Quote for the day:
"The actions of a responsible executive are contagious." -- Joe D. Batton
Operational excellence with AI: How companies are boosting success with process intelligence everyone can access
The right tooling can make a company’s processes visible and accessible to
more than just its process experts. With strategic stakeholders and lines of
business users involved, the very people who best know the business can
contribute to innovation, design new processes and cut out endless wasted
hours briefing process experts. AI, essentially, lowers the barrier to entry
so everyone can come into the conversation, from process experts to
line-of-business users. This speeds up time-to-value in transformation. ...
Rather than simply ‘survive,’ companies can use AI to build true resilience —
or antifragility — in which they learn from system failures or cybersecurity
breaches and operationalize that knowledge. By putting AI into the loop on
process breaks and testing potential scenarios via a digital twin of the
organization, non-process experts and stakeholders are empowered to mitigate
risk before escalations. ... Non-process experts must be able to make
data-driven decisions faster with AI powered insights that recommend best
practices and design principles for dashboards. Any queries that arise should
be answered by means of automatically generated visualizations which can be
integrated directly into apps — saving time and effort.
Why Security Leaders Are Opting for Consulting Gigs
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CISOs are asked to balance business objectives alongside product and
infrastructure security, ransomware defense, supply chain security, AI
governance, and compliance with increasingly complex regulations like the
SEC's cyber-incident disclosure rules. Increased pressure for transparency
puts CISOs in a tough situation when they must choose between disclosing an
incident that could have adverse effects on the business or not disclosing it
and risking personal financial ruin. ... The vCISO model emerged as a
practical solution, particularly for midsize companies that need
executive-level security expertise but can't justify a full-time CISO's
compensation package. ... The surge in vCISOs should serve as a warning
to boards and executives. If you're struggling to retain security leadership
or considering a virtual CISO, you need to examine why. Is it about
flexibility and cost, or have you created an environment where security
leaders can't succeed? The pendulum will inevitably swing back as
organizations realize that effective security leadership requires consistent,
dedicated attention. ... Your CISO is working hard to protect your
organization. So who will protect your CISO? Now is a great time to check in
on them. Make sure they feel like they're fighting a winnable fight.
How to Build a Reliable AI Governance Platform
An effective AI governance platform includes four fundamental components: data
governance, technical controls, ethical guidelines and reporting mechanisms,
says Beena Ammanath, executive director of the Global Deloitte AI Institute.
"Data governance is necessary for ensuring that data within an organization is
accurate, consistent, secure and used responsibly," she explains in an online
interview. Technical controls are essential for tasks such as testing and
validating GenAI models to ensure their performance and reliability, Ammanath
says. "Ethical and responsible AI use guidelines are critical, covering
aspects such as bias, fairness, and accountability to promote trust across the
organization and with key stakeholders." ... "AI governance requires a
multi-disciplinary or interdisciplinary approach and may involve
non-traditional partners such as data science and AI teams, technology teams
for the infrastructure, business teams who will use the system or data,
governance and risk and compliance teams -- even researchers and customers,"
Baljevic says. Clark advises working across stakeholder groups. "Technology
and business leaders, as well as practitioners -- from ML engineers to IT to
functional leads -- should be included in the overall plan, especially for
high-risk use case deployments," she says.
Reality Check: Is AI’s Promise to Deliver Competitive Advantage a Dangerous Mirage?
What happens when AI makes our bank’s products completely commoditized and
undifferentiated? It’s not a defeatist question for the industry. Instead, it
suggests a shortcoming in bank and credit union strategic planning about AI,
Henrichs says. "Everyone’s asking about efficiency gains, risk management, and
competitive advantages from AI," he suggests. "The uncomfortable truth is that
if every bank has access to the same AI capabilities [and increasingly do
through vendors like nCino, Q2, and FIS], we’re racing toward commoditization
at an unprecedented speed." ... How can boards lead the institution to use AI
to amplify existing competitive advantages? It’s not just about the
technology. It’s "the combination of technology stack," say Jim Marous,
Co-Publisher of The Financial Brand, with "people, leadership and willingness
to take risks that will result in the quality of AI looking far different from
bank A to bank Z. AI [is about] rethinking what we do. Further, fast follower
doesn’t cut it because trying to copy… ignores the fundamental strategic
changes [happening] behind the scenes." Creativity is not exactly a top
priority in an industry accountable day-in and day-out to regulators, yet it’s
required as technology applies commoditization pressure.
A strategic playbook for entrepreneurs: 4 paths to success
To make educated choices as an entrepreneur, Scott and Stern recommend a
sequential learning process known as test two, choose one for the four
strategies within the compass. This is a systematic process where
entrepreneurs consider multiple strategic alternatives and identify at least
two that are commercially viable before choosing just one. As the authors
write in their book, “The intellectual property and architectural strategies
are worth testing for entrepreneurs who prefer to put in the work developing
and maintaining proprietary technology; meanwhile, value chain and disruption
may work better for leaders looking to execute quickly.” Scott referred to
Vera Wang as a classic example of sequential learning. As a Ralph Lauren
employee and bride-to-be at 35, Wang told her team that she felt there was an
untapped market for older women shopping for wedding dresses. The company
disagreed, so Wang opened her own shop — but she didn’t launch her line of
dresses immediately. Instead, Scott said, Wang filled her shop with
traditional dresses and offered only one new dress of her own. The goal was to
see which types of customers were interested, as well as which aesthetics
ultimately sold, before she started designing her new line. “[Wang] was able
to take what she learned about design, customer, messaging, and price point
and build it into her venture,” Scott said.
Increasing Engineering Productivity, Develop Software Fast and in a Sustainable Way
The real problem comes when speed means cutting corners - skipping tests,
ignoring telemetry, rushing through code reviews. That might seem fine in the
moment, but over time, it leads to tech debt and makes development slower, not
faster. It’s kind of like skipping sleep to get more done. One late night? No
problem. But if you do it every night, your productivity tanks. Same with
software - if you never take time to clean up, everything gets harder to
change. ... Software engineering productivity and sustainability are
influenced by many factors and can mean different things to different people.
For me, the two primary drivers that stand out are code quality and efficient
processes. High-quality code is modular, readable, and well-documented, which
simplifies maintenance, debugging, and scaling, while reducing the burden of
technical debt. ... if the developers are not complaining enough, it’s
probably because they’ve become complacent with, or resigned to, the status
quo. In those cases, we can adopt the "we’re all one team" mindset and
actually help them deliver features for a while – on the very clear
understanding that we will be taking notes about everything that causes
friction and then going and fixing that. That’s an excellent way to get the
ground truth about how development is really going: listening, and hands-on
learning.
Rethinking System Architecture: The Rise of Distributed Intelligence with eBPF
In an IT world driven by centralized decision-making, gathering insights and
applying intelligence often follows a well-established — yet limiting —
pattern. At the heart of this model, large volumes of telemetry,
observability, and application data are collected by “dumb” data collectors.
For analysis, these collectors gather information and ship it to centralized
systems, such as databases, security information, event management (SIEM)
platforms, or data warehouses. ... By processing data at its origin, we
significantly reduce the amount of unnecessary or irrelevant data sent over
the network, resulting in lower information transfer overhead. This minimizes
the load on the infrastructure itself and cuts down on data storage and
processing requirements. The scalability of our systems no longer needs to
hinge on the ability to expand storage and analytics power, which is both
expensive and inefficient. With eBPF, distributed systems can now analyze data
locally, allowing the system to scale out more efficiently as each node can
handle its own data processing needs without overwhelming a centralized point
of control — and failure. Instead of transferring and storing every piece of
data, eBPF can selectively extract the most relevant information, reducing
noise and improving the overall signal quality.
How Explainable AI Is Building Trust in Everyday Products
Explainable AI has already picked up tremendous momentum in almost every
industry. E-commerce platforms are now starting to avail detailed insight to
the user on why a certain product is recommended to them. This reduces
decision fatigue and improves the overall shopping experience. Even streaming
services such as Netflix and Spotify make suggestions like “Because you
watched…” or “Inspired by your playlist.” These insights make users much more
connected with what they consume. In healthcare and fitness, the stakes are
higher. Users literally rely on apps for critical insight into their health
and well-being. Take a dietary suggestion or an exercise recommendation: If
explainable AI provides insight into the whys, then users are more likely to
feel knowledgeable and confident in those decisions. Even virtual assistants
like Alexa and Google Assistant have added explainability features that
provide much-needed context for their suggestions and enhance the user
experience. ... Explainable AI has quite a number of challenges that stand in
the way of its implementation. The need for simplifying such a very complex AI
decision to some explainable form consumable by users is not a trivial task.
The balance lies in clear explanations without oversimplification or
misrepresentation of the logic.
IT execs need to embrace a new role: myth-buster
It’s more imperative than ever that IT leaders from the CIO on down educate
their colleagues. It’s far too easy for eager early adopters to get into tech
trouble, and it’s better to head off problems before your corporate data winds
up, say, being used to train a genAI model. This teaching role is critical for
high-ranking execs (C-level execs, board members) in addition to those on the
enterprise front lines. CFOs tend to fall in love with promised efficiencies
and would-be workforce reductions without understanding all of the
implications. CEOs often want to support what their direct reports want — when
possible — and board members rarely have any in-depth knowledge of technology
issues. It’s especially critical for IT Directors, working with the CIO, to
become indispensable sources of tech truth for any company. Not so long ago,
business units almost always had to route their technology needs through IT.
No more. It’s not a battle that can be won by edicts or directives. IT
directives are often ignored by department heads, and memo mayhem won’t help.
You have to position your advice as cautionary, educational — helpful even —
all in a bid to spare the business unit various disasters. You are their
friend. Only then does it have a chance of working.
Increased Investment in Industrial Cybersecurity Essential for 2025
“The software used in machine controls and other components should be
continuously updated by manufacturers to close newly discovered security
gaps,” said the CEO of ONEKEY. He cites typical examples such as manufacturing
robots, CNC machines, conveyors, packaging machines, production equipment,
building automation systems, and heating and cooling systems, which, in some
cases, rely on outdated software, making them targets for hackers. ...
Firmware, the software embedded in digital control systems, connected devices,
machines, and equipment, should be systematically tested for cyber resilience,
advises Jan Wendenburg, CEO of ONEKEY. However, according to a report, less
than a third (31 percent) of companies regularly conduct security checks on
the software integrated into connected devices to identify and close
vulnerabilities, thereby reducing potential entry points for hackers. ...
Current practices fall far behind the required standards, as shown by the “OT
+ IoT Cybersecurity Report” by ONEKEY. ... “Manufacturers should align their
software development with the upcoming regulatory requirements,” advised Jan
Wendenburg. He added, “It is also recommended that the industry requires its
suppliers to guarantee and prove the cyber resilience of their products.”
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