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From AI models that boost sales to robots that slash production costs, advanced technologies are transforming both top-line growth and bottom-line efficiency. Business leaders dont need to be technology experts to grasp this shift; they need vision and urgency. Today, that timeline is shrinking dramatically.
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.
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.
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. We ought to heed Collingridge’s warning that technology evolves in uncertain ways. We ought to heed Collingridge’s warning that technology evolves in uncertain ways.
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!
It’s a hot topic, and as technologies continue to evolve at a rapid pace, the scope of the cloud continues to expand. These servers are busy storing, managing, and processing data that enables users to expand or upgrade their infrastructure and retrieve files on demand. The capabilities and breadth of the cloud are enormous.
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.
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.
As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. 1] Retaining outdated technology may seem like a cautious approach but there are mounting inherent dangers. NTT DATAs Coding with Azure OpenAI is a prime example of just such a solution.
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.
The Middle East is rapidly evolving into a global hub for technological innovation, with 2025 set to be a pivotal year in the regions digital landscape. Looking ahead to 2025, Lalchandani identifies several technological trends that will define the Middle Easts digital landscape.
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.
Data analytics is incredibly valuable for helping people. More institutions are recognizing this, so the market for data analytics in education is projected to be worth over $57 billion by 2030. We have previously talked about the many ways that big data is disrupting education.
South Korea’s sudden political upheaval has raised fresh concerns for its economy and global supply chains, with analysts warning of potential disruptions to its critical technology exports. Its dominance in critical areas like memory chips makes it indispensable to industries worldwide.
Data-driven ecommerce companies have a strong advantage over their competitors. As we stated before, data-driven marketing strategies are extremely valuable for ecommerce companies. What kind of ROI can big data offer for the ecommerce sector? What data does your online store need to transfer?
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.
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. These reinvention-ready organizations have 2.5
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.
Taking too long on AI projects Extracting value from AI is a key CEO priority today , and many IT leaders have in turn reshaped their IT agendas to emphasize projects centered on the technology. AI technology is changing so fast that projects taking more than a month can end up built on out-of-date technology, he says.
Agentic AI was the big breakthrough technology for gen AI last year, and this year, enterprises will deploy these systems at scale. 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. Infrastructure modernization In December, Tray.ai
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.
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.
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.
This shift not only reduces the chances of human error but also elevates the quality of outputs across various departments, which reflects a broader trend of harnessing technology to drive meaningful transformation in the workplace. Such investments position enterprises to respond more effectively to market changes and customer demands.
For CIOs and IT leaders, this means improved operational efficiency, data-driven decision making and accelerated innovation. Agentic AIs ability to assess changing conditions in service operations (ServiceOps) and proactively recommend steps to reduce change failures sets the technology apart from traditional AI and automation tools.
As the study’s authors explain, these results underline a clear trend toward more personalized services, data-driven decision-making, and agile processes. Even beyond customer contact, bankers see generative AI as a key transformative technology for their company.
However, successful AI implementation requires more than cutting-edge technology. 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.
Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.
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.
Training models isn’t well understood yet, at least not within companies that haven’t already invested significantly in technology (in general) or AI (in particular). Nor are building data pipelines and deploying ML systems well understood. is having the greatest influence on data science: that’s where the skilled people are.
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.
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. What are GRC certifications? Why are GRC certifications important?
DeepSeeks advancements could lead to more accessible and affordable AI solutions, but they also require careful consideration of strategic, competitive, quality, and security factors, says Ritu Jyoti, group VP and GM, worldwide AI, automation, data, and analytics research with IDCs software market research and advisory practice.
Agentic AI, the more focused alternative to general-purpose generative AI, is gaining momentum in the enterprise, with Forrester having named it a top emerging technology for 2025 in June. The technology is in its early days, and several questions remain open chief among them, how AI agents will be priced.
Intelligent new services and infrastructure can optimize cost and performance, but the rapidly evolving technology environment also introduces complexity. Business transformation is a journey Great modern enterprises are only as good as their technology, which must keep pace with changing business demands.
Adopting emerging technology to deliver business value is a top priority for CIOs, according to a recent report from Deloitte. As technology rapidly evolves, the need for the number of developers will undoubtedly decrease, especially with entry-level roles over time,” Hafez says. But that will change. “As
The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.
Aligning ESG and technological innovation At the core of this transformation is the CIO, a pivotal player whose role has expanded beyond managing technological innovation to overseeing how these innovations contribute to ESG goals. It provides CIOs a roadmap to align these technologies with their organizations’ ESG goals.
AI poses a number of benefits and risks for modern businesses. AI technology is helping with cybersecurity in a myriad of ways. Cybersecurity is the practice of taking precautions to protect data privacy, security, and reliability from being compromised online. One of the most striking examples is in the field of cybersecurity.
As CIOs seek to achieve economies of scale in the cloud, a risk inherent in many of their strategies is taking on greater importance of late: consolidating on too few if not just a single major cloud vendor. This is the kind of risk that may increasingly keep CIOs up at night in the year ahead.
During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. The second-most significant barrier was the availability of quality data. Relatively few respondents are using version control for data and models. Respondents.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Data is the heart of our business, and its centralization has been fundamental for the group,” says Emmelibri CIO Luca Paleari.
Large language models (LLMs) are very good at spotting patterns in data of all types, and then creating artefacts in response to user prompts that match these patterns. Assuming a technology can capture these risks will fail like many knowledge management solutions did in the 90s by trying to achieve the impossible.
The technology has the potential to disrupt and benefit your organization, and CIOs are well-positioned to support the board, prepare the organization for it, and lead the business into the age of AI. AI allows organizations to use growing data more effectively , a fact recognized by the entire leadership team.
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