AI Strategy Assessment Quiz

AISWITCH™
AI Maturity Assessment (Preview)

Evaluate your organization's AI Practice Competitive Leverage, across 6 critical dimensions of AISWITCH™ (AI Strategy-Workforce-Information-Technology-Culture-Human augmentation) Enterprise AISM framework.

NOTE: This is a 'preview' assessment to give you an example. Our full-fledged analyst-led assessment involves comprehensive evaluation of your organization's AI capabilities across key dimensions, including People, Process, Tools, and Outcomes (Metrics).

1. AI Strategy

1.1 Do you have structured formal AI Strategy formulation and review process and practices in place, that are widely practiced across the enterprise and partner ecosystems?

No formal AI strategy formulation and review process exists. AI strategy is discussed informally but not documented. A documented AI strategy formulation process exists and is shared with key stakeholders. Clear, well-documented AI strategy formulation and review process and practices exist and are fully functional, integrated with overall business strategy and communicated across enterprise and ecosystems.

1.2 Do you have a formal and functional AI financial management process, to track and benchmark AI services and usecases TCO, cost, RoI?

Minimal to no visibility into AI financials. Project-level cost visibility only. Input costs e.g. API costs and overall computing and services costs are tracked. Full-fledged AI financial management process is in place, that continuously monitors and manages AI costs vs benefits for all enterprise AI services and usecases, with industry benchmarks in terms of AI outcomes and RoI based on business KPIs.

1.3 Do you have formal and fully in-practice AI risk management process across the organization and partner ecosystems?

No formal AI risk management process. Ad-hoc, project-by-project basis AI risk management. Centralized basic risk management for AI experimentation exists. A detailed formal AI risk management process is in place and is communicated and practiced across organization and ecosystems, covering all relevant GRC, security and legal requirements.

1.4 How do you have a formal AI Strategic Outcomes management process, to measure, benchmark and discover best practices for optimal impact of AI in a 360 manner e.g. across financial, customer, internal and knowledge dimensions?

Success is not formally measured. Measures by technical metrics only (e.g., model accuracy). Measures by limited project-level business outcomes (e.g., cost savings). There is a well-practices AI strategic outcomes management process through which strategic business impact and transformation (e.g., new revenue streams, market share) are measured, peer-benchmarked, also showcased to market, with board-level visibility on value dashboards.

2. AI Workforce

2.1 Do you have a formal AI CoE setup and review process, with resources planned and allocated, based on industry best practices?

No formal AI CoE setup. A small experimental AI CoE is in place, but no formal process/ mandate/ funding/ resource plans. A centralized CoE is in place, with adequate resources and funds. A well-governed, unified data and AI CoE is in place, with clear mandate, sponsorship and funding models, senior leadership commitment and board visibility, integrated with the risk and governance offices and CIO, CSO, CISO offices. The CoE review process and practices are also in place, to improve based on market and competition inputs.

2.2 What is the state of your AI upskilling, cross-skilling and resource management process?

No formal process exists. Some point solutions and programs are delivered and run by partners and providers. Formal, well-practiced AI skills management process is in place, that constantly works with businesses and technology teams on identifying relevant AI training & certification requirements and plans and provides the necessary resources. A scalable, dynamic need-based intelligent AI skills management process is in place, that scans the tech supply and competitive environment for new AI leverage opportunities, and proactively trains relevant sections of workforce.

2.3 Do you have a well-established role-KPI-KRA and career roadmapping process in the AI CoE as well as the enterprise AI practice?

No formal process. Basic roles in place for AI CoE. A formal AI leveraged role-KPI-KRA and career management process is in place but no visibility into peer best practices and benchmarks. Robust AI role enhancement and career mapping processes are fully functional both within the AI CoE and across different user groups, and benchmarked with peer best practices and external advisor ecosystems, for optimal talent retention and AI leverage across enterprise.

2.4 Do you have an established process for AI workforce cost management?

No visibility into AI-related workforce and talent costs. Project-level resource visibility exists. A standardized resource cost model is available. AI workforce cost management process is fully functional across AI developer/ user/ analyst/ leaders – for talent acquisition, training, L&D, certifications, revenue/ impact, R&R and retention activities. Within the AI CoE and across the enterprise ecosystem, industry benchmarks are used regularly.

3. Information (Data)

3.1 What is the status of your AI Information/ data security management process?

No specific AI information security management process. Basic data security systems are in place but accountability not clearly defined. A mix of enterprise data security systems and roles, with limited visibility into data catalog, data ownership and accountability for privacy and GRC. A robust fully functional AI information security management process is in practice for all AI applications DataOps, integrated with the CISO offices and GRC specialists. Industry gold standards and best practices are established, along with role-specific risks and accountability for any data-related incidents.

3.2 How robust is your Information/ data relevance, model latency and lifecycle management process?

No clear Information relevance/ lifecycle management process in place. Basic dataops and modelops tools in place but process and KPIs are not defined. AI CoE leaders have established fully functional DataOps and ModelOps processes and tools, with clear accountability and auditability. Widespread adoption of information relevance and lifecycle management process and tools, for all AI applications, within the AI CoE as well as across the enterprise user communities.

3.3 What is the status of your AI Information/ data quality management process e.g. bias, noise, change management?

No clearly established data quality management process for AI. Project-specific data quality management. Data quality management tools are widely utilized but are limited in terms of quality assurance, audit results and impact. Full-fledged data quality management process is practiced for all AI development and applications, along with extensive use of tools and quality dashboards. Clear accountability and visibility into data quality are established, e.g. for biases and noise in training and test data. Red teaming and extreme testing for data quality checks is a regular practice.

4. Technology

4.1 Do you have a formal AI technology infra and ops services management process to manage availability, capacity, reliability, SLM, BCP-DRP of AI applications and services?

No, AI applications are managed like any other IT app. Some critical AI applications infra and ops are managed with specific relevant processes and partner tools, within siloes. Yes, a full-fledged AI tech infra and ops management process is defined and running, to manage availability, capacity, SLM, reliability and service continuity for all enterprise AI applications, from chip to services. A comprehensive AI tech infra and ops management process is in place and it leverages AIOps tools as well as uses AI for autonomous resolutions e.g. with agentic workflows as apt, with human by exception workflows.

4.2 Do you have a green, energy-efficient AI tech infrastructure design and delivery process?

There is no established process to promote green AI. Developers are aware of green, energy-efficient AI techniques but not mandated by any established process to prioritize them. There is a clear process set up to encourage developers, testers and users to choose and optimize on energy-efficient, green AI infrastructure and sandboxes. Green and responsible AI is mandated by a formal process that ensures clear accountability and is regularly audited for energy efficient options and benchmarks. Industry best practices for green AI are discovered and adopted, across internal and external ecosystems.

4.3 AI technology change and innovation management process?

No specific AI technology innovation and change management process in place. A basic AI technology change management process is in place. A proactive AI technology innovation and change management process is established and mandated for the AI CoE leaders. The AI technology innovation and change management process is proactively utilized and adopted by the entire organization where all stakeholders scan the tech supply environment and suggest potential options and innovations based on new technologies and their competitive applications.

5. AI Culture

5.1 Have you established an AI Organizational change management process?

No, there is no organizational change process associated with AI. Limited project-specific change management initiatives are run by AI partners but no formal, repeatable process. A formal AI-specific organizational change management process is mandated, developed, tested and adopted, across the organization. It has clear responsibilities and accountabilities defined for various leaders and stakeholders. A proactive and mandatory AI organizational change management process is running across the organization including its partner ecosystems, with clear accountability and initiatives defined in place. The process has board level visibility and is factored into CXO KPIs and KRAs.

5.2 Do you have a formal process for AI adoption management and governance?

No there is no such process in place. A basic AI adoption management process is in place but not widely practiced. A formal AI adoption management process is in place, with clear accountability and governance. A comprehensive AI adoption management process is in place, with clear accountability, governance, and metrics to track adoption and impact.

5.3 Do you have a formal AI ethics and responsible AI process?

No formal AI ethics process exists. Basic AI ethics guidelines are available but not enforced. A formal AI ethics and responsible AI process is in place, with clear guidelines and governance. A comprehensive AI ethics and responsible AI process is in place, with clear guidelines, governance, and metrics to track compliance and impact.

6. Human AI Augmentation

6.1 Do you have a formal process for AI-human collaboration and augmentation?

No formal process exists. Basic AI-human collaboration guidelines are available but not enforced. A formal AI-human collaboration process is in place, with clear guidelines and governance. A comprehensive AI-human collaboration process is in place, with clear guidelines, governance, and metrics to track collaboration effectiveness and impact.

6.2 Do you have a formal process for AI-enabled decision making?

No formal process exists. Basic AI-enabled decision making guidelines are available but not enforced. A formal AI-enabled decision making process is in place, with clear guidelines and governance. A comprehensive AI-enabled decision making process is in place, with clear guidelines, governance, and metrics to track decision quality and impact.

6.3 Do you have a formal process for AI-enabled innovation and creativity?

No formal process exists. Basic AI-enabled innovation guidelines are available but not enforced. A formal AI-enabled innovation process is in place, with clear guidelines and governance. A comprehensive AI-enabled innovation process is in place, with clear guidelines, governance, and metrics to track innovation effectiveness and impact.

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AI Maturity Assessement

AISWITCH™
AI Maturity Assessment (Preview)

1. AI Strategy

2. AI Workforce

3. Information (Data)

4. Technology

5. AI Culture

6. Human AI Augmentation

Your AI Maturity Assessment Results

Your AI Maturity Scores

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Services

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Company

© 2025, PROSIGHT™ is part of DIGITALFABRIC® Group of Companies. All Right Reserved.

Built by IMAGENIE™

AI Engineering Capabilities Setup

AI Technology Management & GRC

AI Service Management

AISWITCH™ (IP)

AISTATE™ (IP)

GREAI™

CHIP2EXPERIENCE™

Perspectives

AI Maturity Assessement

1. AI Strategy

2. AI Workforce

3. Information (Data)

4. Technology

5. AI Culture

6. Human AI Augmentation

Almost there!

Your AI Maturity Scores

Thank You!

PROSIGHT™ is a pure-play AI Services Company.

Services

Frameworks

Company

© 2025, PROSIGHT™ is part of DIGITALFABRIC® Group of Companies. All Right Reserved.

Built by IMAGENIE™

AI Engineering
Capabilities Setup

AI Technology
Management & GRC

AI Service
Management