Sustainable AI
Sustainable Data
Sustainable
Operations
Sustainable AI
Sustainable AI
Current State
-
AI introduces ethical concerns, security and privacy vulnerabilities, workforce adaptation issues and significant energy consumption. Many initiatives lack governance and business alignment.
-
Poorly designed AI agents destroy productivity by producing “workslop” and accelerating bad processes.*
-
AI opportunities exist but are unprioritized, delaying productivity gains and responsible scale.
Target state
-
AI systems are reliable, ethical, compliant, secure and energy‑efficient, aligned to workforce needs and business goals.
-
AI value is unlocked responsibly, and governance mechanisms provide effective oversight.
-
Human‑centric design enables adoption while maintaining transparency and fairness
Notional Timeline
-
Month 1-3: High value process assessment
-
Month 3-9: AI governance process design
-
Month 3-12:Sustainable AI pilots & AI gov process implementation
-
Month 7-18: Replication on other critical business processes
​
Sustainable Data
Data Governance
Current State
-
Data fragmentation and inconsistent quality slow AI adoption and undermine decision-making.
-
Unclear governance and insufficient controls increase regulatory, security, and operational risks.
-
Siloed models and legacy technologies limit visibility, agility, and enterprise integration.
Our approach
-
Conduct data maturity assessments to identify critical gaps in governance, quality and compliance.
-
Design a data strategy and roadmap aligned to business and AI outcomes and the target operating model (TOM) to implement it.
-
Deploy metadata, lifecycle management and stewardship processes that bring directly value to the business.
Target state
-
Data is trusted, accessible and aligned to business and AI strategy. Strong governance ensures accountability and resilience.
-
Modernized data architecture enables interoperability, automation and secure sharing across the enterprise.
-
Clear operating model empowers teams with defined stewardship roles and lifecycle controls.
Notional Timeline
-
Month 1-3: Maturity Assessment
-
Month 4-6: Roadmap/TOM
-
Month 4-12: Pilot Implementation
-
Month 7-18: Process implementation (quality, compliance, MDM, data use…)
Data Privacy
Current State
-
Organizations face legal and reputational risks from privacy breaches. Employees and customers expect transparency and trust in data handling.
-
Lack of clear rules on personal data use hinders innovation, data sharing and AI adoption.
-
Privacy-enhancing technologies are underdeveloped, causing organizations to rely on manual processes that create bottlenecks for the businesses.
Our approach
-
Perform privacy assessment and risk mitigation roadmap calibrated to regulatory exposure.
-
Design privacy TOM and implement privacy‑by‑design practices across key workflows.
-
Deploy processes and tooling for inventory, DPIA, rights management, third party risk management and breach response.
Target state
-
A transparent, risk‑based privacy program enables responsible use of personal data while supporting innovation.
-
Data subjects’ rights are continuously respected and efficiently managed at scale.
-
Seamless and scalable privacy processes are embedded in the company’s value chain. Privacy requirements are embedded by design across processes, products and technologies.
Notional Timeline
-
Month 1-3: Privacy Assessment
-
Month 4-6: Roadmap/TOM
-
Month 4-12: Pilot Implementation
-
Month 7-18: Process implementation (Data Subject Rights, Privacy by Design, Incident Response Management)
Data Protection
Current State
-
Organizations often fail to prevent data breaches from internal or external sources. Inability to manage identities and access, and misunderstanding data assets, are key obstacles.
-
Security measures lack prioritization based on business value. Data protection technologies are underdeveloped, causing organizations to rely on manual processes that create bottlenecks for the businesses.
-
Companies struggle with inconsistent protection measures due to balancing internal and outsourced cybersecurity.
Our approach
-
Perform Crown Jewels Inventory, NIST/ISO 27001 assessment and data discovery exercises to locate sensitive information and vulnerabilities.
-
Define data protection strategies focused on business strategy and crown jewels protection.
-
Integrate data protection as part of the organization’s business as usual processes.
Target state
-
Crown Jewels are identified and protection measures prioritized based on their classification. A zero-trust identity and access management policy is in place.
-
Automated state of art data protection technologies enable seamless cybersecurity (posture management, AI enabled data discovery, Data leakage prevention)
-
Internal and external capabilities are coordinated through a well‑defined model, optimizing cost and impact.
Notional Timeline
-
Month 1-3: Crown Jewel Inventory/Assessment/Data Discovery
-
Month 4-6: Strategy/Roadmap
-
Month 4-12: Pilot Implementation
-
Month 7-18: Process implementation (Process implementation (e.g. IAM recast, DLP…)
Data Sovereignty
Current State
-
Dependence on foreign cloud and technology providers creates potential geopolitical and supply chain exposure.
-
Confidential data, including national security, trade secrets and intellectual property, is exposed to foreign intelligence.
-
Data subjects' privacy is at risk from foreign access and potentially illegally used to train AI models and fuel innovations.
-
Technology lock‑ins reduce flexibility, innovation and negotiation leverage with strategic vendors.
Our approach
-
Rapid assessment of data flows, vendor dependencies and legal exposure.
-
Define sovereign target architecture options, financing models and transition planning.
-
Execute migration, encryption and continuity operations using local and open‑source solutions.
Target state
-
Sovereign data and infrastructure ensure operational independence and continuity under any scenario.
-
Confidential data and personal information are safe from foreign intelligence.
-
Technology choices are strategic, compliant and diversified, reducing concentration risk.
Notional Timeline
-
Month 1-3: Sovereignty Assessment
-
Month 4-6: Target Architecture
-
Month 4-12: Pilot Implementation
-
Month 7-18: Data Migration
Sustainable Operations
ESG Strategy
Current State
-
ESG expectations rise from investors, regulators and employees, while organizations lack a consistent framework for accountability and disclosure.
-
Limited visibility into climate and social risks constraints strategic planning, regulatory readiness and access to capital.
-
ESG initiatives exist but remain isolated, without measurable outcomes or executive‑level alignment.
Our approach
-
Benchmark ESG maturity, conduct double materiality assessment and define KPIs linked to productivity and value creation.
-
Deploy frameworks (such as the Theory of Change), policies and training to operationalize sustainable practices.
-
Support ESG reporting aligned to global standards and continuous improvement systems.
Target state
-
ESG and sustainability are integrated into strategy, operations and culture, driving resilience and productivity gains.
-
Credible reporting builds stakeholder trust and enhances reputation and investment attractiveness.
-
Performance is tracked against targets informed by material business impacts.
Notional Timeline
-
Month 1-3: Maturity Assessment
-
Month 4-9: Framework and policy design
-
Month 4-12: Framework and policies implementation
-
Month 7-18: Continuous deployment & monitoring
Procurement
Current State
-
Supply chains face growing scrutiny on human rights, carbon footprint and regulatory compliance. Organizations rely heavily on suppliers regarding data ethics, compliance, quality, security and privacy.
-
Procurement lacks tools and criteria to integrate ESG performance into purchasing decisions and supplier segmentation.
-
Visibility on supply chain risk is limited, exposing the company to disruptions and reputational damage.
Our approach
-
Assess procurement sustainability maturity and risks across full supplier lifecycle.
-
Perform a supply chain double materiality and ESG risk/controversies assessment.
-
Develop ESG criteria, policies and training to embed sustainability into purchasing decisions.
-
Deploy monitoring, certification and reporting tools to ensure compliance and improvement.
Target state
-
Sustainable procurement delivers long‑term value through responsible sourcing and supplier partnerships. Suppliers data practices are aligned with your organization’s policies regarding ethics, compliance, quality, security and privacy.
-
Full visibility into supply chain ESG performance reduces risks and supports compliance.
-
Sustainability metrics inform decision‑making, improving transparency and business outcomes.
Notional Timeline
-
Month 1-3: RiskAssessment
-
Month 4-9: Framework and policy design
-
Month 4-12: Framework and policies implementation
-
Month 7-18: Continuous deployment & monitoring