In a world where AI is moving from autocomplete to agents that act, most organizations don’t need another tool demo—they need a way to decide what work humans will keep, what work AI will take on, and how they want that partnership to feel day to day. FountainBlue’s Humans Over the Loop and Humans In the Loop micro‑trainings give leaders and teams a shared, practical language for that work: six HOTL modules that anchor vision, ethics, experimentation, and workforce voice, and six HITL modules that translate those choices into real workflows, metrics, and routines. Instead of “AI literacy” in the abstract, participants redesign their actual meetings, decisions, and handoffs, leveraging AI, so they can move from fear and AI‑washing to hope, agency, and measurable human value.
Humans Over the Loop and Humans In the Loop is a twelve‑part asynchronous micro training series with optional one-on-one live coaching calls that helps leaders, managers, and teams build practical, human‑centered AI capabilities across strategy, ethics, experimentation, workforce, and day‑to‑day execution.
More Choice, Less Noise
In the Age of AI, we don’t need more noise, we need more choice—about what to build, what to stop, and how we want humans and machines to dance together. Here are eight concrete opportunities I’m seeing with leaders and teams who are embracing that dance on purpose.
1. Unlock the AI‑enabled organization
To get out of the noise of “random pilot mode”, rally your team behind a small set of real business problems, redesign the workflows around them, and agree on a basic toolset. Let people participate in the design and implementation so they can collectively shape the results, instead of feeling like AI is something done to them.
2. Connect humans and AI agents
AI is brilliant at suggesting, but humans are still on the hook for deciding. Consider creating simple lines in the sand: “AI drafts the analysis; humans decide what goes to the exec team”. This might sound like a manager saying, “The bot’s suggestion provides a range of options, but we chose the best option, given what we know about this customer.”
3. Design shared services
Shared services are where a lot of AI value—and pain—will show up first. The opportunity is to let AI handle the repeatable tickets and lookups, while humans focus on nuance, exception, and care. For example, the bot might reset a password in seconds; but that only happens after human HR partners handle the messy, emotional employee‑relations cases that shouldn’t be automated.
4. Maximize value streams
Org charts don’t feel the work; customers and employees feel the flow. When you watch an end‑to‑end journey and ask, “Where should AI help? Where is it actually adding friction?” you start moving from structure to flow. Investigate whether your AI “helper” is generating so much rework that turning it off actually sped everything up rather than adopting AI for the sake of checking off the ‘adopt AI’ box.
5. Clarify and expand core offerings
AI makes it easier than ever to ship “one more feature,” but that doesn’t mean you should. The real move is to clarify which offerings are truly core—and use AI to go deeper there, while simplifying or stopping the rest. Ask: “Are we automating a service we shouldn’t be offering at all, or are we using AI to strengthen what customers already value most?”
6. Elevate workflow performance
Real performance isn’t just “more output”; it’s sustainable output plus learning and well‑being. Simple questions can open big insights: “What did AI save you yesterday?” and “Where did it make life harder?” Patterns that emerge will point you to concrete places to tweak prompts, processes, and even policies.
7. Deliver strategic advantage
Fairness, access, and inclusion are not side quests; they’re core to trust and brand. Ask, “Is this model accurate for our intended audience?” and “Who is missing from this data or decision?” Then bring diverse perspectives into design, testing, and governance so you can truly serve your broad client base—not just the loudest or most represented segments.
8. Embrace agility and flow
In this Age of AI, we’re asked to shift and pivot strategically while still rallying around the core purpose of the organization and the needs of our customers and our people. AI will keep reshaping products and processes; our job is to move product, process, and people together, with a clear “why” and visible learning loops. Treat AI changes like market shifts: explain, listen, adjust—in cycles everyone can see and influence.
FountainBlue’s HOTL (Humans Over the Loop) and HITL (Humans In the Loop) trainings are designed as practical playbooks, mapping to these eight evolving AI trends, to help leaders set vision, ethics, and focus, and to help managers redesign workflows, metrics, and routines for an AI‑rich world.
The six Humans Over the Loop modules focus on where humans stay firmly “over” AI—setting context and vision in the age of AI, communicating impact transparently, defining ethical guardrails and non‑negotiable red lines, running strategic experiments with clear learning loops, and empowering the workforce with trust, psychological safety, and voice.
The six Humans In the Loop modules turn that vision into operational practice—integrating AI into work with a “this‑and” mindset, redesigning workflows and checkpoints, maintaining data and quality vigilance, reskilling people to partner with AI, and using AI “by your side” as a thinking partner for decisions, problem‑solving, and scenario planning.
Across the twelve modules, we map your real work to eight big shifts in the Age of AI—from unlocking an AI‑enabled organization to embracing agility and flow—so you can stop chasing random pilots and start building an operating model where humans and AI dance together on purpose. Each module is asynchronous, bite‑sized, and scaffolded with toolkits, reflection prompts, and optional one‑on‑one coaching, so learning fits into real schedules and sticks in real workflows.
These twelve modules can be combined for organizations, offered as a focused track for individuals, or embedded into HR and learning‑tech platforms as white‑label content.
HOTL and HITL Training offers more choice, less noise
| Embrace the Opportunities in the Age of AI | HOTL focus (leaders “over the loop”) | HOTL module | HITL focus (managers/teams “in the loop”) | HITL module |
| Unlock the AI‑enabled organization | Set AI ambition and principles, choose priority domains, define where humans stay over the loop, and redesign operating models accordingly. | HOTL 1 – Vision · Process · Technology: Clarify your AI‑era vision, reshape key processes, and converge on a simple, endorsed toolset so AI work has direction, scaffolding, and support. | Implement AI in concrete workflows, with clear trust–verify–override rules and feedback loops on performance and value. | HITL 1 – Adoption & Use Cases: Decide where AI belongs in products, processes, and people systems, redesign work for safe human–AI collaboration, and equip people so humans own outcomes, not just tools. |
| Connect Humans and AI agents | Establish collaboration principles between humans and AI agents, clarify non‑delegable decisions, and fund large‑scale capability building. | HOTL 3 – Ethics & Guardrails: Set and live ethical guardrails with Use/Caution/Never zones and active oversight so humans—not tools—stay responsible for impact. | Practice daily human–agent collaboration, coach teams on when to trust or question AI, and capture edge cases to improve systems. | HITL 2 – Workflows & Hand‑offs: Make AI‑enabled work visible end‑to‑end so humans know who does what, where AI acts, and how handoffs and overrides work, instead of relying on opaque, AI‑driven flows. |
| Design AI-first shared services | Decide end‑to‑end AI‑first service vision, risk appetite, and investment roadmap; align global standards and governance. | HOTL 2 – Communication & Change Management: Communicate AI’s impact so people understand what is changing, what is not, and how AI affects efficiency, roles, and training, replacing vague slogans with concrete stories. | Design and run AI‑enabled operations (queues, routing, exceptions), manage quality sampling and review, and repurpose freed capacity to higher‑value work. | HITL 3 – Data & Quality Vigilance: Treat quality, fairness, and integrity as ongoing responsibilities, knowing AI will amplify whatever data it is given, and use continuous vigilance to keep outputs trustworthy. |
| Maximize value streams | Architect around value streams, remove siloed ownership, and align incentives and KPIs to end‑to‑end flow at the productivity frontier. | HOTL 4 – Strategic Experimentation: Use guardrailed AI experiments to test how changes in roles, prompts, and workflows affect flow, then make explicit scale/stop/change decisions. | Map real workflows with AI touchpoints, spot bottlenecks and rework, and refine prompts/data/process so AI improves, not fragments, work. | HITL 4 – Learning, Impact & Metrics: Run small, structured AI tests with clear metrics so teams know what to scale, stop, or change, turning tiny tweaks into safe, compounding learning instead of unmanaged risk. |
| Clarify and focus core offerings | Choose a few strategic priorities, allocate capital and talent, and decide what to stop, simplify, or divest to protect the core. | HOTL 4 – Experimentation & Priorities: Use experimentation to see which AI initiatives truly serve the core and which to stop or spin down, turning tests into a portfolio tool for focus. | Use AI to remove low‑value tasks, align team OKRs and workload to the core, and run weekly “stop/streamline/automate” reviews. | HITL 4 – Efficiency Metrics: Help teams use AI to stop, streamline, or automate non‑core work and align daily tasks and metrics with what matters most. |
| Elevate workflow performance | Define a performance system that blends operational results with culture and well‑being and invest in distinctive management practices. | HOTL 5 – Workforce Voice & Design: Build an empowered workforce in the middle of AI change, with real voice, safety, and growth paths so people can shape AI use and build AI‑era skills. | Run AI‑assisted stand‑ups, retros, and 1:1s that track both productivity and human health, and run small experiments to improve both. | HITL 5 – Safety & Inclusivity: Give employees voice, psychological safety, and regular forums to discuss how AI affects their work, tuning both performance and well‑being so adoption sticks and trust grows. |
| Deliver strategic advantage | Make DEI a strategic performance lever, with measurable goals and diverse representation in AI design and oversight. | HOTL 5 – Equity & Representation: Ensure diverse, empowered voices shape AI strategy, design, and oversight and tie DEI outcomes to leadership accountability so AI doesn’t quietly encode inequities. | Apply fairness checks in daily decisions (hiring, promotions, assignments, pricing), monitor AI outputs for bias, and escalate patterns with concrete examples. | HITL 5 – Distributed Access: Equip managers to spot and challenge biased patterns in AI‑assisted decisions and make fairness part of everyday loops, not just periodic audits. |
| Embrace agility and flow | Model purpose‑anchored, human‑centered, self‑aware leadership and connect AI and change to a meaningful narrative of value and hope. | HOTL 6 – Agility & Leadership: Link product, process, and workforce agility into one system, guided by clear purpose and human impact, so you can scale AI without losing control or trust. | Translate that narrative into local behavior: psychological safety, curiosity with AI, transparent communication, and coaching through transition. | HITL 6 – Managed Flow: Organize around end‑to‑end value flow so AI‑related work moves quickly and stays visible and governable, turning rapid AI‑driven change into something teams co‑design, not endure. |
In a world of AI‑washing and “forever layoffs,” many leaders are under pressure to show AI productivity gains while their people feel anxious, overloaded, and left behind.
FountainBlue’s Humans Over the Loop and Humans In the Loop micro‑trainings help leaders, managers, and ambitious professionals become truly AI fluent—able to redesign their work with AI while keeping human value, ethics, and hope at the center.
Only about 5% of U.S. workers are currently “AI fluent,” yet they are 4.5 times as likely to report higher wages and 4 times as likely to report a promotion tied to their AI capabilities.
This offering is designed to move your people from casual AI use to confident, human‑centered AI fluency in weeks, not years.
Why choose FountainBlue’s Humans Over the Loop and Humans In the Loop micro training”
- Research + inquiry + action: Each module combines up‑to‑date research with inquiry prompts and concrete follow‑up actions, plus toolkits, frameworks, and reflection questions so learners translate insight into practice.
- Built‑in measurement: A pre‑training survey, post‑training survey, and structured reflection before the 1:1 consultation make learning visible and measurable—for individuals and for organizations.
- Real‑world, vetted content: Every module centers on example use cases, grounded in curated, credible research, and invites practical application of concepts and learnings.
- Chunked, scaffolded learning: Short, structured lessons “chunk” key ideas and pair them with complete toolkits so learners can apply one concept at a time without overwhelm.
- Experienced, human‑centered design: The curriculum is designed and taught by a Linda Holroyd, a seasoned instructor with global experience working with executives and managers across technology‑driven industries, bringing both empathy and edge to every module.
- Integration optimization: Optional 15-minute one-on-one follow-up calls optimize integration and learning, building on surveys, reflections, toolkits, and homework. Additional coaching and consultation are also available for individuals, teams, and partners.
Why This, Why Now?
Healthy, profitable companies are laying off workers in a slow, constant “drip,” often citing AI or efficiency, and many employees now assume that no role is truly safe. At the same time, organizations that invest in human skills alongside AI—judgment, empathy, sense‑making, ethical reasoning—outperform those that treat AI purely as a cost‑cutting lever.
FountainBlue’s Human‑Centered Training offering develops uniquely human skills at both levels of the system – leaders Over the Loop and managers In the Loop – so strategy, culture, and daily execution all shift together, rather than in fragmented, one‑off trainings.
This offering helps you:
- Align strategy with focus, connecting AI to real strategic and operational priorities.
- Empower uniquely human strengths as a deliberate advantage.
- Cultivate a high‑value, high‑trust culture despite continuous change.
- Position your people and business for sustainable growth in the Agentic Age.
Unlike generic AI literacy courses or tool demos, this offering focuses on lived workflows, ethics, and culture, helping real humans become AI fluent in their actual roles—not just pass a quiz.
Research on AI as a differentiator
FountainBlue’s Humans Over the Loop and Humans in the Loop micro trainings are anchored in emerging global research on ethical, human‑centered AI, organizational learning, and workforce well‑being.
UNESCO’s Recommendation on the Ethics of Artificial Intelligence is the first global standard on AI ethics adopted by all UNESCO Member States (193/194 countries), and places human rights, human dignity, transparency, fairness, and human oversight of AI systems at its core. https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence
- Gartner projects that by 2026, organizations that operationalize AI transparency, trust, and security will see approximately 50% improvement in AI adoption and goal attainment, underscoring the importance of explicit guardrails and governance rather than ad‑hoc deployment. https://www.linkedin.com/posts/gartner-for-it-leaders_gartnerit-ai-artificialintelligence-activity-7185312103627378689-maBl
- McKinsey’s 2025 State of AI work finds that top performers are almost 3× more likely to redesign workflows end‑to‑end, invest heavily in reskilling, and treat AI as an operational reinvention rather than a bolt‑on tool. McKinsey Report 2025 https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai and synthesis: https://digitalstrategyai.substack.com/p/state-of-ai-2025-mckinsey-report
- Long‑term opportunity: up to 4.4 trillion dollars in added productivity growth from corporate AI use cases, but only if organizations reconfigure work and roles, not just add tools.
- AI agents are moving from experiments to operations: 62% of organizations use or experiment with agents, and nearly a quarter are scaling them in at least one function.
- McKinsey‘s 2026 State of AI work finds that 55% of leaders expect exponential productivity gains from human-AI collaboration, nearly 3 in 4 organizations are affected by geopolitical and economic upheaval, but fewer than 25% of organizations sustain improvements in people and operational excellence over time https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations
- Forbes – “AI Productivity’s $4 Trillion Question” (2026) https://www.forbes.com/sites/guneyyildiz/2026/01/20/ai-productivitys-4-trillion-question-hype-hope-and-hard-data/
- Documented task‑level productivity gains of 14–55% (e.g., support reps handling more inquiries per hour, consultants working 25% faster with 40% higher quality, developers completing 21% more tasks).
- At the same time, 95% of enterprise AI initiatives fail to deliver durable business impact, largely because organizations don’t redesign workflows, accountability, and training around AI.
- Developer study: AI increased throughput (more pull requests and code), but review times jumped 91% and bug rates rose 9%.
- Deloitte’s board‑level research warns that many boards still lack active oversight of AI, even as they acknowledge that trustworthy, ethical AI and clear risk guardrails are now critical governance responsibilities. “Governance of AI: A critical imperative for today’s boards”:
https://www.deloitte.com/us/en/insights/topics/leadership/successful-ai-oversight-may-require-more-engagement-in-the-boardroom.html - EY’s US workforce survey reports that roughly two‑thirds of employees feel anxious about how to use AI ethically and worry about legal and cybersecurity risks, while approximately 80% say more training, upskilling, and visible leadership on responsible AI would significantly increase their comfort and engagement. “EY research shows most US employees feel AI anxiety”: https://www.ey.com/en_us/newsroom/2023/12/ey-research-shows-most-us-employees-feel-ai-anxiety
According to Google/Ipsos AI Works for America poll from Feb 2025 https://www.ipsos.com/en-us/googleipsos-ai-works-america-poll:
- “AI fluent” workers are those who have redesigned or reorganized significant portions of their work with AI, not just experimented with tools casually.
- AI fluent workers report saving a median of about eight hours per week with AI, versus roughly three hours for more casual users.
- AI‑fluent workers are 4.5 times as likely to report higher wages and 4 times as likely to report a promotion linked to their AI capabilities compared with early‑stage users.
Barriers and concerns, from the same report:
- Only 40% of U.S. workers are using AI at all, which means most people have not yet crossed into this fluency group.
- Among workers not using AI, over half (53%) say they do not think AI is relevant to their job.
- Only about 14% have been offered AI training in the past year, and just 37% say their organization gives clear guidance on using AI at work.
- Only about ~5% meet the “AI‑fluent” threshold—redesigning significant parts of their work with AI.
Sample use cases for having HOTL and HITL
Organizations using HOTL + HITL in customer service, HR, operations, and knowledge work are already reporting double‑digit efficiency gains, shorter cycle times, and better employee and customer experience when they pair human governance with concrete workflow redesign.
- Customer Support & Service Operations
- Use HITL to redesign queues, routing, and response templates with AI triage and drafting, helping teams cut average handling time by 20–40% while improving first‑contact resolution. https://www.ibm.com/think/topics/ai-agents-in-customer-service
- Use HOTL to define what must stay human, set sampling rules for AI‑assisted responses, and communicate clearly with customers about when they’re interacting with AI vs. people, supporting CSAT gains of 5–15 points where communication is proactive. https://www.mckinsey.com/capabilities/operations/our-insights/the-future-of-customer-experience-embracing-agentic-ai
- HR, Hiring, and Talent Systems
- Use HITL to map where AI can safely help—summarizing feedback, drafting reviews, pre‑screening applicants—reducing manual screening and paperwork time by 30–50% while keeping humans in charge of final decisions. https://www.skan.ai/blogs/9-key-use-cases-for-agentic-ai-in-healthcare-banking-insurance
- Use HOTL to set fairness checks, DEI expectations, and escalation paths to track selection‑rate and adverse‑impact ratios as you go. Organizations that add regular AI bias audits and human review have reported 20–50% reductions in measured selection‑rate gaps for underrepresented groups over time, while also improving legal defensibility under the EEOC four‑fifths rule. https://scale.jobs/blog/how-to-audit-ai-hiring-tools-for-bias
- Shared Services and Internal Operations
- Use HITL to design AI‑enabled flows in finance, IT, or facilities (ticket routing, invoice triage, knowledge‑base upkeep), often cutting resolution times by 25–35% and shrinking backlogs without adding headcount. https://agility-at-scale.com/ai/agents/enterprise-ai-agent-use-cases/
- Use HOTL to choose where to centralize AI‑first services, align standards and governance, and decide how freed capacity will be repurposed to higher‑value work. Of “AI high performers” in a recent study, are about three times more likely to have fundamentally redesigned workflows and embedded AI into business processes, rather than running scattered pilots. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- Knowledge Work and Decision Support
- Use HITL to let agents draft analyses, summarize long documents, and prepare options, with field experiments showing 40–60% more output per worker on complex text tasks when collaboration is designed well. https://news.bloomberglaw.com/artificial-intelligence/ai-agents-make-humans-more-productive-mit-researchers-say
- Use HOTL to define non‑delegable decisions, set trust–verify–override rules, and agree on what “good” looks like. In an MIT field experiment with more than 2,300 participants creating real ads, human–AI teams produced about 50–60% more output per worker, sent 23% fewer social messages, and generated higher‑quality ad copy than human‑only teams—showing how well‑governed AI support can boost knowledge‑work productivity and quality at the same time.
https://mitsloan.mit.edu/ideas-made-to-matter/5-heavy-lifts-deploying-ai-agents
- Experimentation and Continuous Improvement
- Use HITL to run sustained, small, structured AI experiments in real workflows (prompts, routing rules, approvals), measuring changes in cycle time, error rates, and rework so teams can add up to massive impact. Most sustained AI value comes from many small workflow changes—often in the 5–10% efficiency range each—stacked across processes, rather than from a single “big bang” deployment. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- Use HOTL to treat these experiments as a portfolio: deciding which to scale, stop, or redesign, and focusing investment on the small share of AI initiatives that actually drive revenue, margin, or experience improvements.
- Gartner estimates that at least 30% of generative‑AI projects will be abandoned after proof of concept by the end of 2025 due to unclear value, poor data, and weak controls. https://www.apmdigest.com/gartner-30-of-genai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025
- Forbes analyses suggest that roughly 5–10% of AI initiatives account for most of the measurable ROI, with around 90–95% delivering little or no return—a pattern that makes deliberate “scale / stop / change” decisions a leadership responsibility, not a technical detail. https://www.forbes.com/sites/andreahill/2025/08/21/why-95-of-ai-pilots-fail-and-what-business-leaders-should-do-instead/
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