Quote for the day:
“Wisdom equals knowledge plus courage. You have to not only know what to do and when to do it, but you have to also be brave enough to follow through.” -- Jarod Kintz
What happens when AI becomes the customer?
If the first point of contact is no longer a person but an AI agent, then
traditional tactics like branding, visual merchandising or website design will
have reduced impact. Instead, the focus will move to how easily machines can
find and understand product information. Retailers will need to ensure that
data, from specifications and availability to pricing and reviews, is accurate,
structured and optimised for AI discovery. Products will no longer be browsed by
humans but scanned and filtered by autonomous systems making selections on
someone else’s behalf. ... This trend is particularly strong among younger and
higher-income consumers. People under 35 are far more likely to use AI
throughout the buying process, particularly for everyday items like groceries,
toiletries and clothes. For this group, convenience matters. Many are
comfortable letting technology take over simple tasks, and when it comes to low
cost, low risk products, they’re happy for AI to handle the entire purchase. ...
These developments point to the rise of the agentic internet – a world in which
AI agents become the main way consumers interact with brands. As these tools
search, compare, buy and manage products on users’ behalf, they will reshape how
visibility, loyalty and influence work. Retailers have less than five years to
respond. That means investing in clean, structured product data, adapting
automation where it’s welcomed, and keeping the human touch where trust
matters. The overlooked cyber risk in data centre cooling systems
Data centre operations are critically dependent on a complex ecosystem of OT
equipment, including HVAC and building management systems. As operators adopt
closed-loop and waterless cooling to improve efficiency, these systems are
increasingly tied into BMS and DCIM platforms. This expands the attack surface
of networks that were once more segmented. A compromise of these systems could
directly affect temperature, humidity or airflow, with clear implications for
the availability of services that critical infrastructure asset owners rely on.
... Resilience also depends on secure remote access, including multi-factor
authentication and controlled jump-host environments for vendors and third
parties. Finally, risk-based vulnerability management ensures that critical
assets are either patched, mitigated, or closely monitored for exploitation,
even where systems cannot easily be taken offline. Taken together, these
controls provide a framework for protecting data centre cooling and building
systems without slowing the drive for efficiency and innovation. ... As the UK
expands its data centre capacity to fuel AI ambitions and digital
transformation, cybersecurity must be designed into the physical systems that
keep those facilities stable. Cooling is not just an operational detail. It is a
potential target — and protecting it is essential to ensuring the sector’s
growth is sustainable, resilient, and secure.
Rethinking Regression Testing with Change-to-Test Mapping
Regression testing is essential to software quality, but in enterprise projects
it often becomes a bottleneck. Full regression suites may run for hours,
delaying feedback and slowing delivery. The problem is sharper in agile and
DevOps, where teams must release updates daily. ... The need for smarter
regression strategies is more urgent than ever. Modern software systems are no
longer monoliths; they are built from microservices, APIs, and distributed
components, each evolving quickly. Every code change can ripple across modules,
making full regressions increasingly impractical. At the same time, CI/CD costs
are rising sharply. Cloud pipelines scale easily but generate massive bills when
regression packs run repeatedly. ... The core idea is simple: “If only part of
the code changes, why not run only the tests covering that part?” Change-to-test
mapping links modified code to the relevant tests. Instead of running the entire
suite on every commit, the approach executes a targeted subset – while retaining
safeguards such as safety tests and fallback runs. What makes this approach
pragmatic is that it does not rely on building a “perfect” model of the system.
Instead, it uses lightweight signals – such as file changes, annotations, or
coverage data – to approximate the most relevant set of tests. Combined with
guardrails, this creates a balance: fast enough to keep up with modern delivery,
yet safe enough to trust in production-grade environments.
Is A Human Touch Needed When Compliance Has Automation?
Even with technical issues, automation may highlight missing patches, but humans
are the ones who must prioritize fixes, coordinate remediation, and validate
that vulnerabilities are closed. Audits highlight this divide even more clearly.
Regulators rarely accept a data dump without explanation. Compliance officers
must be able to explain how controls work, why exceptions exist, and what is
being done to address them. Without human review, automated alerts risk creating
false positives, blind spots, or alert fatigue. Perhaps most critically,
over-dependence on automation can erode institutional knowledge, leaving teams
unprepared to interpret risk independently. ... By eliminating repetitive
evidence collection, teams gain the capacity to analyze training effectiveness,
scenario-plan future threats, and interpret regulatory changes. Automation
becomes not a replacement for people, but a multiplier of their impact. ...
Over-reliance on automation carries its own risks. A clean dashboard may mask
legacy systems still in production or system blind spots if a monitoring tool
goes down. Without active oversight, teams may not discover gaps until the next
audit. There’s also the danger of compliance becoming a “black box,” where staff
interact with dashboards but never learn how to evaluate risk themselves. CIOs
need to actively design against these vulnerabilities.
14 Challenges (And Solutions) Of Filling Fractional Leadership Roles
Filling a fractional leadership role is tough when companies underestimate the
expertise required to thrive in such a role. Fractional leaders need both
autonomy and seamless integration with key stakeholders. ... One challenge of
fractional leadership is grasping the company culture and processes with limited
time on site. Without that context, even the most skilled leader can struggle to
drive meaningful change or build credibility. ... Finding the right culture fit
for a fractional leadership role can be challenging. High-performing leadership
teams are tight-knit ecosystems, and a fractional leader’s challenges with
breaking into them and fitting into their culture can be daunting. ... One
challenge is unrealistic expectations—wanting full-time availability at
part-time cost. The key is to define scope, decision rights and deliverables
upfront. Treat fractional leaders as strategic partners, not stopgaps. Clear
onboarding and aligned incentives are essential to driving value and trust. ...
A common hurdle with fractional roles is misaligned expectations—impact is
needed fast, but boundaries and authority aren’t always defined. The fix? Be
upfront: outline goals, decision-making limits and integration plans early so
leaders can add value quickly without friction.
Will the EU Designate AI Under the Digital Markets Act?
There are two main ways in which the DMA will be relevant for generative AI
services. First, a generative AI player may offer a core platform service and
meet the gatekeeper requirements of the DMA. Second, generative AI-powered
functionalities may be integrated or embedded in existing designated core
platform services and therefore be covered by the DMA obligations. Those
obligations apply in principle to the entire core platform service as
designated, including features that rely on generative AI. ... Cloud computing
is already listed as a core platform service under the DMA, and thus,
designating cloud services would be a much faster process than creating a new
core platform service category. Michelle Nie, a tech policy researcher formerly
with the Open Markets Institute, says the EU should designate cloud providers to
tackle the infrastructural advantages held by gatekeepers. Indeed, she has
previously written for Tech Policy Press that doing so “would help address
several competitive concerns like self-preferencing, using data from businesses
that rely on the cloud to compete against them, or disproportionate conditions
for termination of services.” ... Introducing contestability and fairness, the
stated goals of the DMA, into digital ecosystems increasingly relied on by
private and public institutions could not be more critical.
The Looming Authorization Crisis: Why Traditional IAM Fails Agentic AI
From copilots booking travel to intelligent agents updating systems and
coordinating with other bots, we’re stepping into a world where software can
reason, plan, and operate with increasing autonomy.This shift brings immense
promise and significant risk. The identity and access management (IAM)
infrastructures that we rely upon today were built for people and fixed service
accounts. They weren’t designed to manage self-directing, dynamic digital
agents. And yet that’s what Agentic AI demands. ... The road to a comprehensive
and internationally accessible Agentic AI IAM framework is a daunting task. The
rapid pace of AI development demands accelerated IAM security guidance,
especially for heavily regulated sectors. Continued research, continued
development of standards, and rigorous interoperability are required to prevent
fragmentation into incompatible identity silos. We must also address the ethical
issues, such as bias detection and mitigation in credentials, and offer
transparency and explainability of IAM decisions. ... The stakes are high.
Without a comprehensive plan for managing these agents—one that tracks who they
are, what they can perceive, and when their permissions expire—we risk disaster
through way of complexity and compromise. Identity remains the foundation of
enterprise security, and its scope must reach rapidly to shield the autonomous
revolution.
How immutability tamed the Wild West
One of the first lessons that a new programmer should learn is that global
variables are a crime against all that is good and just. If a variable is passed
around like a football, and its state can change anywhere along the way, then
its state will change along the way. Naturally, this leads to hair pulling and
frustration. Global variables create coupling, and deep and broad coupling is
the true crime against the profession. At first, immutability seems kind of
crazy—why eliminate variables? Of course things need to change! How the heck am
I going to keep track of the number of items sold or the running total of an
order if I can’t change anything? ... The key to immutability is understanding
the notion of a pure function. A pure function is one that always returns the
same output for a given input. Pure functions are said to be deterministic, in
that the output is 100% predictable based on the input. In simpler terms, a pure
function is a function with no side effects. It will never change something
behind your back. ... Immutability doesn’t mean nothing changes; it means values
never change once created. You still “change” by rebinding a name to a new
value. The notion of a “before” and “after” state is critical if you want
features like undo, audit tracing, and other things that require a complete
history of state. Back in the day, GOSUB was a mind-expanding concept. It seems
so quaint today.
What Lessons Can We Learn from the Internet for AI/ML Evolution?
One of the defining principles of the Internet was to keep the core simple and
push the intelligence to the edge. The network and its host computers just
simply delivered packets reliably without dictating or controlling applications.
That principle enabled the explosion of the Web, streaming, and countless other
services. In AI, similar principles should be considered. Instead of
centralizing everything in “one foundational model”, we should empower
distributed agents and edge intelligence. Core infrastructure should stay simple
and robust, enabling diverse use cases on top. ... One of the most important
lessons of all from the Internet is that there be no single company nor
government-owned or controlled TCP/IP stack. It is neutral governance that
created global trust and adoption. Institutions such as ICANN, and the regional
Internet registries (RIRs) played a key role by managing domain names and IP
address assignments in an open and transparent way, ensuring that resources were
allocated fairly across geographies. This kind of neutral stewardship allowed
the Internet to remain interoperable and borderless. On the other hand, today’s
AI landscape is controlled by a handful of big-tech companies. To scale AI
responsibly, we will need similar global governance structures—an “IETF for AI,”
complemented by neutral registries that can manage shared resources such as
model identifiers, agent IDs, coordinating protocols, among others.
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