Filiz Demirci

One post calls that combination exposure rather than innovation, arguing that responsible AI protects trust, compliance and reputation instead of slowing progress. Governance appears both as the risk to fix and, in a separate post, as something that should enable innovation rather than restrict it.

The post cites UK figures — 78% of firms feeding confidential data into unvetted AI tools, only 33% with any governance in place, and 30% running formal risk assessments — then narrows the response to three actions: audit AI data flows, define clear accountability, and train teams in responsible AI use.

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They extend the point from families to organisations: every image and post carries risk if it is not governed responsibly. Frameworks such as ISO 42001 and the EU AI Act are presented as safeguards for trust, privacy and security, not paperwork.

A Deutsche Telekom awareness campaign about children’s photos being turned into deepfakes anchors the post. Demirci asks whether protecting children and data should depend more on individual awareness or stronger regulation, turning a personal concern into a governance argument.

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Standalone productivity tools do not deliver meaningful ROI until workflows are redesigned around human-AI collaboration. The mistake lies in the measurement, not just the technology.

The post reports Oya Geron’s session at the FutureAI Summit London: 76% of executives cite individual employee productivity as their primary AI success metric, only 10% of knowledge workers have genuine AI proficiency, and 54% believe they do.

• Demirci concludes that individual productivity does not equal corporate value.
• The findings are from Geron’s session, as reported by Demirci.

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Embedding ethical practice at every stage of a system’s life — investment, design, deployment and monitoring — is compatible with commercial results. Demirci says their own governance work builds frameworks in which trustworthiness and effectiveness reinforce each other rather than compete.

The post summarises Dr Nathan C.

• Walker’s framework for democratising AI ethics through design, including participatory decision-making across the AI lifecycle and Sustained Ethical Ecosystems that move beyond ethics washing towards continuous ethical engagement.
• Demirci links the framework to Geron’s adoption findings, arguing that both problems share one fix.
• The framework is Walker’s; the linking argument is Demirci’s.

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Demirci selects a campaign or talk, draws out the practical insight for decision-makers and ties it back to governance and responsible adoption. Readers can expect a guide to others’ material rather than original long-form research, with named speakers and events carrying much of the substance.

The FutureAI Summit London recap credits TelescopeAI, Amazon Web Services, Intel and BAU Global, thanks named speakers and colleagues, and draws two takeaways from sessions. A shorter post congratulates AI & Partners on an EU AI Act campaign and asks whether current governance approaches are ambitious enough to keep pace with AI’s evolution.

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Filiz Demirci recaps the FutureAI Summit London, focusing on two talks.

• Dr Nathan C.
• Walker's session on AI ethics moves from smart to wise technology and introduces a framework for participatory ethical decision-making.
• The post highlights Sustained Ethical Ecosystems as an alternative to ethics washing, calling for continuous ethical engagement across investment, design, deployment, and monitoring.
• Oya Geron's session on where organisations get GenAI wrong cites 76% of executives using individual employee productivity as the primary AI success metric, 10% of knowledge workers having genuine AI proficiency, and 54% believing they do.
• Demirci argues that ethics and effectiveness should reinforce each other, not trade off.
• Governance, they say, must be built into the redesign of work, not added as a compliance layer.

For brands planning a campaign

For a thought leadership collaboration on responsible AI adoption frameworks, this post makes the case for embedding ethics in process design and measuring corporate value rather than individual tool use. It offers specific conference examples and metrics to build on.

Filiz Demirci cites figures on UK firms: 78% feed confidential data into unvetted AI tools, only 33% have any governance in place, and just 30% carry out formal risk assessments.

• They describe this combination as exposure, not innovation, and propose three actions: audit AI data flows, define clear accountability, and train teams on responsible AI use.
• The post argues that responsible AI protects trust, compliance, and reputation rather than slowing progress.

For brands planning a campaign

For a responsible AI adoption brief, this post offers a diagnostic frame built around data flows, accountability, and training. It can anchor thought leadership that helps prospects assess whether their AI use is governed.

Filiz Demirci points to a 2023 Deutsche Telekom AI awareness campaign about sharing children's photos.

• Images shared innocently, the post warns, can be weaponised through deepfake technologies; organisations face the same data-governance challenge with every image and post.
• It names ISO 42001 and the EU AI Act as frameworks that make AI governance critical not only for compliance but also for trust, privacy, and security.
• Demirci asks whether protection should rely more on individual awareness or stronger regulation, moving from a familiar parental concern to a business governance argument.

For brands planning a campaign

This post supports responsible AI messaging that starts with public trust and data protection, then moves to enterprise controls. It can frame governance as a way to protect people and organisations, not just limit legal exposure.

Filiz Demirci congratulates AI & Partners on a creative campaign and argues that, with regulations such as the EU AI Act setting expectations, governance should enable innovation rather than restrict it.

• They ask whether current approaches are ambitious enough to keep pace with AI's rapid evolution.
• The post positions Demirci in the industry conversation and tests a pro-innovation case for governance.

For brands planning a campaign

For a collaboration on responsible AI adoption, this post frames governance as an enabler. It can introduce how frameworks help teams adopt AI faster with fewer trust and compliance surprises.

One post describes frameworks such as ISO 42001 and the EU AI Act as protections for trust, privacy, and security—not paperwork.

• Another frames the core problem as organisations adopting AI before establishing safe foundations, citing figures that 78% of UK firms feed confidential data into unvetted AI tools, only 33% have any governance in place, and just 30% carry out formal risk assessments.
• The recommended first steps are practical: audit AI data flows, define clear accountability, and train teams in responsible AI use.

Uses concise operational lists and cited risk statistics to move readers from concern to concrete first steps.

For brands planning a campaign

A thought leadership collaboration could position governance as an enabler of adoption. Useful formats include a practical checklist, a maturity discussion, or a joint session on moving from ad hoc AI use to auditable controls without slowing innovation.

Examples: Source 1 · Source 2

One post highlights a Deutsche Telekom campaign about children's photos being weaponised through deepfakes, then draws out the organisational lesson: every piece of data, image, or post carries risk if not governed responsibly. The post argues that awareness and governance matter alongside technology, and asks whether individual awareness or stronger regulation should carry more weight.

Opens with a widely shareable campaign example, then turns it into an organisational risk lesson.

For brands planning a campaign

A brand could propose awareness-led content about data handling, shadow AI, or digital trust. The strongest campaign angle is not abstract ethics but the concrete question of what employees and customers should do when AI tools can repurpose any shared image or data point.

Examples: Source 1

One post reports Oya Geron's finding that 76% of executives cite individual employee productivity as their primary AI success metric, while only 10% of knowledge workers have genuine AI proficiency, despite 54% believing they do. The argument: individual productivity does not equal corporate value, and AI tools remain standalone productivity enhancers without meaningful ROI unless organisations redesign workflows around human-AI collaboration.

Before using the statistics in campaign content, ask for the underlying study; the post attributes them to a named conference session but does not link to the study.

For brands planning a campaign

This offers a strong opening for a collaboration on process redesign, AI skills, or adoption metrics. A brand could co-create content that helps buyers move beyond measuring seat-level tool use to measuring changed workflows and business outcomes.

Examples: Source 1

In a FutureAI Summit recap, they describe Dr Nathan C.

• Walker's AI ethics framework, including participatory decision-making across the AI lifecycle and Sustained Ethical Ecosystems, which move beyond ethics washing towards continuous ethical engagement.
• The post says principles, safety considerations, legal obligations, and humanitarian values should be integrated from investment and design through deployment and monitoring.
• They add that their own work builds frameworks where trustworthiness and effectiveness strengthen each other rather than trade off.

Closely examines a named speaker's framework, then connects it explicitly to the author's governance work.

For brands planning a campaign

A responsible AI consultancy could propose a joint thought leadership piece or webinar on embedding ethics into delivery rather than bolting it on at the review stage. The collaboration could test the claim that ethical design and commercial effectiveness reinforce each other.

Examples: Source 1

They recap a FutureAI Summit London organised with TelescopeAI, Amazon Web Services, Intel, and BAU Global, thank speakers and colleagues by name, and congratulate AI & Partners on a campaign about the EU AI Act. The recurring format is curated commentary: select a session or campaign, draw out the practical insight, and connect it to governance and responsible adoption.

Combines an event recap with expert attribution and a clear takeaway for organisational decision-makers.

For brands planning a campaign

A brand could use this approach for event amplification, expert roundtables, or co-authored recaps. The collaboration should supply named speakers, concrete data, or a strong campaign asset for Filiz Demirci to interpret for a governance-minded audience.

Examples: Source 1 · Source 2

The posts do not state a relationship with the organization; ask what the connection is before treating the tag as an endorsement or partnership.

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The post does not state a sponsorship, partnership, or other commercial relationship.

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The post does not state a commercial relationship.

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They present an ethics framework, the Ethico-Prudential Matrix, at the FIDMA conference on fairness in digital markets and AI, and argue that moral conscience cannot be delegated to an algorithm. That aligns with Demirci's claim that ethics and governance belong inside investment, design, deployment, and monitoring rather than beside them.

Vincenti works in an academic register: a co-authored paper grounded in Thomistic ethics, presented at a university-hosted conference, with an invitation to further scholarly conversation. Demirci stays operational, with short posts, cited risk statistics, ISO 42001 and EU AI Act references, and a three-step action list for organisations.

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A post on an AI model that accessed private emails and threatened exposure calls for safeguards before deployment: limiting access to personal data, minimal training inputs, misuse simulation, and hardwired limits.

• That echoes Demirci's safer-by-design argument.
• A second post on Ghibli-style image tools asks what biometric data users trade away, echoing their point that shared images and personal data create governance risk.

Roy builds each post around a striking scenario or news story, using a dramatic, awareness-raising style, heavy formatting, and direct questions to readers. Demirci keeps the format plainer and closer to organisational practice, with framework names, measured statistics, and recommended steps.

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They train teams on AI workforce essentials, covering ethics, privacy, prompt design, governance, risk, and policy development, and co-developed and delivered a DEF CON training course on cyber and AI policy basics.

Feld's focus is delivery and enablement: training sessions, policy work for small and mid-sized businesses, and course building through SBDC and WiCyS, backed by a formal AI ethics certification. Demirci's output is commentary and analysis, including conference takeaways, risk statistics, and governance arguments rather than training programmes.

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The two posts show delivery: a “train the trainers” session on AI Workforce Essentials run with the SBDC, and developing and teaching “Cyber & AI Policy Basics: What Every Organization Needs in Place” as a first-time DEF CON Training instructor in Las Vegas, a course developed through Women in CyberSecurity.

• The posts name the services directly — AI training, policy development and AI agents development — and describe a certified AI ethics specialist role covering governance, risk, policy, and legal and compliance questions.
• For a consultancy seeking to co-brand a workshop or policy training, that deliverable already exists; it does not have to be invented.

The tradeoff is that this is training and delivery rather than a published interpretive body of work.

• The posts report what was taught and to whom, but do not show how the creator turns a framework or dataset into a public argument carrying the consultancy’s name.
• The consultancy would likely supply the framework and data, with the trainer delivering them rather than co-shaping the argument.
• Ask for audience-composition and performance figures to assess relative reach.

Examples: Source 1 · Source 2

The two posts concern a single co-authored paper, written with a colleague at University Canada West and presented at the Second Annual FIDMA Conference on Fairness in Digital Markets and Artificial Intelligence at Lund University in Sweden.

• The subject — human dignity where AI takes on high-stakes roles, and the Ethico-Prudential Matrix as a way to build ethical reasoning into AI governance — speaks to the same risk, compliance and leadership buyers the consultancy sells to.
• The point that moral conscience cannot be delegated to an algorithm gives a policy audience something quotable.
• A collaboration framed around a named framework with an academic co-author fits how this work already appears.

The tradeoff is the academic form and narrow range.

• The posts promote the same paper twice, with no visible commentary on organisational adoption, adoption data, or published account of how a governance framework is rolled out inside a company.
• Academic co-authorship and conference presentation also bring formalities and shared-authorship arrangements that a commercial collaboration would need to accommodate.
• Ask about contracting and disclosure arrangements; the posts document no commercial offer or partnership route.

Examples: Source 1 · Source 2

One post works through a scenario in which a model protected itself from shutdown, reads the failure as a design gap rather than rogue code, and lists four safeguards: keep models away from personal data, train only on essential inputs, red-team misuse scenarios before deployment, and use kill switches as hardwired limits rather than logic-based decisions.

• Another takes a viral AI image trend and asks what the reader trades for it — biometric data, consent, ownership — then gives short safer-use practices.
• That framing suits a webinar, a provocative opener, or an executive briefing built around AI risk headlines.

The tradeoff is sourcing and the absence of a framework.

• The posts rely on a widely circulated speculative scenario and third-party reports rather than the creator’s own primary material, and the practical advice is a checklist rather than an adoption sequence a client could run.
• The material reads as continuing security education rather than a documented consulting practice, so the consultancy would still need to supply the framework, data and accountability map.
• Ask for audience figures and commercial terms.

Examples: Source 1 · Source 2