AI-Shoring and the Great Enterprise AI Disconnect
AI-Shoring and the Great Enterprise AI Disconnect
AI-Shoring and the Great Enterprise AI Disconnect
Insights
AI-Shoring and the Great Enterprise AI Disconnect
AI-Shoring and the Great Enterprise AI Disconnect
22 January, 2025 - By Conor Twomey, Co-founder & CEO, AI One
Enterprise spending on AI is skyrocketing. Snowflake recently reported $943 million in quarterly revenue, a 28% jump year-over-year, while NVIDIA’s data center revenues soared 93% to a staggering $35.6 billion. These numbers suggest widespread AI adoption. But for many enterprises, these investments have yet to produce real operational change.
The hard truth? Most enterprise AI spending isn’t about intelligence—it’s about infrastructure bloat. Companies are pouring billions into data lake migrations, cloud contracts, and sprawling vendor ecosystems under the assumption that AI success starts with an expensive IT overhaul. Yet these investments rarely translate into tangible automation, decision-making improvements, or workforce efficiency gains.
From Offshoring to AI-Shoring
For decades, enterprises relied on offshoring to reduce costs and handle routine IT operations. But that model is breaking down. Rising offshore labor costs, inflation, and the complexity of managing distributed teams are eroding the benefits. More critically, businesses are losing direct control over their most vital workflows.
A better model is emerging: AI-Shoring. Unlike offshoring, AI-Shoring doesn’t relocate tasks to distant human teams—it replaces manual operations with intelligent automation. AI-driven agents can handle monitoring, maintenance, data reconciliation, and routine analytics 24/7, reducing errors and freeing up human capital for higher-value work.
Companies that have implemented AI-Shoring at scale are seeing measurable, immediate results:
A Fortune 100 bank cut IT operational costs by 27% after automating infrastructure monitoring and routine data reconciliation.
A top healthcare provider reduced administrative processing times by 70% by deploying intelligent automation in patient records and claims management.
A global energy firm decreased supply chain inefficiencies by 40%, leveraging AI-driven predictive maintenance instead of manual system checks.
Why So Many Enterprises Are Stuck in Legacy Thinking
Despite these clear gains, many enterprises remain trapped in outdated thinking, spending billions on data lakes, cloud migrations, and vendor lock-in instead of directly deploying automation. Industry reports estimate that 85% of enterprise AI projects fail—not because AI lacks potential, but because companies make the mistake of assuming that massive IT overhauls are a prerequisite.
Why does this misconception persist?
Cloud Vendors Push Infrastructure Over Automation – Tech providers profit from selling storage, compute, and data pipelines, not AI-driven operational efficiency.
Enterprise Inertia – AI investment is often equated with hardware and cloud spending rather than with automation and workflow redesign.
Fear of Disruption – Many CIOs hesitate to implement AI that directly improves operations, fearing it will clash with existing IT architectures.
The result? Enterprises are spending billions to modernize data infrastructure while ignoring the AI-driven automation that could provide instant efficiency gains.
A Smarter Approach: AI-Shoring Over Infrastructure Bloat
To break this cycle, enterprises need to stop approaching AI as a cloud infrastructure problem and start treating it as an operational intelligence opportunity. The AI-Shoring model delivers:
Faster ROI – AI-driven automation delivers measurable results in months, not years.
Reduced Vendor Dependence – Eliminates reliance on costly offshore teams and sprawling cloud contracts.
Operational Agility – AI-driven systems learn, adapt, and improve continuously, unlike static offshore teams.
Instead of another multi-year, multi-million-dollar cloud migration, companies should focus on targeted automation deployments that deliver immediate efficiency gains.
The Bottom Line
The future of enterprise efficiency isn’t in bigger data lakes or larger outsourcing contracts—it’s in AI-driven automation that makes existing systems work smarter.
Companies that embrace AI-Shoring will move faster, cut costs, and regain operational control—while those clinging to outdated offshoring and infrastructure-heavy AI strategies risk falling further behind.
For CIOs and enterprise leaders, the question isn’t whether to invest in AI.
The question is: Are you funding real intelligence—or just more infrastructure bloat?
22 January, 2025 - By Conor Twomey, Co-founder & CEO, AI One
Enterprise spending on AI is skyrocketing. Snowflake recently reported $943 million in quarterly revenue, a 28% jump year-over-year, while NVIDIA’s data center revenues soared 93% to a staggering $35.6 billion. These numbers suggest widespread AI adoption. But for many enterprises, these investments have yet to produce real operational change.
The hard truth? Most enterprise AI spending isn’t about intelligence—it’s about infrastructure bloat. Companies are pouring billions into data lake migrations, cloud contracts, and sprawling vendor ecosystems under the assumption that AI success starts with an expensive IT overhaul. Yet these investments rarely translate into tangible automation, decision-making improvements, or workforce efficiency gains.
From Offshoring to AI-Shoring
For decades, enterprises relied on offshoring to reduce costs and handle routine IT operations. But that model is breaking down. Rising offshore labor costs, inflation, and the complexity of managing distributed teams are eroding the benefits. More critically, businesses are losing direct control over their most vital workflows.
A better model is emerging: AI-Shoring. Unlike offshoring, AI-Shoring doesn’t relocate tasks to distant human teams—it replaces manual operations with intelligent automation. AI-driven agents can handle monitoring, maintenance, data reconciliation, and routine analytics 24/7, reducing errors and freeing up human capital for higher-value work.
Companies that have implemented AI-Shoring at scale are seeing measurable, immediate results:
A Fortune 100 bank cut IT operational costs by 27% after automating infrastructure monitoring and routine data reconciliation.
A top healthcare provider reduced administrative processing times by 70% by deploying intelligent automation in patient records and claims management.
A global energy firm decreased supply chain inefficiencies by 40%, leveraging AI-driven predictive maintenance instead of manual system checks.
Why So Many Enterprises Are Stuck in Legacy Thinking
Despite these clear gains, many enterprises remain trapped in outdated thinking, spending billions on data lakes, cloud migrations, and vendor lock-in instead of directly deploying automation. Industry reports estimate that 85% of enterprise AI projects fail—not because AI lacks potential, but because companies make the mistake of assuming that massive IT overhauls are a prerequisite.
Why does this misconception persist?
Cloud Vendors Push Infrastructure Over Automation – Tech providers profit from selling storage, compute, and data pipelines, not AI-driven operational efficiency.
Enterprise Inertia – AI investment is often equated with hardware and cloud spending rather than with automation and workflow redesign.
Fear of Disruption – Many CIOs hesitate to implement AI that directly improves operations, fearing it will clash with existing IT architectures.
The result? Enterprises are spending billions to modernize data infrastructure while ignoring the AI-driven automation that could provide instant efficiency gains.
A Smarter Approach: AI-Shoring Over Infrastructure Bloat
To break this cycle, enterprises need to stop approaching AI as a cloud infrastructure problem and start treating it as an operational intelligence opportunity. The AI-Shoring model delivers:
Faster ROI – AI-driven automation delivers measurable results in months, not years.
Reduced Vendor Dependence – Eliminates reliance on costly offshore teams and sprawling cloud contracts.
Operational Agility – AI-driven systems learn, adapt, and improve continuously, unlike static offshore teams.
Instead of another multi-year, multi-million-dollar cloud migration, companies should focus on targeted automation deployments that deliver immediate efficiency gains.
The Bottom Line
The future of enterprise efficiency isn’t in bigger data lakes or larger outsourcing contracts—it’s in AI-driven automation that makes existing systems work smarter.
Companies that embrace AI-Shoring will move faster, cut costs, and regain operational control—while those clinging to outdated offshoring and infrastructure-heavy AI strategies risk falling further behind.
For CIOs and enterprise leaders, the question isn’t whether to invest in AI.
The question is: Are you funding real intelligence—or just more infrastructure bloat?
22 January, 2025 - By Conor Twomey, Co-founder & CEO, AI One
Enterprise spending on AI is skyrocketing. Snowflake recently reported $943 million in quarterly revenue, a 28% jump year-over-year, while NVIDIA’s data center revenues soared 93% to a staggering $35.6 billion. These numbers suggest widespread AI adoption. But for many enterprises, these investments have yet to produce real operational change.
The hard truth? Most enterprise AI spending isn’t about intelligence—it’s about infrastructure bloat. Companies are pouring billions into data lake migrations, cloud contracts, and sprawling vendor ecosystems under the assumption that AI success starts with an expensive IT overhaul. Yet these investments rarely translate into tangible automation, decision-making improvements, or workforce efficiency gains.
From Offshoring to AI-Shoring
For decades, enterprises relied on offshoring to reduce costs and handle routine IT operations. But that model is breaking down. Rising offshore labor costs, inflation, and the complexity of managing distributed teams are eroding the benefits. More critically, businesses are losing direct control over their most vital workflows.
A better model is emerging: AI-Shoring. Unlike offshoring, AI-Shoring doesn’t relocate tasks to distant human teams—it replaces manual operations with intelligent automation. AI-driven agents can handle monitoring, maintenance, data reconciliation, and routine analytics 24/7, reducing errors and freeing up human capital for higher-value work.
Companies that have implemented AI-Shoring at scale are seeing measurable, immediate results:
A Fortune 100 bank cut IT operational costs by 27% after automating infrastructure monitoring and routine data reconciliation.
A top healthcare provider reduced administrative processing times by 70% by deploying intelligent automation in patient records and claims management.
A global energy firm decreased supply chain inefficiencies by 40%, leveraging AI-driven predictive maintenance instead of manual system checks.
Why So Many Enterprises Are Stuck in Legacy Thinking
Despite these clear gains, many enterprises remain trapped in outdated thinking, spending billions on data lakes, cloud migrations, and vendor lock-in instead of directly deploying automation. Industry reports estimate that 85% of enterprise AI projects fail—not because AI lacks potential, but because companies make the mistake of assuming that massive IT overhauls are a prerequisite.
Why does this misconception persist?
Cloud Vendors Push Infrastructure Over Automation – Tech providers profit from selling storage, compute, and data pipelines, not AI-driven operational efficiency.
Enterprise Inertia – AI investment is often equated with hardware and cloud spending rather than with automation and workflow redesign.
Fear of Disruption – Many CIOs hesitate to implement AI that directly improves operations, fearing it will clash with existing IT architectures.
The result? Enterprises are spending billions to modernize data infrastructure while ignoring the AI-driven automation that could provide instant efficiency gains.
A Smarter Approach: AI-Shoring Over Infrastructure Bloat
To break this cycle, enterprises need to stop approaching AI as a cloud infrastructure problem and start treating it as an operational intelligence opportunity. The AI-Shoring model delivers:
Faster ROI – AI-driven automation delivers measurable results in months, not years.
Reduced Vendor Dependence – Eliminates reliance on costly offshore teams and sprawling cloud contracts.
Operational Agility – AI-driven systems learn, adapt, and improve continuously, unlike static offshore teams.