Sam Sharma

An agent that can reach a CRM, read customer records, update tickets, trigger workflows or search internal documents is part of the operating model, not just a work assistant.

• Sam Sharma says these systems need the same controls as human employees: a clear purpose, owner, identity, access boundary, risk level, approval process and audit trail.
• The principle: the more autonomy an agent has, the stronger its governance must be.

The post runs through the questions leaders should answer before widening access: what an agent should be allowed to access, which decisions it should influence, what actions it should take, who owns the outcome when it gets something wrong, how to monitor it in real time, and how to stop a useful pilot from becoming uncontrolled agent sprawl.

Read original

The danger is not a sudden turn to evil but the gradual delegation of powers to increasingly capable systems before their behaviour can be reliably controlled.

• That shifts the question from whether AI will destroy humanity to what powers should be handed to AI before they can be proven controllable.
• The post also asks: if AI companies believe their systems are becoming dangerously powerful, why are they still racing to build them, and who benefits if tighter regulation squeezes smaller competitors?

The argument spans cybersecurity, financial systems, scientific research, critical infrastructure, military decision-making and autonomous agents, and closes with a clear principle: capability should not outrun controllability.

Read original

The recurring sequence is to constrain, integrate and govern systems, keeping humans accountable before increasing autonomy. Greater capability without boundaries means greater risk.

Sam Sharma cites reporting that Anthropic placed a highly capable cyber model behind a specific security workflow: it scans for vulnerabilities and recommends fixes while humans retain approval.

• They compare AI to electricity in a building: employees do not need the power station, but safely designed systems that deliver power where it creates value.
• A second post uses a lock-picking analogy for Anthropic's Mythos and Project Glasswing, arguing that power without structure creates exposure rather than advantage.
• A third draws the same lesson from an OpenAI and Hugging Face incident: AI agents need only a goal, access and insufficient controls to cause serious consequences, while accountability for external harm remains unclear.

Read original · Original 2 · Original 3

When coding agents follow different instruction files, developers in the same organisation can end up with competing versions of how things are done.

• The proposed fix is a shared organisational source of truth covering data, standard operating procedures, coding standards, security policies and governance frameworks for people and agents alike.
• The related claim: the question is no longer whether to adopt AI, but which processes it should own, which it should augment and where humans should stay in control.

Sam Sharma responds to Shopify CEO Tobi Lütke's reported threat to ban Claude Code.

• As described in the post, Lütke's concern is that developers' agents read different instruction files, such as CLAUDE.md and AGENTS.md, effectively automating inconsistency across the organisation.
• Separately, the launch of ChatGPT Work illustrates AI connecting to Gmail, Slack, CRM, Drive, calendars and internal documents to perform scheduled work.
• Sam Sharma frames 2026 as the year AI becomes a digital workforce rather than a digital assistant.

The digital-workforce framing in the ChatGPT Work post goes further than earlier posts, which treated agents as a governance problem to contain. The two positions meet in a simple formula: give agents real work, then bound it.

Read original · Original 2

Buying tools first and justifying the spend later fails, especially as AI competes for budget with cybersecurity, cloud infrastructure, analytics and automation.

• The winning pattern is to identify where AI increases revenue, reduces friction, improves decisions or delivers measurable productivity gains, then scale what works.
• Technology does not create ROI; business outcomes do.

The post contrasts tool-led adoption with outcome-led prioritisation, naming revenue, friction, decision quality and productivity as the four areas to assess for measurable value before scaling an initiative.

Read original

Organisations lack clarity, process and consistent execution; high-performing leaders refine systems rather than chase tools. The same logic applies to AI: combine leadership thinking with intelligent systems and AI-driven execution to scale.

The post describes a lunch at the Institute of Directors in London with Dan Hindman, a senior sales leader at Sales Xceleration. The post attributes the other side of the conversation to Hindman rather than presenting it as Sam Sharma's view.

Read original

Customers now ask AI tools to compare products, summarise reviews, recommend brands and narrow options before visiting a site.

• Visibility depends on whether those tools can understand a brand's products, trust its reviews and include it in the answer.
• Reviews, structured content and trust signals now form part of a new discovery layer.

The ecommerce thread also describes AI shopping assistants that give thousands of customers individualised bots to answer questions and surface discounts at the right moment, which the post says Elevate AI Tech is deploying now.

Together with the governance posts, the ecommerce thread reveals a tension: personalised agents at scale for consumers, strict access boundaries for enterprise agents.

• Sam Sharma's resolution is that control should match consequence.
• For a buyer, the deployment claims in the ecommerce post still need substantiation: ask for client names and results.

Read original · Original 2

Newcomers should start here: it frames the governance problem in plain operational terms, not abstract risk language.

Sam Sharma argues that AI agents are moving from experiments into real enterprise workflows, where they can reach data, connect to systems, make decisions and act with growing independence.

• The post contrasts a chatbot that drafts an email with an agent that can access a CRM, review customer records, update tickets, trigger workflows and search internal documents.
• Their central claim: govern AI agents like digital workers, each with a clear purpose, owner, identity, access boundary, risk level, approval process and audit trail.
• The more autonomy an agent has, the stronger its governance must be.

For brands planning a campaign

A useful anchor for a co-authored article or webinar on governing autonomous agents in enterprise workflows. It offers a concrete framework—purpose, owner, identity, access boundary, risk level, approval, audit trail—that a B2B AI governance consultancy could adapt into a practical checklist.

Newcomers should start here to understand the controllability argument that runs through much of their other work.

Sam Sharma explores why recent warnings from AI researchers, safety leaders and CEOs have grown more serious, and asks uncomfortable questions about incentives: if companies believe their systems are becoming dangerously powerful, why are they still racing to build them?

• They argue the debate is often framed incorrectly.
• The risk is not that AI suddenly becomes evil, but that increasingly capable systems gain more autonomy, permissions and access to the real world before their behaviour can be reliably controlled.
• The post sums it up: “capability should not outrun controllability.”

For brands planning a campaign

A thought-leadership angle on AI controllability rather than catastrophe. For a governance consultancy, this supports content on pacing autonomy, permissions and oversight in real business systems.

Newcomers should start here for a concrete illustration of “maximum capability within intelligently designed boundaries.”

Sam Sharma uses Anthropic's cybersecurity move to make a broader point about enterprise AI: the most powerful AI may be the AI employees never directly touch.

• Adoption does not mean giving everyone access to increasingly powerful models.
• They compare AI to electricity in a building: employees do not need access to the power station; they need safely designed systems that deliver power where it creates value.
• The post notes that Anthropic puts its highly capable cyber model behind a specific security workflow, scanning for vulnerabilities and recommending fixes while humans retain approval.

For brands planning a campaign

A strong case study for a governance consultancy's content on human-in-the-loop design, least-privilege access and keeping powerful models behind controlled workflows.

Newcomers should start here: it turns an executive headline into a clear governance problem.

Sam Sharma reads Shopify CEO Tobi Lütke's threat to ban Claude Code as a sign that AI adoption is moving faster than governance.

• Developers using different AI coding agents may follow different instruction files, so the same organisation can give its AI developers different versions of how things are done.
• They call this automated inconsistency.
• The enterprise question: what is the organisational source of truth for every human and AI agent?

For brands planning a campaign

A co-authored piece could explore how organisations define and enforce a single source of truth for people and AI agents, especially in software, operations and regulated workflows.

Newcomers interested in the commercial side of AI should start here.

Sam Sharma argues that traditional search is no longer the only place where ecommerce decisions begin.

• Customers now ask AI tools to compare products, summarise reviews, recommend brands and narrow choices before visiting a website.
• The visibility challenge is not just ranking on Google, but whether AI tools can understand a brand's products, trust its reviews and include it in an answer.
• Reviews, structured content and trust signals are part of a new discovery layer.

For brands planning a campaign

A useful angle for an ecommerce or answer-engine-optimisation content brief on preparing product data, reviews and structured content for AI-mediated discovery.

Newcomers should start here to understand Sam Sharma's commercial framing and why governance work must connect to measurable value.

Sam Sharma says most AI budgets are approved with one question hanging over them: “Show me the money.”

• The popular approach fails because organisations buy AI tools first and try to justify the investment later.
• As AI competes for budget against cybersecurity, cloud, analytics and automation, every investment must prove its value.
• They argue that winning organisations identify where AI can increase revenue, reduce friction, improve decision-making or create measurable productivity gains, then scale what works.
• Technology does not create ROI; business outcomes do.

For brands planning a campaign

A useful angle for a webinar or article on building a business case for governed AI, linking controls and oversight to measurable productivity and revenue outcomes.

The posts argue that agentic AI changes the risk conversation: a chatbot drafting an email is different from an agent touching a CRM, reviewing customer records, updating tickets, or triggering workflows.

• The proposed response is to treat agents less like productivity features and more like digital workers, each with a clear purpose, owner, identity, access boundary, risk level, approval process, and audit trail.
• The recurring principle is that greater autonomy requires stronger governance, with human approval retained when consequences are high.

Uses direct questions and control checklists: what should an agent access, decide, and do; who owns outcomes; how is it monitored; how is sprawl prevented.

For a detailed treatment, ask for control frameworks, audit standards, or regulatory mappings.

For brands planning a campaign

Strong fit for a co-authored article or webinar on governing autonomous AI agents in enterprise workflows. Sam Sharma can supply the executive framing and control questions; add a security architect or compliance specialist if the audience needs implementation-level detail.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6

Sam Sharma argues the real danger may not be a sudden evil AI but the progressive delegation of powers to increasingly capable systems before their behavior can be reliably controlled.

• Posts cover Anthropic’s cybersecurity approach, Anthropic’s Mythos/Project Glasswing framing, and an OpenAI and Hugging Face incident as examples of agentic capability outrunning containment and accountability.
• The recurring line is that capability should not outrun controllability.

Anchors abstract risk in current news stories, then translates them into governance or accountability lessons. Uses analogies such as a machine that can pick any lock or electricity delivered safely into a building.

The posts reference longer articles. Ask for the full texts to assess the depth of the analysis.

For brands planning a campaign

Good fit for executive explainers, keynotes, or panels on AI risk, responsible deployment, and why controllability matters. For a technical security audience, pair Sam Sharma with a practitioner who can discuss model evaluation, red-teaming, and incident-response specifics.

Examples: Source 1 · Source 2 · Source 3 · Source 4

One post uses Shopify’s Claude Code discussion to argue that when different AI coding agents read different instruction files, an organisation can give its AI developers inconsistent versions of “how we do things here.”

• The proposed answer is an organisational source of truth for data, standard operating procedures, coding standards, security policies, and governance frameworks that every human and AI agent follows.
• Another post argues that ChatGPT Work shifts the question from whether to adopt AI to which processes AI should own, augment, or leave under human control.

Takes a current enterprise AI controversy or product launch, identifies the hidden governance inconsistency, and reframes it as an operating-model design question.

Ask for a specific implementation model for maintaining agent instructions or context, and examples of companies that have adopted one.

For brands planning a campaign

Useful for content or workshops on AI operating models, process redesign, agent inventories, and instruction/context management. Ask Sam Sharma to show how the source-of-truth idea works in practice, not just assert that it matters.

Examples: Source 1 · Source 2

The popular approach fails because organisations buy AI tools first and try to justify the investment later.

• As AI competes for budget with cybersecurity, cloud, analytics, and automation, leaders need each investment to prove value.
• The organisations winning with AI are framed as those identifying where AI can increase revenue, reduce friction, improve decisions, or create measurable productivity gains, then scaling what works.
• The stated principle is that technology does not create ROI; business outcomes do.

Uses a blunt budget-approval lens and contrasts tool-led adoption with outcome-led prioritisation.

Ask for financial benchmarks, client examples, or measurement frameworks to support the argument.

For brands planning a campaign

Fits CFO/CIO-facing content and vendor-neutral webinars on building AI business cases and prioritising use cases. Sam Sharma can contribute the prioritisation argument; ask a research partner for benchmarks if the campaign promises quantitative guidance.

Examples: Source 1

The post contrasts this with generic popups and chatbots and argues that one-size-fits-all customer journeys are over. It states that Elevate AI Tech is building and deploying this capability now.

Paints a concrete customer-experience scenario, then links it to a strategic claim about personalisation at scale.

The post contains no client names, deployment metrics, or performance results to support the claim that the capability is already deployed.

For brands planning a campaign

Relevant for ecommerce, retail, and CX AI campaigns. Before using it as proof of delivery, request a case study or measurable deployment evidence; the current source supports the positioning and concept, not verified outcomes.

Examples: Source 1

Customers now ask AI tools to compare products, summarise reviews, recommend brands, and narrow choices before visiting a website.

• That shifts the visibility challenge from Google ranking alone to whether AI tools can understand a brand’s products, trust its reviews, and include it in generated answers.
• The post points to reviews, structured content, and trust signals as part of the new discovery layer.

Connects a shift in consumer behavior directly to ecommerce content and trust requirements.

The post is a short summary; the linked article contains the substantive treatment.

For brands planning a campaign

Good fit for AEO, ecommerce content strategy, and AI discovery sessions. Review the linked long-form article before a campaign promises a detailed AEO methodology.

Examples: Source 1

ChatGPT Work is described as moving organisations beyond asking AI questions toward AI executing work across Gmail, Slack, CRM, Drive, calendars, and internal documents.

• The question becomes which processes AI should own, which it should augment, and where humans stay in control.
• The posts call for prepared people, governance, redesigned processes, and responsible deployment, and frame 2026 as the year AI becomes a digital workforce rather than just a digital assistant.
• A separate post from a business lunch argues that most businesses do not have a sales problem but a clarity, process, and execution problem.

Combines a technology-change narrative with change-management language and a call for structured systems rather than tool chasing.

The workforce and sales discussions stay at the level of principles and questions. Ask for a change-management methodology, training example, or sales-process case study.

For brands planning a campaign

Fits HR, operations, and transformation events about preparing teams for AI-enabled operating models. For a sales-growth angle, Sam Sharma can speak to clarity and execution, but AI governance is the stronger campaign fit.

Examples: Source 1 · Source 2

Several posts use bold or styled lead-ins, numbered questions, and analogies such as electricity in a building or a machine that can pick any lock.

• Hashtags cluster around AI governance, security, transformation, ecommerce AI, and AEO.
• Calls to action include commenting, direct messaging, reading a linked article, or assessing whether a business is building for AI as a digital workforce.

The recurring method is to take a headline or product launch, identify the governance or operating-model gap, and turn it into leadership questions.

The posts do not state individual dates, so changes over time cannot be mapped chronologically.

For brands planning a campaign

A campaign can adapt this format into a short, provocative LinkedIn post linked to a co-authored long-form piece or webinar. If the campaign needs evidence of depth, request the linked articles and any deployment case studies first.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7 · Source 8

One post says "At Elevate AI Tech, this is how we approach AI transformation: matching powerful technology with governance, human oversight and real business workflows."

• Another says "This is what we're building at Elevate AI Tech: AI shopping assistants that operate at scale," and a third states "At Elevate AI Tech, we help businesses bridge that gap, designing AI systems with humans in the loop, strong governance, and clear business outcomes in mind for 2026 and beyond."
• The posts describe the service offering and use "we."
• They frame this as a partnership but do not say whether Sharma owns the company or works there; marketers should ask about their relationship before treating this as an owned business or employment record.

Original disclosure

Posts attribute the work to Elevate AI Tech using first-person plural language. They do not say whether Sam Sharma owns or is employed by the business, and they provide no sponsorship disclosure.

View example

Hill argues that buyer agents will research vendors, compare options and build shortlists before a human talks to sales, and that scarce skills include translating messy business processes into workable AI and automation. Both treat AI adoption as a business-process and clarity problem rather than a model problem, and write for enterprise GTM and operations audiences.

Chris Hill's centre of gravity is go-to-market and revenue operations: machine-readable product data, pricing clarity, procurement agents, seller agents, and the forward-deployed or workflow engineer role.

• Sam Sharma focuses on governance and executive control of autonomous agents, with ecommerce and sales as secondary applications.
• Readers looking for GTM engineering and agentic commerce should start with Chris Hill; those interested in agent governance, risk and audit framing should start with Sam Sharma.

Read original · Original 2

Adams's posts on US Army cyber and NSA reporting describe agents trained for defined work roles and qualified against human-style standards, while people retain responsibility for operational risk. This echoes Sharma's digital-worker framing and the principle that greater autonomy needs stronger guardrails.

Tori Adams writes from a federal, defence and national-security vantage point, and covers AI as a macroeconomic variable through Federal Reserve policy.

• Sam Sharma writes from a commercial enterprise and ecommerce vantage point, addressing business leaders, governance teams and brands.
• Their governance arguments are similar, but the evidence each draws on comes from a different sector.

Read original · Original 2

Agents operationalise the ontology they are given, scaling bad definitions rather than fixing them.

• Sam Sharma applies the same operating-model diagnosis to AI agents that follow different instruction files or run without a shared source of truth.
• Both argue that trusted, governed foundations matter more than the most capable model.

Mark Bidgood is a consumer-goods and CPG specialist focused on data foundations, retail media, trade promotion, and ERP and CRM alignment.

• Sam Sharma ranges more widely across enterprise AI governance, security, transformation leadership, ecommerce and sales.
• Readers focused on CPG data strategy should follow Bidgood; those focused on agent governance across an enterprise should follow Sharma.

Read original · Original 2

The posts describe buying agents that research vendors, compare options, and build a shortlist before a human sales conversation.

• They argue that product information, pricing, integrations, APIs, documentation, and security details must be machine-readable so agents can evaluate them correctly.
• They also point to a scarce role: the translator who can move from business problem to process, data, AI, automation, controls, and outcome.
• For a consultancy engagement connecting governance to GTM systems, procurement workflows, and the people who redesign them, Chris Hill is a better fit than Sam Sharma.

Chris Hill's framing starts with commercial transformation and workflow design.

• The posts say little about security boundaries, audit trails, or approval authority for autonomous agents.
• A governance consultancy would need to add the formal risk-and-control layer, potentially losing some of the broader GTM argument when narrowing the piece to control questions for a campaign aimed at CFOs.
• The review covers two posts; ask for more examples before assessing the breadth of their work.

Examples: Source 1 · Source 2

The posts analyze recent Army and NSA reporting on AI-enabled cyber defense, protected investment, wargames tied to acquisition, and AI agents trained for defined cyber roles while humans retain responsibility for operational-risk decisions.

• A second post frames AI as a macroeconomic variable and argues that the next bottleneck is organizational capability, not model capability.
• Tori Adams is a better choice than Sam Sharma if the consultancy wants a policy, defense, or regulated-enterprise angle and is prepared to translate that material into commercial governance guidance.

The posts lean toward defense, federal, and macroeconomic policy sources, farther from day-to-day enterprise workflow governance than Sam Sharma's posts.

• A consultancy would need to translate the defense examples into private-sector control practices.
• The posts rely on secondary reporting rather than detailed control frameworks.
• The review covers two posts; ask for more examples to assess the fit for commercial governance work.

Examples: Source 1 · Source 2

The posts argue that many AI failures in consumer goods are definition failures: marketing, supply chain, finance, and retail partners disagree on what product, customer, promotion, margin, and inventory mean.

• They warn that AI agents do not question context; they operationalize it, so a wrong ontology scales faster.
• Mark Bidgood is a better choice than Sam Sharma if the consultancy wants a sector-specific webinar or article showing how governance must begin with a trusted, consistently understood data foundation before agents are given more autonomy.

This alternative narrows the conversation to consumer goods and data-ontology problems.

• It offers less on broad enterprise agent risk, security boundaries, or approval authority across general business workflows, and the posts are older than the other alternatives.
• The consultancy would need to widen the data-governance argument if the engagement is not CPG-specific.
• The review covers two posts; ask for more examples to assess broader relevance.

Examples: Source 1 · Source 2