part 1 of a 3-part blog series: genAI processes
AI is a game-changing technology. While the need to explore AI use cases is urgent, rushing into AI projects without a deep understanding of your organization’s readiness across processes, technology, and people can result in disappointing results. In part one of this three-part blog series, I’ll present the business case for understanding your AI readiness and highlight best practice steps for determining the maturity of your processes.
Artificial intelligence (AI) is emerging as a pivotal force for innovation, promising to drive unprecedented business growth, productivity, and efficiency to give you a competitive edge — with a steep divide between early adopters and those late to the market. According to the Gartner Hype Cycle™ for AI, “Early adoption of [genAI and decision intelligence] innovations will lead to significant competitive advantage and ease the problems associated with utilizing AI models within business processes.”
The need to incorporate AI into your business strategy is pressing, but a haphazard, departmentally siloed, one-off approach to AI runs the risk of sapping resources only to return disappointing results. To avoid this outcome, it’s important to take a step back to assess your organization’s AI readiness across processes, technology, and people — so that you can enhance these before getting started. Early wins can then be scaled into future projects and continuing AI success.
The following guidelines can help steer your organization through assessing your capabilities on the path to transformative AI. These have been built and further fine-tuned based on our observations and learnings from our engagements.
The complete framework should essentially address your level of maturity using five distinct levels across all the dimensions required for project success. The first five dimensions, addressed in this blog, will probe the processes you have in place to support AI at your organization and include assessments of your high-level AI strategy, target use cases, data management practices, planned performance metrics, and ethical considerations.
AI strategy.
As with any major technology initiative, success starts with identifying your desired outcomes and creating a strategy to get there. A successful strategy aligns AI initiatives with broader business objectives, creating a roadmap from initial exploratory stages to deeply integrated, strategic use cases that drive innovation. Begin by assessing your current state, noting the clarity of your business goals and how closely your AI strategy aligns with them.
emergent
- Your AI strategy is nonexistent or very basic.
- Teams have a limited understanding of the potential impacts of AI.
- Existing AI initiatives are ad hoc and lack clear alignment with business goals.
experimental
- You have started developing an AI strategy but are still in the early stages.
- You have some awareness of how AI can benefit the business but lack a comprehensive plan.
- AI projects are more exploratory, without a long-term strategic framework.
proficient
- You have a well-defined AI strategy that aligns with business objectives.
- You have a clear understanding of how AI can drive business value.
- AI initiatives are planned and measured against expected outcomes.
progressive
- Your AI strategy is integrated with your business strategy.
- There is a focus on scaling AI and using it for competitive advantage.
- Your AI strategy is continuously evaluated and adapted.
innovative
- AI is a core part of your business strategy, driving major decisions and innovations.
- Your company is recognized as a leader in AI.
- Your AI strategy is dynamic and forward-thinking and sets the pace for future trends and industry standards.
AI applications and use cases.
The practical application of AI to solve business problems illustrates operational maturity. Use the following to assess the extent and variety of AI applications within your organization and how they are used to solve problems and add value.
emergent
- Production use cases are limited or nonexistent.
- Any operational use cases are rudimentary and in early testing phases.
experimental
- Teams are starting to evaluate AI but use cases may be in pilot stages or limited in scope.
- The focus is on experimenting with AI in operational settings to understand its potential and limitations.
proficient
- Your company has several AI use cases successfully in production.
- AI use cases are stable, integrated into operations, and delivering clear benefits.
- Teams demonstrate proficiency in managing and optimizing AI applications.
progressive
- There are multiple advanced AI use cases in production, demonstrating efficiency and innovation.
- You are expanding and scaling AI applications and continuously improving operations based on AI insights.
innovative
- AI use cases in production are numerous, diverse, and showcase cutting-edge applications of AI.
- Operations have high levels of automation, sophistication, and impact on the company's core business and industry.
data management and quality.
High-quality data combined with advanced data management practices are crucial for effective AI. Your maturity level indicates your ability to collect, process, and leverage data efficiently. Use the prompts below to evaluate how your organization manages and utilizes data.
emergent
- Basic data management practices are in place, with limited structure and organization.
- Data quality is inconsistent, with minimal efforts to cleanse or standardize data.
- Comprehensive data governance or policies are lacking.
experimental
- There is a developing awareness of the importance of data quality and management.
- Data practitioners have taken initial steps toward organizing and cleaning data, but the processes are not fully established or standardized.
- Some data governance policies are established, though they may be rudimentary.
proficient
- IT has established data management practices with a focus on quality and accessibility.
- Regular data cleansing and standardization processes are in place.
- Clear data governance policies and practices are assigned to responsible teams or individuals.
progressive
- IT uses advanced data management strategies to ensure high-quality, reliable data.
- A comprehensive data governance framework is integrated into all data-related activities.
- There is a strong focus on data security, privacy, and ethical handling of data.
innovative
- Leading-edge data management and quality practices set industry standards.
- IT proactively uses modern technologies and methodologies for data management and quality enhancement.
- Data governance is a core aspect of the business strategy, with continuous innovation in data practices.
performance measures.
Effective metrics and evaluation strategies for AI projects are essential for being able to measure success and drive continuous improvement. For this dimension, impartially evaluate how the company measures the performance and impact of AI initiatives, including metrics for success and frameworks for continuous improvement.
emergent
- There are limited or no AI performance metrics.
- Performance is evaluated anecdotally or based on subjective criteria.
- There is no systematic approach to tracking and analyzing outcomes.
experimental
- Key performance indicators (KPIs) have been identified but may be basic and not aligned with business objectives.
- There are some inconsistent or early-stage efforts to track AI performance.
- AI impact analysis lacks depth and comprehensiveness.
proficient
- Well-defined KPIs are aligned with specific business goals.
- There is regular AI performance monitoring and reporting.
- Performance data is used to make informed decisions and adjustments.
progressive
- Advanced performance measurement frameworks integrate quantitative and qualitative metrics.
- AI performance data is used for strategic planning and continuous improvement.
- Robust analytics capabilities help track AI impact and value generation.
innovative
- There are innovative approaches to measuring AI performance, which often set industry benchmarks.
- Teams proactively use predictive analytics and other advanced methods to forecast AI project outcomes.
- Performance measurement is integral to the AI lifecycle, driving innovation and excellence.
ethical AI and governance.
Ethical considerations in AI usage include data privacy and protection, guarding against algorithm bias, and model transparency. Your place along the maturity framework reflects your company's commitment to responsible AI practices.
emergent
- Your organization has a limited awareness of ethical AI principles and governance issues.
- There are no formal policies or guidelines in place for ethical AI use.
- Ethical considerations in AI projects are often an afterthought or not addressed.
experimental
- There is a growing awareness of the importance of ethical AI and governance.
- IT has taken initial steps toward developing policies and guidelines, but these steps are not yet comprehensive or fully implemented.
- Some ethical considerations are discussed in the context of AI projects, but without a structured approach.
proficient
- Established policies and guidelines for ethical AI use are in place.
- Ethical AI considerations are integrated into project planning and execution.
- There are training and awareness programs for staff on ethical AI and governance issues.
progressive
- Advanced ethical AI and governance frameworks are regularly reviewed and updated.
- A strong commitment to ethical AI practices is demonstrated in all AI projects.
- IT actively engages with external bodies or committees on ethical AI standards and practices.
innovative
- Your organization is leading the industry in ethical AI practices and governance models.
- Practitioners set the standards and advocate for responsible AI use, both internally and in the wider community.
- Ethical AI and governance are deeply embedded in the organizational culture and decision-making processes.
where are you on the AI maturity framework?
A complete AI maturity framework serves as a guide to help you determine your readiness for navigating the complexities of AI integration. By assessing and advancing through each dimension, you will chart a clear path toward unlocking the full potential of AI, transforming operations, and setting new standards of excellence for your industry.
Be sure to read the next blog post in the series, which covers the technology dimension of AI maturity.
Where are you on the AI maturity framework? Get in touch to work through the framework with one of our seasoned experts.