The transformation is not “humans to AI.” It is human-only workflows → human–AI workflows → potentially human–AGI organizations.
Artificial intelligence has moved from a distant strategic topic into everyday business work. Experimental and field evidence now shows measurable productivity gains in selected tasks, but it also shows something equally important: AI does not improve every task, and misplaced trust can reduce performance when the technology is used outside its capability frontier.[6]
This paper therefore separates three questions. What did daily work look like before generative AI? What can organizations reliably do with AI today? And what could change if genuinely general, reliable and governable AI systems emerge in the future?
The third category AGI requires particular care. There is no universally accepted operational threshold for AGI, and there is no scientifically reliable arrival date. DeepMind researchers have proposed evaluating progress through dimensions such as performance, generality and autonomy.[4] Accordingly, this white paper treats AGI as a strategic scenario, not as a claim about present-day technology and not as a prediction.
AI adoption is broad. Deep workflow transformation is still early.
Stanford's 2026 AI Index reports that 88% of surveyed organizations used AI in at least one business function in 2025. The same report shows generative AI use in at least one function at 79% of surveyed organizations, while agent deployment remained in the single digits across nearly all business functions.[1]
The ILO's task-level analysis is especially useful because it distinguishes exposure from replacement. It concludes that because most occupations still include tasks requiring human input, job transformation is the more likely outcome for most workers.[2]
Software used to wait for instructions. AI increasingly interprets objectives.
ERP, CRM, spreadsheets and business systems improve productivity, but the person translates the objective into most intermediate actions.
AI assists with drafting, summarization, extraction, code, analysis, classification and increasingly controlled tool use.
A more general system could potentially coordinate broad cross-domain work. This is a scenario, not a present capability claim.
Before AI
“Before AI” does not mean before computers. Companies already had ERP, CRM, databases, search, analytics, workflow automation and business intelligence. The difference was that employees typically operated those systems step by step. A financial analyst gathered data, cleaned spreadsheets and wrote a report. A salesperson researched an account, updated the CRM and wrote the proposal. A developer searched documentation, wrote code, tested it and debugged it.
With AI today
Generative AI changes the interface. The user can increasingly state an objective and provide context, while the AI performs or proposes intermediate cognitive work. This can shift the person from sole producer toward director, reviewer and accountable decision-maker. Human oversight remains essential because generative AI can produce inaccurate or misleading output and creates privacy, cybersecurity, information-integrity, intellectual-property and other risk considerations.[3]
A possible AGI operating model
AGI should not simply mean “a better chatbot.” A useful enterprise scenario is a system with broad capability across domains that can learn unfamiliar tasks, maintain context, plan multi-stage work, use tools and adapt when conditions change. DeepMind's AGI-level framework explicitly discusses performance, generality and autonomy as separable dimensions.[4]
AI can produce real productivity gains and real mistakes.
A peer-reviewed experiment by Noy and Zhang found that professionals using ChatGPT completed selected writing tasks substantially faster and produced higher-quality output on average.[5] In customer support, Brynjolfsson, Li and Raymond found a nearly 14% average productivity increase for agents using a generative AI assistant, with larger benefits for less experienced workers.[7]
Knowledge work also gives us one of the strongest warnings against simplistic adoption. In the “jagged technological frontier” study, participants using AI completed 12.2% more tasks and worked 25.1% faster on tasks inside the model's capability frontier. But on a complex managerial task outside that frontier, AI users were 19% less likely to reach the correct solution.[6]
Experimental evidence supports meaningful gains on defined professional writing tasks, but the result should not be generalized to every knowledge task.[5]
Large field evidence found stronger gains for novice and lower-skilled workers.[7]
Across randomized experiments involving 4,867 developers at three companies, developers with an AI coding assistant completed more tasks.[8]
Performance can deteriorate when people rely on AI in areas where the model is not capable enough.[6]
Productivity at the task level is not automatically productivity at the company level. A randomized field experiment across 66 firms and 7,137 knowledge workers found that active users of an AI tool integrated into their work applications spent roughly two fewer hours per week on email in the second half of the experiment, but the study did not detect broader changes in task quantity or composition from individual-level provision alone.[9]
Danish labor-market research similarly found widespread adoption and new AI-related tasks, yet no statistically meaningful effect larger than 2% on recorded earnings or hours two years after ChatGPT's launch. What changed first was the structure of work new content-generation, oversight and integration tasks appeared.[10]
The common pattern: producer → AI-assisted producer → orchestrator → accountable decision-maker.
The table below combines observable current AI capabilities with a deliberately conservative future scenario. The AGI column should be read as “what could become possible if genuinely general, reliable and governable systems emerge”, not as a statement of today's capability.
| Business function | Before AI | With AI today | Possible AGI scenario | Human value shifts toward |
|---|---|---|---|---|
| Executive management | Gather reports, attend meetings, request analyses and manually compare alternatives. | Summarize documents, meetings and KPIs; assist research and scenario preparation. | A persistent system could monitor the enterprise, coordinate analysis and surface emerging risks continuously. | Purpose, governance, stakeholder leadership, risk appetite and final accountability. |
| Sales | Manual account research, CRM entry, emails, proposals and follow-up. | Research accounts, draft personalized communication, summarize calls and support proposals. | Systems could continuously identify opportunities, coordinate outreach and manage bounded commercial workflows. | Relationships, negotiation, trust and complex deal judgment. |
| Marketing | Manual research, campaign design, content creation and performance analysis. | Accelerate research, content variants, localization, segmentation and experimentation. | Coordinate market research, campaign creation, execution and optimization across channels. | Brand direction, differentiation, values, creativity and market judgment. |
| Customer service | Search knowledge bases, type responses, classify cases and escalate. | Retrieve knowledge, propose responses, summarize cases, translate and assist agents. | Resolve many end-to-end service journeys across connected systems while escalating exceptions. | Empathy, sensitive situations, policy decisions and relationships. |
| Finance & accounting | Manual reconciliation, reporting, spreadsheet analysis and commentary. | Assist document processing, extraction, variance explanation and scenario preparation. | Coordinate continuous analysis, forecasts, anomaly investigation and management reporting. | Fiduciary responsibility, controls, assumptions, capital allocation and sign-off. |
| Human resources | Create job descriptions, organize onboarding, answer routine questions and manage administration. | Assist drafting, policy Q&A, learning materials, workforce analysis and administrative workflows. | Orchestrate personalized onboarding, skills mapping and workforce planning. | Culture, sensitive personnel decisions, coaching, ethics and leadership. |
| Legal & compliance | Search and compare contracts, review policies and prepare documentation. | Assist comparison, extraction, summarization, research and first drafts under expert review. | Continuously map obligations against contracts, controls and operational evidence. | Legal judgment, interpretation, privilege, accountability and consequential decisions. |
| Software & IT | Research documentation, code, test, troubleshoot and document manually. | Generate and explain code, assist debugging, testing, documentation and selected multi-step tasks. | Implement larger requirements across architecture, code, test, deployment and maintenance. | Architecture, requirements, security, verification and technical governance. |
| Cybersecurity | Inspect alerts, logs, vulnerabilities and intelligence feeds. | Assist correlation, summarization, investigation and response preparation. | Continuously investigate, simulate and coordinate defensive actions within strict authority boundaries. | Risk ownership, authorization, adversarial reasoning and governance. |
| Procurement & supply chain | Compare suppliers, prepare requests, track orders and analyze operational data. | Assist supplier comparison, documents, demand analysis, communication and exception detection. | Coordinate sourcing, logistics, inventory and supplier workflows dynamically against constraints. | Supplier relationships, resilience strategy, ethics and exception decisions. |
| Product & engineering | Research, specifications, concepts and iterative prototypes. | Assist ideation, research, simulation preparation, documentation and analysis. | Coordinate multidisciplinary engineering cycles from requirements through testing. | Problem definition, safety, physical validation and engineering accountability. |
| Learning & development | Standard training created manually and delivered broadly. | Personalized explanations, exercises, simulations and knowledge support. | Adaptive tutors could continuously personalize development to employee performance and organizational need. | Mentorship, motivation, assessment integrity and learning strategy. |
The AI-ready professional is not simply a person who knows how to prompt.
The World Economic Forum's Future of Jobs 2025 report says skills gaps are a major barrier to business transformation and projects that technological skills especially AI and big data will grow rapidly in importance. At the same time, analytical thinking, resilience, leadership, collaboration and creative thinking remain important.[12]
Domain knowledge becomes more not less important when AI produces plausible output at high speed. An accountant must recognize an unreasonable conclusion. An engineer must recognize an unsafe design. A security analyst must recognize whether a proposed remediation creates a new vulnerability. A manager must recognize when an impressive-looking analysis rests on the wrong assumption.
For European organizations, AI competence is no longer only a competitive issue.
Article 4 of the EU AI Act has applied since 2 February 2025. European Commission guidance says providers and deployers must take measures supporting AI literacy among staff and others operating AI systems on their behalf; supervision and enforcement provisions apply from August 2026.[13]
Capabilities, uncertainty, failure modes and the difference between assistance and autonomous execution.
Confidentiality, personal data, credentials, access boundaries, prompt injection and unsafe automation.
Source checking, domain review, tests, controls and independent validation for consequential work.
Clear decision rights, approvals and escalation paths for AI-supported business outcomes.
Buying an AI subscription is not the same as transforming a company.
Consider a traditional management-reporting process. Employees collect information from separate systems, copy data into spreadsheets, reconcile discrepancies, build slides and explain the results. Adding AI only to the final presentation step may save time, but it leaves most of the process unchanged.
An AI-native workflow could connect governed data sources, monitor changes, flag anomalies, draft preliminary analysis and present evidence to the responsible manager for validation. The opportunity is not only to automate an old process, but to redesign it.
An organization cannot control when AI advances. It can control whether its own environment is ready.
Policies, procedures and operational know-how must be accessible, governed and current not trapped in inboxes and individual memory.
APIs and integration layers allow intelligent systems to work safely with CRM, ERP, documents, ticketing and analytics.
Agents need identities, permissions, authorization boundaries, logging and revocation not blanket access.
Data classification, lineage, quality and stewardship become more important as automation expands.
Specify what systems may recommend, draft, execute, approve or never do autonomously.
Benchmarks and demos are not enough. Evaluate quality, reliability, cost, risk and outcomes in your own workflows.
“How many employees use AI?” is an adoption metric, not a value metric.
The OECD's review of experimental research concludes that generative AI can produce meaningful gains in tasks such as writing, summarization, editing, translation and coding, but the size and direction of the effect depend on the task, context and user.[11]
| Dimension | Better questions and measures |
|---|---|
| Productivity | Cycle time, throughput, cases resolved, time-to-completion. |
| Quality | Error rate, rework, review findings, acceptance rate. |
| Financial | Cost per transaction, revenue contribution, margin, avoided cost. |
| Human impact | Time shifted from repetitive work to higher-value work; employee experience. |
| Customer impact | Response time, resolution rate, satisfaction, abandonment. |
| Risk | Incorrect outputs, policy breaches, privacy/security incidents, inappropriate autonomy. |
| Learning | AI literacy, verification ability, role-specific proficiency. |
| Innovation | Experiments launched, ideas tested, time-to-prototype, learning velocity. |
AI transformation is increasingly a management project, not only an IT project.
Managers must decide where AI should be used, what data it may access, which actions it may execute, what requires human approval, who verifies output, what happens when confidence is low, and who remains accountable when an AI-supported decision is wrong.
As systems become more capable, management may spend less time specifying individual actions and more time defining objectives, constraints, policies, permissions, resources and success criteria.
AI can remove administrative friction without removing the need for judgment.
Imagine an experienced procurement manager who spends large parts of the week searching information, comparing supplier documents, drafting emails and preparing spreadsheets. AI can already assist with many of those activities. The value of the manager then shifts toward harder questions: Is this supplier strategically reliable? Are the assumptions realistic? What geopolitical or operational risk is missing? When should we override the system? What are the long-term consequences?
The same transition applies to engineers, accountants, teachers, developers, lawyers, security analysts, salespeople and executives.
Preparing for more capable AI is a capability-building program, not a bet on a date.
Waiting for a universally acknowledged AGI milestone would postpone work that already creates value today: knowledge management, secure integration, identity and access control, data governance, workflow redesign, evaluation and employee development.
The World Economic Forum reports that skills gaps remain a major obstacle to business transformation and that many employers expect substantial reskilling and upskilling needs through 2030.[12]
Therefore, preparing employees for current AI is also preparation for whatever comes next.
Start with the work. Then add AI where evidence says it belongs.
Map what people actually do every day, including undocumented workarounds.
Separate repetitive, analytical, creative, relational, sensitive and regulated tasks.
Select use cases with measurable value and acceptable risk.
Run controlled pilots and establish a baseline before claiming impact.
Define data rules, permissions, controls, escalation and human approval.
Connect AI to the systems and information required for the real workflow.
Build role-specific AI literacy, verification skill and safe operating habits.
Track productivity, quality, financial, customer, human and risk outcomes.
Ask whether the process itself should change now that AI exists.
Design the architecture so more capable systems can be introduced safely later.
AI readiness is an organizational capability not a software license.
One path is to leave existing processes unchanged while employees experiment individually with AI. The other is to deliberately build an organization in which people and intelligent systems work together under clear governance.
The second path requires more: workforce development, data architecture, security, integration, process redesign, experimentation and leadership. But it creates something more durable than temporary automation. It creates the ability to absorb future technological change.
Do not wait for AGI.
We are living through an unusual moment in business history.
For decades, digital transformation meant teaching people how to operate increasingly sophisticated software. Artificial intelligence is beginning to change that relationship. We can increasingly explain what we want to achieve and allow software to help determine how to achieve it.
That change should not frighten us. But neither should we underestimate it.
AI should not become an excuse to stop developing people. It should become a reason to develop them faster.
Employees need the knowledge to use AI. Managers need the judgment to govern AI. Companies need the infrastructure to integrate AI. Leaders need to understand that adding a chatbot to an old process is not transformation.
Start with real workflows. Establish measurable baselines. Introduce AI where it creates value. Keep people accountable for consequential decisions. Train employees. Connect organizational knowledge. Improve data. Build governance. Measure results. Improve again.
Our goal should not simply be to make people work faster. It should be to give talented people more time for the activities where human contribution remains especially important: understanding people, creating trust, defining purpose, challenging assumptions, exercising judgment and accepting responsibility.
If increasingly general AI eventually becomes part of business life, organizations that already understand how humans and intelligent systems should work together will enter that transition from a position of strength.
AI readiness is not primarily about buying technology. It is about developing the organization. And that work can start today.
Evidence and further reading
- Stanford Institute for Human-Centered Artificial Intelligence. (2026). The 2026 AI Index Report Economy. hai.stanford.edu
- Gmyrek, P., Berg, J., Kamiński, K., et al. (2025). Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO Working Paper 140. DOI: 10.54394/HETP0387
- Autio, C., Schwartz, R., Dunietz, J., et al. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. NIST AI 600-1. doi.org/10.6028/NIST.AI.600-1
- Morris, M. R., Sohl-Dickstein, J., Fiedel, N., et al. (2024). Levels of AGI for Operationalizing Progress on the Path to AGI. ICML 2024. Google DeepMind
- Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. doi.org/10.1126/science.adh2586
- Dell'Acqua, F., McFowland III, E., Mollick, E. R., et al. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. SSRN 4573321
- Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at Work. The Quarterly Journal of Economics, 140(2), 889–942. Working-paper version: NBER 31161
- Cui, Z. K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. Microsoft Research
- Dillon, E. W., Jaffe, S., Immorlica, N., & Stanton, C. T. (2025). Shifting Work Patterns with Generative AI. NBER Working Paper 33795. doi.org/10.3386/w33795
- Humlum, A., & Vestergaard, E. (2025; revised 2026). Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI. NBER Working Paper 33777. doi.org/10.3386/w33777
- Calvino, F., Reijerink, J., & Samek, L. (2025). The Effects of Generative AI on Productivity, Innovation and Entrepreneurship. OECD Artificial Intelligence Papers, No. 39. doi.org/10.1787/b21df222-en
- World Economic Forum. (2025). The Future of Jobs Report 2025. weforum.org
- European Commission. (2026). AI Literacy Questions & Answers. Shaping Europe’s Digital Future. digital-strategy.ec.europa.eu
Methodological note. This paper deliberately distinguishes observed evidence, present technological capability and future scenarios.
Quantitative findings apply to the specific populations, tools, tasks and experimental conditions studied; they should not be interpreted as guaranteed productivity gains for every organization. The AGI sections are scenario analysis and do not claim that AGI currently exists, predict a specific arrival date or assume that future systems will achieve a particular level of autonomy or reliability.
Organizations should validate AI use cases against their own operational, legal, privacy, security and risk requirements before deployment.