It’s well known that most AI projects get stuck early on, with about 60-80% of them not making it past the PoC phase. One big reason is the cost. Gartner says that the financial strain of developing AI projects from scratch is so expensive and complicated that more than half of companies will give up on their AI plans by 2028.
Not good news for you as a CTO.
Your role has evolved beyond just overseeing architecture, data, and security; now, financial smarts are just as critical. No CTO relishes the thought of explaining to the leadership, after 12 months, why the 'expected magic' failed to materialize due to budget and resource miscalculations.
So, what can you do to keep your AI project on track and within budget?
Data and AI platform designed to help you fulfill your ambitious AI plans while optimizing costs. Its infrastructure changes to fit your needs and resource constraints, including incorporating new data and AI stack. This data and AI operating system is easy to use and scales along with you, ensuring your tools and skills are always in sync with the latest tech trends – without the DevOps burden.
The best part? You can get all these benefits with a simple subscription fee.
Let's look into how a Data and AI Operating System can be a game-changer in reducing these costs and keeping AI initiatives on track and within budget.
DevOps efforts are crucial in AI development, involving significant initial setup and ongoing maintenance costs.
DevOps costs might seem like a standard part of the payroll expenses, but they're a premium investment. Building AI infrastructures from the ground up takes around 12-24 months of intense engineering effort. That's two to three DevOps engineers working full-time, tallying up a hefty $300K to $400K in salaries. Having a structured data and AI Operating System in place eliminates the need for the extensive data engineering resources traditionally used to build and maintain the data infrastructure. Instead, a data platform provides frictionless access to the latest tools in the industry, allowing the data teams to jump in and do what they do best.
For example, the small team of data scientists working behind the scenes for the Cleveland Cavaliers implemented a data and AI OS and were able to roll out application after application in a short timeframe, without involving their busy DevOps team.
Aside from the initial setup costs, there is also the future to consider.
Staying ahead means constantly updating your stack. A data platform frees organizations from the constraints of tools that require steep learning curves or hard-to-find expertise. It offers the flexibility to tailor the tech stack according to the available expertise, either through utilizing current skills, upskilling teams, or bringing in new talent.
Speaking of new talent, when new engineers come on board, they will likely disagree with the past methodology and hesitate to maintain what they see as someone else’s “mess”. Every fresh look at data architecture can result in fundamental changes, leading to a vicious cycle of re-starts that costs the organization big in terms of time and money. A data platform avoids the costly re-start cycle and allows your project to continue even when fresh faces come on board.
Switching to a commercial data and AI platform with fixed fees can reduce these initial setup and maintenance costs by 90%, plus free up DevOps resources for more strategic projects.
When time is money, ease of use can alleviate the time-consuming dependency on DevOps. With an in-house team, deploying a new AI tool could take weeks after red tape, stack configuration, and rigorous testing. But with a data platform, it only takes five minutes and one click to start using a new tool. This simplicity means data scientists don't have to do admin work, and it's easier for engineers without DevOps skills to get their work done.
Without the need to involve themselves in the intricacies of the data stack, data scientists can deliver updates and deploy new features quickly, bypassing the bottleneck of often-limited development resources. For many enterprises using a data platform, what previously took a week to accomplish can now be done in mere minutes.
For example, EnPowered, a leader in clean technology, switched their machine learning (ML) development from personal laptops to the cloud and saw big improvements in their project speed. It also allowed their data scientists to focus more on creating and launching AI solutions instead of setting up their data systems.
AI projects demand substantial computing resources, leading to skyrocketing structural costs. Traditional setups involve a maze of subscription fees for licenses and must-have features like cloud premium services or managed care services like SSO and multi-tenancy, which pile up the expenses. It's like being nickel-and-dimed, but with thousands of dollars!
A data platform rolls all these costs into one manageable monthly fee. For example, a pipeline orchestration tool could cost $170K/year for a dashboard, multi-tenancy, and SSO. These tools are all included in an OS, eliminating the high yearly cost of just one tool. Imagine how your ROI could soar if you have 5+ tools.
Storage isn't just about keeping data; it's a key component of AI projects. However, traditional data lakes can be expensive due to their consumption-based pricing. In contrast, a data platform usually charges a flat fee, making storage costs more predictable and budget-friendly..
In industries where time is of the essence and milliseconds matter, latency can have a substantial economic impact. A data platform that runs on a private cloud/on prem – meaning the system is located physically close to the data source – brings the action closer to home and enables the real-time processing so critical for many applications. It also cuts the latency costs associated with delayed data analysis, providing a crucial competitive advantage in time-sensitive markets.
For many projects, 80% of the DevOps work is basic, while 10-20% is more complex and therefore requires external data and AI specialists with broad view/experience. However, organizations that need assistance are still required to pay the full engagement fee of a consultant. Opting for a data and AI OS means gaining access to a Customer Success team composed of industry experts from firms like Google and AWS, offering high-level consultancy at a fraction of the cost. This approach ensures that organizations get tailored support for their complex needs without the hefty price tag of traditional consulting services.
AI projects can take a while to pay off. With a typical waiting period of 6-18 months before the business sees any benefits, many CFOs get nervous when consumption – and costs – spike way before any ROI can make an appearance. A data platform's flat pricing model helps maintain steady costs despite increased consumption, preventing budget overruns that raise blood pressure levels.
Example: A North American retailer with 200K employees implemented a data and AI operating system but initially experienced slow adoption. However, as the organization recognized the system's value — that ramping up use wouldn’t mean ramping up costs — the tide turned. Even before their first project started, 5 to 6 separate teams were bursting with ideas for new AI applications, leading to an unexpected jam of potential projects. As they launched each initiative, the retailer discovered a pleasant surprise: their expenses stayed flat, even as their AI usage soared.
Deploying a new tool requires risk and compliance resources. When you bring in external vendors, you're taking a risk—any slip in their compliance can jeopardize your business. This means investments in extensive audits and risk management. A data and AI OS minimizes these issues by retaining the data within the organization, thereby keeping the security footprint unchanged. This not only saves the day by reducing the workload for your Governance, Risk, and Compliance (GRC) team but also cuts down on the time and expense spent on audits.
Balancing AI costs demands a strategy, and a data and AI operating system is the answer. More than just reducing expenses, this system transforms AI projects. It cuts DevOps and infrastructure costs, speeding up the journey from idea to live deployment. With expert support included, it's not just about saving cash—it's about boosting speed and innovation.
Want to revolutionize your approach to data and AI? Tap into the power of a data and AI OS. It's your best bet for success in the AI landscape.
It’s well known that most AI projects get stuck early on, with about 60-80% of them not making it past the PoC phase. One big reason is the cost. Gartner says that the financial strain of developing AI projects from scratch is so expensive and complicated that more than half of companies will give up on their AI plans by 2028.
Not good news for you as a CTO.
Your role has evolved beyond just overseeing architecture, data, and security; now, financial smarts are just as critical. No CTO relishes the thought of explaining to the leadership, after 12 months, why the 'expected magic' failed to materialize due to budget and resource miscalculations.
So, what can you do to keep your AI project on track and within budget?
Data and AI platform designed to help you fulfill your ambitious AI plans while optimizing costs. Its infrastructure changes to fit your needs and resource constraints, including incorporating new data and AI stack. This data and AI operating system is easy to use and scales along with you, ensuring your tools and skills are always in sync with the latest tech trends – without the DevOps burden.
The best part? You can get all these benefits with a simple subscription fee.
Let's look into how a Data and AI Operating System can be a game-changer in reducing these costs and keeping AI initiatives on track and within budget.
DevOps efforts are crucial in AI development, involving significant initial setup and ongoing maintenance costs.
DevOps costs might seem like a standard part of the payroll expenses, but they're a premium investment. Building AI infrastructures from the ground up takes around 12-24 months of intense engineering effort. That's two to three DevOps engineers working full-time, tallying up a hefty $300K to $400K in salaries. Having a structured data and AI Operating System in place eliminates the need for the extensive data engineering resources traditionally used to build and maintain the data infrastructure. Instead, a data platform provides frictionless access to the latest tools in the industry, allowing the data teams to jump in and do what they do best.
For example, the small team of data scientists working behind the scenes for the Cleveland Cavaliers implemented a data and AI OS and were able to roll out application after application in a short timeframe, without involving their busy DevOps team.
Aside from the initial setup costs, there is also the future to consider.
Staying ahead means constantly updating your stack. A data platform frees organizations from the constraints of tools that require steep learning curves or hard-to-find expertise. It offers the flexibility to tailor the tech stack according to the available expertise, either through utilizing current skills, upskilling teams, or bringing in new talent.
Speaking of new talent, when new engineers come on board, they will likely disagree with the past methodology and hesitate to maintain what they see as someone else’s “mess”. Every fresh look at data architecture can result in fundamental changes, leading to a vicious cycle of re-starts that costs the organization big in terms of time and money. A data platform avoids the costly re-start cycle and allows your project to continue even when fresh faces come on board.
Switching to a commercial data and AI platform with fixed fees can reduce these initial setup and maintenance costs by 90%, plus free up DevOps resources for more strategic projects.
When time is money, ease of use can alleviate the time-consuming dependency on DevOps. With an in-house team, deploying a new AI tool could take weeks after red tape, stack configuration, and rigorous testing. But with a data platform, it only takes five minutes and one click to start using a new tool. This simplicity means data scientists don't have to do admin work, and it's easier for engineers without DevOps skills to get their work done.
Without the need to involve themselves in the intricacies of the data stack, data scientists can deliver updates and deploy new features quickly, bypassing the bottleneck of often-limited development resources. For many enterprises using a data platform, what previously took a week to accomplish can now be done in mere minutes.
For example, EnPowered, a leader in clean technology, switched their machine learning (ML) development from personal laptops to the cloud and saw big improvements in their project speed. It also allowed their data scientists to focus more on creating and launching AI solutions instead of setting up their data systems.
AI projects demand substantial computing resources, leading to skyrocketing structural costs. Traditional setups involve a maze of subscription fees for licenses and must-have features like cloud premium services or managed care services like SSO and multi-tenancy, which pile up the expenses. It's like being nickel-and-dimed, but with thousands of dollars!
A data platform rolls all these costs into one manageable monthly fee. For example, a pipeline orchestration tool could cost $170K/year for a dashboard, multi-tenancy, and SSO. These tools are all included in an OS, eliminating the high yearly cost of just one tool. Imagine how your ROI could soar if you have 5+ tools.
Storage isn't just about keeping data; it's a key component of AI projects. However, traditional data lakes can be expensive due to their consumption-based pricing. In contrast, a data platform usually charges a flat fee, making storage costs more predictable and budget-friendly..
In industries where time is of the essence and milliseconds matter, latency can have a substantial economic impact. A data platform that runs on a private cloud/on prem – meaning the system is located physically close to the data source – brings the action closer to home and enables the real-time processing so critical for many applications. It also cuts the latency costs associated with delayed data analysis, providing a crucial competitive advantage in time-sensitive markets.
For many projects, 80% of the DevOps work is basic, while 10-20% is more complex and therefore requires external data and AI specialists with broad view/experience. However, organizations that need assistance are still required to pay the full engagement fee of a consultant. Opting for a data and AI OS means gaining access to a Customer Success team composed of industry experts from firms like Google and AWS, offering high-level consultancy at a fraction of the cost. This approach ensures that organizations get tailored support for their complex needs without the hefty price tag of traditional consulting services.
AI projects can take a while to pay off. With a typical waiting period of 6-18 months before the business sees any benefits, many CFOs get nervous when consumption – and costs – spike way before any ROI can make an appearance. A data platform's flat pricing model helps maintain steady costs despite increased consumption, preventing budget overruns that raise blood pressure levels.
Example: A North American retailer with 200K employees implemented a data and AI operating system but initially experienced slow adoption. However, as the organization recognized the system's value — that ramping up use wouldn’t mean ramping up costs — the tide turned. Even before their first project started, 5 to 6 separate teams were bursting with ideas for new AI applications, leading to an unexpected jam of potential projects. As they launched each initiative, the retailer discovered a pleasant surprise: their expenses stayed flat, even as their AI usage soared.
Deploying a new tool requires risk and compliance resources. When you bring in external vendors, you're taking a risk—any slip in their compliance can jeopardize your business. This means investments in extensive audits and risk management. A data and AI OS minimizes these issues by retaining the data within the organization, thereby keeping the security footprint unchanged. This not only saves the day by reducing the workload for your Governance, Risk, and Compliance (GRC) team but also cuts down on the time and expense spent on audits.
Balancing AI costs demands a strategy, and a data and AI operating system is the answer. More than just reducing expenses, this system transforms AI projects. It cuts DevOps and infrastructure costs, speeding up the journey from idea to live deployment. With expert support included, it's not just about saving cash—it's about boosting speed and innovation.
Want to revolutionize your approach to data and AI? Tap into the power of a data and AI OS. It's your best bet for success in the AI landscape.
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