Outsourcing trust to technology

Trust has always been the glue of any functioning society, but historically, it was rooted in direct human perception: we trusted what we could see, hear, feel, and verify with our own senses, as well as the reputation and consistency of others. The digital era already strained this: when most interactions moved online, we lost our full sensory toolkit and leaned almost entirely on visual perception, the image, the video, the text, to decide what’s real. It worked because we assumed photos don’t lie, videos show what happened, and a “realistic” look signals authenticity. Generative AI breaks that last pillar. When you can’t trust your eyes alone, because anything can be synthetically created to look “real”, the burden shifts from perception to verification. So the new trust model is:          • Not what you see is what you get, but what you can prove is what you can trust.          • Not your senses, but the systems you rely on: provenance, credentials, reputation, technical proofs.          • Not a passive act, but an active practice: constant checking, validating, and re-checking. In this sense, the big shift isn’t that trust is new, it’s that its foundation is moving from our senses to our systems. We’ve never had to outsource trust to technology at this scale before. That’s what’s fundamentally different now. #TrustInTheDigitalAge #ContentAuthenticity #VerifyDontTrust #SeeingIsNotBelieving #ProvenanceMatters #visualcontent #visualtech

There is a trust paradox forming between humans and machines We've relied on search engines and AI to answer our questions for nearly two decades. But this relationship is evolving, demanding a new level of engagement. We feed massive amounts of information to machines. They, in turn, must compute what to trust and how to rank it. Yes, ranking results have existed for a long time, but the dialogue-based (not keyword-based) approach offers far more ways to rank content and data. Machines use signals like frequency, consistency, and recency. At its core, it's a simple trust assessment, and it works. Humans circle back, asking questions through search and AI interfaces. Based on their data processing, the machines return ranked, "trusted" results. I'm expecting machines to start to reason in new ways. I recently tested SearchGPT for local storage units. The AI didn't rank solely by proximity and reviews. It factored in current offers from websites, which is a massive change. It was essentially analyzing which storage unit companies had the best current offers. ME: "Can you tell me why you put each of those businesses in that particular order?" SearchGPT: "The order in which the self-storage options were listed is based on the proximity to Mayport, FL, and the variety of features and prices they offer." That's value-based reasoning. That is a HUGE change. Try this yourself. Ask AI to find you some local businesses (on any topic) and then ask, "Why did you rank those businesses in that order?" We're on the cusp of a shift as OpenAI works on its "strawberry project" to enhance reasoning in models. It will change how we structure information for machines and what information will give you an edge in showing up. One concern is whether our dialogue will just pass on our own cognitive biases, but realistically, this already happens. We are probably okay with outsourcing portions of decision-making to AI so long as the AI can easily explain why it ranked or returned the results it did. IMAGINE THAT! A Machine tells you WHY it ranked your search results exactly how it did. Machines are becoming more than information repositories. AI reasoning is expanding. It's moving beyond simple data matching to value assessment. Stop thinking about your AI Strategy and think more about your Data Strategy (to the AI).

That online purchase may look harmless. A staff member orders shoes during lunch using a company computer. But what if the website isn’t secure? What if it plants malware that steals passwords and opens the door to your donor database, your payroll system, and your email accounts? One small click can cost an organisation a lot. Including donor trust, and even compliance with data protection laws. Organizations today operate in an increasingly digital environment. Managing donor databases, running online fundraising campaigns, and using cloud-based accounting systems. Technology has brought efficiency and transparency, but it has also introduced new risks that directly impact compliance. Cybercriminals target organizations through fake websites and emails. A single compromised device can lead to fraudulent transactions or leaked donor data. Personal activities on company devices, including online shopping, streaming, or downloading apps, can introduce malicious software without anyone noticing. The tools we love for speed for instance AI platforms for content creation or analysis can become threats if staff upload sensitive donor or beneficiary data. Many AI systems store prompts, meaning your confidential information may end up outside your control. Even trusted third-party services (payroll, accounting, cloud storage) can suffer breaches. Outsourcing does not outsource responsibility. You are still accountable to donors and regulators. Organizations can set clear digital-use policies for example, no personal transactions on company devices. Train staff to recognize phishing, risky sites, and AI risks, approve and control AI tools, use secure systems for fundraising, payroll, and donor engagement and regularly audit digital systems as part of the compliance program.

Trust Betrayed. Again. Anthropic—the company that branded itself as “privacy-first” and “safety-driven”—just torched its own moat. Starting now, Claude will train on your chat transcripts and coding sessions unless you manually opt out by September 28. Five years of storage replaces the old 30-day deletion rule. Free, Pro, Max, Claude Code—no exceptions. This is not an update. It is a betrayal. → Hypocrisy laid bare: The self-proclaimed “responsible” AI company now runs the same playbook as the rest—harvest first, ask forgiveness later. → Compliance nightmare: Sensitive conversations, contracts, legal docs, and code can now sit in Anthropic’s servers for half a decade. Opt-out ≠ consent. → Structural exposure: For governments and enterprises that bought Claude for its privacy promises, the foundation just cracked. → Pattern confirmed: In the end, every closed model company caves to the same growth imperative: extract more data, hold it longer, and lock users in. The last fig leaf of “privacy-first AI” has fallen. The message is simple: sovereignty and control cannot be outsourced. The question for every policymaker, CIO, and enterprise is now clear: how many more times will you let “responsible AI” vendors betray your trust before you build systems you truly control? https://lnkd.in/gm2J-T6h

I’ve been thinking a lot about the shifts we’re seeing in outsourcing. For two decades, the model has been clear: businesses outsource to save costs, scale faster, and access expertise. It worked, and it worked well. But AI is rewriting that script. Not in theory, in practice. Clients are no longer asking “who can take this off my plate?” They’re asking, “who can make this faster, smarter, and more reliable with technology baked in?” And here’s the catch: if outsourcing continues to be treated as a handoff, it will get disrupted. Simple as that. The way forward isn’t to fear AI. The shift must start with reframing outsourcing itself, as a partnership of human expertise and intelligent tools. That’s where the value will lie:  in teams trained to collaborate with AI,  in processes redesigned for insight, not just output,  and in outcomes that reflect both speed and depth. AI won’t replace outsourcing. But outsourcing that ignores AI? That’s what’s at risk.The disruption is inevitable. The readiness isn’t. #FutureOfOutsourcing #AIandHumanSynergy #SmartOutsourcing #DisruptWithAI