What IT hiring looks like heading into 2025
AI isn’t replacing jobs so much as it is reshaping the nature of work, said
Elizabeth Lascaze, a principal in Deloitte Consulting’s Human Capital
practice. She, too, sees evidence that entry-level roles focused on tasks like
note-taking or basic data analysis are declining as organizations seek more
experienced workers for junior positions. “Today’s emerging roles require
workers to quickly leverage data, generate insights, and solve problems,” she
said, adding that those skilled in using AI, such as cybersecurity analysts
applying AI for threat detection, will be highly sought after. Although the
adoption of AI has led to some “growing pains,” many workers are actually
excited about it, Lascaze said, with most employees believing it will create
new jobs and enhance their careers. “Our survey found that just 24% of early
career workers and 14% of tenured workers fear their jobs will be replaced by
AI,” Lascaze said. “Tenured workers are more likely to lead organizational
strategy, so they may prioritize AI’s potential to improve efficiency,
sophistication, and work quality in existing roles rather than AI’s potential
to eliminate certain positions. “These workers reported being slightly more
focused on building AI fluency than early-career employees,” Lascaze
said.
The Future of AI (And Travel) Relies on Synthetic Data
Synthetic data enhances accuracy and fairness in AI models as organic data
can be biased or unbalanced, leading to ML models failing to represent
diverse populations accurately. With synthetic data, researchers can create
datasets that more accurately reflect the demographics they intend to serve,
thereby minimizing biases and improving overall model robustness. ...
Synthetic data can be a double-edged sword. While it addresses data privacy
and availability challenges, it can inadvertently carry or magnify biases
embedded in the original dataset. When source data is flawed, those
imperfections can cascade into the synthetic version, skewing results — a
critical concern in high-stakes domains like healthcare and finance, where
precision and fairness are paramount. To counteract this, having a human in
the loop is super important. While there’s a temptation to use synthetic
data to fill in every gap for better accuracy and fairness, we understood
that running synthetic searches for every flight combination possible
globally for our price tracking and predictions feature could overwhelm our
booking system and impact real travelers organically searching for flights.
Synthetic data has limitations that go beyond bias.
9 Cloud Service Adoption Trends
Most organizations are building modern cloud computing applications to
enable greater scalability while reducing cost and consumption costs.
They’re also more focused on the security and compliance of cloud systems
and how providers are validating and ensuring data protection. “Their main
focus is really around cost, but a second focus would be whether providers
can meet or exceed their current compliance requirements,” says Will
Milewski, SVP of cloud infrastructure and operations at content management
solution provider Hyland. ... There’s a fundamental shift in cloud
adoption patterns, driven largely by the emergence of AI and ML
capabilities. Unlike previous cycles focused primarily on infrastructure
migration, organizations are now having to balance traditional cloud ROI
metrics with strategic technology bets, particularly around AI services.
According to Kyle Campos, chief technology and product officer at cloud
management platform provider CloudBolt Software, this evolution is being
catalyzed by two major forces: First, cloud providers are aggressively
pushing AI capabilities as key differentiators rather than competing on cost
or basic services. Second, organizations are realizing that cloud strategy
decisions today have more profound implications for future innovation
capabilities than ever before.
We’ve come a long way from RPA: How AI agents are revolutionizing automation
As the AI ecosystem evolves, a significant shift is occurring toward
vertical AI agents — highly specialized AI systems designed for specific
industries or use cases. As Microsoft founder Bill Gates said in a recent
blog post: “Agents are smarter. They’re proactive — capable of making
suggestions before you ask for them. They accomplish tasks across
applications. They improve over time because they remember your activities
and recognize intent and patterns in your behavior. “ Unlike traditional
software-as-a-service (SaaS) models, vertical AI agents do more than
optimize existing workflows; they reimagine them entirely, bringing new
possibilities to life. ... The most profound shift in the automation
landscape is the transition from RPA to multi-agent AI systems capable of
autonomous decision-making and collaboration. According to a recent Gartner
survey, this shift will enable 15% of day-to-day work decisions to be made
autonomously by 2028. These agents are evolving from simple tools into true
collaborators, transforming enterprise workflows and systems. ... As AI
agents progress from handling tasks to managing workflows and entire jobs,
they face a compounding accuracy challenge. Each additional step introduces
potential errors, multiplying and degrading overall performance.
8 reasons why digital transformations still fail
“People got really excited about, ‘We’re going to transform,’” Woerner says,
but she believes part of the problem lies with leaders who “didn’t have the
discipline to make the hard choices early on” to get employee buy-in. Ranjit
Varughse, CIO of automotive paint and equipment firm Wesco Group, agrees.
“The first challenge is getting digital transformation buy-in from teams at
the outset. People are creatures of habit, making many hesitant to change
their existing systems and processes,” he says. “Without a clear change
management strategy to get a team aligned, ERP implementations in particular
can be slow, stall, or even fail entirely.” ... Digital transformation isn’t
a technology problem, it’s about understanding how people actually work, not
how we think they should work, Wei says. “At PropertySensor, we scrapped our
first version after realizing real estate agents needed mobile-first
solutions, not desktop dashboards,” he says. ... “People, process, and
technology” is a common phrase technology leaders use when discussing the
critical elements of a transformation. “But the real focus should be people,
people, people,” echoes Megan Williams, vice president of global technology
strategy and transformation at TransUnion.
How companies can address bias and privacy challenges in AI models
Companies understand that AI adoption is existential to their survival, with
the winners of tomorrow being determined by their ability to harness AI
effectively. Furthermore, they understand that their brand’s reputation is
one of their most valuable assets. Missteps with AI—especially in
mission-critical contexts (think of a trading algorithm going awol, a breach
of user privacy, or a failure to meet safety standards)—can erode public
trust and harm a company’s bottom line. This could have dire consequences.
With a company’s competitiveness and potentially its very survival at stake,
AI governance becomes a business imperative that they cannot afford to
ignore. ... Certainly, we see a lot of activity from the government – both
at the state and federal levels – which is creating a fragmented approach.
We also see leading companies who understand that adopting AI is crucial to
their future and want to move fast. They are not waiting for the regulatory
environment to settle and are taking a leadership position in adopting
responsible AI principles to safeguard their brand reputations. So, I
believe companies will act intelligently out of self-interest to accelerate
their AI initiatives and increase business returns.
Ensuring AI Accountability Through Product Liability: The EU Approach and Why American Businesses Should Care
In terms of a substantive law regulating AI (which can be the basis of the
causality presumption under the proposed AI Liability Directive), the
European Union’s Artificial Intelligence Act (AI Act) entered into force on
August 1, 2024, becoming the first comprehensive legal framework for AI
globally. The AI Act applies to providers and developers of AI systems that
are marketed or used within the EU (including free-to-use AI technology),
regardless of whether those providers or developers are established in the
EU or a separate country. The EU AI Act sets forth requirements and
obligations for developers and deployers of AI systems in accordance with
risk-based classification system and a tiered approach to governance, which
are two of the most innovative features of the AI Act. The Act classifies AI
applications into four risk categories: unacceptable risk, high risk,
limited risk, and minimal or no risk. AI systems deemed to pose an
unacceptable risk, such as those that violate fundamental rights, are
outright banned. ... High-risk AI systems, which include areas such as
health care, law enforcement, and critical infrastructure, will face
stricter regulatory scrutiny and must comply with rigorous transparency,
data governance, and safety protocols.
Agentic AI is evolving into specialised assistants, enabling the workforce to focus on value-adding tasks
A structured discovery approach is required to identify high impact areas
for AI adoption rather than siloed use-cases. Infosys Topaz comprises
verticalised blueprints, industry catalogues and strategic AI value map
analysis capabilities. We have created playbooks for industries that lay out
a structured roadmap to embed and mature GenAI into core processes and
operations and across the IT landscape. This includes the right use-cases
across the value stream spanning operations, customer experience, research
and development, etc. As part of our Responsible AI by Design approach, we
implement robust technical and process guardrails to ensure privacy and
security. These include impact assessments, audits, automated policy
enforcement, monitoring tools, and runtime safeguards to filter inputs and
outputs for generative AI. We also use red-teaming and advanced testing
tools to identify vulnerabilities and fortify AI models. Additionally, we
employ privacy-preserving techniques such as Homomorphic Encryption and
Secure Multi-Party Computation to enhance the security and resilience of our
AI solutions. ... AI-driven monitoring tools detect inefficiencies in IT
infrastructure, leveraging predictive analytics and forecasting techniques
to improve utilisation in real time.
Security leaders top 10 takeaways for 2024
One of the most significant new rules, which has received the lion’s share
of press attention, is the ‘materiality’ component, or the need to report
“material” cybersecurity incidents to the SEC within four business days of
discovery. At issue is whether the incident led to significant risk to the
organization and its shareholders. If so, it’s defined as material and must
be reported within four days of this determination being made (not its
initial discovery). “Materiality extends beyond quantitative losses, such as
direct financial impacts, to include qualitative aspects, like reputational
damage and operational disruptions,” he says. McGladrey says the SEC’s
materiality guidance underscores the importance of investor protection in
relation to cybersecurity events and, if in doubt, the safest path is
reporting. “If a disclosure is uncertain, erring on the side of transparency
safeguards shareholders,” he tells CSO. ... As a virtual or fractional CISO
service, Sage has observed startups engaging vCISO services earlier, in
pre-seed and Series A stage and, in some cases, before they’ve finalized
their minimum viable product. “Small technology consulting and boutique
software development groups are looking for ISO 27001 certifications to
ensure they can continue serving their larger customers,” she tells CSO.
Emotional intelligence in IT management: Impact, challenges, and cultural differences
While delivering results is the primary goal of any leader, you can’t forget
that you’re managing people, not machines. Emotional intelligence helps
balance the need for productivity with fairness and empathy. One way to
illustrate this balance is through handling difficult conversations about
career moves. Managing a team of over 100 support specialists for several
years gave me the opportunity to conduct an interesting experiment. Many
employees tend to hide the fact that they are exploring job opportunities
elsewhere until the last minute. This creates unnecessary tension and can
lead to higher turnover. However, if a manager removes the stigma around job
interviews and treats them as part of market research, it encourages open
communication. ... Emotionally intelligent managers possess the ability to
identify the core of a conflict without letting it escalate. Attempting to
gather every single piece of information is not always helpful. Instead,
managers should focus on resolving conflicts, as often the solution is
already within the team. This does not mean conducting surveys or asking for
feedback from each person, as delicate situations require a more refined
approach. A manager should observe, analyze, and extract the most
significant points quickly and intuitively, enabling conflict resolution
before it grows into a larger issue.
Quote for the day:
“Things come to those who wait, but only the things left by those who
hustle” -- Abraham Lincoln
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