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Learn how leaders prove follow-through.
Most programs can show activity, not follow-through. These articles, whitepapers, and tools help OD, L&D, AI adoption, compliance, and governance leaders close that gap with evidence sponsors trust.
Articles, whitepaper & microsites
Recent thinking from ReliablyME on trust infrastructure, behavioral data, and governed AI execution.
Making the Intangible Tangible on a Blockchain
Alex Todd's early thesis that trusted promises underpin intangible value, and the vision that helped inspire ReliablyME.
Read the essay WhitepaperGoverned AI proliferation, evidence, and accountability
Introduces TECP — the trust enablement control plane that makes governance enforceable at runtime, with signed authorization and immutable execution receipts.
Read on LinkedIn ArticleUnlocking AI adoption with behavioral data
Why many enterprise GenAI pilots stall before P&L impact — and what changes when leaders capture the human execution layer.
Read on LinkedIn ArticleBuilding scalable trust through behavioral architecture
How OD teams translate post-retreat momentum into observable follow-through at scale — with structured commitments, follow-ups, and evidence.
Read on LinkedIn Founder essayReplacing a senior engineer with AI: possible, getting easier, still dangerous
Alex Todd on what AI can and cannot replace — and why responsibility is the part trust infrastructure has to carry.
Read on LinkedIn MicrositeTECP whitepaper — Governed AI proliferation, evidence & accountability
The full TECP technical whitepaper: governance enforceable at runtime for agentic AI.
Open whitepaper MicrositeIdentic AI — trust architecture for human–AI collaboration
How teams and AI systems build verifiable trust with clear evidence and scoped execution.
Open micrositePlaybooks, templates & briefs — request early access
These resources are in development. Tell us which would be useful.
The follow-through evidence playbook
A practical guide for OD, L&D, and AI adoption leaders building 30–90 day journeys with sponsor-ready evidence.
Discuss this resource WebinarFrom AI usage to AI accountability
Why dashboards stall, what proof packs change for sponsors, and how to design your first follow-through cohort.
Discuss this resource BriefTECP architecture brief
How signed authorization, policy enforcement, and execution receipts fit alongside ReliablyME follow-through.
Discuss this resource TemplateSponsor-ready proof pack template
A reporting structure for executives, boards and review teams to assess.
Discuss this resource GuideDesigning your AI rollout for follow-through
A framework for tying AI adoption commitments to the business KPI the investment was meant to move.
Discuss this resourceThe gap between AI experimentation and operational value
Industry research consistently identifies a gap between AI experimentation and measurable operational value. We cite sources here rather than leaning on a single headline number.
MIT Media Lab (NANDA initiative), "The GenAI Divide: State of AI in Business 2025"
Reports that only a small minority of enterprise GenAI pilots reach measurable P&L impact. Widely cited, though methodology and sample have been debated — treat the specific figure as indicative, not definitive.
Analyst and consultancy surveys of enterprise AI programs (2024–2025)
Repeatedly find that pilots outnumber scaled deployments, and that adoption is often measured by usage rather than by changes in how operational work is performed.
We do not present any single study as proof of ReliablyME's outcomes. Our own pilot evidence is shared in proof packs scoped to each engagement.
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