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Navigating the Tech Hype in 2024: Choosing the Right Path for Your Business

Louis Ridout
Celsior Technologies

The first step is for companies to take a close look internally at their business and the direction the business is heading. What are the business goals, and how does the business plan to achieve them? It's essential to determine the path to success and the steps required of people, processes, and technology to reach those milestones. Only then can businesses identify the areas where technology can play a crucial role in supporting these goals.

A thorough examination of people, processes, and technology will help you identify areas where innovation can be a catalyst for business growth. As you meticulously analyze your goals, you will be able to develop a custom roadmap of potential projects to chart the course to future success. Of course, these projects must be evaluated from an ROI and possibly an NPV standpoint to determine which ones are worth doing. Once you identify the most worthwhile projects from a theoretical standpoint, you will next need to identify practical gaps or impediments to implementing the projects.

Some of the more common gaps or impediments are skills gaps, capability deficiencies, and funding problems, which require further internal analysis.

The funding component is ubiquitous and must be addressed. Often, relying solely on the corporate budget will not be sufficient. This is where tough decisions come into play. Countless organizations have failed due to issues related to funding. If the lack of funds influences you to make decisions based on an inadequate budget without considering the downstream impact, you risk becoming diverted from the path you've created, thus resulting in a partial or ineffective technology solution.

Additionally, funding that is not secure can result in the dreaded "bridge to nowhere" with a partially completed effort gathering dust and rust. One approach to avoid this is to look internally for areas from which you could free up budget dollars to augment your corporate-approved funding. Perhaps you can phase out older, costlier, less effective technology to make room for innovation. Look at outdated technologies and functions that are not delivering the desired business value. Next, consider reducing, eliminating, or outsourcing them and earmark the savings for your new strategic efforts.

Skills and capability gaps represent another dilemma you will face.

Do you develop these skills and capabilities internally?

Can you afford to do so?

Should you use vendor partner(s) for this?

Most companies will land on a hybrid model where critical skills and capabilities are at least partially in-house, and vendors are used strategically. Vendor models may include training of internal or external personnel, staffing augmentation, and SLA-based solutions. If you use an outside vendor, make sure that they have a do-it-with-you philosophy that avoids loss of control. To the extent that it is important to you, also make sure that your contract has provisions for appropriate knowledge transfer back to your own organization so that you are not captive to your partner.

Finally, businesses must remain focused on their unique goals and aspirations. By carefully examining internal capabilities, making tough decisions, and selecting vendors strategically, companies can navigate the ever-evolving tech landscape and position themselves for success in the digital era.

Louis Ridout is Director of Customer Solutions at Celsior Technologies

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Navigating the Tech Hype in 2024: Choosing the Right Path for Your Business

Louis Ridout
Celsior Technologies

The first step is for companies to take a close look internally at their business and the direction the business is heading. What are the business goals, and how does the business plan to achieve them? It's essential to determine the path to success and the steps required of people, processes, and technology to reach those milestones. Only then can businesses identify the areas where technology can play a crucial role in supporting these goals.

A thorough examination of people, processes, and technology will help you identify areas where innovation can be a catalyst for business growth. As you meticulously analyze your goals, you will be able to develop a custom roadmap of potential projects to chart the course to future success. Of course, these projects must be evaluated from an ROI and possibly an NPV standpoint to determine which ones are worth doing. Once you identify the most worthwhile projects from a theoretical standpoint, you will next need to identify practical gaps or impediments to implementing the projects.

Some of the more common gaps or impediments are skills gaps, capability deficiencies, and funding problems, which require further internal analysis.

The funding component is ubiquitous and must be addressed. Often, relying solely on the corporate budget will not be sufficient. This is where tough decisions come into play. Countless organizations have failed due to issues related to funding. If the lack of funds influences you to make decisions based on an inadequate budget without considering the downstream impact, you risk becoming diverted from the path you've created, thus resulting in a partial or ineffective technology solution.

Additionally, funding that is not secure can result in the dreaded "bridge to nowhere" with a partially completed effort gathering dust and rust. One approach to avoid this is to look internally for areas from which you could free up budget dollars to augment your corporate-approved funding. Perhaps you can phase out older, costlier, less effective technology to make room for innovation. Look at outdated technologies and functions that are not delivering the desired business value. Next, consider reducing, eliminating, or outsourcing them and earmark the savings for your new strategic efforts.

Skills and capability gaps represent another dilemma you will face.

Do you develop these skills and capabilities internally?

Can you afford to do so?

Should you use vendor partner(s) for this?

Most companies will land on a hybrid model where critical skills and capabilities are at least partially in-house, and vendors are used strategically. Vendor models may include training of internal or external personnel, staffing augmentation, and SLA-based solutions. If you use an outside vendor, make sure that they have a do-it-with-you philosophy that avoids loss of control. To the extent that it is important to you, also make sure that your contract has provisions for appropriate knowledge transfer back to your own organization so that you are not captive to your partner.

Finally, businesses must remain focused on their unique goals and aspirations. By carefully examining internal capabilities, making tough decisions, and selecting vendors strategically, companies can navigate the ever-evolving tech landscape and position themselves for success in the digital era.

Louis Ridout is Director of Customer Solutions at Celsior Technologies

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...