Enterprise AI Executive

Executive AI Enterprise serves as a trusted resource for thousands of senior decision-makers driving adoption and shaping the future of enterprise AI.

Drawing on extensive experience advising executives on AI strategy, transformation, and architecture, and analyzing hundreds of AI best practices, case studies, and innovations each week, we distill what actually drives value — and why it’s important for professional and enterprise success.

Subscribe here: https://enterpriseaiexecutive.ai/subscribe

Advisory is currently at capacity, but do reach out regarding future requirements.

Executive Director – AI Strategy, Transformation & Architecture

Stealth Startup

Founder

Deloitte

Advanced to lead various AI engagements and initiatives.

Serving as a trusted advisor to executives, shaping AI strategies, operating models, and roadmaps aligned with business objectives.

Leading enterprise AI transformations to deploy AI solutions at scale that drive efficiency and enhance customer experience. 

Overseeing end-to-end design, architecture, and delivery of AI, ensuring enterprise integration, quality, scalability, and security.

Hands-on experience architecting and developing AI solutions with technologies from OpenAI, Microsoft and Google. 

Driving business development by identifying opportunities, co-developing propositions with partners, and securing engagements.

Mentoring high-performing teams, promoting continuous learning, innovation, and technical excellence.

Artificial Intelligence Lead

Goldman Sachs

Summer Analyst

Helping you level up with Generative AI. Follow for daily insights. Deloitte AI, Goldman Sachs

Hi, Lewis here. How can I help?

Thousands of enterprise AI best practices, case studies, market dynamics, and innovation insights — spanning agentic AI, generative AI, and AI agents — have been assessed.

Here’s what’s driving value: enterpriseaiexecutive.ai

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Over the past decade, I’ve partnered with dozens of executives to drive enterprise strategy, transformation, and architecture initiatives leveraging agentic AI, generative AI, AI agents, and other advanced technologies.

→ Serving as a trusted advisor to executives, shaping AI strategies, operating models, and roadmaps aligned with business objectives.
→ Leading enterprise AI transformations to deploy AI solutions at scale that drive efficiency and enhance customer experience.
→ Overseeing end-to-end design, architecture, and delivery, ensuring integration, quality, scalability, and security.
→ Hands-on experience architecting and developing AI solutions with technologies from OpenAI, Microsoft and Google. 
→ Driving business development by identifying opportunities, co-developing propositions with strategic partners, and securing new engagements.
→ Mentoring high-performing teams, fostering continuous learning, innovation, and technical excellence.

Since the rise of generative AI, agentic AI, and AI agents, the enterprise AI landscape has become increasingly crowded and complex.

As a result, many leaders, preoccupied with high-stakes strategic decisions, struggle to seize AI opportunities and manage emerging risks.

To address this, Executive AI Enterprise was created as a practical resource for executives and professionals at the forefront of AI.

Drawing on extensive experience advising executives on AI strategy, transformation, and architecture, and analyzing hundreds of AI best practices, case studies, and innovations each week, we distill what actually drives value — and why it’s important for professional and enterprise success.

The results have been exceptional.

Today, Executive AI Enterprise is a trusted resource for thousands of senior decision-makers driving adoption and shaping the future of enterprise AI.

Want to join them? 

Subscribe here: https://enterpriseaiexecutive.ai/subscribe

Want to transform your processes with AI?
Here's an example from BCG:

Financial crime remains one of the most resource-intensive areas for banks, despite years of investment in KYC, screening, and transaction monitoring.

BCG analysis shows financial crime operations can reach 5% of bank costs, with nearly half of client refreshes still done manually at leading institutions.

Among financial crime, KYC (onboarding and ongoing due diligence) stands out as both the most resource-intensive and ripest for transformation.

The graphic below outlines BCG’s KYC target-state vision where AI orchestrates onboarding, validation, risk rating, and monitoring.

Full insights: https://lnkd.in/eJxWg9yZ  

2195 more AI use cases: https://lnkd.in/ebbYBnfq

P.S. Repost to help your network with AI.

Lewis Walker ➲

Get the top 1% of agentic/gen AI (View my newsletter)

McKinsey just published its State of AI 2025.
(best practices on page 21)

The 1,993-respondent, 105-country survey finds that 88% use AI in at least one business function, up from 78% last year, with many exploring AI agents.

1\ Nearly two-thirds of firms remain in the AI experimentation (32%) or piloting (30%) stages, while only 31% report scaling AI enterprise-wide.

2\ Most respondents say less than 5% of EBIT comes from AI, but 56% have seen cost reductions in software engineering and manufacturing this year.

3\ Sixty-two percent of organizations are at least experimenting with AI agents, yet fewer than one in ten report deploying them at scale.

4\ Perceptions of workforce impact differ: 32% expect reductions, 43% predict no change, and 13% anticipate headcount growth within a year.

5\ High-performing firms employ best practices like structured Human-in-the-Loop (HITL) validation and rewiring processes (page 21).

Report below and here: https://lnkd.in/egNcRMBi

For more: https://lnkd.in/eBBHY3H7

New: McKinsey State of AI 2025

PowerPoint wasn't built to create 47 personalized proposals in one afternoon.

Yet that's exactly what my team needed last month.

Here's what we used instead: Gamma

Most people know it as "that AI tool that makes presentations."

But that's just the surface.

Gamma turns ideas into structured, quality outputs, fast and at scale. Presentations, documents, websites, even social media carousels.

Spend less time on fonts and formatting, and more time refining your message.

The Gamma API handles:

• Proposal customization for hundreds of clients.
• Localized content generation across regions.
• Report creation across different teams, and more.

Each output is tailored automatically with your data, messaging, and branding.

See in the PDF how the Gamma API builds 47 proposals.

Want more details?

Repost or comment below, and I’ll send you the:

1\ Process document
2\ Video walkthrough
3\ Code

Faster proposals, better results

AWS releases new AI transformation blueprint.
It addresses five themes:

The 22-page executive AI transformation blueprint from AWS spans five chapters with contributions from Capgemini, IBM, and PwC.

1\ Principles for AI success, AWS (p.2)
2\ Building data foundations, Devoteam (p.5)
3\ Agentic use cases, Capgemini (p.10)
4\ Responsible AI governance, IBM (p.14)
5\ Operationalizing AI, PwC (p.18)

Full insights below.

Free executive AI playbooks: https://lnkd.in/e5VBUzsB

New AWS Executive AI Transformation

Patrick van Abbema, MBA, PMP, CBAP, CSP

The safest place for enterprises to vibe code?


From rapid prototypes to full enterprise apps, Replit lets you build and refine through plain-English conversations with an AI agent.

Type what you want. Replit builds it.

Databases, authentication, integrations, and enterprise-grade security. All built in.

What you can build:

1\ Rapid prototypes

Prototype product ideas for customer demos and feedback. Iterate on it or hand it off to engineering for continued development.

2\ Internal tools

Finally build the internal tools you’ve always needed but never got. Automate workflows and improve your productivity.

3\ Customer-facing apps

Ship new production apps faster via rapid prototyping and more seamless handoff from product to engineering teams.

Replit has SSO/SAML, SOC 2 compliance, and robust enterprise controls. It’s also available on Azure Marketplace and Google Cloud Marketplace.

Try it yourself:

1. Go to replit.com
2. Click Start
3. Enter your prompts
4. Review your app

Repost to help your network deliver value faster.

P.S. Prompting best practices/examples below. 

Save it for later.

Brand partnership • Get the top 1% of agentic/gen AI (View my newsletter)

Want a practical path to scaling AI?
OpenAI just shared its approach:

The organizations that reach production consistently focus on four connected phases that form a repeatable system for scaling AI:

1\ Set the foundations (page 9)

Establish the structures that let experimentation scale safely and mature over time; executive alignment, governance, data access, and clear goals.

Enterprise examples: Figma, Chime


2\ Create AI fluency (page 12)

Develop the skills, confidence, and culture that make AI part of everyday work rather than a separate initiative.

Enterprise examples: BBVA, San Antonio Spurs, Wayfair


3\ Scope and prioritize (page 15)

Create a clear, repeatable system for capturing, evaluating, and prioritizing opportunities across the organization.

Enterprise example: Promega


4\ Build and scale products (page 19)

Develop a consistent, reliable method for turning new ideas and use cases into internal and external products.

Enterprise examples: Uber, Intercom, Lowes


Actions to get started in the playbook below. 

For more like this: https://lnkd.in/e5VBUzsB

New OpenAI Enterprise Scaling Playbook

OpenAI just shared a leaders guide to AI agents.
It covers four key areas:

1\ What AI agents are and their core components
2\ How they work and where they add value
3\ How to delegate, supervise, and optimize agents
4\ How to organize agents and avoid complexity

For more AI agent playbooks: https://lnkd.in/eEFSnMk2

New OpenAI Agent Insights

How your role could change with AI agents/robots?
McKinsey just broke it down:

The new 60-page McKinsey report explores the emerging work partnership between people, agents (nonphysical automation), and robots (physical). 

1\ Current technologies could automate over half of US work hours. As it unfolds, some roles will shrink, others grow or shift, while new ones emerge.

2\ Over 70% of human skills apply across both automatable and non-automatable tasks. Skills stay relevant, but how and where they’re used changes.

3\ The Skill Change Index (p.34) shows digital/info-processing skills face greatest automation exposure, while assisting/care-oriented skills change least.

4\ Demand for AI fluency, the ability to use, direct, and manage AI tools, has risen sevenfold in two years, outpacing every other skill across US job postings.

5\ By 2030, $2.9T in value could be unlocked in the US if organizations prepare their people and redesign work around humans, agents, and robots.

See page 52 for how leadership and management skills will need to change.

For more: https://lnkd.in/eBBHY3H7

New McKinsey Future of Work

Open just released its AI-native engineering guide.
28 steps to get started:

The 20-page guide outlines how each SDLC phase changes with coding agents and provides steps for teams to start operating as AI-native.

Plan

1\ Identify common processes that require alignment between features and source code. Common areas include feature scoping and ticket creation.

2\ Begin by implementing basic workflows, for example tagging and deduplicating issues or feature requests.

3\ Consider more advanced workflows, like adding sub tasks to a ticket based on an initial feature description. 

Design

4\ Use a multi-modal coding agent that accepts both text and image input.

5\ Integrate design tools via MCP with coding agents.

6\ Programmatically expose component libraries with MCP, and integrate them with your coding model.

7\ Build workflows that map designs → components → implementation of components.

8\ Utilize typed languages (e.g. Typescript) to define valid props and subcomponents for the agent.

Build

9\ Start with well specified tasks.

10\ Have the agent use a planning tool via MCP, or by writing a PLANmd file that is committed to the codebase.

11\ Check that the commands the agent attempts to execute are succeeding.

12\ Iterate on an AGENTSmd file that unlocks agentic loops like running tests and linters to receive feedback.

Test

13\ Guide the model to implement tests as a separate step, and validate that new tests fail before moving to feature implementation.

14\ Set guidelines for test coverage in your AGENTSmd file.

15\ Give the agent specific examples of code coverage tools it can call to understand test coverage.

Review

16\ Curate examples of gold-standard PRs that have been conducted by engineers including both the code changes and comments left. Save this as an evaluation set to measure different tools.

17\ Select a product that has a model specifically trained on code review. OpenAI finds that generalized models often nitpick and provide a low signal to noise ratio.

18\ Define how your team will measure whether reviews are high quality. OpenAI recommends tracking PR comment reactions as a low-friction way to mark good and bad reviews.

19\ Start small but rollout quickly once you gain confidence in the results of reviews.

Document

20\ Experiment with documentation generation by prompting the coding agent

21\ Incorporate documentation guidelines into your AGENTSmd

22\ Identify workflows (e.g. release cycles) where documentation can be automatically generated

23\ Review generated content for quality, correctness, and focus

For the remaining five steps on Deploy & Maintain (page 19), and more on how agents are changing the SDLC, check out the full guide below.

P.S. Want to become AI-native faster?
Join us: https://lnkd.in/eGruXbbb

New OpenAI AI-Native Best Practices

Google just shared 5 new AI agent guides.
250+ pages of practical guidance:

1\ Introduction to AI Agents (50 pages)

This covers the 5 levels of AI agents, from basic reasoning to self-evolving systems, plus security and architecture patterns that work at scale.
↳ https://lnkd.in/ebwVAKHq

2\ Agent Tools and Model Context Protocol (50 pages)

This breaks down tool design, the MCP, and the security risks hiding in plain sight, including MCP servers adding new tools without your approval. 
↳ https://lnkd.in/eepCvuNK

3\ Context Engineering and Memory (70 pages)

Agents with zero long-term memory treat every session like a first meeting. Learn to build memory systems that actively curate context.
↳ https://lnkd.in/eT4Tvxvm

4\ Agent Quality (50 pages)

Traditional QA fails for agents. This covers the four pillars of agent quality (effectiveness, efficiency, robustness, safety) and how to evaluate reasoning.
↳ https://lnkd.in/ec_ri234

5\ Prototype to Production (40 pages)

Learn evaluation gates, circuit breakers, and evolution loops that close the gap between demo and trusted production deployment.
↳ https://lnkd.in/eJg3CkF7

For 16 more top AI agent guides: https://lnkd.in/eEFSnMk2

P.S. Repost to your network with AI agents.

6 insights for execs to maximize agentic AI ROI:
(according to Accenture)

“1\ Agentic AI marks a shift in economic history. It is changing the rules of competition and redefining the balance of economic value creation (p.4)
2\ The agentic workforce will reshape how all industries create value. Agentic enterprises are around the corner (p.6)
3\ If the company’s agentic AI agenda doesn’t excite investors, the ambition isn’t bold enough (p.8)
4\ Winners will place agentic AI where it unlocks 10x value, not 10% savings (p.9)
5\ Companies must race where agentic AI will shift the competitive frontier. Hesitate and risk being left behind (p.13)
6\ Accenture's "one-two punch" strategy focused on value and key enabling capabilities will put ambitious leaders ahead of the competition (p.14)”

See the full insights below.

P.S. I reviewed 147 AI playbooks from the past 7 months. These 18 are essential reads for leaders: https://lnkd.in/e5VBUzsB

New Accenture Agentic AI

Data is the Achilles’ heel of enterprise AI transformation. Here are 15 actions to change that:

Organizations that tap into their most valuable data, and have a clear vision of what they want to achieve deliver better AI-powered results.

To identify best practices, IBM with Oxford Economics surveyed 1,700 Chief Data Officers (CDOs) across 19 industries and 27 geographies.

The research identified five key areas: strategy, scale, resilience, innovation, and growth, supported by fifteen recommended actions for CDOs.


Strategy: Don’t just collect data. Deploy it on a mission.

1\ Develop a forward-looking data strategy, in partnership with other business/tech leaders, that incorporates emerging technologies and trends.

2\ Optimize your data estate by mapping workflow needs, then assess that data’s quality, as well as whether it can be accessed and used by AI.

3\ Advocate for data literacy across the C-suite, ensuring all understand the strategic value of data and support data-driven decision making.

Scale: Give AI agents a fast track to data. 

4\ Build a scalable enterprise data architecture with unified, composable platforms that integrate data sources with real-time access/governance.

5\ Design data products that address business unit pain points with clear ownership, user feedback loops, and continuous improvement cycles.

6\ Adopt hybrid cloud solutions to optimize data storage and processing, enabling seamless data sharing and real-time collaboration.

Resilience: Build unbreakable data pipelines.

7\ Introduce AI agents to monitor data quality, detect anomalies, and feed insights into your data governance framework.

8\ Address emerging data and AI sovereignty issues by regularly auditing data handling practices and maintaining a map of your AI footprint.

9\ Build an AI agent marketplace that enables agents to create/use data products that unlock value without exposing source data.

Innovation: Deliver data to every desk.

10\ Make every role a data role by embedding AI into daily decisions and cultivating a mindset focused on applying data to drive outcomes.

11\ Invest in intuitive interfaces that simplify access to business data for non-technical users, enabling faster, more confident decisions.

12\ Become a talent magnet by showcasing a strong data culture, highlighting literacy programs and clear pathways for candidates with critical skills.

Growth: Actions 13, 14 and 15 can be found on page 37 in the report below.

For more executive AI: https://lnkd.in/eBBHY3H7

New IBM Best Practices 1700 CDOs

What 4000 leaders revealed about enterprise AI:


Microsoft commissioned IDC to survey 4,000+ enterprise leaders across 16 countries responsible for decision-making on gen AI and agentic AI.

Here are 10 insights from the new 54-page report:

1\ AI investments are on the rise, with nearly 40% of organizations planning to increase AI spending by up to 19% over the next 24 months. 

2\ 24% are repurposing existing IT budgets, 13% are repurposing a portion of the non-IT budget, 34% are adding net new investment.

3\ Organizations are expanding from prebuilt gen AI apps (40% today) to customized or custom-built gen AI solutions (70% in the next 24 months). 

4\ 68% of all respondents use gen AI, with 26% not currently using but planning to use it in the next 12 months. 

5\ Most organizations are seeing returns on their AI investments, with an average ROI of 2.8 times for gen AI users. 

6\ The average ROI realization for gen AI projects is 15 months. 

7\ 37% of all respondents currently use agentic AI, another 25% are experimenting with it, and 24% are planning to use in the next 24 months. 

8\ Agentic AI users reported an average ROI of 2.3 times. Average ROI realization for agentic AI projects is 13 months. 

9\ Nearly 30% identify security, privacy, governance, and compliance are the top challenges to scaling AI.

10\ Over 75% of organizations rate transparency as very or extremely important, emphasizing the need for explainability and human oversight. 

Full report below. How does this compare to adoption in your organization?

For more: https://lnkd.in/eBBHY3H7

New Microsoft IDC AI Insights

Building reliable voice AI is harder than it looks.

Models are only one part of the equation.

Enterprises also need predictable logic, realistic testing, low-latency telephony, and continuous improvement at scale.

Synthflow has built something that connects all of that, shaped by hundreds of enterprise deployments.

Until now, deploying voice automation often meant accepting significant uncertainty.

Prototypes looked promising but scaling them required months of trial and error.

There was no reliable way to drive consistency across calls, regions, or integrations.

To address this, Synthflow created BELL, a repeatable framework built directly into its voice AI platform.

See how it works below.

Reliability is the New Baseline

Integrating enterprise AI agents can be hard.
These 5 layers make it easier: 

1\ Smart business layer

Treat agents as a system actor, with clear roles, responsibilities & interaction boundaries like any other microservice

Meet users where they work by integrating the agent into existing enterprise surfaces (e.g. CRM, ERP, collaboration tools)

2\ Core transaction layer

Use MCP to connect agents through existing integration backbones (e.g. service meshes, API gateways) for “free” standard logging & security

3\ AI layer

Integrate agents via stable & well-defined MCP servers for APIs, events & service calls, with lifecycle management

Design for asynchrony & retries, using events/queues for hand-off & resumption and “once-only” semantics for retries

4\ Data layer

Ensure agents act on behalf of a user or service identity, using existing Identity & Access Management (IAM) to never bypass access controls

5\ Infrastructure & cloud layer

Let middleware abstract complexity, and only expose domain-level actions to agents, e.g. “submit expense report”


P.S. MCP is a key enabler easing agent integration. The graphic below covers:

→ Why AI models need MCP
→ What MCP does
→ How MCP works

Try MCP yourself in Replit:

→ Set up an MCP environment: https://lnkd.in/eqUgcZDq
→ Run a practical example (details below)
→ Customize for your needs

McKinsey just addressed limited AI P&L impact.
+ why the next three years should be different:

The 22-slide presentation covers:

1\ The potential to unlock trillions in value (p.2)
2\ Not all sectors are impacted equally (p.3)
3\ Gen AI/agentic enterprise impact examples (p.4-9)
4\ AI outperforms humans on key tests (p.10)
5\ Gen AI is everywhere except the P&L (p.11)
6\ Limited P&L impact stems from seven factors (p.12)
7\ Why the next three years should differ (p.13)
8\ AI agents: More integration & automation (p.14)
9\ Estimated automation potential of AI agents (p.15)
10\ Agentic AI tackling complex workflows (p.16)
11\ Impact with more holistic agents (p.17)
12\ From "interns" to experienced peers (p.18)
13\ Challenges scaling agentic AI (p.19)
14\ “Rewiring” organizations to scale AI (p.20)
15\ Risk should not be under-estimated (p.21)

Full insights below. 

Source: https://lnkd.in/eA3_QKKK

For more: https://lnkd.in/eBBHY3H7

P.S. Repost this to help your network with AI.

New McKinsey AI Agents POV

Deloitte just shared its 5 agentic AI lessons:


SFL Scientific, a specialist technical AI arm of Deloitte Consulting, outlined its five lessons from developing and deploying gen AI and agentic AI.

GenAI and agentic AI excel where RPA and traditional ML struggle, especially with large amounts of unlabeled and unstructured text and images.

As gen AI is a principal component of agentic AI, SFL Scientific refers to both simply as agentic AI for brevity in these insights.

Five lessons learned:

1\ Agentic AI should start small; its broad potential makes focus difficult, and tight scoping prevents misaligned expectations while driving steady value.

2\ Evaluating agentic AI is challenging (unstructured/novelty of outputs); define success early and build feedback loops to avoid optimizing the wrong goals.

3\ Strong user experience and change management are key; intuitive workflows often outperform technical accuracy in driving user adoption and business value.

4\ Shipping agentic AI requires addressing uncertainty and hallucinations; pairing LLMs with RAG, citations, ReAct, etc. creates more reliable systems that scale.

5\ Agentic AI is developing rapidly; new protocols, governance, and solution patterns unlock value but also introduce added complexity/cost.

While prototyping AI is faster than ever, full deployment still takes time, shifting effort from data prep to post-hoc validation, guardrails, and change management.

The full insights below break down each lesson in more detail, plus 25 key considerations across strategy, development, and change management.

For more agentic AI: enterpriseaiexecutive.ai

New Deloitte Agentic AI Lessons Learned

Marketing just got faster and more cost-effective.
AI is reinventing how ads are created:

✅ A/B testing is radically cheaper, generate unlimited ad variations fast.
✅ Produce as many ads as you want, no editors or long timelines.
✅ Show your product from any angle with new scripts, voices, avatars.
✅ One product URL can produce a complete video ad in under a minute

All you need to create a video ad? Just the product page URL from your website and Higgsfield AI's Click-to-Ad feature.

Drop it in, and 40 seconds later you have the advert. Check out the Tesla Cybertruck example below.

Try it: https://lnkd.in/eCVeiuKe

Less than 10% are scaling AI agents.
What’s holding others back?

Agent adoption continues to lag its potential across enterprises, as highlighted by McKinsey’s latest State of AI 2025 and other studies.

Leaders often cite security risks, operational complexity, and data accuracy as key barriers. 

Unsecured MCP servers and weak governance create vulnerabilities.

Operational complexity slows secure rollout across teams. Data-quality concerns add hesitation to critical workflows.

These are all challenges Airia addresses. Airia centralizes agent orchestration and AI security on one platform.

Enterprises can integrate LLMs with their systems without exposing sensitive data or compromising compliance.

AI adoption should be reliable, secure, and scalable.

That’s what Airia delivers.

What’s blocking AI agent adoption in your enterprise?

Register here: https://lnkd.in/ejWnAN73

Want to secure AI in your enterprise?
(summary table on page 5)

Darktrace, a leader in AI cybersecurity, just released a 15-page vendor-neutral guide outlining the cybersecurity scope of AI.

1\ The guide presents five categories that form distinct layers of protection across the AI lifecycle, each with related risks and safeguards (page 5).

2\ Layers cover defending against misuse, monitoring AI behavior, protecting AI development, securing supply chains, and improving oversight.

3\ The paper guides leaders in translating AI security complexity into clear executive actions centered on trust, accountability, and resilience (page 12).

Many executives still lack a concise definition of what “securing AI” encompasses. Few resources cohesively align risks with safeguards. 

Broader standards like ISO/IEC 42001 set governance principles but lack practical guidance to operationalize enterprise AI security.

For more: https://lnkd.in/eBBHY3H7

P.S. Repost to help others secure enterprise AI.

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