Daily Tech Digest - June 02, 2025


Quote for the day:

"The best way to predict the future is to create it." -- Peter Drucker


Doing nothing is still doing something

Here's the uncomfortable truth, doing nothing is still doing something – and very often, it's the wrong thing. We saw this play out at the start of the year when Donald Trump's likely return to the White House and the prospect of fresh tariffs sent ripples through global markets. Investors froze, and while the tariffs have been shelved (for now), the real damage had already been done – not to portfolios, but to behaviour. This is decision paralysis in action. And in my experience, it's most acute among entrepreneurs and high-net-worth individuals post-exit, many of whom are navigating wealth independently for the first time. It's human nature to crave certainty, especially when it comes to money, but if you're waiting for a time when everything is calm, clear, and safe before investing or making a financial decision, I've got bad news – that day is never going to arrive. Markets move, the political climate is noisy, the global economy is always in flux. If you're frozen by fear, your money isn't standing still – it's slipping backwards. ... Entrepreneurs are used to taking calculated risks, but when it comes to managing post-exit wealth or personal finances, many find themselves out of their depth. A little knowledge can be a dangerous thing – and half-understanding the tax system, the economy, or the markets can lead to costly mistakes.


The Future of Agile Isn’t ‘agile’

One reason is that agilists introduced too many conflicting and divergent approaches that fragmented the market. “Agile” meant so many things to different people that hiring managers could never predict what they were getting when a candidate’s resume indicated s/he was “experienced in agile development.” Another reason organizations failed to generate value with “agile” was that too many agile approaches focused on changing practices or culture while ignoring the larger delivery system in which the practices operate, reinforcing a culture that is resistant to change. This shouldn’t be a surprise to people following our industry, as my colleague and LeadingAgile CEO Mike Cottmeyer has been talking about why agile fails for over a decade, such as his Agile 2014 presentation, Why is Agile Failing in Large Enterprises… …and what you can do about it. The final reason that led “agile” to its current state of disfavor is that early in the agile movement there was too much money to be made in training and certifications. The industry’s focus on certifications had the effect over time of misaligning the goals of the methodology / training companies and their customers. “Train everyone. Launch trains” may be a short-term success pattern for a methodology purveyor, but it is ultimately unsustainable because the training and practices are too disconnected from tangible results senior executives need to compete and win in the market.


CIOs get serious about closing the skills gap — mainly from within

Staffing and talent issues are affecting CIOs’ ability to double down on strategic and innovation objectives, according to 54% of this year’s respondents. As a result, closing the skills gap has become a huge priority. “What’s driving it in some CIOs’ minds is tied back to their AI deployments,” says Mark Moccia, a vice president research director at Forrester. “They’re under a lot of cost pressure … to get the most out of AI deployments” to increase operational efficiencies and lower costs, he says. “It’s driving more of a need to close the skills gap and find people who have deployed AI successfully.” AI, generative AI, and cybersecurity top the list of skills gaps preventing organizations from achieving objectives, according to an April Gartner report. Nine out of 10 organizations have adopted or plan to adopt skills-based talent growth to address those challenges. ... The best approach, Karnati says, is developing talent from within. “We’re equipping our existing teams with the space, tools, and support needed to explore genAI through practical application, including rapid prototyping, internal hackathons, and proof-of-concept sprints,” Karnati says. “These aren’t just technical exercises — they’re structured opportunities for cross-functional learning, where engineers, product leads, and domain experts collaborate to test real use cases.”


The Critical Quantum Timeline: Where Are We Now And Where Are We Heading?

Technically, the term is fault-tolerant quantum computing. The qubits that quantum computers use to process data have to be kept in a delicate state – sometimes frozen to temperatures very close to absolute zero – in order to stay stable and not “decohere”. Keeping them in this state for longer periods of time requires large amounts of energy but is necessary for more complex calculations. Recent research by Google, among others, is pointing the way towards developing more robust and resilient quantum methods. ... One of the most exciting prospects ahead of us involves applying quantum computing to AI. Firstly, many AI algorithms involve solving the types of problems that quantum computers excel at, such as optimization problems. Secondly, with its ability to more accurately simulate and model the physical world, it will generate huge amounts of synthetic data. ... Looking beyond the next two decades, quantum computing will be changing the world in ways we can’t even imagine yet, just as the leap to transistors and microchips enabled the digital world and the internet of today. It will tackle currently impossible problems, help us create fantastic new materials with amazing properties and medicines that affect our bodies in new ways, and help us tackle huge problems like climate change and cleaning the oceans.


6 hard truths security pros must learn to live with

Every technological leap will be used against you - Information technology is a discipline built largely on rapid advances. Some of these technological leaps can help improve your ability to secure the enterprise. But every last one of them brings new challenges from a security perspective, not the least of which is how they will be used to attack your systems, networks, and data. ... No matter how good you are, your organization will be victimized - This is a hard one to swallow, but if we take the “five stages of grief” approach to cybersecurity, it’s better to reach the “acceptance” level than to remain in denial because much of what happens is simply out of your control. A global survey of 1,309 IT and security professionals found that 79% of organizations suffered a cyberattack within the past 12 months, up from 68% just a year ago, according to cybersecurity vendor Netwrix’s Hybrid Security Trends Report. ... Breach blame will fall on you — and the fallout could include personal liability - As if getting victimized by a security breach isn’t enough, new Securities and Exchange Commission (SEC) rules put CISOs in the crosshairs for potential criminal prosecution. The new rules, which went into effect in 2023, require publicly listed companies to report any material cybersecurity incident within four business days.


Are you an A(I)ction man?

Whilst individually AI-generated action figures have a small impact - a drop in the ocean you could say - trends like this exemplify how easy it is to use AI en masse, and collectively create an ocean of demand. Seeing the number of individuals, even those with knowledge of AI’s lofty resource consumption, partaking in the creation of these avatars, makes me wonder if we need greater awareness of the collective impact of GenAI. Now, I want to take a moment to clarify this is not a criticism of those producing AI-generated content, or of anyone who has taken part in the ‘action figure’ trend. I’ve certainly had many goes with DALL-E for fun, and taken part in various trends in my time, but the volume of these recent images caught my attention. Many of the conversations I had at Connect New York a few weeks ago addressed sustainability and the need for industry collaboration, but perhaps we should also be instilling more awareness from an end-user point of view. After all, ChatGPT, according to the Washington Post, consumes 39.8 million kWh per day. I’d be fascinated to see the full picture of power and water consumption from the AI-generated action figures. Whilst it will only account for a tiny fraction of overall demand, these drops can have a tendency to accumulate. 


The MVP Dilemma: Scale Now or Scale Later?

Teams often have few concrete requirements about scalability. The business may not be a reliable source of information but, as we noted above, they do have a business case that has implicit scalability needs. It’s easy for teams to focus on functional needs, early on, and ignore these implicit scaling requirements. They may hope that scaling won’t be a problem or that they can solve the problem by throwing more computing resources at it. They have a legitimate concern about overbuilding and increasing costs, but hoping that scaling problems won't happen is not a good scaling strategy. Teams need to consider scaling from the start. ... The MVP often has implicit scalability requirements, such as "in order for this idea to be successful we need to recruit ten thousand new customers". Asking the right questions and engaging in collaborative dialogue can often uncover these. Often these relate to success criteria for the MVP experiment. ... Some people see asynchronous communication as another scaling panacea because it allows work to proceed independently of the task that initiated the work. The theory is that the main task can do other things while work is happening in the background. So long as the initiating task does not, at some point, need the results of the asynchronous task to proceed, asynchronous processing can help a system to scale. 


Data Integrity: What It Is and Why It Matters

By contrast, data quality builds on methods for confirming the integrity of the data and also considers the data’s uniqueness, timeliness, accuracy, and consistency. Data is considered “high quality” when it ranks high in all these areas based on the assessment of data analysts. High-quality data is considered trustworthy and reliable for its intended applications based on the organization’s data validation rules. The benefits of data integrity and data quality are distinct, despite some overlap. Data integrity allows a business to recover quickly and completely in the event of a system failure, prevent unauthorized access to or modification of the data, and support the company’s compliance efforts. By confirming the quality of their data, businesses improve the efficiency of their data operations, increase the value of their data, and enhance collaboration and decision-making. Data Quality efforts also help companies reduce their costs, enhance employee productivity, and establish closer relationships with their customers. Implementing a data integrity strategy begins by identifying the sources of potential data corruption in your organization. These include human error, system malfunctions, unauthorized access, failure to validate and test, and lack of Governance. A data integrity plan operates at both the database level and business level.


Backup-as-a-service explained: Your guide to cloud data protection

With BaaS, enterprises have quick, easy access to their data. Providers store multiple copies of backups in different locations so that data can be recovered when lost due to outages, failures or accidental deletion. BaaS also features geographic distribution and automatic failover, when data handling is automatically moved to a different server or system in the event of an incident to ensure that it is safe and readily available. ... With BaaS, the provider uses its own cloud infrastructure and expertise to handle the entire backup and restoration process. Enterprises simply connect to the backup engine, set their preferences and the platform handles file transfer, encryption and maintenance. Automation is the engine that drives BaaS, helping ensure that data is continuously backed up without slowing down network performance or interrupting day-to-day work. Enterprises first select the data they need backed up — whether it be simple files or complex apps — backup frequency and data retention times. ... Enterprises shouldn’t just jump right into BaaS — proper preparation is critical. Firstly, it is important to define a backup policy that identifies the organization’s critical data that must be backed up. This policy should also include backup frequency, storage location and how long copies should be retained.


CISO 3.0: Leading AI governance and security in the boardroom

AI is expanding the CISO’s required skillset beyond cybersecurity to include fluency in data science, machine learning fundamentals, and understanding how to evaluate AI models – not just technically, but from a governance and risk perspective. Understanding how AI works and how to use it responsibly is essential. Fortunately, AI has also evolved how we train our teams. For example, adaptive learning platforms that personalize content and simulate real-world scenarios are assisting in closing the skills gap more effectively. Ultimately, to become successful in the AI space, both CISOs and their teams will need to grasp how AI models are trained, the data they rely on, and the risks they may introduce. CISOs should always prioritize accountability and transparency. Red flags to look out for include a lack of explainability or insufficient auditing capabilities, both of which leave companies vulnerable. It’s important to understand how it handles sensitive data, and whether it has proven success in similar environments. Beyond that, it’s also vital to evaluate how well the tool aligns with your governance model, that it can be audited, and that it integrates well into your existing systems. Lastly, overpromising capabilities or providing an unclear roadmap for support are signs to proceed with caution.

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