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When executives got tired of long project timelines, unclear requirements, and missed deadlines, Agile seemed like the ultimate solution since it promised faster delivery, more flexibility, and closer alignment to business needs so the strategy could be realized quicker and with a higher ROI. Agile can’t fix this. No methodology can.
Past shifts to agile methodologies helped as teams now had a product owne r to prioritize backlogs and adopted agile principles that empowered them to commit to a realistic amount of work. But many enterprises stopped their agile transformations at this layer.
In the State of Enterprise Architecture 2023 , only 26% of respondents fully agreed that their enterprise architecture practice delivered strategic benefits, including improved agility, innovation opportunities, improved customer experiences, and faster time to market.
Speed and agility bring in the top transformation prize. Go all-in with agile Another way to ensure IT can quickly deliver transformative results is to go all-in with modern approaches, starting with a full embrace of agile development. The 2024 State of Agile report from Digital.ai
2024 GEP Procurement & Supply Chain Tech Trends Report — explores the biggest technological trends in procurement and supply chain, from generative AI and the advancement of low-code development tools to the data management and analytics applications that unlock agility, cost efficiency, and informed decision-making.
Structured frameworks such as the Stakeholder Value Model provide a method for evaluating how IT projects impact different stakeholders, while tools like the Business Model Canvas help map out how technology investments enhance value propositions, streamline operations, and improve financial performance.
Rather than view this situation as a hindrance, it can be framed as an opportunity to reassess the value of existing tools, with an eye toward potentially squeezing more value out of them prior to modernizing them. A first step, Rasmussen says, is ensuring that existing tools are delivering maximum value.
Many IT teams use agile methodologies to iteratively deliver feature-rich releases, improve capabilities, address technical debt, and experiment with emerging technologies. I recently moderated Adaptavist’s “Agile Back to Basics” roundtable, which included three authors of the Agile Manifesto.
That means you need not just someone who knows cloud features, but also someone who knows how to design a hybrid application model with one foot in a static data center and the other kicking around in an agile ether. That, it turns out, is very hard to find. Talent is the problem, enterprises say.
Speaker: Peter Taylor, Speaker/Author, The Lazy Project Manager
Business agile is an approach that gives the right business flexibility and fast decision-making in a volatile environment, providing a great capacity for innovation, adaptation and change. Businesses everywhere are trying to “get business agile”—but it’s not easy to adapt to becoming this adaptive.
Gen AI-driven application modernization tools are revolutionizing mainframe modernization strategies, accelerating time to value, and closing mainframe skills gaps by enabling developers to modernize or build applications faster and more efficiently,” the study stated.
Instead of succumbing to the pressure, CIOs should consider collaborative approaches: Empower and inspire agile teams by avoiding rigid delivery roadmaps, highlighting what performance improvement areas are worth focusing on, and giving teams time to reset after major releases.
Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs. Optimize data flows for agility. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. Cloud storage. Cloud computing.
As the study’s authors explain, these results underline a clear trend toward more personalized services, data-driven decision-making, and agile processes. AI as differentiator According to the study’s authors, banks that are strategically positioned in AI and agility can achieve transformative growth and efficiency.
Speaker: Leo Zhadanovsky, Principal Solutions Architect, Amazon Web Services
To get there, Amazon focused on decomposing for agility, making critical cultural and operational changes, and creating tools for software delivery. Whether you're developing for a small startup or a large corporation, learning the tools for CI/CD will make your good DevOps team great. The "two pizza" team culture.
And he believes these tools not only streamline management and allow for more precise administration of resources, but also open up a range of possibilities to personalize the customer experience. We train and equip our teams with the necessary tools to integrate technology into their daily work, fostering constant and natural innovation.
An ideal working environment for this is based on three pillars: autonomy, collaboration and agility. This hybrid integration model made it possible to maintain the Rookout teams innovative strength and agility while making optimal use of the strengths of the existing development infrastructure.
The agile methodology, which facilitates collaboration between stakeholders, teams, and customers during software development, is fast gaining prominence in today’s enterprises. The Scrum master leads this process, providing guidance to the team and product owner and ensuring agile practices are followed by team members.
Altehed also says that Volvo’s systems span five decades, starting with mainframe environments from the 1970s and into modern technologies and tools recently launched. Paring down agile Another change the digital organization has gone through recently is to start backing away from a pure agile approach.
Innovation starts when you unleash the creativity and passion of your people, giving them the tools to navigate change, leverage analytics, and help build agility into the business.
What CIOs can do: Avoid and reduce data debt by incorporating data governance and analytics responsibilities in agile data teams , implementing data observability , and developing data quality metrics. AI debt that will require significant rework Gen AI tools and capabilities are introducing new sources of technical debt.
Decisions made in isolation lead to inefficiencies, slower responses to market changes, and a lack of agility that stifles innovation. Architects help organizations remain agile, innovative, and aligned by bridging gaps between strategy and technology. The future of leadership is agile, adaptable and architecturally driven.
AI is really the brain driving humanoid robots like Agility, Tesla Optimus, and Boston Dynamics Atlas. Today, key vendors xAI, Meta, IBM, Boston Dynamics, Agility Robotics, Apptronik, Figure.ai, FourierIntelligence, and Sanctuary.ai have plans to develop AI humanoid robots that can reason and adapt.
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]
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. In 2025, data masking will not be merely a compliance tool for GDPR, HIPPA, or CCPA; it will be a strategic enabler.
Technology investments, such as in generative AI, are a priority in addressing the need to meet rising expectations while also driving operational agility and resilience. Agility and innovation are no longer competitive advantages theyre necessities, Barnett states.
The COVID-19 pandemic has influenced the networking arena in a number of ways, including the rise of fully automated remote offices, the need to support a "branch of one," and the growth of new communications software tools. One of the biggest trends we are seeing is business agility.
There’s a balance to strike, but with the right tools and procedures in place, developers can be empowered to uphold high governance standards while achieving greater agility, cost-efficiency, and creativity in serverless environments.
Data is the foundation of innovation, agility and competitive advantage in todays digital economy. Key recommendations include investing in AI-powered cleansing tools and adopting federated governance models that empower domains while ensuring enterprise alignment. Data is spread across disconnected tools and legacy systems.
Many workers are not waiting for guidance and permission when it comes to adopting AI tools, leading to the emergence of shadow AI. In late 2023, a report from ISACA suggested that up to two-thirds of workers are using unsanctioned AI tools, despite only 11% organisations having a formal policy permitting its use.
With the right tools. Stage 2: Operational EA Delivering business optimization and agility Once the foundational structure is established, EA can shift from static documentation to active execution. By embedding EA into execution, organizations can achieve agility, improve innovation cycles, and scale transformation initiatives.
The first is to foster a culture of agility, collaboration, and AI-driven innovation, driven in part by our new Office of AI. The driver for the Office was the initial need for AI ethics policies, but it quickly expanded to aligning on the right tools and use cases. Its a three-pronged effort.
Whether youre in an SMB or a large enterprise, as a CIO youve likely been inundated with AI apps, tools, agents, platforms, and frameworks from all angles. Theyre using approved tools and exploring others too, increasing the risk of leaking data. of IT budgets by 2027. the same time, people are experimenting.
Transformational CIOs continuously invest in their operating model by developing product management, design thinking, agile, DevOps, change management, and data-driven practices. AI tools exacerbate the issue by exposing these data pockets, creating new security risks.
But this past fall, the engineer retested a gen AI tool and found the accuracy of its outputs had significantly improved. Implementing AI tools isnt enough; we must invest in training, coaching, and support for our teams so they can fully integrate these capabilities into their work. Rather, AI is an augmentation tool.
What companies need to do in order to cope with future challenges is adapt quickly: slim down and become more agile, be more innovative, become more cost-effective, yet be secure in IT terms. If these don’t have a modern foundation, then the whole transformation project will be doomed to failure. That’s why the issue is so important today.
The dynamic nature of the cloud — and the need for continually optimize operations — often drives requirements unique to a CIO’s enterprise, meaning that even some popular third-party cloud cost optimization tools may no longer fit an enterprise’s specific requirements. Garcia gives the example of the AWS cURL file, written three times daily.
The Zscaler ThreatLabz team observed that phishing threats have reached unprecedented levels of sophistication in the past year, driven by the proliferation of generative AI tools. However, the questions surrounding the secure adoption of these AI tools and the defense against AI-driven threats remain unresolved.
Think of IT service management (ITSM) as the glue that brings the disparate parts of IT together: the policies, processes, and tools to design and enhance the IT equipment, resources, and services. That’s because their approach is often not agile enough to keep pace with today’s evolving business landscape.
The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. Business objectives must be articulated and matched with appropriate tools, methodologies, and processes. But this scenario is avoidable. Check out this webinar to get the most from your cloud analytics migration.
On top of that, IT teams have adopted DevOps, agile and SRE practices that drive much greater frequency of change into IT systems and landscapes. At the same time, the scale of observability data generated from multiple tools exceeds human capacity to manage. These challenges drive the need for observability and AIOps.
In the event of eliminating or reducing agile work, 68% of Italians would remain where they are but would start looking for a new job, and 7% would leave immediately even without an alternative, according to recruiting company Hays Italia and law firm Daverio & Florio. million compared to about 3.6
Procurement teams are also held back by operational efficiencies stemming from inconsistent workflows, leading to delays that hinder operational agility. Making matters worse, many organizations struggle to integrate procurement systems with other enterprise tools, which creates data silos that hurt collaboration.
He also noted Googles approach to project management, emphasizing long-term investments in core technologies like AI and Quantum, while remaining agile and adaptable in product development, willing to course correct when necessary.
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