Articles | ISG

What AI-Informed Sourcing Contracts Can Do for Healthcare | ISG

ISG — Wed, 23 Sep 2026 07:00:00 GMT

AI is reshaping revenue cycle management (RCM), but many healthcare contracts still rely on outdated labor-based models that fail to capture the value of automation and transformation. Modern RCM contracting can align incentives around measurable outcomes, enable greater flexibility and accountability, and help health systems unlock significant cost savings, stronger revenue performance and sustainable financial impact.

The AI Productivity Trap: Why Faster Marketing Does Not Guarantee Value | ISG

Kaveri Camire — Fri, 18 Sep 2026 07:00:00 GMT

Nearly every marketing organization can point to productivity gains from AI. Reports are generated faster. Content is produced in greater volume. Campaign cycles are compressed. Yet many executive teams still struggle to see an equivalent improvement in revenue, profitability or enterprise value.

Index Insider | Will Local AI Models Change Who Owns the Risk? | ISG

Stanton Jones — Fri, 18 Sep 2026 07:00:00 GMT

Hello. This is Stanton Jones with what’s important in the IT and business services industry this week.

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The Affordability Control Tower: How Utility CIOs Can Engineer Enterprise Economics | ISG

Bill Huber; Jon Brock; Korey Barnard — Wed, 16 Sep 2026 07:00:00 GMT

From Rate Cases to Enterprise Decisions: Rethinking Utility Affordability

For decades, affordability in utilities was primarily viewed through the lens of rate cases, customer assistance programs and regulatory oversight. Technology organizations enabled those efforts, but they were rarely expected to directly influence affordability.

That paradigm is changing quickly. Affordability is no longer the downstream consequence of regulatory decisions. Customers face increasing bill pressure and regulators increasingly expect every investment to demonstrate measurable customer value.

That is why affordability is becoming an emergent property of thousands of operational, technological and commercial decisions made across the enterprise every day. Utilities today face unprecedented pressure to modernize aging infrastructure, strengthen cybersecurity, improve grid resilience and reliability, prepare for AI-driven load growth, integrate distributed energy resources and deliver better customer experiences. Every investment now has both an operational consequence and an affordability consequence.

As utilities become more digital, affordability is evolving from a financial metric into an enterprise-design objective. It must be engineered continuously rather than measured retrospectively. That shift fundamentally changes the role of the utility CIO.

From Technology Delivery to Enterprise Decision Quality

Historically, CIOs were measured by technology performance. Today, technology success is increasingly measured by business outcomes rather than system performance alone. As a result, CIOs are also becoming accountable for the economic consequences of technology decisions. Every major technology investment now influences how efficiently the utility acquires assets, operates infrastructure, maintains systems and serves customers. Investments in customer information systems, enterprise asset management, grid operations, field workforce management, analytics, cloud platforms and AI all influence operating costs, workforce productivity, asset utilization, grid resilience and reliability and service quality. Collectively, these decisions determine the utility's cost-to-serve.

As a result, CIOs are becoming stewards of enterprise-wide decision quality, helping the organization understand not only whether technology works, but whether it improves affordability while maintaining reliability and resilience.

That responsibility also exposes an important limitation in how most utilities manage affordability today.

The Missing Link Between Operations and Customer Bills

Most utilities can explain why rates have increased. Far fewer can explain which operational decisions created those increases, or predict which decisions being made today will flow into customer bills three years from now. The underlying challenge is fragmentation, both organizational and technical. Individual business functions optimize within their own boundaries, while the economics emerge across the enterprise.

The fragmentation often looks like this:

Customer data resides in the Customer Information System (CIS) and the Customer Relationship Management (CRM) system
Usage data lives in Advanced Metering Infrastructure (AMI) and Meter Data Management System (MDMS)
Financial and cost information sits in Enterprise Resource Planning (ERP) systems
Asset performance is tracked in Enterprise Asset Management (EAM) and work management applications
Operational technology platforms such as Geographic Information System (GIS), Supervisory Control and Data Acquisition (SCADA), Advanced Distribution Management System (ADMS), and Outage Management System (OMS) often maintain their own data models.

Each system explains its own performance. None explains enterprise affordability.

The Future of Worker Safety: AI-Driven Risk Management in Manufacturing | ISG

John Lytle; Anamika Sarkar — Mon, 14 Sep 2026 07:00:00 GMT

The State of Worker Safety Today

Manufacturers are facing a global labor shortage and – at the same time – are continuously working to implement measures and technology to eliminate workplace injuries. Labor shortages, an aging workforce and evolving regulatory expectations are driving organizations to rethink worker safety. And they are taking it not merely as a compliance obligation but as a strategic business priority that directly influences productivity, operational resilience and talent retention.

Because production environments combine machinery, energy sources, repetitive motion, chemicals, heat, noise, vehicle movement, maintenance work and contractor activity, worker safety remains a core business risk for manufacturers. Workplace incidents extend far beyond medical costs, disrupting production, increasing labor and insurance expenses, delaying customer deliveries and damaging organizational reputation. As a result, leading manufacturers are embedding safety into their broader operational strategy, recognizing that safer workplaces deliver more reliable operations, stronger workforce engagement and improved business performance.

Industry Trends Driving Change

Worker safety is shifting from a reactive model based mainly on incident reporting, audits and compliance documentation to a more predictive, data-driven safety management approach. Companies are increasingly using AI, machine learning, advanced sensing and data analytics to identify leading indicators of risk, such as unsafe machine interactions, fatigue patterns, near misses, maintenance gaps, heat exposure and repeated ergonomic strain before they result in injuries. The National Institute for Occupational Safety and Health (NIOSH) notes that advanced manufacturing is adopting technologies such as artificial intelligence (AI), advanced sensing, data analytics, robotics and digital supply-chain integration and emphasizing that these technologies must be introduced responsibly to protect workers.

Wearable devices and IoT-enabled connected worker solutions are also gaining importance in manufacturing plants, with tools such as ergonomic sensors, smart helmets, exposure monitors, location alerts and exoskeletons helping companies monitor risk in real time. Enterprises adopting this kind of tech must address privacy, cost, usability and evidence of effectiveness.

At the same time, the convergence of operational technology (OT), information technology (IT) and environmental, health and safety (EHS) platforms is creating a more connected safety ecosystem. Digital inspections, automated compliance workflows, real-time dashboards and integrated risk platforms are providing organizations with enterprise-wide visibility into operational, environmental and workforce risks.

Coupled with increasing ESG reporting requirements and climate-related workplace challenges, these trends are elevating worker safety from an operational function to a strategic capability that supports resilience, regulatory readiness and long-term business value.

Why This Matters Now

Global enterprises are undergoing a fundamental shift in how they manage operational risk, compliance and sustainability. What was once the domain of EHS systems, which are focused largely on compliance and incident tracking, is rapidly evolving into a broader, more strategic framework: safety, sustainability, security, health and environment (SSSHE).

As a result, enterprise leaders are no longer selecting solutions purely for compliance. They are investing in integrated risk and performance platforms that connect safety, environmental, health, security and sustainability domains.

Historically, EHS solutions focused on tracking incidents, ensuring regulatory compliance and supporting audits and inspections. Today, global manufacturers face a more complex reality, including:

Operational risks (e.g., process failures, supply disruptions) are interconnected with environmental and safety outcomes
ESG reporting is now board-level, investor-driven and regulated
Security risks (physical and cyber-physical) are increasingly tied to operational continuity

This convergence demands a unified system of record and intelligence, not siloed tools. Traditional EHS systems emphasize incident reporting, root cause analysis and compliance metrics. Modern SSSHE platforms emphasize: leading indicators (including near misses, unsafe conditions), predictive analytics (including risk forecasting, pattern detection) and scenario modeling (including environmental and operational impact).

This shift mirrors broader trends toward data-driven operational resilience for enterprises. Many organizations that are aiming to align with the goals of Industry 5.0 are at the forefront of this transformation. Particularly enterprises in Energy, Chemicals, Automotive and Life Sciences are looking for solutions to address their high-risk operational environments, complex global regulatory exposure and tight integration between operations, quality and safety.

When a Sneaker Brand Becomes an AI Company, That’s Strategic Drift | ISG

Dr. Dorotea Baljević — Tue, 08 Sep 2026 07:00:00 GMT

Forget AI drift. The more immediate risk facing enterprises is strategic drift. This is when organizations start chasing the AI narrative faster than their own mission.

Reimagining the Life Sciences Workforce for the AI Era | ISG

Pankaj Mishra; Anamika Sarkar — Fri, 04 Sep 2026 07:00:00 GMT

How will AI change work in the Life Sciences industry?

AI will redesign Life Sciences workflows and roles before it eliminates occupations at scale. Some roles will shrink, and the evidence on entry-level effects remains unsettled. That forecast is directionally right, but it understates the management challenge. The next phase will be defined less by AI model capability than by whether organizations keep changing AI systems under control, verify capacity before claiming it, preserve the learning work that makes experts and capture productivity rather than leave it inside outsourcing contracts priced by hours or headcount.

ISG’s State of Enterprise AI Adoption study, based on 1,200 priority use cases representing $2.6 billion in spending, shows value shifting from AI pilots to AI work that is governed, priced and measured. For Life Sciences firms, that means four moves: validate the workflow, protect the entry-level learning path, measure verification-adjusted capacity and reprice outsourced work, so productivity gains translate into value for the Life Sciences client.

Validate the Workflow, Not Just the Models

Computer-system validation in regulated Life Sciences operations was designed for relatively stable software: a defined system, controlled change and revalidation on known triggers. Technology vendor-hosted foundation models strain those assumptions: the model’s behavior can vary with prompts, retrieved data and connected tools, while the AI vendor may update the underlying model on a schedule the Life Sciences company does not control. The unit of assurance should therefore be a bounded workflow and a control framework, not the model.

Five controls matter most when Life Sciences enterprises implement AI-enabled workflows in regulated operations:

Pin and bound: Where feasible, fix the version of the AI model used by the workflow; define permissions, intended use and acceptance thresholds.
Ground the output: Require each AI-generated answer, draft or recommendation to cite supporting sources; reject or route unsupported claims for review.
Retest on change: rerun a versioned regression pack after material model, prompt, retrieval or tool changes.
Keep the record: retain execution records; model version, retrieved sources, tool calls, outputs, approvals.
Preserve exit: secure contractual change-notification, rollback and substitution rights from technology vendors. Model portability is quality control, not a procurement preference.

FDA’s January 2025 draft guidance ties AI credibility to context of use; predetermined change control plans pre-authorize defined software modifications; and the 2026 computer software assurance guidance expressly applies its risk-based framework to AI/ML tools used in medical-device production or quality management systems. The EU AI Act classifies certain product-integrated AI systems as high risk. Together they support context-bounded, risk-based assurance. ISO/IEC 42001 adds an auditable AI management system standard. In medical devices, the same logic covers field service and post-market surveillance analytics.

Human Review Is a Layer, Not the Load-Bearing Wall

The FDA launched its internal, large language model-powered generative AI tool Elsa in June 2025 and deployed agentic capabilities agency-wide that December. Reporting on Elsa’s early rollout was mixed: RAPS reported that some FDA staff found the tool accurate and helpful, while others encountered erroneous answers. CNN later reported that current and former FDA employees said Elsa had generated nonexistent studies or misrepresented real research. These accounts represent reported user experiences, not a formal FDA finding.

The broader lesson remains that regulated institutions can move quickly, but human review alone is a weak control. High-consequence workflows need source grounding, automated checks, independent sampling, reviewer calibration and stop rules. Agents belong in reversible, low-consequence actions until reliability and auditability are demonstrated.

Protect the Entry-Learning Path

The quieter workforce risk is that AI absorbs tasks through which juniors learn. Stanford and ADP researchers reported a 16% relative employment decline among workers aged 22-25 in the most AI-exposed occupations; Economic Innovation Group researchers dispute the attribution, finding junior and senior postings fell in parallel amid a broader slowdown. The cause remains unsettled, but the operational risk is clear: organizations can automate entry-level work faster than they redesign how junior staff learn.

Routine work is often where that learning happens. Regulatory associates learn by tracing claims to requirements; pharmacovigilance staff learn by coding ambiguous narratives; quality analysts learn by reconstructing deviations. If AI performs the first pass and juniors merely confirm it, throughput may improve but judgment formation will likely deteriorate. Managers should therefore assign some cases without AI assistance, require independent first passes and rotate junior staff through exceptions. Functional and talent leaders should track competency milestones and time to independent proficiency alongside throughput. Life Sciences enterprises need not preserve busy work; they need to preserve the learning loop.

Index Insider | You Can’t Be a Managed Service Provider If You Can’t Measure | ISG

Alex Bakker; Paul Reynolds — Fri, 04 Sep 2026 07:00:00 GMT

Hello. This is Alex Bakker and Paul Reynolds with what’s important in the IT and business services industry this week.

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The Enterprise Is Still in the Multi-Provider Incident Path | ISG

David Schmidt — Tue, 01 Sep 2026 07:00:00 GMT

In a multi-provider IT outage, the enterprise often discovers something uncomfortable: the work was outsourced, but the accountability was not. When a major issue crosses providers, the client still receives the business escalation, still must interpret competing accounts of what happened and still must push recovery over the line.

Index Insider | The Agentic Pyramid: Context Is for People, Too | ISG

Alex Bakker; John Boccuzzi, Jr. — Fri, 28 Aug 2026 07:00:00 GMT

Hello. This is Alex Bakker and John Boccuzzi with what’s important in the IT and business services industry this week.

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