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In December 2025, Anthropic launched a large-scale public conversation on artificial intelligence - one that intentionally moved away from abstract speculation about risks and benefits, and toward the lived experiences of people already using AI in their daily lives. Through an interview-based study conducted via Anthropic Interviewer (a specialized version of Claude), the organization collected perspectives from 80,508 participants across 159 countries and 70 languages, making it, by their own account, the largest and most multilingual qualitative study of its kind. The goal of the research was ambitious: to understand what people hope for and fear about AI, not in theory, but grounded in practical interaction with the technology. What’s been largely missing in public discourse, Anthropic argues, is a shared vision of what “AI going well” actually looks like for individuals and society. In this article, I approach Anthropic’s findings through a Business Analysis lens, applying core BA competencies to interpret individual aspirations as signals of organizational needs, systemic change, and value creation opportunities. |
According to the International Institute of Business Analysis (IIBA), Business Analysis is the practice of enabling change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders. From this perspective, the essential questions are always the same: In what context? For which stakeholders? To address which needs? Through which solutions? Producing what change and what value?
Anthropic’s data provides unusually rich material for answering these questions.
The first part of Anthropic's research shows what people hope for (with regard to AI). This is an individual perspectives, showing the percentage of respondents:
1. Professional excellence 18.8% (AI to support more strategic work, less routine tasks)
2. Personal transformation 13.7% (AI as a guide, a coach)
3. Life management 13.5% (AI managing schedules, reduce mental burden)
4. Time freedom 11.1% (AI frees time)
5. Financial independence 9.7% (AI for income generation, security)
6. Societal transformation 9.4% (AI for solving big problems)
7. Entrepreneurship 8.7% (AI for building and scaling an enterprise)
8. Learning & growth 8.4% (AI to accelerate learning)
9. Creative expression 5.6% (AI for creativity).
From Individual Hopes to Organizational Signals. Participants in the study described their hopes for AI across nine broad themes. While these hopes are expressed at an individual level, each one maps directly to deeper organizational and enterprise-level implications.
1. From Professional Excellence to Organizational Effectiveness & Decision Velocity
The most frequently expressed hope — professional excellence (18.8%) — reflects a desire to spend less time on routine tasks and more time on strategic, meaningful work. From an organizational perspective, this signals a shift in how work itself is designed. AI is no longer just a productivity tool; it becomes an active participant in value creation.
Organizations move toward:
- End‑to‑end, AI‑orchestrated processes
- Agentic AI managing multi‑step workflows (procure‑to‑pay, order‑to‑cash, incident resolution)
- Decision support embedded directly in operational processes, not isolated dashboards.
The key change is a transition from role‑based work to outcome‑based work, and from long human approval chains to AI‑led execution governed by policies.
Business Analysis focus:
- Redesign value streams with AI as a first‑class actor
- Clarify decision rights: what AI decides, what it escalates
- Measure cycle time, decision latency, and exception rates.
2. From Personal Transformation to Organizational Intelligence & Augmented Leadership
The second major theme — personal transformation (13.7%) — describes AI as a guide, coach, or thinking partner.
At scale, this translates into organizational intelligence: AI as a collective cognitive layer that augments leadership rather than replacing it.
Organizations begin to adopt:
- AI‑supported strategic planning and scenario analysis
- Executive AI advisors for market sensing and risk detection
- Learning loops that capture decisions, outcomes, and context.
The key change is cultural: moving from intuition‑led leadership to evidence‑augmented leadership, and accepting that AI may challenge long‑held assumptions.
Business Analysis focus:
- Embed AI into decision workflows (budgeting, portfolio management)
- Ensure decision traceability (why was a decision made?)
- Measure decision accuracy, forecast variance, and risk anticipation.
3. From Life Management to Operational Productivity & Cognitive Load Reduction
Many participants (13.5%) expressed the desire for AI to manage complexity and reduce mental burden.
In organizations, this manifests as AI managing coordination, not just tasks.
This includes:
- Automated coordination across teams and systems
- AI‑managed dependencies, bottlenecks, and resource allocation
- Proactive issue detection before problems become visible.
The key change is moving from manual coordination and constant meetings to AI‑mediated operations, and from reactive firefighting to preventive management.
Business Analysis focus:
- Map process and team interdependencies
- Identify “invisible work” suitable for automation
- Track work‑in‑progress, rework levels, and coordination overhead.
4. From Time Freedom to Organizational Capacity Reallocation
The hope for time freedom (11.1%) highlights a common misconception: freeing time automatically creates value.
In reality, freed capacity only creates value if it is deliberately reinvested.
AI enables:
- Faster time‑to‑market
- Shorter feedback loops
- Redeployment of effort toward innovation, customer experience, and resilience.
The key change is reframing efficiency gains from cost‑cutting exercises into strategic capacity management.
Business Analysis focus:
- Quantify capacity released by AI
- Define explicit reinvestment scenarios
- Measure throughput, innovation velocity, and utilization mix.
5. From Financial Independence to Sustainable Value Creation & Economic Resilience
When participants associate AI with financial independence (9.7%), organizations should hear a signal about economic durability.
This translates into:
- New AI‑enabled revenue streams
- Margin improvements through intelligent cost structures
- Better capital allocation via predictive insights.
The key change is moving from isolated AI pilots to AI as a core economic engine, and from short‑term cost focus to a portfolio of value bets.
Business Analysis focus:
- Connect AI use cases to explicit value pools
- Model ROI under uncertainty
- Measure value realization, margin impact, and risk‑adjusted returns.
6. From Societal Transformation to Corporate Purpose & System‑Level Impact
Approximately 9.4% of respondents see AI as a driver of societal transformation.
For organizations, this implies a role as system shapers, not passive technology adopters.
This includes:
- AI applied to sustainability, safety, health, and inclusion
- Ecosystem‑level collaboration across public and private sectors
- Responsible AI as a license to operate.
The key change is shifting from compliance‑based ethics to ethical‑by‑design systems, and from isolated firms to interconnected ecosystems.
Business Analysis focus:
- Define measurable societal outcomes
- Balance financial KPIs with impact KPIs
- Track ESG impact, trust metrics, and regulatory resilience.
7. From Entrepreneurship to Organizational Agility & Internal Venture Building
The entrepreneurial hope (8.7%) points to AI lowering the cost of experimentation.
Within enterprises, this enables:
- Rapid testing of products, services, and business models
- Internal startups powered by AI
- Faster scaling from idea to execution.
The key change is moving from risk avoidance to managed experimentation, and from rigid annual planning to continuous portfolio rebalancing.
Business Analysis focus:
- Design lightweight governance for experiments
- Track hypothesis‑to‑value cycles
- Measure experiment velocity, kill‑rate, and scale success.
8. From Learning & Growth to Organizational Learning at Scale
With 8.4% focused on learning and growth, AI emerges as a mechanism for continuous capability upgrading.
Organizations evolve toward:
- Dynamic upskilling linked to processes and outcomes
- Skills ecosystems replacing static job descriptions
- Knowledge captured from execution rather than manuals.
The key change is shifting from static org charts to skills‑based organizations, and from training events to learning embedded in daily work.
Business Analysis focus:
- Link skills to value streams
- Forecast future skill demand using AI
- Measure skill coverage and learning‑to‑performance lag.
9. From Creative Expression to Organizational Innovation & Cultural Change
Finally, creative expression (5.6%) highlights AI’s potential to multiply creativity—if culture allows it.
Organizations can enable:
- AI‑assisted ideation and design
- Faster iteration on products and experiences
- Wider participation in innovation.
The key change is cultural: from “creativity belongs to experts” to psychological safety for experimentation with AI.
Business Analysis focus:
- Integrate AI into innovation pipelines
- Measure diversity and throughput of ideas
- Track idea‑to‑impact rate and innovation cycle time.
A Single Converging Transformation. From a Business Analysis perspective, these nine hopes converge into a single, profound shift: organizations are moving from human‑centered execution to human‑AI co‑governed systems.
This transformation carries clear BA implications:
- Processes become adaptive systems
- Roles evolve into fluid capabilities
- Governance shifts from rigid rules to policies and guardrails
- Strategy becomes continuous sensing and responding.
Anthropic’s research does not simply tell us what people want from AI. Interpreted through Business Analysis, it reveals how organizations must evolve if AI is to truly “go well”.
This article represents the first part of the analysis of Anthropic's research through the lens of Business Analysis. Further research findings will be the subject of future articles.