April 06, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
The culture that pushes people to be leaders frequently sugarcoats a position like that by showing all the advantages, juicy challenges, fancy bonuses and sparkly cars. The reality is that the responsibility is heavy and being a leader is a lot closer to being a psychologist/coach/rescuer/mom/dad than a hands-on worker. A leadership position calls for emotional intelligence growth, great adaptability and, believe it or not, ego detachment. A great leader is one who does not hoard talent but lets people fly, knows that the best team is made of people who are different from and better than they are, learns how to hold pressure and remain calm and, above all, can be trusted. Show workers this truth. ... Sometimes, not wanting a leadership position may indicate simply that one is afraid of it and not that one doesn’t want it. We all know that. Companies can and must push people out of their comfort zones but also need to maintain a balance of respecting their preferences. How? Training them before a leadership role. Yes. Most companies train leaders after they have assumed a leadership role.?
Unfortunately, most IT teams today have limited ability to discern how the performance of Internet services is impacting their applications. There are, of course, Internet performance management (IPM) tools capable of surfacing network performance metrics. The challenge and opportunity now is to surface those metrics in context with all the other telemetry data that DevOps teams collect from the various application performance management (APM) and observability platforms they rely on to monitor and troubleshoot application environments. ... Broadly, there are three major classes of blind spots that impact distributed application performance. The first and arguably most opaque are the services provided by third-party vendors. Ranging from content delivery networks (CDNs) to software-as-a-service (SaaS) application, each of these services is controlled by an external service provider that typically doesn’t allows a DevOps team to collect telemetry data by deploying agent software in their IT environments. At best, they may expose an application programming interface (API) to enable an agentless approach to collecting data, but that method doesn’t typically provide the level of control required to optimize application performance.
Ruby on Rails has always been promoted as a tool that a single person can use to create a web application — that’s why it was so popular with Web 2.0 entrepreneurs. The Rails website in April 2005 described the framework as “a full-stack, open-source web framework in Ruby for writing real-world applications with joy and less code than most frameworks spend doing XML sit-ups.” While XML is no longer a factor in 2024, DHH continues to do interviews espousing the “joy and less code” philosophy. In an interview with the devtools.fm podcast last month, he even suggested this approach will help developers adapt in the current generative AI era. “As we are now facing perhaps an existential tussle with AI,” he said, “I think it’s never been more important that the way we design programming languages is designed for the human first. The human needs all the help the human can get, if we’re going to have any chance to remain not only just valuable, but relevant as a programmer. And maybe that’s a lost cause anyway, but at least in the last 20 years that I’ve been working with Ruby on Rails, I’ve seen that bet just pay [off] over and over again.”
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Business leaders can no longer afford to wait until disruptions occur to measure their financial impact. They need insights to protect the customer and their financial bottom line as quickly and seamlessly as possible. AI and ML provide the means to achieve such agility, offering “quick wins” in the form of immediate financial value. By harnessing accelerators to automate data capture and deliver intelligent insights at the point of disruption, reducing lead time to capture data from several weeks to near real time, they obtain optimized recommendations at the point of disruption across the value network, thus protecting the customer experience and the financial impact on the business in near real time. ... AI contributes to decision intelligence in supply chains. A good example of decision-making processes that have been enhanced by AI is the Amazon Scan, Label, Apply & Manifest (SLAM) process. When a customer places an order, there are multiple microservices and intelligent algorithms that run to find the most optimal way to fulfill it, based on the customer promise and best financial business outcome.?
GenAI simply isn’t ready yet. Just like the internet of 1999, the genAI tools of 2024 will eventually get there. But in the meantime, I predict, as Gartner would put it, we’re heading quickly to the “Trough of Disillusionment.” That’s where the initial burst of excitement over a new technology runs out and everyone realizes the reality isn’t close to what we all dreamed it would be. I’ve seen too many of these bubbles over the years and still we fall for it every time. What’s different now, and why the coming fall will hurt so much, is that almost every company has fallen under the genAI spell. Not only are businesses planning to move to it, they’re already replacing the people they need to get their work done with half-baked AI models. This is going to greatly accelerate the coming crash. Don’t get me wrong. GenAI will eventually replace some jobs. But former US. Treasury Secretary and current OpenAI board member Larry Summers gets it right. He recently said, “If one takes a view over the next generation, this could be the biggest thing that has happened in economic history since the Industrial Revolution.”
The first use case I would start with is your developers. It’s the most mature generative AI scenario. And as you build new applications for this, why not build them with generative AI? Then I would think about the out-of-the-box AI, gen AI that you’ll get from us if you start introducing it to Teams and Office. You’ll hit a ton of use cases there that are sort of horizontal across the whole business. Then, you’ll be left with a set of custom use cases. These could be things like I would start; you could start with a contact center, just enhancing what you currently have in your contact center. You don’t have to rip out your contact center, either. It’s just about sort of adding the capabilities on top. Building a knowledge base is also a great way of learning how to use this inside the organization. ... One of the things we did as well was create this concept of a citizen developer or citizen data scientist. You could just take a set of data, and we can prompt you to say, “It looks like you need one of these models, that could be sentiment analysis or something.” Then, it will build a model with the data.