September 19, 2025 • 9 mins
AI initiatives run in every department. HR screens candidates, Finance processes invoices, Operations predicts maintenance. Each shows success individually, but enterprise value remains elusive. As AI spending reaches record levels, the pressure to demonstrate measurable returns has intensified. Siloed AI initiatives prevent organizations from achieving the strategic advantages board members expect from these investments. This pattern appears across Fortune 500 companies.
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Episode Transcript
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(00:00):
Siloed AI Blocks Enterprise Value.
(00:02):
AI initiatives run in every department.
HR screens candidates, Finance processes invoices, Operations predicts maintenance.
Each shows success individually, but enterprise value remains elusive.
As AI spending reaches record levels,the pressure to demonstrate measurable returns has intensified.
Siloed AI initiatives prevent organizations from achieving the strategic advantages board members expect from these investments.
(00:30):
This pattern appears across Fortune 500 companies.
Recent research by IDC found that 88% of POCs didn't make it into widespread deployment.
McKinsey's 2025 research shows that over 80% of organizations aren't seeing a tangible enterprise-level EBIT impact from their AI investments.
The failure stems from fragmented automation strategy decisions, not technical limitations.
(00:56):
Organizations treat AI like individual tools instead of building integrated capabilities.
Consider a typical scenario.
An AI system identifies a high-risk transaction, but Finance still triggers approval manually.
The AI insight gets buried in manual handoffs.
AI quickly flags the decision, yet execution remains stuck in manual workflows.
(01:19):
CIO.
com surveyed over 900 IT leaders in 2025 and found that 80% of CIOs are leading efforts to research and evaluate new AI tools for their organizations.
But this collaborative push has exposed serious integration problems.
Why McDonald's AI Hiring Tool Exposed 64 Million Records.
(01:41):
McDonald's recent AI hiring breach illustrates precisely how this can go wrong.
The fast-food giant's AI-powered recruitment platform exposed personal data from 64 million job applicants due to basic security failures.
The system used passwords like "123456" and lacked proper access controls.
(02:01):
The AI system itself functioned as intended, but governance and oversight were missing.
McDonald's had no security oversight,weak authentication,
and zero integration with their existing IT security framework.
ScriptRunner's policy-driven governance would have flagged weak passwords and enforced security standards before the AI system went live.
(02:24):
The AI hiring tool operated without adequate security oversight,
proper authentication protocols,or integration with McDonald's broader IT security framework.
McDonald's wanted to streamline recruitment processes but created massive legal liability and regulatory exposure instead.
The breach affects applicants across multiple countries and violates numerous data protection regulations.
(02:48):
The McDonald's breach is not an isolated failure.
Across industries,AI adoption has revealed security and compliance gaps that traditional automation frameworks alone cannot address.
Without enterprise-level governance, organizations expose themselves to regulatory risk.
PowerShell Needs Enterprise Governance for AI.
(03:09):
PowerShell remains the backbone of enterprise automation, supporting most critical workflows.
To safely integrate AI-driven decisions into these workflows at scale,
enterprises need a governance and execution layer that enforces policies and ensures controlled,
compliant automation.
ScriptRunner delivers this layer,enhancing PowerShell to operate securely in AI-enabled environments.
(03:34):
Most enterprise automation runs on PowerShell workflows built for predictable, rule-based processes.
AI disrupts this model (03:40):
a single model might suddenly require ten times the usual processing power or trigger cascading actions across multiple platforms.
Traditional deterministic scripts were never designed to handle this kind of probabilistic,
event-driven decision-making.
Security becomes exponentially more complex.
(04:01):
Each AI implementation brings its own authentication model,
audit requirements,and compliance standards.
PowerShell handles the majority of enterprise automation in Microsoft ecosystems.
Organizations already use PowerShell for their automation strategy,
and AI workflows are no exception.
The challenge becomes adding enterprise governance that makes PowerShell secure and scalable for AI automation.
(04:26):
ScriptRunner transforms standard PowerShell into an AI-ready enterprise platform with built-in governance,
security controls,and integration capabilities.
The integration challenge compounds with scale.
Every new AI tool requires custom connections to existing systems.
What starts as simple automation becomes a tangled mess of custom connections that teams struggle to maintain.
(04:50):
Enterprise IT leaders report spending more on maintaining disconnected AI-PowerShell integrations than they spent on the original manual processes.
Transform your PowerShell automation for AI workflows.
Book a strategy call with our experts to see ScriptRunner's automation platform in action.
What Works (05:09):
From Fragmentation to Scalable Platforms.
The enterprises that succeed with AI don't treat it as another project.
They rebuild their automation strategy as a scalable platform.
A fundamental mindset shift drives successful implementations.
They build strategic foundations designed for reuse across departments and use cases.
(05:31):
Instead of bolting AI onto existing automation,they develop new systems with AI built in from day one.
Three things make this approach successful (05:38):
1.
API-first integration.
Your AI systems must communicate with other tools through standard connections.
Teams can link different applications across their setup and pivot quickly when needs change.
2.
Policy-driven execution.
Establish governance rules once, then apply them consistently everywhere.
(06:00):
Consistent rules prevent conflicting decisions and keep AI operating within your boundaries.
3.
Event-based automation.
When your AI spots something important,it should kick off the following action automatically - no more passing things along manually.
Event-driven setups link AI insights directly to the actions your systems take,
(06:21):
allowing you to track everything that happens.
Together,these principles provide a scalable foundation that lets CIOs manage AI automation securely.
Business teams gain access to intelligent functions through self-service interfaces,
while IT retains full oversight and control over compliance.
Innovative organizations evolve their existing automation capabilities instead of starting over.
(06:46):
Fragmented automation makes AI unmanageable.
With ScriptRunner, CIOs gain the policy-driven PowerShell foundation that enterprise AI requires.
3 Actions Every CIO Must Take Now to Unify Their Automation Strategy.
From experience, CIOs who overcome AI fragmentation follow three strategic priorities (07:01):
1.
Audit Your AI Landscape.
Start by auditing your AI landscape across departments.
Most CIOs discover they're running 12-15 disconnected AI tools when they map their systems.
2.
Define AI-Ready Architecture Standards.
(07:23):
Require API-first design for all new AI implementations.
Set policy-driven governance as a non-negotiable requirement.
No AI system goes live without integration capabilities.
3.
Build Enterprise-Scale Platform Capabilities.
Invest in a unified automation infrastructure that enables the enhancement of existing workflows through the use of AI.
(07:47):
Focus on reusable components rather than custom point solutions.
Jae Evans,Global CIO and Executive Vice President at Oracle,
emphasizes the importance of data control in AI strategy.
Her approach recognizes that an AI automation strategy requires unified data management before it can deliver enterprise value.
(08:08):
"As a large enterprise, we have vast amounts of data from disparate sources," Evans explains.
Using generative AI and analytics tools can transform raw data into actionable insights that arrive when teams need them in the proper context.
Successful CIOs build AI automation capabilities as strategic platforms rather than collecting individual tools.
(08:30):
Leading technology executives report seeing potential for AI agents to communicate with each other,
taking on complex tasks with minimal human intervention.
Technology leaders estimate that it will take at least two to three years for AI to become mainstream across the enterprise.
Organizations that utilize this time to establish an integrated AI automation infrastructure will gain a significant competitive advantage.
(08:56):
Companies still managing fragmented AI implementations will face escalating integration costs and compliance risks that board members won't tolerate.
Budget trends also confirm this shift.
Research shows IT leaders expect AI to account for nearly 20% of tech budgets in 2025,
with spending expected to almost triple compared to last year.
(09:18):
As GenAI adoption accelerates rapidly from early pilot stages to enterprise deployment,
it is expected to reach 42% by the end of this year.
But these results come from integrated implementations, not siloed experiments.
Siloed AI Blocks Enterprise Value.
(00:02):
AI initiatives run in every department.
HR screens candidates, Finance processes invoices, Operations predicts maintenance.
Each shows success individually, but enterprise value remains elusive.
As AI spending reaches record levels,the pressure to demonstrate measurable returns has intensified.
Siloed AI initiatives prevent organizations from achieving the strategic advantages board members expect from these investments.
(00:30):
This pattern appears across Fortune 500 companies.
Recent research by IDC found that 88% of POCs didn't make it into widespread deployment.
McKinsey's 2025 research shows that over 80% of organizations aren't seeing a tangible enterprise-level EBIT impact from their AI investments.
The failure stems from fragmented automation strategy decisions, not technical limitations.
(00:56):
Organizations treat AI like individual tools instead of building integrated capabilities.
Consider a typical scenario.
An AI system identifies a high-risk transaction, but Finance still triggers approval manually.
The AI insight gets buried in manual handoffs.
AI quickly flags the decision, yet execution remains stuck in manual workflows.
(01:19):
CIO.
com surveyed over 900 IT leaders in 2025 and found that 80% of CIOs are leading efforts to research and evaluate new AI tools for their organizations.
But this collaborative push has exposed serious integration problems.
Why McDonald's AI Hiring Tool Exposed 64 Million Records.
(01:41):
McDonald's recent AI hiring breach illustrates precisely how this can go wrong.
The fast-food giant's AI-powered recruitment platform exposed personal data from 64 million job applicants due to basic security failures.
The system used passwords like "123456" and lacked proper access controls.
(02:01):
The AI system itself functioned as intended, but governance and oversight were missing.
McDonald's had no security oversight,weak authentication,
and zero integration with their existing IT security framework.
ScriptRunner's policy-driven governance would have flagged weak passwords and enforced security standards before the AI system went live.
(02:24):
The AI hiring tool operated without adequate security oversight,
proper authentication protocols,or integration with McDonald's broader IT security framework.
McDonald's wanted to streamline recruitment processes but created massive legal liability and regulatory exposure instead.
The breach affects applicants across multiple countries and violates numerous data protection regulations.
(02:48):
The McDonald's breach is not an isolated failure.
Across industries,AI adoption has revealed security and compliance gaps that traditional automation frameworks alone cannot address.
Without enterprise-level governance, organizations expose themselves to regulatory risk.
PowerShell Needs Enterprise Governance for AI.
(03:09):
PowerShell remains the backbone of enterprise automation, supporting most critical workflows.
To safely integrate AI-driven decisions into these workflows at scale,
enterprises need a governance and execution layer that enforces policies and ensures controlled,
compliant automation.
ScriptRunner delivers this layer,enhancing PowerShell to operate securely in AI-enabled environments.
(03:34):
Most enterprise automation runs on PowerShell workflows built for predictable, rule-based processes.
AI disrupts this model (03:40):
a single model might suddenly require ten times the usual processing power or trigger cascading actions across multiple platforms.
Traditional deterministic scripts were never designed to handle this kind of probabilistic,
event-driven decision-making.
Security becomes exponentially more complex.
(04:01):
Each AI implementation brings its own authentication model,
audit requirements,and compliance standards.
PowerShell handles the majority of enterprise automation in Microsoft ecosystems.
Organizations already use PowerShell for their automation strategy,
and AI workflows are no exception.
The challenge becomes adding enterprise governance that makes PowerShell secure and scalable for AI automation.
(04:26):
ScriptRunner transforms standard PowerShell into an AI-ready enterprise platform with built-in governance,
security controls,and integration capabilities.
The integration challenge compounds with scale.
Every new AI tool requires custom connections to existing systems.
What starts as simple automation becomes a tangled mess of custom connections that teams struggle to maintain.
(04:50):
Enterprise IT leaders report spending more on maintaining disconnected AI-PowerShell integrations than they spent on the original manual processes.
Transform your PowerShell automation for AI workflows.
Book a strategy call with our experts to see ScriptRunner's automation platform in action.
What Works (05:09):
From Fragmentation to Scalable Platforms.
The enterprises that succeed with AI don't treat it as another project.
They rebuild their automation strategy as a scalable platform.
A fundamental mindset shift drives successful implementations.
They build strategic foundations designed for reuse across departments and use cases.
(05:31):
Instead of bolting AI onto existing automation,they develop new systems with AI built in from day one.
Three things make this approach successful (05:38):
1.
API-first integration.
Your AI systems must communicate with other tools through standard connections.
Teams can link different applications across their setup and pivot quickly when needs change.
2.
Policy-driven execution.
Establish governance rules once, then apply them consistently everywhere.
(06:00):
Consistent rules prevent conflicting decisions and keep AI operating within your boundaries.
3.
Event-based automation.
When your AI spots something important,it should kick off the following action automatically - no more passing things along manually.
Event-driven setups link AI insights directly to the actions your systems take,
(06:21):
allowing you to track everything that happens.
Together,these principles provide a scalable foundation that lets CIOs manage AI automation securely.
Business teams gain access to intelligent functions through self-service interfaces,
while IT retains full oversight and control over compliance.
Innovative organizations evolve their existing automation capabilities instead of starting over.
(06:46):
Fragmented automation makes AI unmanageable.
With ScriptRunner, CIOs gain the policy-driven PowerShell foundation that enterprise AI requires.
3 Actions Every CIO Must Take Now to Unify Their Automation Strategy.
From experience, CIOs who overcome AI fragmentation follow three strategic priorities (07:01):
1.
Audit Your AI Landscape.
Start by auditing your AI landscape across departments.
Most CIOs discover they're running 12-15 disconnected AI tools when they map their systems.
2.
Define AI-Ready Architecture Standards.
(07:23):
Require API-first design for all new AI implementations.
Set policy-driven governance as a non-negotiable requirement.
No AI system goes live without integration capabilities.
3.
Build Enterprise-Scale Platform Capabilities.
Invest in a unified automation infrastructure that enables the enhancement of existing workflows through the use of AI.
(07:47):
Focus on reusable components rather than custom point solutions.
Jae Evans,Global CIO and Executive Vice President at Oracle,
emphasizes the importance of data control in AI strategy.
Her approach recognizes that an AI automation strategy requires unified data management before it can deliver enterprise value.
(08:08):
"As a large enterprise, we have vast amounts of data from disparate sources," Evans explains.
Using generative AI and analytics tools can transform raw data into actionable insights that arrive when teams need them in the proper context.
Successful CIOs build AI automation capabilities as strategic platforms rather than collecting individual tools.
(08:30):
Leading technology executives report seeing potential for AI agents to communicate with each other,
taking on complex tasks with minimal human intervention.
Technology leaders estimate that it will take at least two to three years for AI to become mainstream across the enterprise.
Organizations that utilize this time to establish an integrated AI automation infrastructure will gain a significant competitive advantage.
(08:56):
Companies still managing fragmented AI implementations will face escalating integration costs and compliance risks that board members won't tolerate.
Budget trends also confirm this shift.
Research shows IT leaders expect AI to account for nearly 20% of tech budgets in 2025,
with spending expected to almost triple compared to last year.
(09:18):
As GenAI adoption accelerates rapidly from early pilot stages to enterprise deployment,
it is expected to reach 42% by the end of this year.
But these results come from integrated implementations, not siloed experiments.
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