『Tech Transformed』のカバーアート

Tech Transformed

Tech Transformed

著者： EM360Tech
無料で聴く

【Amazonプライム会員限定】今ならプレミアムプランが4か月 月額99円。

10月19日まで。※適用条件あり
Explore how tech is shaping the future of business and share best practices for implementing these innovations. With expert interviews, in-depth analysis, and practical advice, you'll stay ahead of the curve and make informed decisions for your enterprise. Join us to debunk myths, dive into the latest trends, and cut through the AI noise with “Tech Transformed.” Tune in and transform your understanding of technology and its potential.Copyright 2026 EM360Tech マネジメント マネジメント・リーダーシップ 政治・政府 経済学
エピソード
  • What Is Self-Driving Production? The Future of AI SRE Explained

    What Is Self-Driving Production? The Future of AI SRE Explained
    2026/10/07

    Envision this: A ServiceNow ticket arrives at a big bank, and 50 customers can't log into their accounts. All the dashboards show a red indicator. About 60 to 100 people end up in a war-room meeting and remain there for about two hours. They discover the problem was a slow SQL query in a database ten hops down the stack, a system neither the login team nor their monitoring system had originally identified.

    That is the situation which Anish Agarwal, CEO of Traversal, alludes to when explaining why incident response is becoming more difficult and why he believes the solution must be designed differently from the observability tools that most enterprises currently operate.

    In the recent episode of the Tech Transformed podcast, Dan Twing, President and COO of Enterprise Management Associates (EMA), sat down with Agarwal, also a professor at Columbia University. They discuss how code laid out by artificial intelligence (AI) is affecting production incidents.

    They also talk about what is needed to trace symptoms back to their causes within a portfolio of 5,000 applications, and how businesses decide when to switch from using AI suggestions to adopting autonomous remediation.

    In terms of reliability, Agarwal says that the enterprise has to question whether the task was not just initiated but completed. However, it's also possible for a job to finish, yet the answer might not be the one you want.

    Key Takeaways
    • "Did it run?" no longer proves an AI-built system worked.
    • Coding agents are trained on compile-and-run, so they can sideline the real goal.
    • Same configuration, three runs, three different results.
    • Engineers now write specs; agents write code, so code comprehension drops.
    • Testing improves, but failures still happen at the seams between systems.
    • Agents need to search far more data than dashboards were built for.
    • Traversal's production world model maps entities, connections and live telemetry.
    • Customers are using it to validate CMDBs, which was unplanned.
    • Causal analysis separates upstream and downstream anomalies from spurious ones.
    • Multiple paths converging on one cause builds confidence in a root cause.
    • L4 cut a war room from 60–100 people to five to 10.
    • Reported MTTR fell from two or three hours to 20–30 minutes.
    • Coding agents now call Traversal's MCP to write production-aware code.
    • Decide up front when to build in-house versus buy.

    Chapters
    • 00:00 Introduction to Hybrid Cloud Challenges
    • 02:53 The Role of AI in Production Systems
    • 06:34 Complexity of Troubleshooting in AI Systems
    • 12:03 Causal Analysis in Production Environments
    • 18:03 Agentic Systems and Human Collaboration
    • 22:56 Framework for Agentic Readiness in Reliability
    • 27:03 Future of Self-Driving Production
    • 29:55 Practical Steps for Tech Leaders

    Visit traversal.com.

    agentic SRE, self-driving production, site reliability engineering, AI SRE, AI incident response, root cause analysis, causal AI, MTTR, mean time to resolution, production world model, CMDB, CMDB validation, ServiceNow, observability, AIOps, incident management, Traversal, Anish Agarwal, Dan Twing, Tech Transformed, EM360Tech, Enterprise Management 360, AI-generated code, coding agents, DevOps, enterprise IT
    続きを読む 一部表示
    33 分
  • Vibe-Coding: Will Software Engineers Disappear by 2028?

    Vibe-Coding: Will Software Engineers Disappear by 2028?
    2026/09/28
    “Vibe coding is already present in your company,” according to David Hsu, CEO of Retool and governance is the only way to secure it.Let's put this into context. Think of a Fortune 500 company’s CIO who has recently come across a vibe-coded application spotted on the public internet. This app was developed by an employee of the Fortune 500 company in question. The developer obtained a data dump from Salesforce pertaining to customers, then fed this data to an external AI tool not part of the company’s set of AI tools. The employee prompted the tool to host the resulting application on a public URL.Since it was an external AI tool, a security review couldn’t be carried out, nor could the IT department view it. Without any internal visibility into a tool created by the employee, the actual customer data was live on the open web. It’s a scary situation, a breach of trust, so imagine the CIO’s wrath upon seeing it.This is why, in the recent episode of the Tech Transformed podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down for a conversation with David Hsu, CEO and Founder of Retool. They talk about the repercussions of vibe coding in addition to examining why most enterprises have no conception of how much of it is already taking place within their own enterprise.The discussion then turns from the immediate issue of governance concerning uncontrolled AI app-building to Hsu's prediction that software engineering as a profession could vanish within two to three years.“All the CIOs that I speak to are genuinely frightened about vibe coding, and even if you don't want to admit it, vibe coding is already present in your company,” he tells Dua. This is partly why Hsu founded Retool.Retool secures vibe coding for enterprises. Hsu explains that Retool can be connected to a company’s Snowflake database, for instance or their Databricks data lake. “Users should be able to have access to particular tables only,” he says. Access controls are required at the group level.“If you're in this particular Okta group, you can only access this table, and it's read-only. Whereas if you're in this other Okta group, you can actually write back to this other Salesforce over here." So one Okta group could be granted read-only access to a particular table, while another group might be given write access to a connected Salesforce instance. All applications that are built on top of that connection automatically inherit the same restrictions.Once rules of such a nature are put in place, people could then be set free, explaining why he views governance as something that enables freedom rather than something that restricts.TakeawaysVibe coding is already happening inside most enterprises, sanctioned or notA Fortune 500 CIO found a vibe-coded app exposed on the public internetOne customer's AI coding spend hit 30x their original budget estimateTwo employees independently built duplicate apps that returned different numbersHsu forecasts fully automated coding arriving by late 2027 or early 2028By 2030, Hsu estimates 90% of company work will be done by AI employeesThe CIO's future role: a "chief people officer" governing AI agents, not humansChapters00:00 The Rise of No-Code and Low-Code Development04:52 Governance in the Age of Rapid Development12:40 Centralisation and Control in Software Development20:04 The Future of Software Engineering and AI25:13 The Role of Humans in an AI-Driven Worldvibe coding, AI governance, enterprise AI, CIO, AI agents, software engineering, Retool, David Hsu, enterprise software, AI security
    続きを読む 一部表示
    27 分
  • The Rise of Autonomous Customer Experience: How Should Enterprise Leaders Prepare Today?

    The Rise of Autonomous Customer Experience: How Should Enterprise Leaders Prepare Today?
    2026/09/28
    AI is reshaping how businesses interact with customers, from handling routine enquiries to delivering more personalised and responsive experiences. As organisations automate more of the customer journey, however, customer support is increasingly being reconsidered not simply as a cost to reduce, but as an important part of how businesses build lasting relationships with their customers.On this episode of Tech Transformed, host Christina Stathopoulos welcomed back Pranay Jain, CEO and co-founder of Enterprise Bot, to discuss what this shift means in practice. The conversation moves beyond the usual AI hype to consider how businesses can scale automation without losing meaningful human connection, while navigating growing regulatory expectations across Europe and the UK.Jain has been building EnterpriseBot for almost a decade, since, as he puts it, "artificial intelligence was more artificial than intelligent." This vantage point shapes a conversation that's less about what AI can do and more about how enterprises should think about doing it.Stop Treating AI as a Cost-Cutting ToolJain sees AI as an opportunity to rethink how customer service teams work, using automation to handle routine tasks while giving people more time to focus on interactions where human judgement and empathy matter most. Password resets, invoice lookups and basic status checks can be handled instantly by AI, allowing human agents to spend more time with customers during moments that require greater care, such as a major life event or an insurance claim following an accident.He points to IKEA as a real-world example of this working. Rather than laying off staff as AI took over routine tasks, the company retrained employees to help customers plan their homes in a more personal, consultative way and saw sales increase as a result. It's a useful reframe that automation isn't about removing people from the equation; it's about repositioning them where their judgment and empathy are still needed.Jain also flags a metric that trips up a lot of contact centres, which is average handling time. Teams are often rewarded for keeping calls short, but a one-minute call with a 40 per cent resolution rate is worse than a three-minute call that actually solves the problem. His recommendation is to automate the parts of a call that eat time without adding value - identity verification alone can consume up to a quarter of a call, so agents can spend the time they save on getting things resolved properly, not just quickly.Why AI Needs GuardrailsGiven EnterpriseBot’s European roots, regulation naturally comes into the conversation. Jain explains how the EU AI Act, Spain’s new customer service rules and the UK’s FCA expectations are starting to shape what “autonomous” really means in practice. For example, businesses can track sentiment at a general level, but analysing an agent’s tone of voice or emotions crosses into much more sensitive territory. The same applies to financial transactions. An AI agent can’t simply be left to make those decisions on its own. There needs to be a clear workflow and defined controls around what it can and cannot do.His concern is that a lot of the AI tooling on the market is built with a US-first lens and simply wasn't designed with these boundaries in mind. This creates real exposure for enterprises that adopt a slick-looking solution without checking if it can actually operate within EU or UK rules. Jain sees these regulatory changes as a positive step, particularly as AI becomes more embedded in customer interactions. "I actually think it's very important that you do have the right regulation, and there's a very good reason why these things are there."This point extends into data sovereignty too. With geopolitical tension pushing some organisations to reconsider public cloud dependence, Jain suggests flexibility- the ability to run on-prem, in a private cloud, or across different regions depending on the customer; this is becoming one of the more undervalued priorities on a CIO's list.Connected Customer Experience With AIThe conversation closes on something every customer feels but rarely thinks about, which is the disjointedness of switching between voice, chat, email, and messaging with the same company. Jain's push is for a single AI agent that carries context across every channel and every language. He uses his own background in Switzerland, a country with four national languages, to illustrate the scale of the problem. Without a unified system, a company could end up managing a dozen separate AI agents just to cover language and channel combinations.His final advice focuses on what enterprise leaders can put into practice now. Start by actually listening to your customer data to figure out where automation adds value and where human time matters most. Then be honest about whether building AI in-house is really core to your business, or if it's a distraction from what you're actually good at. As he ...
    続きを読む 一部表示
    27 分
adbl_web_anon_alc_button_suppression_t1
まだレビューはありません