Loading form...
Your company
Industry Average
1. Strategy & Business Alignment
Score 1 – 2: Emerging
Recommendations
- Conduct workshops to define how AI can support business goals
- Brainstorm practical, quick-win AI use cases that demonstrate clear value
- Build a small pilot project and use success stories to secure funding and executive sponsorship
Score 3: Developing
Recommendations
- Shift from AI pilots to a more structured, documented AI roadmap
- Establish clear KPIs for each AI project
- Create an AI governance body to align AI projects with strategic priorities
Score 4: Proficient
Recommendations
- Ensure AI goals are well-articulated, and each initiative has a clear plan
- Look for high impact, enterprise-wide AI projects
- Continuously monitor AI’s financial and operational impact
Score 5: Leading
Recommendations
- Make AI a standard consideration in all new initiatives and product planning
- Explore cutting-edge AI to differentiate in the market
- Encourage a fail-fast, learn-fast mentality, with AI powering growth
2. Data Readiness
Score 1 - 2: Emerging
Recommendations
- Identify key data sources, integrate them to reduce silos and inconsistencies
- Define ownership for critical data sets and draft privacy & compliance policies
- Fix obvious data errors and establish processes for regular data cleansing
Score 3: Developing
Recommendations
- Ensure data standards and policies are known and enforced
- Provide self-service data tools or dashboards to key stakeholders
- Introduce ETL/ELT tools and data integration platforms
Score 4: Proficient
Recommendations
- Adopt cloud or hybrid data solutions that handle large volumes with high performance
- Automate compliance checks, add metadata management, and implement data lineage tracking
- Encourage cross-team data collaboration through formal processes and centralized data catalogs
Score 5: Leading
Recommendations
- Ensure that your data ecosystem is highly integrated, secure, and in real-time
- Use ML to automate data quality checks and anomaly detection
- Consider next-level capabilities (e.g., data virtualization) to stay ahead
3. Talent & Skills
Score 1 - 2: Emerging
Recommendations
- Bring on expert talent or partner with specialized vendors to fill skill gaps
- Launch AI workshops for both technical and non-technical employees
- Find individuals in each department who can advocate for AI adoption
Score 3: Developing
Recommendations
- Offer skill development for both technical and business roles (AI literacy)
- Tackle an initial AI project with a small team of business and technical staff
- Define specific roles (e.g., AI engineer) and offer a clear progression
Score 4: Proficient
Recommendations
- Provide AI education (e.g., internal academies) for your staff
- Embed AI roles into business teams and vice versa to deepen synergies
- Incentivize teams that successfully use AI to improve processes or outcomes
Score 5: Leading
Recommendations
- Ensure everyone understands and contributes to AI initiatives
- Build an AI CoE and offer competitive benefits to retain top talent
- Sponsor AI conferences, contribute to AI communities, and publish research
4. Technology & Infrastructure
Score 1 - 2: Emerging
Recommendations
- Identify gaps in compute, storage, and network capacity for AI experiments
- Start with cost-effective cloud-based AI services for small pilots
- Ensure at least basic cybersecurity measures are in place
Score 3: Developing
Recommendations
- Invest in mid-level computing resources to handle complex AI workloads
- Roll out ML libraries, pipeline orchestration tools and experiment tracking
- Incorporate AI vulnerabilities into existing cybersecurity audits and policies
Score 4: Proficient
Recommendations
- Implement end-to-end platforms that handle data ingestion, model development, deployment, and monitoring
- Leverage GPU/TPU clusters or cloud services with auto-scaling for production-grade AI
- Formal AI security reviews and real-time monitoring for threats targeting AI pipelines
Score 5: Leading
Recommendations
- Use CI/CD pipelines for rapid, reliable AI deployments across the enterprise
- Invest in specialized hardware for deep learning or large-scale AI
- Integrate security frameworks tailored to protect AI models against attacks
5. AI Governance & Ethical AI
Score 1 - 2: Emerging
Recommendations
- Do basic training on bias, fairness, and transparency in AI
- Identify relevant data and AI regulations (GDPR, CCPA, etc.) and start assessing compliance needs
- Periodically check model outputs for errors or biases
Score 3: Developing
Recommendations
- Draft guidelines on fairness, data privacy, and appropriate model use
- Use frameworks/external tools to interpret model decisions for stakeholders
- Form a small committee to evaluate AI risks and model performance
Score 4: Proficient
Recommendations
- Define governance processes, roles, and responsibilities around AI ethics
- Encourage model interpretability techniques in critical systems
- Implement real-time tools to detect anomalies or drift and alert teams
Score 5: Leading
Recommendations
- Ensure ethical considerations are integral to data collection, model design, deployment, and user feedback
- Maintain version control, model documentation, and robust logging for compliance and transparency
- Publish best practices, collaborate with industry groups, and help shape future AI regulations
Unlock the full potential of your business with our cutting-edge Data and AI services.
