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Forbes
7 hours ago
- Business
- Forbes
How Engineering Teams Are Reimagining Work Through AI
Bratin Saha, Chief Product and Technology Officer at DigitalOcean, tech executive with 20 years of experience across AI and cloud computing. AI is one of the most transformative technologies of our time. Given the rapid pace of AI innovation, it seems that practically every leader is thinking about how they can reimagine their work using AI. While this may be daunting and will take persistence, I believe the reward is well worth it. Having worked on many projects using AI, I want to discuss the factors that are critical to ensuring the success of these projects—from an iterative mindset to new mechanisms and rigor in tracking metrics. AI For Coding AI is rapidly emerging as an indispensable tool for software engineers. As teams explore AI-powered coding assistants, it's essential to look beyond 'lines of code generated' and consider impact on code quality, security and long-term maintainability. Many engineering teams are now experimenting with tools like GitHub Copilot, and we've personally seen up to 40% increases in code generation. But productivity isn't the only metric worth tracking—security, code quality and maintainability are just as critical. One helpful practice is to implement internal evaluation systems that compare AI-generated and human-authored code for defects, rollback frequency and overall impact on velocity. Our initial findings suggest that AI-generated code can match or even outperform human benchmarks in some areas, though consistent monitoring remains essential. For leaders considering similar integrations, there are a few principles that can help guide responsible adoption. Define baseline metrics early, evaluate AI output with the same rigor applied to human-authored code and build feedback loops to inform ongoing tool selection. Thoughtful experimentation combined with clear evaluation criteria is key to realizing the value of AI without compromising quality or trust. AI For Rootcausing Cloud Incidents One way of figuring out where to add AI is to understand where your employees are spending the most time and then automating that activity with AI. Cloud engineers, for instance, typically spend over 20% of their time troubleshooting incidents. Additionally, high availability is critical for customers who rely on cloud services. AI can play a powerful role in accelerating incident resolution, especially when engineers are spending a significant portion of time on root cause analysis. As an example, we developed a GenAI-powered site reliability engineer (SRE) agent that assists engineers by analyzing real-time logs and telemetry to find root causes autonomously during incidents. Engineers can ask follow-up questions and rate accuracy. By eliminating the need to assemble multiple engineering teams for incident triage and diagnosis, this approach can help reduce the time and effort required to resolve issues and restore the service faster. Accuracy is one of the crucial measures of the agent's effectiveness, and achieving that was an iterative process. The agent needs to be trained on high-quality, representative data; it needs to be tightly integrated into incident response workflows with real-time access to observability systems; and it must have mechanisms to learn from new incidents and user feedback. Besides encouraging engineer feedback, one thing we found beneficial is to incorporate the agent into the post-incident review (PIR) process. This retrospective analysis refined the agent's accuracy and functionality by clarifying incident causes and guiding engineers on prompt optimization. Once the agent meets your predefined success metrics, you can then expand its role. In our case, we extended the agent role beyond reactive incident response. By embedding the agent earlier in the incident lifecycle to monitor system alerts, the agent can automatically assess alerts and propose root cause solutions, significantly reducing investigative time for engineers. These continuous and targeted improvements are key to building a successful GenAI agent. AI For Server Maintenance Another way to figure out how to use AI is to consider operations where data analysis can be used to avoid undesirable outcomes and help teams move from a reactive mode (fire fighting) to a proactive mode. For example, server downtime in a data center is undesirable because it directly impacts the service uptime. Servers usually do not fail out of the blue; there is a pattern of malfunction that can be detected by closely monitoring server health with AI tools. At DigitalOcean, we use AI to analyze logs in real time, providing a confident root cause to engineers. This analysis can help repair machines faster while reducing repeat outages. We also collect messages that are emitted by the operating system or the out-of-band management controllers of servers, and perform a rules-based evaluation to trigger the removal of workloads from at-risk machines. If a stick of RAM issues a hardware warning or a disk array degrades in a production hypervisor, AI can automatically migrate customer and internal workloads to healthy machines. Companies can use similar techniques that use AI to perform a real-time analysis of relevant metrics to drive operational improvements and use predictive rather than reactive operations. In Conclusion AI is already changing every aspect of how we work, and it is important for leaders to get in front of it. The most important part is to get started; identify some workflows that are ripe for automation, put together a tiger team and give them the latitude to experiment till they get the AI right. Even if the initial experiments do not work, the learnings are invaluable and set you up for success down the road. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
18-06-2025
- Business
- Forbes
Talent Vs. Toil: Taking Care Of Business
Agur Jõgi, CTO of Pipedrive and expert in scaling technology and organizations. Experienced as an innovator, founder and C-level manager. getty One of the lenses through which tech leaders view their plans for success should be balancing talent and tedium. That is, the skills, attitudes and capabilities of your team versus the toil they must overcome in their day-to-day activities. One of the major keywords you've probably already flashed up is "burnout." Most of us can't maintain our best focus and output over either a long or acutely stressful period. The easy analogy is that of an athlete. A 100-meter sprinter trains for their event, and they may well be pretty good in a longer race. However, they haven't prepared for a marathon—mentally or physically. Tech workers are—either naturally or by career development and practice—primed for certain types of roles and responsibilities. If these are inconsistent, too onerous or often simply too tedious, then attention can slip, and the risk of burnout or disengagement rises. For teams with complex and mentally taxing tech roles that are facing mercurial economic pressures and rapidly changing tools and products, it helps to have a methodical approach to monitoring and supporting the right working environment. Time And Motion, Toil And Team In the mid-20th century, time-and-motion studies became a big business efficiency technique for improving work methods. Factories (or anywhere where there was physical motion, such as assembly lines) were increasingly optimized for better business efficiency. This kind of thinking influenced businesses of all kinds as it evolved, and the IT industry may be the most obvious inheritor of this style of process management. It would be reasonable to say workers didn't tend to get the better end of the drive for efficiency in times past. Speak of "the factory floor" or an "assembly line worker," and many people may have a bias that such working practices make a person a cog rather than an active agent. It's now well understood that employee experience and productivity are known to be entwined. Only leaders who keep their finger on the pulse of the holistic employee and business experience will keep their project and business performance in the green over the medium to long term. Leaders must understand the processes of their teams and be on hand to offer the benefit of experience. They must also advocate if the cost of toil and poor experience ever degrades their ability to deliver on business goals. KPIs, OKRs and metrics define the company goals and deliverables, but these must be translated into "human-readable" behaviors and processes to avoid work becoming a rote lever-pulling exercise. Starting Right And Continuing The Same Way Culture begins in many places, one of them at the point of hiring. Right from the get-go, find people during recruiting who know why they want to work for this company, fit in and strengthen the existing culture. A person with the right "why" will collaborate on the "how." Of course, it's good sense to offer great pay and benefits to go with a great culture as part of the whole employee experience. Equally importantly, choose people who want to develop and want to do it themselves rather than waiting for someone to develop them. Showing agency and a future orientation is a great way for employees to show they can overcome challenges, show resilience and positively support their teams. From there, every manager has a major task—to ensure the continuous professional and cultural development of their people and help out those whose desire for development has stopped. As a guide, my team members know that if they decide to leave, they will generally be trained and experienced enough to get a job offer from the market that's a level higher. Other companies will see a mid-level Pipedrive developer as a new senior as a result of our culture and drive for individual development and excellence. Experience Supporting Excellence The "greed is good/work 18 hours a day in the boiler room" style of management doesn't build a culture of excellence or long-term success. Collaboration and trust are what's needed to unlock really compounding strength and value. That's not to say the best teams don't have some high targets, tight deadlines or some healthy stress. That's how all athletes and professionals maintain a winning mindset and overcome challenges. What's needed is a culture of trust and a great working experience that supports teams in delivering their best over sustained periods. Working experience is very hard to get perfect. It's probably not perfect. People and their varied circumstances are always changing. Leaders at every level must regularly consider the kind of environment they want for their talent and make the right choices to balance experience, resources and expediency to stay on top of the challenge. Leaders must avoid "setting and forgetting." Culture changes with every act made and impression received. A poor hire, the wrong decision, a disruptive customer demand—anything can change it. Culture is made up of so many parts that it doesn't take much to send it down a different path. The mission/vision set from the top is a great start, but it must be backed by evidence that it's taken seriously and meaningfully across the majority of working activities. Taking Care Of Business "Taking care of business" in terms of making a great working experience means tending to factors like employee autonomy and empowerment. Merely taking a temperature check as part of an annual review cycle is a great way to uncover problems a long time after they should have been solved. Some areas, like recognition and appreciation, don't require much more than a thoughtful and empathetic approach to management. Toil must be transformed into meaningful work, and taking care of business doesn't merely refer to delivering on company goals. The company is an organization of people collectively. When they pull together, they grow collectively. When they lose the rhythm, that growth is hampered. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Malay Mail
13-06-2025
- Business
- Malay Mail
From fishing family to Big Tech: French CEO Fidji Simo joins OpenAI as second-in-command
NEW YORK, June 13 — At just 39 years old, Fidji Simo is poised to become OpenAI's second-in-command after leaving her mark at two other major tech firms, including Meta. Reporting directly to CEO Sam Altman, the move to the ChatGPT-maker represents the latest chapter in a career that has taken Simo from a fishing family in France's Mediterranean port of Sete to the heights of Silicon Valley. As the current CEO of grocery delivery platform Instacart, she cuts a unique profile: a French woman in the male-dominated American tech landscape — who resists advice to blend in. 'I can put all my energy trying to be someone else or I can be myself and pour all of that energy into what I can create,' she told CNBC in February. This philosophy will likely be on display when she appears yesterday at the VivaTech conference in Paris. Raised in Sete, Simo attended the elite HEC business school before joining eBay in 2006, first in France then in California. 'People expect a very business-like story for why I decided to come to the US. It wasn't. The American Dream was on TV every night and that was an incredibly appealing thing,' she said. 'Never Intimidated' In 2011, Simo joined Facebook, now Meta. She was given responsibility for video and monetisation in 2014, a role she considers the defining moment of her career. Simo championed the company's pivot to video, which became central to Meta's strategy despite initial internal skepticism. 'She never let herself be intimidated,' recalled David Marcus, who worked at Meta alongside Simo and now serves as CEO of online payment company Lightspark. 'She had an ability to challenge Mark (Zuckerberg) and push him, when others would have hesitated.' Joining Instacart in 2021, Simo inherited a company that had been bleeding money for a decade. Under her leadership, the grocery delivery platform achieved profitability in 2022 through aggressive diversification: data monetisation, expanded retail partnerships and a robust advertising business. Now Simo faces her biggest test yet. As OpenAI's number two, she'll free up CEO Altman to focus on research and infrastructure while she tackles the company's operational challenges. Despite being one of history's most highly funded startups and ChatGPT's phenomenal success, OpenAI is burning cash at an alarming rate. The company has also weathered significant leadership turnover, including Altman's own brief ouster and reinstatement in 2023, raising questions about management stability. But French investor Julien Codorniou, who worked alongside Simo at Facebook, said she will more than rise to the occasion. 'Fidji's arrival is a declaration of ambition by OpenAI,' he said. — AFP
