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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.
Organizations will always be transforming , whether driven by growth opportunities, a pandemic forcing remote work, a recession prioritizing automation efficiencies, and now how agentic AI is transforming the future of work.
Experienced CIOs know there is never a blank check for transformation and innovation investments, and they expect more pressure in 2025 to deliver business value from gen AI investments. As gen AI heads to Gartners trough of disillusionment , CIOs should consider how to realign their 2025 strategies and roadmaps.
Forrester Research this week unleashed a slate of predictions for 2025. Noting that companies pursued bold experiments in 2024 driven by generative AI and other emerging technologies, the research and advisory firm predicts a pivot to realizing value. 40% of highly regulated enterprises will combine data and AI governance.
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. Despite these limitations and concerns among CIOs over AI costs, real progress has been made this year and we can expect to see this grow further in 2025.
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.
This year saw emerging risks posed by AI , disastrous outages like the CrowdStrike incident , and surmounting software supply chain frailties , as well as the risk of cyberattacks and quantum computing breaking todays most advanced encryption algorithms. To respond, CIOs are doubling down on organizational resilience.
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. Development velocity grinds to a halt.
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.
I dont think anyone has any excuses going into 2025 not knowing broadly what these tools can do for them, Mason adds. Do we have the data, talent, and governance in place to succeed beyond the sandbox? These, of course, tend to be in a sandbox environment with curated data and a crackerjack team.
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] 4] On their own AI and GenAI can deliver value.
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.
Big data has become a highly invaluable aspect of modern business. More companies are using sophisticated data analytics and AI tools to overhaul their business models. Some industries have become more dependent on big data than others. The e-commerce sector has been one of the most affected by major advances in data technology.
But 2025 and 2026 will bear good news, according to Deloitte. 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.
Meanwhile, Gartner predicts at least 30% of gen AI projects will be abandoned after the proof-of-concept stage by 2025. Gen AI must be driven by people who want to implement the technology,” he says. Currently, we don’t have gen AI-driven products and services,” he says. “We However, emerging technology must be used carefully.
Imagine standing at the entrance of a vast, ever-expanding labyrinth of data. This is the challenge facing organizations, especially data consumers, today as data volumes explode and complexity multiplies. The compass you need might just be Data Intelligenceand it’s more crucial now than ever before.
Big data is disrupting the healthcare sector in incredible ways. The market for data solutions in healthcare is expected to be worth $67.8 billion by 2025 , which is a remarkable 303% increase from 2017. There are a lot of different applications for big data in the healthcare sector. Better patient outcomes with big data.
As concerns about AI security, risk, and compliance continue to escalate, practical solutions remain elusive. as AI adoption and risk increases, its time to understand why sweating the small and not-so-small stuff matters and where we go from here. The latter issue, data protection, touches every company.
There are risks around hallucinations and bias, says Arnab Chakraborty, chief responsible AI officer at Accenture. So far, over half a million lines of code have been processed but human supervision is required due to the risk of hallucinations and other quality problems. And the data is also used for sales and marketing.
The report underscores a growing commitment to AI-driven innovation, with 67% of business leaders predicting that gen AI will transform their organizations by 2025. The data also shows growing momentum around AI agents, with over half of organizations exploring their use. However, only 12% have deployed such tools to date.
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. This can be great for technically-savvy customers but has the risk of not being sufficiently abstracted from AI costs to hold value over time, he says.
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.
On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.
They will be handing over customer data to AI companies that reserve the right to use it for their own purposes,” Fernandes says. The BloomScale AI, owned by Bloomin Blinds and scheduled to fully launch in early 2025, automates inbound and outbound sales, with an AI-powered call center. And you select from this constellation of tools.”
Predicts 2021: Data and Analytics Leaders Are Poised for Success but Risk an Uncertain Future : By 2023, 50% of chief digital officers in enterprises without a chief data officer (CDO) will need to become the de facto CDO to succeed. Through 2023, up to 10% of AI training data will be poisoned by benign or malicious actors.
Enterprises must reimagine their data and document management to meet the increasing regulatory challenges emerging as part of the digitization era. The cost of compliance These challenges are already leading to higher costs and greater operational risk for enterprises. zettabytes in 2020 to 181 zettabytes in 2025.
Companies are leaning into delivering on data intelligence and governance initiatives in 2025 according to our recent State of Data Intelligence research. Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives.
It is fundamental for AI and essential for reducing cybersecurity risks or streamlining cloud migration processes, among other things. While compliance, risk, resources, and performance metrics might already be there, look for metrics from which you could also derive KPIs for strategic alignment.
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.
Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?
Gartner also recently predicted that 30% of current gen AI projects will be abandoned after proof-of-concept by 2025. Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. What comes up must come down.”
As a result, software supply chains and vendor risk management are becoming ever more vital (and frequent) conversations in the C-suite today, as companies seek to reduce their exposure to outages and the business continuity issues of key vendors their businesses depend on.
In this article, we decided to cover the tendencies in banking loan software in 2022 and give a brief market outlook of AI-driven lending software as a whole. Using up-to-date lending software, banks solely in the North American market have an opportunity to save over $70 billion by 2025. Digital banking market.
The industrial manufacturing industry produces unprecedented amounts of data, which is increasing at an exponential rate. Worldwide data is expected to hit 175 zettabytes (ZB) ?by by 2025, and 90 ZB of this data will be from IoT devices. Can you correlate data across all departments for informed decision- making ?
Organizations today risk falling into a similar scenario known as Shadow AI , where teams turn to public clouds or API service providers in their rush to build or adopt AI solutions. Prioritize an “on-prem first” strategy that brings AI to your data Cost is just one consideration in an increasingly AI-driven world.
It is projected that there will be over 77 million smart homes in the United States by 2025. Fortunately, data scalability has made smart technology more accessible. The growth of smart technology is one of the most beneficial trends brought on by advances in AI. Many factors are driving the demand for smart technology. The impact.
But unknown to many is Indias meteoric rise to become a global leader in AI adoption and it is one to watch: what happens in the Indian market in 2025 will set the scene for the rest to follow. Firstly, India is home to the worlds largest pool of mobile data and is the second-fastest-growing data market globally.
Lineos reduces manual tasks and empowers finance teams to boost productivity and uncover hidden potential within their data RALEIGH, N.C. Lineos supports finance professionals by simplifying complex data into actionable insights, addressing real-world challenges, and enabling confident decision-making.
Cyber risk is increasingly a top executive priority, due in large part to the rising number of unplanned outages, driven by the increasingly sophisticated cyberattacks and widening skills gap. What’s the answer to coping with the dynamic nature of risks? And the problem can’t be ignored. Find more about it here. [1]
It is estimated that the market for artificial intelligence is going to be worth nearly $400 billion by the year 2025. Another major trend influencing the online gaming industry is the growing number of companies turning to AI with data collection. The market for AI is changing in spectacular ways.
Organizations are managing more data than ever. In fact, the global datasphere is projected to reach 175 zettabytes by 2025, according to IDC. With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing.
Deep automation transforms enterprises into living organisms, integrating technologies, processes, and data for self-adjustment. AI-integrated tractors, planters, and harvesters form a data-driven team, optimizing tasks and empowering farmers. Prioritize data quality to ensure accurate automation outcomes.
As organizations shape the contours of a secure edge-to-cloud strategy, it’s important to align with partners that prioritize both cybersecurity and risk management, with clear boundaries of shared responsibility. But outsourcing operational risk is untenable, given the criticality of data-first modernization to overall enterprise success.
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