5 reasons your company could be dealing with a data quality issue
Data is a window into understanding not just how your business operates, but how consumers engage with, and select, your solutions. But according to this annual analyst report from Precisely and Drexel University, 67 percent of organizations don’t trust the data they are using for decision making. That means that your organization could also be dealing with unreliable data. When poor data leads to incorrect decisions, it can mean lost opportunities and wasted resources -- which costs your organization money. In uncertain times, a business's livelihood could even be on the line.
Data quality -- especially when it comes to Marketing, Advertising and Sales data -- impacts how teams assess and move forward in the marketplace. These teams lean on datasets to adjust outreach activities and awareness campaigns. The insights used from data are often the underpinning foundation for how a business not just orients itself for short-term quarterly goals, but also how an organization pivots to gain an edge on competition in the long term.
Making AI agents 'smarter' with connected data
The latest buzzy concept in the world of AI is now transcending its initial hype. Advances in Agentic AI are accelerating the development of autonomous business systems, building upon progress in machine learning. Acting as an independent ‘agent’, the technology can make informed decisions based on the multimodal data and algorithms they’re built on and then ‘learn’ from its experiences.
But even more excitingly, Agentic AI aims to execute tasks independently. It’s this ability to adapt, plan, and execute complex tasks without direct human intervention is what differentiates Agentic AI from its predecessors.
Why auto-scaling is key to cost-effective cloud management
Today’s fast-paced digital businesses are dependent on scalable and adaptable public cloud infrastructures to keep up with the growing needs of their customers, with spending on global cloud infrastructure services predicted to increase by 19 percent this year. Faced with the constant challenge of how to optimise their cloud resources to avoid potential performance issues and unnecessary costs, the best prepared organizations must seek out tools to help achieve the most effective cloud management possible.
Auto-scaling is one such tool, designed to automatically adjust public cloud resources depending on real-time demand. It can help businesses keep their applications agile, responsive and available at the right level, so it is well worth considering its benefits.
Not if, but when -- Why every organization needs a cyber resilience strategy
Because of AI, data has become the most valuable competitive asset for organizations regardless of industry. However, cyber-attacks are continuing to escalate, so the need for robust security measures is more important than ever. It is no longer enough to focus solely on prevention, so organizations must shift their mindset and resources toward rapid recovery and resilience.
For years, IT leaders have funneled time and investments into hardening their networks, building firewalls, and implementing the latest access controls to prevent breaches from malicious threat actors. Yet, despite these efforts, the unfortunate reality remains: cyberattacks, particularly ransomware attacks, are no longer a question of "if," but "when."
What is an AI agent and why should you build one?
AI agents are having a moment. From automating customer service to optimizing supply chains, AI agents are constantly promising to transform how organizations operate -- faster, smarter and more efficiently. In fact, recent research from Salesforce shows that 93 percent of IT leaders plan to implement AI agents in the next two years. But what exactly is an AI agent?
An AI agent is a software system that can autonomously perform tasks like answering customer inquiries and translating documents in multiple languages, improving overall efficiency and customer experience. Unlike traditional automation tools that follow static rules, AI agents continuously learn from data and adapt to changing conditions to make decisions on their own, in real time. That’s what makes AI agents powerful and risky.
What does the future of AI-powered software development look like -- and how secure is it?
AI and machine learning tools have had an important role in software development for many years, helping to drive efficiency and automation. The new generation of AI tools has the potential to supercharge this transformation, bringing even greater improvements to efficiency, cost-effectiveness, and innovation cycles.
However, these tools also come with new risks, including security vulnerabilities, governance challenges, and regulatory uncertainty. As with any new technological approach, organizations bringing new AI tools and specifically AI-generated code into their development lifecycles must balance benefits with the potential risks.
Four common AI pitfalls -- and how to avoid them
Artificial intelligence (AI) is transitioning from an emerging technology to a business mainstay. While many businesses are already reaping the benefits of strategic AI implementation, others are adopting AI solutions without first considering how to integrate the tools strategically. While some AI tools offer tangible gains in automation and efficiency, others overpromise and underdeliver, leading to costly investments with little return.
Distinguishing marketing buzz from real-world impact is critical for businesses looking to make AI a true driver of operational success. Despite AI’s potential, many businesses fall into common pitfalls that prevent them from realizing the full value of innovative technology. From unclear objectives to poor integration and security risks, these challenges can turn AI from a competitive advantage into an expensive mistake.
Enhancing data security in an AI-driven era
For many years, the IT community has consistently emphasized the inherent value and significance of data. Data is one of the greatest resources within a business, even referred to as an organization’s crown jewels, and as a result, has become a vital part of business’ security strategies.
However, as the global interconnectivity of technology continues to grow, securing data and its integrity has become one of the most complex parts of cybersecurity. The driving factor behind this increasing complexity is the broadening use of generative AI (GenAI) and large language models (LLMs), for which training data has largely become the world’s publicly available data.
Beyond words: What AI is really learning -- and what it knows that we never taught it
Imagine you had to finish every sentence in every book ever written -- with just your best guess of the next word. That’s how large language models (LLMs) like GPT-4 start learning.
LLMs use self-supervised learning, meaning they don’t need someone to label or explain the data to them. Instead, they learn by reading vast amounts of text from books, code, academic papers, Wikipedia (and its 57 million+ articles), Reddit forums, and news articles, in addition to billions of others, and then predicting what word comes next in a sentence -- over and over again.
Is AI adoption the next great risk to data resilience?
With cyberattacks surging across every sector from critical national infrastructure to commercial businesses, it’s never been more vital for organizations to get control of their digital footprint and restrict access to their most sensitive data. Instead, organizations are being pulled in the opposite direction by AI, which is demanding access to as much data as possible to deliver much-hyped business solutions.
Organizations worldwide are pouring resources into AI innovation, with spending set to hit an astronomical $632 billion by 2028, according to Gartner. Some are even redesigning their organizational structure, introducing new AI-focused roles and even rerouting workflows as they deploy generative AI into day-to-day operations. At the same time, AI organizations are generating unthinkable amounts of investment with OpenAI raising another $40 billion already this year. It’s clear that AI is here to stay, but have organizations lost sight of their data resilience in a bid to keep up with the AI race?
Why 2025 is the year AI will revolutionize construction
Generative AI is the tech buzzword of the decade -- and the money has followed obediently. In 2025, American Big Tech firms alone announced an investment of $300bn in AI infrastructure. A year earlier, global venture capital investment into AI startups reached approximately $97 billion.
One sector that runs the risk of falling behind is construction. ONS Business Insights report only 12 percent of UK construction businesses use AI, which likely reflects (and contributes to) a more skeptical view of AI in the industry. Compared with UK employees across other industries, 11 percent fewer construction employees are excited by the prospect of AI in the workplace and 34 percent of construction workers are worried about the technology.
Why companies may be overthinking their cloud transformation
Cloud migration is an essential step for companies looking to scale, optimize, and future-proof their operations, but many organizations find themselves overwhelmed by the complexity of migration, often overthinking the process and getting stuck in a paralysis of what if rather than keeping focused on the more important why.
The problem with this hesitancy is that it is slowing progress for their overall digital transformation. Indeed, cloud migration is a complex journey, especially if the organization in question has multiple sites and decades of sunk costs in legacy technologies. But the cloud migration process can still be simplified and executed successfully, provided organizations focus on the right strategies.
The shift from on-call engineers to agentic incident management
Every engineering team I’ve been a part of has had a 10x engineer. They were active contributors to design reviews -- developing a deep, intuitive understanding of the product and every new feature. Beyond writing code, they reviewed every pull request, tracked every change across the product, and kept a mental map of how all the pieces fit together. They were in the right Slack channels, constantly evaluating process or infrastructure changes to understand how their team might be impacted.
They built operational dashboards, and spent the first 15 minutes of their day scanning key metrics to learn what “normal” looked like so they could spot anomalies instantly. They knew their upstream and downstream dependencies, tracked bugs and releases, and stayed up to date on the tools and platforms their team was built on. All of this context led to one inevitable, risky outcome: whenever something broke, they were the only one who knew where to look and how to fix it.
The intersection of wellness and technology: How AI is revolutionizing personalized health
Today’s patients expect medical care with a level of efficiency, accuracy, and convenience than ever before. Unfortunately, in a world where medical professionals are overworked and there is a significant shortage in the availability of labor in the healthcare industry, achieving this is easier said than done, which is why many medical professionals have turned to tools like artificial intelligence to boost their efficiency.
In the medical industry, AI has already been used by medical researchers for years, helping them with their experiments and research. However, innovators throughout the health and wellness industry -- including doctors and leaders of supplement companies -- are beginning to find ways to leverage the power of AI to make their operations more efficient and effective.
The core pillars of cyber resiliency
As we enter a new era of cybersecurity threats, which has prompted the evolution of new vulnerabilities, organizations are challenged on how to best respond to these evolving attacks. The threat landscape is more complex than ever causing organizations to grapple with new tactics to safeguard their critical data.
In 2024, ransomware surged rapidly in acceleration and sophistication, accounting for 23 percent of all intrusions in 2023 compared with 18 percent in 2022 according to Mandiant’s annual M-Trends report. Since the introduction of AI, the ability to automate its deployment can also be attributed to its exponential growth. Most notably, increasing its attack surface to target critical infrastructure, sensitive data, and operational capabilities.
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