C‑Suite Leaders Redefine AI Strategy Amid Widespread IT Talent Shortage

Breaking: Global executives accelerate AI and automation while talent gaps slow progress

A worldwide survey of nearly 4,300 C‑suite leaders shows organizations are pushing AI and automation to modernize operations, even as a persistent shortage of skilled IT talent threatens to derail ambitious timelines.

Budget pressures, rising cybersecurity threats, and lasting skill gaps are identified as top obstacles to delivering on AI-driven strategies.The findings underscore that human expertise remains essential in cybersecurity, infrastructure management, and ongoing application support, even as automation programs expand.

Key insights include that 44% of leaders view AI and automation as the leading capabilities needed to support both immediate and long‑term IT initiatives. Meanwhile, 36% say skills gaps are already limiting growth opportunities, and a striking 98% report IT talent shortages are hampering their ability to execute technology strategies.

Despite strong interest in automation,executives caution that AI cannot operate in isolation. The workforce is still critical for security, system stability, and maintaining core applications. Even with ERP systems largely meeting business needs (97%), about a quarter of workforce time is spent maintaining existing systems, diverting talent from innovation.

To cope, many organizations are outsourcing key IT services to supplement internal teams.Third‑party support is increasingly used to address gaps in cybersecurity operations, infrastructure, and application maintenance, helping to reduce risk and stabilize core systems.

Executives emphasize a desire to modernize without being trapped in costly vendor upgrade cycles. By stabilizing and maximizing the ERP foundation, they aim to redirect time and resources toward strategic, AI‑driven initiatives that yield more meaningful results.

at a glance: key findings

Metric	Finding	Why it matters
AI/Automation priority	44% identify as the top capability for IT goals	Shows a strong push to embed AI in both short- and long‑term plans
Skills gaps impact	36% say gaps limit growth opportunities	Talent shortages constrain expansion and efficiency gains
Talent shortages	98% report shortages affect strategy execution	Widespread constraint across industries and regions
ERP readiness	97% say ERP largely meets business needs	ERP remains a stable platform for modernization
Maintenance time	About 25% of workforce time	Maintenance diverts talent from innovation
Outsourcing trend	Near-worldwide reliance on third parties for cybersecurity, infra, and app maintenance	External support helps reduce risk and stabilize core systems

Beyond the numbers, the report paints a coordinated shift: AI and automation will remain central to long‑term strategy, but success will hinge on balancing tech investments with sustained human talent. Leaders warn that automation accelerates transformation only when people, processes, and partnerships are aligned.

Evergreen takeaways for readers: A durable modernization plan requires a dual track-drive AI and automation to unlock value while investing in skills development and strategic outsourcing to maintain security, reliability, and innovation. Adopting vendor‑agnostic roadmaps and prioritizing critical skills can definitely help organizations remain resilient as technologies evolve.

Two questions for readers

1) how is your association balancing automation investments with ongoing workforce development in the coming year?

2) Which IT domains do you anticipate outsourcing most as AI initiatives advance?

Share your perspectives in the comments and tell us how your company is navigating AI adoption in the era of talent constraints.

For broader context on AI and workforce trends, see analyses by McKinsey & company.

Disclaimer: This article is a synthesis of a global business survey and reflects industry perspectives. Results can vary by sector and region.

>: IBM’s “AI Skills Academy” and Coursera’s “Generative AI Professional Certificate” have become mainstream for employee progress.

The 2025 IT Talent Landscape: Numbers That Matter

• global shortfall: The World Economic Forum estimates 4.3 million unfilled AI‑focused positions worldwide, a 12 % increase from 2023.
• Skill mismatch: Burning Glass data shows 68 % of open AI roles require expertise in machine learning engineering, data engineering, or AI ethics, yet only 22 % of applicants meet all three criteria.
• Turnover pressure: A Deloitte 2024 talent report reveals 58 % of senior IT professionals consider leaving their current employer within the next 12 months, driven by burnout and limited growth pathways.

These figures force CEOs, CFOs, CIOs, and CIOs (the C‑suite) to rethink AI roadmaps before projects stall due to staffing gaps.

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Why C‑Suite Leaders Are Redefining AI strategy

1. Cost‑risk balance – Traditional AI development pipelines (data acquisition → model training → deployment) are 40 % more expensive when talent scarcity forces reliance on external consultants.
2. Speed to market – Competitive pressure to launch AI‑driven products within 6-9 months compels leaders to adopt rapid‑deployment frameworks.
3. Regulatory scrutiny – New EU AI Act provisions (effective Jan 2025) demand robust governance that cannot be built ad‑hoc; it requires dedicated expertise.

These drivers push C‑suite executives to prioritize agility, partnership, and internal capability building over the classic “build‑it‑in‑house” model.

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Strategic Shifts Shaped by Talent Constraints

1. Low‑Code/No‑Code AI Platforms

• Accelerated prototyping: Tools such as Microsoft Power Platform, Google Vertex AI Studio, and DataRobot allow business analysts to create predictive models without writing code.
• Reduced dependency: 73 % of CIOs surveyed by Gartner (2025) report a 30 % drop in reliance on senior data scientists for pilot projects.

2. AI‑as‑a‑Service (AIaaS) & Cloud Partnerships

• Scalable infrastructure: Leveraging AWS Bedrock, Azure OpenAI Service, and Alibaba Cloud’s Genie model reduces the need for on‑prem GPU farms.
• Managed security: Cloud providers now embed AI‑specific compliance controls (e.g.,data residency,model audit logs),easing the governance burden on internal teams.

3. Upskilling & Internal Talent Pipelines

• Micro‑credential programs: IBM’s “AI Skills Academy” and Coursera’s “Generative AI Professional Certificate” have become mainstream for employee development.
• Internal AI Communities of Practice (CoP): 61 % of Fortune 500 firms (McKinsey, 2025) report that CoPs shorten the learning curve for new AI tools by 4-6 weeks.

4. Strategic Outsourcing & Remote AI Talent

• Hybrid delivery models: Companies pair a small core AI team with offshore model‑training labs (e.g., in Poland, India, and Brazil) to fill skill gaps.
• Gig‑economy platforms: Specialized marketplaces like Upwork AI Talent and Toptal ML provide vetted consultants on a project basis,reducing overhead.

5. Governance, Ethics, and Risk Management

• AI Ethics Boards: Boards now include legal, compliance, and data‑privacy leaders to satisfy emerging AI legislation.
• Model‑Ops maturity: Adoption of MLOps frameworks (e.g., Kubeflow Pipelines, MLflow) standardizes monitoring, versioning, and rollback-critical when external talent builds models.

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Real‑World Examples

JPMorgan Chase’s AI Talent Alliance (2024‑2025)

• Partnership: Joined forces with Stanford AI Lab and MIT Sloan to create a joint research fellowship.
• Outcome: Delivered a fraud‑detection model that cut false‑positive rates by 22 % while using a 16‑person internal team rather of the planned 40‑person external vendor.
• Key takeaway: Academic partnerships can augment scarce talent and accelerate time‑to‑value.

Siemens’ Hybrid AI Model (2025)

• Approach: Integrated low‑code IoT analytics with Azure OpenAI to predict equipment failures across 12 k+ factories.
• Result: Achieved a 15 % reduction in unplanned downtime while staffing the AI effort with 45 % fewer data engineers than the previous legacy system.
• Key takeaway: Combining low‑code tools with AIaaS allows digital conversion at scale despite a thin talent pool.

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Practical Tips for Executives

Action	Why It Matters	Fast Implementation Steps
Audit AI skill inventory	Identify exact gaps before investing	Use HR analytics dashboards to map current AI competencies vs. project needs; prioritize critical roles.
Adopt low‑code AI	Cuts reliance on senior engineers	Pilot a low‑code solution on a non‑critical use case (e.g.,demand forecasting) and measure time‑to‑deployment.
Forge academic & ecosystem partnerships	Diversifies talent sources	Sign MOUs with top universities; sponsor capstone projects aligned with business objectives.
Standardize MLOps pipelines	Ensures quality and compliance	deploy a baseline MLflow stack; train core staff on CI/CD for models.
Leverage AIaaS for compute‑intensive workloads	Reduces CAPEX and talent demand	Migrate a selected model training job to Azure Bedrock; monitor cost vs. on‑prem baseline.
Create an AI Ethics Board	Mitigates regulatory risk	Appoint cross‑functional members; define charter aligned with EU AI Act and upcoming US AI Bill of Rights.
Implement a tiered upskilling program	Builds enduring internal capability	Tier 1: No‑code basics; Tier 2: Model‑building with Python; Tier 3: Advanced MLOps. Offer certifications after each tier.
Utilize remote AI talent marketplaces	Adaptability for peak demand	Set up vendor governance policies; start with a 3‑month trial for a specific project.

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Benefits of a Re‑Engineered AI Strategy

• Accelerated ROI: companies report average 28 % faster realization of AI‑driven revenue streams when combining low‑code and AIaaS.
• Talent cost containment: Outsourcing and platform use can lower per‑model development cost by up to 45 % (Deloitte,2024).
• Regulatory compliance: Integrated governance frameworks reduce the risk of fines associated with AI violations by 70 % (McKinsey, 2025).
• Employee engagement: Upskilling initiatives boost internal mobility and reduce turnover, with a reported 15 % increase in AI‑related job satisfaction scores.

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Key Metrics for Ongoing Success

1. Time‑to‑Model‑Deployment (TTMD) – Target ≤ 8 weeks for pilot models.
2. AI Talent utilization Ratio – Percentage of projects staffed internally vs. outsourced; aim for ≥ 60 % internal.
3. Model Accuracy vs. Baseline – Track incremental performance gains after each governance checkpoint.
4. compliance Pass Rate – Ratio of models passing AI ethics audit on first review; target ≥ 90 %.
5. Upskilling Completion Rate – Percentage of staff completing micro‑credential tracks; target ≥ 75 % annually.

Monitoring these KPIs ensures that the C‑suite’s AI strategy remains agile, cost‑effective, and compliant despite the persistent IT talent shortage.