Emerj Artificial Intelligence Research

18/06/2026

AI’s move into physical operations is forcing enterprises to confront a new layer of infrastructure decisions with real operational and financial consequences.

Drew Henry, Executive Vice President for Physical AI at Arm, joins Emerj’s Daniel Faggella CEO and Head of Research, to examine how leaders can prepare for model‑driven systems across logistics, manufacturing, and other asset‑heavy environments

Drew highlights how executives should think about:

• Compute and hardware choices for safety‑critical environments
• Understanding power, reliability, and integration constraints
• Using simulation to validate changes before touching physical systems
• Grounding AI initiatives in real operational problems, not model novelty

Listen to the full episode for a clear view of the architectural steps required to move from fixed automation to model‑driven control: https://zurl.co/H1hby

18/06/2026

Finance leaders are being asked to influence outcomes in real time — while operating on architectures built for delay, aggregation, and manual reconciliation.

Emerj’s Dan Faggella is joined by Alex Curran, CEO at Aptitude Software, on this AI in Business Podcast episode to examine why finance functions are hitting structural limits.

Curran breaks down how event‑level capture, continuous checks, and full lineage enable CFOs to surface exceptions immediately and act before outcomes harden.

Listen to this Aptitude Software–sponsored episode to learn the specific architectural shifts that unlock real‑time finance: https://zurl.co/1zyau

17/06/2026

CX is becoming the function that determines whether enterprise AI delivers business value or creates new operational risk.

In this NiCE–sponsored episode of the AI in Business podcast, Shri Nandan, VP of AI Experiences at Comcast, joins Emerj Editor Yolandi de Weerdt to discuss what it takes to scale agentic AI across customer operations while maintaining trust, consistency, and control.

Comcast serves approximately 32 million customers across broadband, cable, and streaming.

The conversation explores:

• Three data foundations required to maintain context continuity in production

• Why human-AI cooperation produces stronger outcomes than complete automation

• How a shared North Star across CX, IT, and operations prevents fragmentation as AI scales

One theme stands out:

Scaling AI in customer operations is not primarily a technology challenge. It is a coordination challenge.

For senior CX and operations leaders, this episode provides a practical framework for moving beyond proof-of-concept projects and building AI systems that can operate reliably at enterprise scale.

https://zurl.co/bL09F

16/06/2026

Enterprise workflows built around human oversight begin to fail when agentic AI operates at machine speed.

Approval gates, audit checkpoints, and overnight batch windows were designed for human-paced decisions.

Agentic systems change that operating assumption entirely.

In this episode of the AI in Financial Services Podcast, Chris Caldwell joins Emerj’s Yolandi de Weerdt to examine why regulated enterprises remain operationally unprepared for agentic deployment at scale.

The conversation explores:

• why machine-speed transactions break traditional governance models
• why digital delegates outperform the digital twin concept in enterprise settings
• how bounded authority becomes critical once agents begin making operational decisions
• why poorly tuned agents can create higher operational costs than the humans they replace

The discussion also highlights a broader issue inside financial services:

Enterprise infrastructure was designed around controlled human throughput.

Agentic systems introduce continuous ex*****on pressure across workflows that were never designed for autonomous coordination.

For leaders evaluating agentic AI deployment timelines, these are the operational friction points worth examining before scale introduces governance failure.
https://zurl.co/ZaiHG

15/06/2026

AI is cutting service costs. It’s also accelerating organizational risk faster than most enterprises can absorb.

The latest article distills the conversation with Robert Rose, Senior Director of Customer Experience at Adobe where he breaks down what enterprises keep getting wrong about scaling AI to the customer layer, and what must be true before generative systems can operate safely.

Three insights stood out:

• Trust threshold as a deployment map
• Deterministic AI foundation as the prerequisite for generative personalization
• Escalation design as the measure of service AI maturity

Read the full article for Robert’s insights into scaling AI in customer operations, and the fault lines that matter.

https://zurl.co/ePOQk

15/06/2026

Supply chain executives are losing margin and time because their organizations still can’t evaluate structural options at the speed disruptions demand.

Joris Wijpkema, Executive Vice President for Solutions and Strategy at Optilogic, joins Emerj’s Marilie Fouché to explain why ex*****on‑oriented planning systems can’t support large‑scale scenario evaluation.

He lays out why enterprises now need a dedicated modeling layer capable of testing thousands of options in minutes.

The episode offers an outline of how this layer

• strengthens resilience
• accelerates alignment, and
• feeds higher‑quality decisions back into planning cycles.

To understand the strategic implications for supply chain workflow listen to thie full Optilogic-sponsored episode on the AI in Business podcast: https://zurl.co/bpMOZ

12/06/2026

Centene’s AI strategy points to a core payer challenge:

How do you improve member experience and health outcomes across 28 million lives without adding operational drag?

Centene Corporation is a leading healthcare enterprise delivering services through Medicaid, Medicare, and Health Insurance Marketplace programs.

With over 28 million members across all 50 states, Centene is the largest Medicaid managed care organization in the U.S.

In 2024, the company reported $163.1 billion in revenue, a 5.89% year-over-year increase, and employed more than 60,000 professionals globally.

Against that scale, Centene is integrating artificial intelligence into core operations.

Investment figures remain undisclosed, but the direction is clear:

AI is being deployed to improve efficiency, support member health outcomes, and optimize service delivery at scale.

Our article analyzes two AI use cases at Centene for healthcare leaders:

1. Using machine learning and natural language processing to streamline customer correspondence, reduce response times, and improve satisfaction.

2. Leveraging predictive analytics to identify at-risk members based on clinical and demographic data, enabling early interventions that improve outcomes and reduce costs.

For healthcare leaders, the lesson is not that AI belongs in isolated pilots.

It belongs where scale, workflow pressure, and measurable outcomes intersect.

Read the full breakdown here:
https://zurl.co/UuPZZ

Centene Corporation is a leading healthcare enterprise that is committed to helping people live healthier lives through government-sponsored and commercial healthcare programs. The company serves as a managed care organization providing a comprehensive range of healthcare services, primarily through...

10/06/2026

Pharma field teams are losing HCP engagement opportunities because commercial intelligence arrives too late.

That timing problem is rarely caused by a lack of data.

In this ODAIA-sponsored episode of the AI in Business podcast, Damion Nero, Global Head of Statistics at Daiichi Sankyo, joins Emerj editor Yolandi de Weerdt to explain why fragmented data pipelines create a disconnect between commercial insight and field ex*****on.

The discussion explores how legacy infrastructure and disconnected systems create delays that cause field teams to miss critical engagement windows.

It also examines why AI initiatives struggle to scale in pharma and why organizations see stronger commercial results when they begin with routine, high-certainty use cases before expanding into broader automation efforts.

For pharma leaders focused on commercial operations, the conversation offers a practical framework for moving beyond isolated AI pilots and turning insight into action.
https://zurl.co/lp9oQ

10/06/2026

AI agents fail in production not because of the model, but because they lack the context required to make reliable decisions.

That challenge sits beneath a large share of stalled agentic AI initiatives.

In this Arango-sponsored episode of the AI in Business podcast, Ravi Marwaha, Chief Operating Officer and Chief Technology Product Officer at Arango, joins Emerj editor Yolandi de Weerdt to discuss why context has become a foundational infrastructure problem for enterprise AI.

Arango's multi-model data platform combines graph, document, key-value, vector, and search capabilities into a unified system designed to provide governed, consistent context for AI applications.

The conversation explores why real-time reasoning, explainability, and relevance determine whether AI systems create measurable value in environments such as customer support, semiconductor design, and clinical research.

A key takeaway:

As enterprises move from AI assistants to AI agents, context becomes a business requirement, not a technical enhancement.

Leaders responsible for scaling agentic AI will find practical guidance on architecture, governance, and production readiness.

Listen here:
https://zurl.co/9C6Ul

Where have you seen context become the limiting factor in enterprise AI deployment?

09/06/2026

Insurance leaders do not need another broad AI explainer.

They need a clear view of where AI can support the insurance value chain.

Emerj’s AI in Insurance: Executive Cheat Sheet gives leaders a practical scan of core AI capabilities already showing up across the sector, including:

Natural language processing for chatbots, claims support, and customer service

Predictive analytics for risk scoring, claims payout estimates, and personalized policies

Machine vision for property underwriting, catastrophe risk, and auto claims assessment

The guide also covers examples from Progressive, Allstate, AXA, Cape Analytics, Ant Financial, Tractable, Geico, State Farm, and Liberty Mutual.

For insurance executives, the question is not whether AI belongs in the roadmap.

The question is where it can create measurable value across underwriting, claims, customer service, and policy pricing without adding risk, workflow friction, or vendor confusion.

Download the AI in Insurance: Executive Cheat Sheet to see where AI is being applied across the sector and how insurance leaders can evaluate practical use cases with clearer context.

Get the cheat sheet here:
https://zurl.co/ChFse