepreneurship #Innovation #Leadership #ArtificialIntelligence #Podcast
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Anu Shukla reposted thisAnu Shukla reposted thisSapan Sarvaiya, Senior Engineering Leader at Meta, drawing from experience across Microsoft and Meta, including large-scale enterprise services, Instagram Direct, and Facebook Messenger, this conversation explores how engineering leaders balance technical execution with business strategy, customer engagement, competitive pressures, and the growing role of AI in product development. **In this episode, we cover:** 1. Why modern engineering leadership requires end-to-end ownership of the product, including understanding customer needs, business objectives, operational challenges, and the technical decisions necessary to deliver both immediate and long-term value. 2. How the right engineering approach changes depending on the maturity of the product, and why early-stage initiatives may benefit more from quickly launching, validating product-market fit, and collecting customer feedback than building technically perfect systems designed to scale for the next decade. 3. Why understanding customer needs requires combining multiple sources of information, including direct customer feedback, product usage patterns, behavioral data, competitive analysis, and experimentation, rather than assuming customers can always articulate the problems they need solved. 4. How engineering leaders determine which problems deserve attention first by evaluating business priorities, customer loyalty, competitive pressures, and the immediate needs of their most important user segments before deciding where to allocate resources. 5. Why meaningful engineering metrics must be directly connected to value for either the customer or the business, including improvements in product quality, customer experience, engagement, operational efficiency, and infrastructure capacity rather than simply measuring technical output. 6. How product strategy differs for highly engaged and less engaged users, including Sapan's discussion of Facebook Messenger's focus on improving experiences for existing power users while understanding how to help lower-engagement users become more active over time. 7. How AI is expanding beyond code generation into engineering productivity, competitive research, business analysis, and strategic problem exploration, allowing technical leaders to examine problems from new perspectives and discover opportunities they may not have considered previously. 8. Why effective AI adoption requires balancing automation with human judgment and oversight, ensuring that as AI takes on more responsibility across engineering workflows, teams remain intentional about how systems are evaluated, how decisions are made, and whether the resulting products actually meet customer needs. \#Meta #EngineeringLeadership #SoftwareEngineering #FacebookMessenger #Instagram #ArtificialIntelligence #AI #ProductStrategy #CustomerCentricity #TechnicalLeadership #BusinessStrategy #ProductDevelopment #Podcast Make the episode description more conciseAdd a stronger listener-focused opening
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Anu Shukla reposted thisAnu Shukla reposted thisYash Parikh, Applied Science Manager at Amazon, drawing from experience across Deutsche Bank, SAP Concur, and Amazon Payments, this conversation explores how technical leaders identify meaningful customer problems, anticipate needs customers have not yet articulated, adapt solutions across different markets, and turn promising technical ideas into initiatives that earn organizational support. **In this episode, we cover:** 1. Why customer experience should guide technical development, and how even the most sophisticated engineering solution can fail to create value if customers find it difficult to understand, adopt, or use in their everyday lives. 2. Why understanding customer needs involves more than simply listening to feature requests, and how anticipating needs customers have not yet expressed can create opportunities for meaningful product innovation. 3. How customer behavior, payment preferences, and regulations vary across countries and customer segments, and why a solution that succeeds in one market may not translate directly to another without understanding its unique constraints. 4. Why identifying the right problem requires a measurable business outcome, and how vague objectives such as improving customer experience become actionable only when tied to specific metrics that demonstrate whether a solution is actually moving the needle. 5. How communicating complex technical ideas in simple business language can determine whether an initiative receives leadership support, including why engineering leaders need to explain the customer problem, expected value, and potential return in a way that decision makers can understand. 6. Why the transition from individual contributor to engineering manager requires rethinking where time creates the most value, shifting from direct technical execution toward securing stakeholder alignment, developing talent, mentoring engineers, and helping promising initiatives gain organizational support. 7. How psychological safety and open communication encourage innovation, and why engineering teams need the freedom to challenge existing approaches, propose early-stage ideas, and question the status quo while remaining accountable to evidence and measurable results. 8. Why strong technical leadership requires balancing conviction with adaptability, encouraging experimentation and new ideas while being willing to change direction when customer feedback, data, or results challenge the original hypothesis. #Amazon #AppliedScience #MachineLearning #ArtificialIntelligence #AI #EngineeringLeadership #DataScience #CustomerCentricity #ProductInnovation #BusinessStrategy #TechnicalLeadership #Leadership #Podcast
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Anu Shukla reposted thisAnu Shukla reposted thisMike L. Swafford, Vice President of Software Engineering at Microsoft, joins the podcast to break down how great engineering leaders create value by understanding what customers are truly trying to accomplish, simplifying systems that have become too complex, and using AI to improve software fundamentals rather than simply generating more code. Drawing from nearly three decades at Microsoft across email protocols, Exchange Server, Exchange Online, developer tooling, engineering systems, and AI for software development, this conversation explores how technical leadership changes when the focus moves from building impressive systems to building systems that customers can trust, operate, scale, and actually use. **In this episode, we cover:** 1. Why understanding the customer starts with identifying what they are trying to accomplish, not simply what feature they ask for, because customers may not know which technical path will best solve their real problem. 2. How Mike learned that his own technical perspective was not always the same as the customer’s perspective, especially when serving information workers, IT professionals, enterprises, and customers operating in very different industries or cultures. 3. Why meeting customers where they are matters, including understanding their workflows, constraints, pain points, and the way they want to operate before deciding what the technical solution should look like. 4. How simpler solutions can sometimes create more value than highly complex ones, especially when decades-old codebases, extensibility layers, integrations, and customizations make systems more fragile than they need to be. 5. What enterprise software leaders can learn from the evolution of Exchange, including how rewriting and simplifying core parts of a platform over time can make systems less fragile while still supporting more product surface area. 6. Why AI is shifting the bottleneck in software engineering from writing code to validating intent, reviewing correctness, managing customer impact, and making sure generated changes do not create security, compliance, or technical debt issues. 7. Where AI still struggles today, especially with large legacy codebases, older build systems, repository-specific practices, unclear intent, context limitations, and situations where English instructions are not precise enough to produce the right outcome. 8. What will become more valuable for software engineers in the AI era, including clear intent, eval-driven validation, judgment, perseverance, adaptability, creativity, communication, and the ability to direct AI without losing ownership of the final outcome. #Microsoft #SoftwareEngineering #EngineeringLeadership #ArtificialIntelligence #AI #DeveloperTools #TechnicalLeadership #EnterpriseSoftware #CustomerCentricity #SoftwareArchitecture #Productivity #Podcast
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Anu Shukla reposted thisAnu Shukla reposted thisAs September comes to a close, we want to share one more nod to Ovarian Cancer Awareness Month. 🩵 Finding the right clinical trial can be a complex part of the cancer journey. OncoTrial Match, developed for UCLA Health and powered by Botco.ai, helps clinicians identify fitting oncology trials through a structured AI-guided workflow—and then helps move the process forward with an automated referral to the selected trial site. From trial matching to the next step in care, AI can help reduce friction in connecting patients with potential treatment opportunities. Visit our Podcast Appearances page to watch the rest of Rebecca Clyde's interview with CX Gurus: https://buff.ly/a5yNzMR https://buff.ly/FYyxQhF #OvarianCancerAwarenessMonth #ClinicalTrials #Oncology
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Anu Shukla reposted thisAnu Shukla reposted thisRaghav Karnam, Engineering Leader at Amazon, joins the podcast to break down how engineering leaders create real impact by combining machine learning, data systems, customer-backward thinking, strategic architecture, and the judgment to know which problems are actually worth solving. Drawing from experience across consulting, entrepreneurship, large-scale consumer data, machine learning, Plume, and Amazon Kindle, this conversation explores how technical leaders move from building complex systems to building the right systems, the ones that scale, serve real customer needs, and create long-term business value. **In this episode, we cover:** 1. How an early interest in data mining, pattern recognition, and business intelligence shaped Raghav’s path into machine learning, analytics, and using data to influence business decisions. 2. Why entrepreneurship changed the way he thought about engineering, especially after realizing that strong technology alone is not enough without a clear customer, market, and business problem. 3. How customer needs often evolve after the initial solution is built, and why engineers need to think not only about what customers ask for, but how the system will be used globally and at scale. 4. Why strategic architecture matters more as AI changes software development, especially when AI can copy old patterns that work today but will not scale for the future. 5. How engineering leaders balance product needs with long-term horizontal technology layers that can solve multiple use cases instead of only serving one narrow feature request. 6. How Amazon’s customer-backward process, including PR FAQs and future customer questions, helps teams identify the right problem before building the solution. 7. Why entrepreneurship develops business empathy, showing engineers the importance of sales, marketing, customer adoption, and choosing the simplest solution that makes customers successful. 8. How Gen AI is changing hiring and engineering expectations, making judgment, domain expertise, design thinking, debugging ability, and the skill of co-driving with AI more important than simply producing more code. #Amazon #EngineeringLeadership #MachineLearning #DataEngineering #ArtificialIntelligence #AI #TechnicalLeadership #CustomerCentricity #Kindle #SoftwareEngineering #BusinessStrategy #Podcast
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Anu Shukla liked thisA bit late sharing this, but ABI Research’s recent piece on Vultr Steps Up for Open Source Just as DigitalOcean Steps Back captures a massive shift in the AI infrastructure market. A few takeaways from the analysis: Billion-Dollar Scale: Vultr’s US$1.2 billion order for AMD Helios racks (built with HPE) marks a major rack-scale commitment. Open Ecosystem Strategy: Between our Open Source Credits Program and expanded Cloud Alliance (with Wasabi, VAST, and SoftwareOne), Vultr is betting on open, composable stacks (ROCm, PyTorch, vLLMs) to eliminate vendor lock-in. Dual-Stack Flexibility: Running both NVIDIA GB300 systems and AMD Helios clusters positions Vultr as the leading independent cloud infrastructure for enterprise workloads. As the industry moves away from single-vendor dominance, open software is becoming the real battleground for switching costs. Worth a read.Anu Shukla liked thisEarlier this week I wrote a blog for ABI Research about Vultr’s US$1.2B order for AMD Helios racks and its new Open Source Credits Program, launched days after DigitalOcean closed its own. 💡 In the post, I look at why those two announcements belong together. I also cover what Helios could mean for NVIDIA’s grip on AI infrastructure, and what has to go right for Vultr's bet to pay off. 🔧 Link to the full post in the first comment 👇 #AI #OpenSource #CloudComputing #Vultr
Patents
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Apparatus and method for providing a marketing service
Issued US US8744928 B2
Similar to previously granted patent US8234375 B2 but applied to newer marketing channels that came into existance after the first patent application was filed in 2005.
Other inventorsSee patent -
Apparatus and method for providing a marketing service
Issued US US8744928 B2
Similar to previously granted patent US8234375 B2 but applied to newer marketing channels that came into existance after the first patent application was filed in 2005.
Other inventorsSee patent -
Rewards Based currency processing system
Issued US US8533036B2
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