Quote for the day:
"You don’t have to be great to start, but you have to start to be great." — Zig Ziglar
Why tomorrow’s best devs won’t just code — they’ll curate, coordinate and command AI
It is not just about writing code anymore — it is about understanding systems,
structuring problems and working alongside AI like a team member. That is a tall
order. That said, I do believe that there is a way forward. It starts by
changing the way we learn. If you are just starting out, avoid relying on AI to
get things done. It is tempting, sure, but in the long run, it is also harmful.
If you skip the manual practice, you are missing out on building a deeper
understanding of how software really works. That understanding is critical if
you want to grow into the kind of developer who can lead, architect and guide AI
instead of being replaced by it. ... AI-augmented developers will replace large
teams that used to be necessary to move a project forward. In terms of
efficiency, there is a lot to celebrate about this change — reduced
communication time, faster results and higher bars for what one person can
realistically accomplish. But, of course, this does not mean teams will
disappear altogether. It is just that the structure will change. ... Being
technically fluent will still remain a crucial requirement — but it won’t be
enough to simply know how to code. You will need to understand product thinking,
user needs and how to manage AI’s output. It will be more about system design
and strategic vision. For some, this may sound intimidating, but for others, it
will also open many doors. People with creativity and a knack for
problem-solving will have huge opportunities ahead of them. The Wild West of Shadow IT
From copy to deck generators, code assistants, and data crunchers, most of them
were never reviewed or approved. The productivity gains of AI are huge.
Productivity has been catapulted forward in every department and across every
vertical. So what could go wrong? Oh, just sensitive data leaks, uncontrolled
API connections, persistent OAuth tokens, and no monitoring, audit logs, or
privacy policies… and that's just to name a few of the very real and dangerous
issues. ... Modern SaaS stacks form an interconnected ecosystem. Applications
integrate with each other through OAuth tokens, API keys, and third-party
plug-ins to automate workflows and enable productivity. But every integration is
a potential entry point — and attackers know it. Compromising a lesser-known
SaaS tool with broad integration permissions can serve as a stepping stone into
more critical systems. Shadow integrations, unvetted AI tools, and abandoned
apps connected via OAuth can create a fragmented, risky supply chain. ...
Let's be honest - compliance has become a jungle due to IT democratization. From
GDPR to SOC 2… your organization's compliance is hard to gauge when your
employees use hundreds of SaaS tools and your data is scattered across more AI
apps than you even know about. You have two compliance challenges on the table:
You need to make sure the apps in your stack are compliant and you also need to
assure that your environment is under control should an audit take place.
Edge Computing: Not Just for Tech Giants Anymore
A resilient local edge infrastructure significantly enhances the availability
and reliability of enterprise digital shopfloor operations by providing powerful
on-premises processing as close to the data source as possible—ensuring
uninterrupted operations while avoiding external cloud dependency. For
businesses, this translates to improved production floor performance and
increased uptime—both critical in sectors such as manufacturing, healthcare, and
energy. In today’s hyperconnected market, where customers expect seamless
digital interactions around the clock, any delay or downtime can lead to lost
revenue and reputational damage. Moreover, as AI, IoT, and real-time analytics
continue to grow, on-premises OT edge infrastructure combined with
industrial-grade connectivity such as private 4.9/LTE or 5G provides the
necessary low-latency platform to support these emerging technologies. Investing
in resilient infrastructure is no longer optional, it’s a strategic imperative
for organisations seeking to maintain operational continuity, foster innovation,
and stay ahead of competitors in an increasingly digital and dynamic global
economy. ... Once, infrastructure decisions were dominated by IT and boiled down
to a simple choice between public and private infrastructure. Today, with IT/OT
convergence, it’s all about fit-for-purpose architecture. On-premises edge
computing doesn’t replace the cloud — it complements it in powerful ways.
A Reporting Breakthrough: Advanced Reporting Architecture
Advanced Reporting Architecture is based on a powerful and scalable SaaS
architecture, which efficiently addresses user-specific reporting requirements
by generating all possible reports upfront. Users simply select and analyze the
views that matter most to them. The Advanced Reporting Architecture’s SaaS
platform is built for global reach and enterprise reliability, with the
following features: Modern User Interface: Delivered via AWS, optimized for
mobile and desktop, with seamless language switching (English, French, German,
Spanish, and more to come). Encrypted Cloud Storage: Ensuring uploaded files and
reports are always secure. Serverless Data Processing: High-precision processing
that analyzes user-uploaded data and uses data influenced relevant factors to
maximizing analytical efficiencies and lower the cost of processing efforts.
Comprehensive Asset Management: Support for editable reports, dashboards,
presentations, pivots, and custom outputs. Integrated Payments & Accounting:
Powered by PayPal and Odoo. Simple Subscription Model: Pay only for what you
use—no expensive licenses, hardware, or ongoing maintenance. Some leading-edge
reporting platforms, such as PrestoCharts, are based on Advanced Reporting
Architecture and have been successful in enabling business users to develop
custom reports on the fly. Thus, Advanced Reporting Architecture puts reporting
prowess in the hands of the user.
These jobs face the highest risk of AI takeover, according to Microsoft
According to the report -- which has yet to be peer-reviewed -- the most at-risk
jobs are those that are based on the gathering, synthesis, and communication of
information, at which modern generative AI systems excel: think translators,
sales and customer service reps, writers and journalists, and political
scientists. The most secure jobs, on the other hand, are supposedly those that
depend more on physical labor and interpersonal skills. No AI is going to
replace phlebotomists, embalmers, or massage therapists anytime soon. ... "It is
tempting to conclude that occupations that have high overlap with activities AI
performs will be automated and thus experience job or wage loss, and that
occupations with activities AI assists with will be augmented and raise wages,"
the Microsoft researchers note in their report. "This would be a mistake, as our
data do not include the downstream business impacts of new technology, which are
very hard to predict and often counterintuitive." The report also echoes what's
become something of a mantra among the biggest tech companies as they ramp up
their AI efforts: that even though AI will replace or radically transform many
jobs, it will also create new ones. ... It's possible that AI could play a role
in helping people practice that skill. About one in three Americans are already
using the technology to help them navigate a shift in their career, a recent
study found.
AIBOMs are the new SBOMs: The missing link in AI risk management
AIBOMs follow the same formats as traditional SBOMs, but contain AI-specific content and metadata, like model family, acceptable usage, AI-specific licenses, etc. If you are a security leader at a large defense contractor, you’d need the ability to identify model developers and their country of origin. This would ensure you are not utilizing models originating from near-peer adversary countries, such as China. ... The first step is inventorying their AI. Utilize AIBOMs to inventory your AI dependencies, monitor what is approved vs. requested vs. denied, and ensure you have an understanding of what is deployed where. The second is to actively seek out AI, rather than waiting for employees to discover it. Organizations need capabilities to identify AI in code and automatically generate resulting AIBOMs. This should be integrated as part of the MLOps pipeline to generate AIBOMs and automatically surface new AI usage as it occurs. The third is to develop and adopt responsible AI policies. Some of them are fairly common-sense: no contributors from OFAC countries, no copylefted licenses, no usage of models without a three-month track record on HuggingFace, and no usage of models over a year old without updates. Then, enforce those policies in an automated and scalable system. The key is moving from reactive discovery to proactive monitoring.2026 Budgets: What’s on Top of CIOs’ Lists (and What Should Be)
CIO shops are becoming outcome-based, which makes them accountable for what
they’re delivering against the value potential, not how many hours were burned.
“The biggest challenge seems to be changing every day, but I think it’s going to
be all about balancing long-term vision with near-term execution,” says Sudeep
George, CTO at software-delivered AI data company iMerit. “Frankly, nobody has a
very good idea of what's going to happen in 2026, so everyone's placing bets,”
he continues. “This unpredictability is going to be the nature of the beast, and
we have to be ready for that.” ... “Reducing the amount of tech debt will always
continue to be a focus for my organization,” says Calleja-Matsko. “We’re
constantly looking at re-evaluating contracts, terms, [and] whether we have
overlapping business capabilities that are being addressed by multiple tools
that we have. It's rationalizing, she adds, and what that does is free up
investment. How is this vendor pricing its offering? How do we make sure we
include enough in our budget based on that pricing model? “That’s my challenge,”
Calleja-Matsko emphasizes. Talent is top of mind for 2026, both in terms of
attracting it and retaining it. Ultimately though, AI investments are enabling
the company to spend more time with customers. Digital Twin: Revolutionizing the Future of Technology and Industry
The rise of the cyberspace of Things [IoT] has made digital twin
technology more relevant and accessible. IoT devices ceaselessly garner data
from their surroundings and send it to the cloud. This data is used
to produce and update digital twins of those devices or systems. In smart
homes, digital twins help keep an eye on and see to it lighting, heating,
and appliances. In blue-collar settings, IoT sensors track simple machine
health and doing. Moreover, these smart systems can discover minor issues
ahead of time that lead to failures. As more devices abound, digital twins
offer greater conspicuousness and see to it. ... Despite its benefits, digital
twin technology comes with challenges. One major issue is the high cost
of carrying out. Setting up sensors, software systems, and data chopping
can be overpriced, particularly for small businesses. There are also
concerns about the data security system and privacy. Since digital twins rely
on straight data flow, any rift can be risky. Integrating digital twins into
existing systems can be involved. Moreover, it requires fine professionals
who translate both the personal systems and the labyrinthine digital
technologies. A different dispute is ensuring the caliber and truth of
the data. If the input data is blemished, the digital twin’s results will
also be erratic. Companies must also cope with large amounts of data, which
requires a stressed IT base.
Why Banks Must Stop Pretending They’re Not Tech Companies
The most successful "banks" of the future may not even call themselves banks at
all. While traditional institutions cling to century-old identities rooted in
vaults and branches, their most formidable competitors are building financial
ecosystems from the ground up with APIs, cloud infrastructure, and data-driven
decision engines. ... The question isn’t whether banks will become technology
companies. It’s whether they’ll make that transition fast enough to remain
relevant. And to do this, they must rethink their identity by operating as
technology platforms that enable fast, connected, and customer-first
experiences. ... This isn’t about layering digital tools on top of legacy
infrastructure or launching a chatbot and calling it innovation. It’s about
adopting a platform mindset — one that treats technology not as a cost center
but as the foundation of growth. A true platform bank is modular, API-first, and
cloud-native. It uses real-time data to personalize every interaction. It
delivers experiences that are intuitive, fast, and seamless — meeting customers
wherever they are and embedding financial services into their everyday lives.
... To keep up with the pace of innovation, banks must adopt skills-based models
that prioritize adaptability and continuous learning. Upskilling isn’t optional.
It’s how institutions stay responsive to market shifts and build lasting
capabilities. And it starts at the top.Colo space crunch could cripple IT expansion projects
For enterprise IT execs who already have a lot on their plates, the lack of
available colocation space represents yet another headache to deal with, and one
with major implications. Nobody wants to have to explain to the CIO or the board
of directors that the company can’t proceed with digitization efforts or AI
projects because there’s no space to put the servers. IT execs need to start the
planning process now to get ahead of the problem. ... Demand has outstripped
supply due to multiple factors, according to Pat Lynch, executive managing
director at CBRE Data Center Solutions. “AI is definitely part of the demand
scenario that we see in the market, but we also see growing demand from
enterprise clients for raw compute power that companies are using in all aspects
of their business.” ... It’s not GPU chip shortages that are slowing down new
construction of data centers; it’s power. When a hyperscaler, colo operator or
enterprise starts looking for a location to build a data center, the first thing
they need is a commitment from the utility company for the required megawattage.
According to a McKinsey study, data centers are consuming more power due to the
proliferation of the power-hungry GPUs required for AI. Ten years ago, a 30 MW
data center was considered large. Today, a 200 MW facility is considered
normal.
