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Transformational CIOs continuously invest in their operating model by developing product management, design thinking, agile, DevOps, change management, and data-driven practices. In 2025, CIOs should integrate their data and AI governance efforts, focus on data security to reduce risks, and drive businessbenefits by improving data quality.
If expectations around the cost and speed of deployment are unrealistically high, milestones are missed, and doubt over potential benefits soon takes root. The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. But this scenario is avoidable.
But alongside its promise of significant rewards also comes significant costs and often unclear ROI. For CIOs tasked with managing IT budgets while driving technological innovation, balancing these costs against the benefits of GenAI is essential. million in 2026, covering infrastructure, models, applications, and services.
In todays digital economy, businessobjectives like becoming a leading global wealth management firm or being a premier destination for top talent demand more than just technical excellence. Enterprise architects must shift their focus to business enablement. The stakes have never been higher.
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. Below are five examples of where to start.
While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.
In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. The assessment provides insights into the current state of architecture and workloads and maps technology needs to the businessobjectives.
SaaS is a software distribution model that offers a lot of agility and cost-effectiveness for companies, which is why it’s such a reliable option for numerous businessmodels and industries. Flexible payment options: Businesses don’t have to go through the expense of purchasing software and hardware.
Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.
This was one area addressed in the HP-sponsored IDC whitepaper on the benefits of integrating managed device services, published in April 2024 [1]. Adopting a Managed Device Services model significantly reduces many of these end-of-life headaches and helps ensure compliance.
With effective change management, organizations usually realize faster implementations and lower costs. It facilitates an organization’s efforts in assessing the impact of change and making recommendations for target states that support businessobjectives.
A common but critical challenge I hear from CIOs, CTOs, and CDOs every day is that they have a difficult time helping the C-suite understand that IT is the very architecture for the future of business, not a cost center. How do you convince decision-makers to collaborate on linking IT strategy with business strategy?
Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.
Making decisions based on data, rather than intuition alone, brings benefits such as increased accuracy, reduced risks, and deeper customer insights. Challenges in Achieving Data-Driven Decision-Making While the benefits are clear, many organizations struggle to become fully data-driven.
We internally analyzed the improvements we had to provide and, together with the CIOs in all the countries where Mapfre operates, we defined a very solid strategy that aligns with the businessobjectives, and we’re implementing that now. This change in platform also entails a data governance model and operational changes.
Furthermore, the growing importance of AI necessitates the modernization of AI models and data pipelines to prevent issues like model drift and bias. Define clear objectives and secure executive buy-in Articulate challenges and benefits: Communicate the challenges posed by legacy applications and the potential benefits of APMR.
To address this requirement and ensure seamless connectivity, organizations are rapidly adopting consumption-driven NaaS models to balance the cost of their network growth with the digital experience of their stakeholders. Obtaining more insight into hidden costs (e.g., Obtaining more insight into hidden costs (e.g.,
In all of these roles, I’ve come across patterns that enable organizations to build faster business insights and innovation with data. These patterns encompass a way to deliver value to the business with data; I refer to them collectively as the “data operating model.” Execution patterns in an operating model.
Decisions on which and how apps are modernised should be aligned with businessobjectives, with a strategy to integrate systems into the wider enterprise IT estate, which is becoming increasingly hybrid. A successful app modernisation initiative ensures cost savings and improvement in efficiency and customer experience.
This approach to better information can benefit IT team KPIs in most areas, ranging from e-commerce store errors to security risks to connectivity outages,” he says. Easy access to constant improvement is another AI growth benefit. All of these benefits promise to give IT teams additional time to focus on more complex issues.
You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy. What is data modeling?
Rick Boyce, CTO at AND Digital, underscores how a typical IT project mentality toward DevOps can undercut the CIO’s ability to deliver on businessobjectives. CIOs should also weigh in on roles and responsibilities and oversee defining a governance model to avoid overloading individuals or ending up with responsibility gaps.
Here are five best practices to get the most businessbenefit from gen AI. A modern data strategy, after all, needs to address and empower the full IT stack by supporting enterprise objectives for intelligent automation, and a myriad of applications supporting transactions, analytics, and decision-making.
A hybrid approach is clearly established as the optimal operating model of choice. The shift toward hybrid IT has clear upsides, enabling organizations to choose the right solution for each task and workload, depending on criteria such as performance, security, compliance, and cost, among other factors.
When businesses migrate to public cloud, they expect to enjoy greater agility, resiliency, scalability, security, and cost-efficiency. When a cloud strategy team has those chief objectives nailed down, they can plan supporting considerations – such as security, resiliency and scalability – around them more effectively.”
Carter Busse, CIO of no-code enabled automation platform company Workato, adds that APIs are now important connective tissue to integrate and interact with large language models (LLMs) within business processes. “If Besides technical considerations, however, there are unique business implications to consider, adds Bizagi’s Vázquez. “We
A hybrid approach is clearly established as the optimal operating model of choice. The shift toward hybrid IT has clear upsides, enabling organizations to choose the right solution for each task and workload, depending on criteria such as performance, security, compliance, and cost, among other factors.
One approach is to define and seek agreement of non-negotiables with the board and executive committee, outlining criteria of when upgrading legacy systems must be prioritized above other businessobjectives. Many want all the benefits from analytics and machine learning but are slow to adopt proactive data governance.
“Everyone is running around trying to apply this technology that’s moving so fast, but without business outcomes, there’s no point to it,” says Redmond, CIO at power management systems manufacturer Eaton Corp. “We We need to continue to be mindful of business outcomes and apply use cases that make sense.”
After transforming their organization’s operating model, realigning teams to products rather than to projects , CIOs we consult arrive at an inevitable question: “What next?” Splitting these responsibilities without a clear vision and careful plan, however, can spell disaster, reversing the progress begotten by a new operating model.
Customization to Business Needs: In-house development allows for the customization of AI models to align with specific businessobjectives and operational requirements. Companies must weigh these costs against the potential long-term benefits and consider their specific circumstances when deciding on their AI strategy.
Belcorp operates under a direct sales model in 14 countries. The second stage focused on building algorithms and models to predict and simulate intricate biological conditions, accelerate discoveries, reduce risks, and optimize the cost-benefit ratio of technological developments using AI solutions.
The difference is in using advanced modeling and data management to make faster scenario planning possible, driven by actionable key performance measures that enable faster, well-informed decision cycles. A major practical benefit of using AI is putting predictive analytics within easy reach of any organization.
As organizations of all stripes continue their migration to the cloud, they are coming face to face with sometimes perplexing cost issues, forcing them to think hard about how best to optimize workloads, what to migrate, and who exactly is responsible for what. Lacking a clear strategy determined by businessobjectives.
Reimagination of business processes sits at the core of digital transformation, and so, by definition, digital transformation challenges the status quo, throwing we-have-always-done-it-this-way sentiment out of the window. This may require hiring outside experts and/or investing in training and development for existing staff.
It opens not just cloud-native technologies such as containerization, which are empowering organizations to move to more agile delivery models critical in today’s highly competitive app-centric landscape, but also enables cutting-edge innovations from 5G to AI and IoT. The essential enablers to realizing your cloud aspirations.
Clear success criteria: Ensure exploitation delivers incremental business value. Adobe’s transition from packaged software to the Creative Cloud service model is an example of a strategic move to exploit new market opportunities and scale for success with recurrent revenue and a broader user base. This phase maximizes long-term value.
Equally important, the documented vision is a tool for agile teams to make implementation decisions when there are multiple ways to solve problems, each with different benefits and tradeoffs. Business leaders get scared and say, ‘Tell me the plan so I can sleep at night,’” said Ronica Roth, co-founder and principal of The Welcome Elephant.
Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.
What benefit does AI serve to that department? Bring the whole organization on the AI journey CIOs also see the need to bring everyone along on that AI journey, something that takes a well-articulated narrative about the benefits AI can bring to those who are and will be impacted by the technology. Statistics can be very misleading.
Jain says that starts by understanding the definition of “customer,” which Jain defines as “anybody who benefits from your services and products.” Create innovation teams IT departments have moved beyond their old shared services model and are now working closely with business lines. “IT needs to go beyond that.
Just like any other IT solution, adopting a successful hybrid cloud strategy starts with examining how this cloud computing architecture can drive overall businessobjectives. As an initial step, business and IT leaders need to review the advantages and disadvantages of hybrid cloud adoption to reap its benefits.
According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital businessobjectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.
He notes that developing a services portfolio to leverage various cloud services can give IT a flexible operating model. “By By separating the decision-making process from infrastructure, and aligning workloads with suitable cloud models, IT can redirect its focus toward ongoing business enhancement,” he explains.
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