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If 2023 was the year of AI discovery and 2024 was that of AI experimentation, then 2025 will be the year that organisations seek to maximise AI-driven efficiencies and leverage AI for competitive advantage. Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose.
It’s difficult to argue with David Collingridge’s influential thesis that attempting to predict the risks posed by new technologies is a fool’s errand. However, there is one class of AI risk that is generally knowable in advance. We ought to heed Collingridge’s warning that technology evolves in uncertain ways.
In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
The high number of Al POCs but low conversion to production indicates the low level of organizational readiness in terms of data, processes and IT infrastructure, IDCs authors report. Companies pilot-to-production rates can vary based on how each enterprise calculates ROI especially if they have differing risk appetites around AI.
Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network
Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in. You won’t want to miss this webinar!
One of the world’s largest risk advisors and insurance brokers launched a digital transformation five years ago to better enable its clients to navigate the political, social, and economic waves rising in the digital information age.
The Evolution of Expectations For years, the AI world was driven by scaling laws : the empirical observation that larger models and bigger datasets led to proportionally better performance. Security Letting LLMs make runtime decisions about business logic creates unnecessary risk. Its quick to implement and demos well.
This approach will help businesses maximize the benefits of agentic AI while mitigating risks and ensuring responsible deployment. Building trust through human-in-the-loop validation and clear governance structures is essential to establishing strict protocols that guide safer agent-driven decisions.
Call it survival instincts: Risks that can disrupt an organization from staying true to its mission and accomplishing its goals must constantly be surfaced, assessed, and either mitigated or managed. While security risks are daunting, therapists remind us to avoid overly stressing out in areas outside our control.
One of the world’s largest risk advisors and insurance brokers launched a digital transformation five years ago to better enable its clients to navigate the political, social, and economic waves rising in the digital information age.
CIOs perennially deal with technical debts risks, costs, and complexities. While the impacts of legacy systems can be quantified, technical debt is also often embedded in subtler ways across the IT ecosystem, making it hard to account for the full list of issues and risks.
It provides better data storage, data security, flexibility, improved organizational visibility, smoother processes, extra data intelligence, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing costs.
We actually started our AI journey using agents almost right out of the gate, says Gary Kotovets, chief data and analytics officer at Dun & Bradstreet. In addition, because they require access to multiple data sources, there are data integration hurdles and added complexities of ensuring security and compliance.
Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. Two big things: They bring the messiness of the real world into your system through unstructured data.
But supporting a technology strategy that attempts to offset skills gaps by supplanting the need for those skills is also changing the fabric of IT careers — and the long-term prospects of those at risk of being automated out of work. And while AI is already developing code, it serves mostly as a productivity enhancer today, Hafez says.
What is it, how does it work, what can it do, and what are the risks of using it? ChatGPT, or something built on ChatGPT, or something that’s like ChatGPT, has been in the news almost constantly since ChatGPT was opened to the public in November 2022. A quick scan of the web will show you lots of things that ChatGPT can do. It’s much more.
Despite AI’s potential to transform businesses, many senior technology leaders find themselves wrestling with unpredictable expenses, uneven productivity gains, and growing risks as AI adoption scales, Gartner said. Gartner’s data revealed that 90% of CIOs cite out-of-control costs as a major barrier to achieving AI success.
At the same time, the scale of observability data generated from multiple tools exceeds human capacity to manage. Observability builds on the growth of sophisticated IT monitoring tools, starting with the premise that the operational state of every network node should be understandable from its data outputs.
In 2018, I wrote an article asking, “Will your company be valued by its price-to-data ratio?” The premise was that enterprises needed to secure their critical data more stringently in the wake of data hacks and emerging AI processes. Data theft leads to financial losses, reputational damage, and more.
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.
In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. That being said, it seems like we’re in the midst of a data analysis crisis. That being said, it seems like we’re in the midst of a data analysis crisis.
CIOs feeling the pressure will likely seek more pragmatic AI applications, platform simplifications, and risk management practices that have short-term benefits while becoming force multipliers to longer-term financial returns. CIOs should consider placing these five AI bets in 2025.
There are risks around hallucinations and bias, says Arnab Chakraborty, chief responsible AI officer at Accenture. The next evolution of AI has arrived, and its agentic. AI agents are powered by the same AI systems as chatbots, but can take independent action, collaborate to achieve bigger objectives, and take over entire business workflows.
As a business executive who has led ventures in areas such as space technology or data security and helped bridge research and industry, Ive seen first-hand how rapidly deep tech is moving from the lab into the heart of business strategy. The takeaway is clear: embrace deep tech now, or risk being left behind by those who do.
Still, CIOs have reason to drive AI capabilities and employee adoption, as only 16% of companies are reinvention ready with fully modernized data foundations and end-to-end platform integration to support automation across most business processes, according to Accenture. Gen AI holds the potential to facilitate that.
With cybersecurity now fundamental to business operations, it must be considered alongside financial, operational, and reputational risk planning to ensure continuity in the face of disruptions. The survey results underscore the pressing need for organizations to rethink their approach and shift towards resilience by design.
A lot of the current focus with AI projects is to cut costs and drive efficiencies, but organizations also need to think about longer-term innovation, says Taylor Brown, co-founder and COO of Fivetran, vendor of a data management platform. Don’t borrow from the future to get quick wins that your organization won’t be able to scale.”
AI systems can analyze vast amounts of data in real time, identifying potential threats with speed and accuracy. Companies like CrowdStrike have documented that their AI-driven systems can detect threats in under one second. Thats the potential of AI-driven automated incident response.
In today’s fast-paced digital environment, enterprises increasingly leverage AI and analytics to strengthen their risk management strategies. By adopting AI-driven approaches, businesses can better anticipate potential threats, make data-informed decisions, and bolster the security of their assets and operations.
According to research from NTT DATA , 90% of organisations acknowledge that outdated infrastructure severely curtails their capacity to integrate cutting-edge technologies, including GenAI, negatively impacts their business agility, and limits their ability to innovate. [1] The foundation of the solution is also important.
For CIOs and IT leaders, this means improved operational efficiency, data-driven decision making and accelerated innovation. The lack of a single approach to delivering changes increases the risk of introducing bugs or performance issues in production. Agentic AI promises to transform enterprise IT work.
In today’s data-rich environment, the challenge isn’t just collecting data but transforming it into actionable insights that drive strategic decisions. For organizations, this means adopting a data-driven approach—one that replaces gut instinct with factual evidence and predictive insights. What is BI Consulting?
Whether driven by my score, or by their own firsthand experience, the doctors sent me straight to the neonatal intensive care ward, where I spent my first few days. And yet a number or category label that describes a human life is not only machine-readable data. Algorithms tell stories about who people are.
Infor’s Embedded Experiences allows users to create first drafts of text for specific business purposes and summarize insights as well as quickly analyze and interact with data. And its GenAI knowledge hub uses retrieval-augmented generation to provide immediate access to knowledge, potentially from multiple data sources.
I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.
Nor are building data pipelines and deploying ML systems well understood. That doesn’t mean we aren’t seeing tools to automate various aspects of software engineering and data science. We’ve also seen (and featured at O’Reilly’s AI Conference) Snorkel , an ML-driven tool for automated data labeling and synthetic data generation.
GRC certifications validate the skills, knowledge, and abilities IT professionals have to manage governance, risk, and compliance (GRC) in the enterprise. Enter the need for competent governance, risk and compliance (GRC) professionals. Why are GRC certifications important? Is GRC certification worth it?
As a major producer of memory chips, displays, and other critical tech components, South Korea plays an essential role in global supply chains for products ranging from smartphones to data centers. Its dominance in critical areas like memory chips makes it indispensable to industries worldwide. It accounts for 60.5%
From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.
As regulatory scrutiny, investor expectations, and consumer demand for environmental, social and governance (ESG) accountability intensify, organizations must leverage data to drive their sustainability initiatives. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. As digital transformation accelerates, so do the risks associated with cybersecurity.
“ On peut interroger n’importe qui, dans n’importe quel état; ce sont rarement les réponses qui apportent la vérité, mais l’enchaînement des questions. “ “ You can interrogate anyone, no matter what their state of being. “ – Inspector Pastor in La Fée Carabine, by Daniel Pennac. And that’s fine.
It demands a robust foundation of consistent, high-quality data across all retail channels and systems. AI has the power to revolutionise retail, but success hinges on the quality of the foundation it is built upon: data. The Data Consistency Challenge However, this AI revolution brings its own set of challenges.
The implementation of these techniques in medical care will allow the transformation of the way we diagnose, in addition to personalizing treatments, helping to identify risk factors and, in general, improving the results and productivity of the health sector. Using that data and running AI on top will prevent early disease in the future.
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