Firm's strategic priorities for navigating AI-driven identity transformation
| Dimension | Short-term | Medium-term | Long-term |
|---|---|---|---|
| 1. Media and Infrastructure | Assess digital and AI infrastructure readiness; identify key media and communication channels | Invest in secure, scalable and monitored infrastructure; implement safeguards against vulnerabilities and misuse | Enable fluid, adaptive infrastructures for multi-agent AI and hybrid physical-digital environments |
| 2. Product and Services | Identify opportunities for AI-enhanced features in existing products; Settle specific business cases; Ensure ethical design | Co-develop new AI-native products with embedded safety checks and bias mitigation strategies; Foster quality, specialization, velocity and prompt personalization | Design products for hybrid environments (digital/physical) with embedded AI and robotics; revisit traditional formats through AI |
| 3. Content | Experiment with AI-generated content; train teams on editorial and ethical use of generative AI | Establish content monitoring systems to detect hallucinations and bias; define escalation protocols for content validation; foster ethical foresight and Agile and Sprint prototyping | Use generative AI to create dynamic, context-aware narratives; blend vintage aesthetics with AI creativity |
| 4. Client | Map AI touchpoints in the customer journey; ensure transparency and consent, reinforce trust | Monitor AI interactions for fairness, hallucinations and unintended consequences; implement feedback and correction loops | Engage clients in fluid, co-created experiences across intelligent, adaptive environments; enable natural, human-like dialogue with AI |
| 5. Stakeholder Relations | Communicate AI vision internally; reinforce trust; involve employees in early-stage initiatives, for example, through AI ambassador or consultant or co-pilot initiatives | Build trust and alignment through participatory processes and cross-functional teams; Define AI crisis management (security issues bonded and not to AI and how confine AI); Build new ecosystem interactions | Foster distributed agency where stakeholders and AI agents co-shape identity and values; Be ambidextrous to change and prone to manage fluidity, continuous change and ecosystemic homeostasis |
| 6. Core Company Nature | Revisit core values and purpose in light of AI adoption settle AI signature moments; Enhance AI awareness; Assess cultural readiness and adjust; Prepare Governance | Translate values into AI behaviors, decisions and interfaces; embed AI in learning culture and continue to manage Governance issues as cross functional and AI concerns | Embrace identity fluidity as a strategic asset; integrate tradition and innovation in a hybrid human-AI culture; but be ready to manage flashback to past realities, for example, physical locations need to remain open |
| 7. AI signature | Define the desired “cognitive style” of AI (tone, logic, empathy, decision-making), for example, through a gap analysis and conflict analysis; settle right and clear data management practices; define boundaries, task and type of instruments (owned or not) as specific and traditional metrics | Monitor AI outputs for consistency, hallucinations and alignment with brand tone and values, for example, introducing Provenance detectors; Refine metrics and feedback timing to update AI, Develop AI dashboard | Develop a poli-functional system with firm cognitive identity that adapts across contexts, avatars and AI systems; enable AI to express brand essence in fluid, human-like ways |
| Dimension | Short-term | Medium-term | Long-term |
|---|---|---|---|
| 1. Media and Infrastructure | Assess digital and AI infrastructure readiness; identify key media and communication channels | Invest in secure, scalable and monitored infrastructure; implement safeguards against vulnerabilities and misuse | Enable fluid, adaptive infrastructures for multi-agent AI and hybrid physical-digital environments |
| 2. Product and Services | Identify opportunities for AI-enhanced features in existing products; Settle specific business cases; Ensure ethical design | Co-develop new AI-native products with embedded safety checks and bias mitigation strategies; Foster quality, specialization, velocity and prompt personalization | Design products for hybrid environments (digital/physical) with embedded AI and robotics; revisit traditional formats through AI |
| 3. Content | Experiment with AI-generated content; train teams on editorial and ethical use of generative AI | Establish content monitoring systems to detect hallucinations and bias; define escalation protocols for content validation; foster ethical foresight and Agile and Sprint prototyping | Use generative AI to create dynamic, context-aware narratives; blend vintage aesthetics with AI creativity |
| 4. Client | Map AI touchpoints in the customer journey; ensure transparency and consent, reinforce trust | Monitor AI interactions for fairness, hallucinations and unintended consequences; implement feedback and correction loops | Engage clients in fluid, co-created experiences across intelligent, adaptive environments; enable natural, human-like dialogue with AI |
| 5. Stakeholder Relations | Communicate AI vision internally; reinforce trust; involve employees in early-stage initiatives, for example, through AI ambassador or consultant or co-pilot initiatives | Build trust and alignment through participatory processes and cross-functional teams; Define AI crisis management (security issues bonded and not to AI and how confine AI); Build new ecosystem interactions | Foster distributed agency where stakeholders and AI agents co-shape identity and values; Be ambidextrous to change and prone to manage fluidity, continuous change and ecosystemic homeostasis |
| 6. Core Company Nature | Revisit core values and purpose in light of AI adoption settle AI signature moments; Enhance AI awareness; Assess cultural readiness and adjust; Prepare Governance | Translate values into AI behaviors, decisions and interfaces; embed AI in learning culture and continue to manage Governance issues as cross functional and AI concerns | Embrace identity fluidity as a strategic asset; integrate tradition and innovation in a hybrid human-AI culture; but be ready to manage flashback to past realities, for example, physical locations need to remain open |
| 7. AI signature | Define the desired “cognitive style” of AI (tone, logic, empathy, decision-making), for example, through a gap analysis and conflict analysis; settle right and clear data management practices; define boundaries, task and type of instruments (owned or not) as specific and traditional metrics | Monitor AI outputs for consistency, hallucinations and alignment with brand tone and values, for example, introducing Provenance detectors; Refine metrics and feedback timing to update AI, Develop AI dashboard | Develop a poli-functional system with firm cognitive identity that adapts across contexts, avatars and AI systems; enable AI to express brand essence in fluid, human-like ways |
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