AI Maturity Model

Learn what it takes to achieve AI maturity, assess where your organization stands, and mark your progress toward AI-driven business transformation.

Introduction

Many executives rank the ‌successful adoption of AI among their top priorities. But few know exactly where they stand relative to their peers or how to accelerate their own progress. Leaders would benefit from a research-backed playbook for pursuing organizational AI maturity.

The CognitivePath AI Maturity Model guides leaders through the complexities of integrating AI into their strategies and operations. It achieves this by:

Approaching AI adoption through the lens of strategy and operations
Addressing AI as an enterprise-class enabler, not merely a toolset for personal or individual contributor productivity
Offering a holistic approach to AI transformation (people, process, and technology)
Providing practical and achievable guideposts without being overly prescriptive (we understand that every organization is unique)
Painting a picture of an AI-enabled future state that meets the best practices of leading organizations

Developed with insights from industry experts and leading practitioners, the CognitivePath AI Maturity Model establishes a comprehensive roadmap that guides executives, department heads, and other AI decision-makers through a journey consisting of five distinct maturity stages with seven interrelated, competency-based paths.

Leaders can follow the stages, the paths, or a combination of both, to navigate their journey from initial exploration to pioneering leadership in AI.

Click here to jump directly to the CognitivePath AI Maturity Model.

A Five-Stage Journey

Each of the five stages of the AI Maturity Model represents the current state of an organization in motion, adopting the technologies and building the competencies necessary to capture the full benefit of artificial intelligence when applied to internal processes and customer-facing solutions. The stages – Ad Hoc, Experimental, Systematic, Strategic, Pioneering – reflect key attributes typically present in an organization at that point in their journey. These provide benchmarks for organizations assessing their own level of AI maturity.

Although presented as a set of distinct stages, most organizations and their adoption paths are more complex. An organization may not move through all aspects of each stage on a linear path or at the same time and might be relatively advanced or mature in one aspect of their AI adoption while lagging in another.

For most organizations this maturity model is aspirational. Because artificial intelligence is moving so quickly, most organizations are in the early stages of AI maturity. None earns consistently high scores across the board. The AI Maturity Model paints a picture of what’s possible for organizations that truly embrace AI-driven transformation – and provides an achievable roadmap for getting there.

Here are the five stages of AI Maturity:

1. Ad Hoc

The organization has limited understanding of AI. There may be a few isolated projects, but they’re not part of a coordinated strategy, and their objectives and business cases may be unclear. Usage may be happening at the individual contributor level, with or without leadership’s knowledge. Insufficient guidelines, guardrails and governance may put the organization and its employees at risk.

2. Experimental

The organization has started to recognize the benefits of AI and is actively piloting tools, technologies, or systems for selected quick win or high- potential use cases. Projects are more structured and aligned with specific objectives but are not yet integrated into core workflows. Knowledge and experience are uneven across the team, making education and training a key priority. Leaders put basic policies in place to help protect the business, its data, and its intellectual property.

3. Systematic

The organization has a clear, documented approach for how AI optimizes, accelerates, and innovates its strategy and is actively integrating it into key processes and workflows. AI projects are managed systematically and are accessible through a centralized management function, often in the CTO’s office, with defined goals and clear success metrics. The organization has built up its AI capabilities, including a dedicated team of experts and a more extensive knowledge base on tools, use cases, and best practices. The organization has documented and deployed governance guidelines and responsible AI guardrails.

4. Strategic

The organization uses AI systems to drive operational efficiencies, increase competitive advantage and differentiation, , and shape business strategies. AI is deeply integrated into processes, workflows, and decision-making across the entire organization. A well-established AI team works across the organization; some departments or functions engage in a redesign of roles and team structures. A robust ethical AI framework and formal governance programs are in place; the organization regularly participates in industry conversations about responsible use of AI in its business.

5. Pioneering

The organization is a leader in using AI to drive innovation, shape industry standards, and achieve sustained competitive advantage. AI is used not only to enhance performance but also to create entirely new business models and value propositions. The entire workforce has strong expertise; team roles and org structures are optimized for AI-powered work. The industry recognizes the organization as a standard bearer for ethical AI and responsible use.

A Silent Stage Zero

We’d be remiss if we didn’t acknowledge that there are, in fact, some marketing organizations that aren’t yet using AI, even on an ad hoc basis. For example, you might work for a corporation or nonprofit with a blanket ban on generative AI or a more cautious organization that’s taking a wait-and-see approach. While we didn’t include the status quo as a distinct stage, we do think our model offers a pathway for any organization that has yet to begin their AI journey.

Seven Paths

The journey toward AI Maturity requires leaders to take a big picture, multi-disciplinary approach that transforms their entire enterprise. Based on confidential conversations with more than 100 business leaders, and work with clients, organizations must build competencies and take action across seven key areas to advance through the five stages of the maturity cycle.

These are:

Approach
Technology
Data
Governance
Expertise
Team (within a department or function)
Alignment (across the enterprise)

By mapping these attributes across the five stages, the Maturity Model presents a high-level comprehensive roadmap for the AI-driven transformation of departments and entire organizations.

Approach

The organization’s approach to AI reflects its level of strategic maturity and the extent to which is has laid the groundwork for confident, consistent implementation. The organizations AI strategic approach provides the foundation for the other six paths.

The path to a mature AI approach underscores the evolution from experimental and isolated efforts to strategic and integrated implementations that drive significant digital transformation and business innovation. While AI must be “strategic”, a stand-alone AI strategy separate from the core business strategy is often counterproductive. Instead, AI should be woven into the fabric of the organization’s core strategy and work to achieve the same goals.

Key milestones on the path toward a mature approach include:

Increased Intention: Intentionality is crucial for transforming AI from a novel experiment to a fundamental capability aligned to an organization’s core goals. Intentional initiatives are measured against specific outcomes, and consistently contributes to the organization’s overall success.

Strategic Alignment and Integration: AI is a means to achieve strategic success, not a goal in itself. For more mature organizations, AI becomes more than an add-on. Instead, it is woven into core strategy, embedded into relevant operational systems and workflows, and aligned to key objectives.

Consistency and Clarity: At higher maturity levels, rising strategic focus and operational rigor provide the necessary direction leaders need to choose and use AI systems with confidence, prioritize the right use cases, scale AI initiatives, and replicate successes.

Results Measurement: More mature organizations evaluate the effectiveness of AI applications against metrics that matter. AI’s impact on the business is quantitatively evidenced through improved measures such as operational efficiencies, time savings, improved productivity, better performance, growth, and innovation.

Here’s how an organization’s approach progresses through the five stages of AI Maturity:

Ad Hoc: Initially, the organization lacks a clear direction for AI, with sporadic efforts that aren’t integrated into the broader enterprise strategy. The approach is exploratory, without defined goals or understanding of potential outcomes. Where AI is in use, it is characterized by isolated, exploratory efforts. These are often one-off initiatives with no clear integration into broader corporate strategies or operations.

Experimental: Organizations at the experimental stage begin identifying specific AI opportunities. where AI can provide tangible benefits, guiding experimental efforts towards areas with the potential for impact. At this stage, organizations start conducting pilot projects to explore the potential of AI, often focused on near-term wins and early proofs of concept. Pilots are generally pre-scale, designed to validate the viability and impact of AI applications before wider implementation. Scopes are narrow and tied to specific workflows, functions or within a limited touchpoint or task.

Systematic: By this stage, AI is more systematically integrated into key programs and processes. The organization follows a structured approach for identifying, scoring, and prioritizing AI use cases that have clear goals and metrics, ensuring that efforts contribute directly to strategic objectives. Successful implementations are scaled up, replicated and/or repeated, indicating a more structured and consistent approach to leveraging AI. Corporate and departmental decision-makers commit to a standard suite of core enterprise-ready AI tools, develop AI-enabled workflows, invest in workforce upskilling, and scale repeatable processes and programs.

Strategic: AI is integral to the organization’s overarching strategy, influencing decision-making, and offering new avenues for differentiation. Organizations at this stage leverage AI not just as a toolset but as a strategic asset driving competitive advantage. AI becomes a fundamental part of core operational processes and workflows. It plays a critical role in decision-making, significantly influencing strategies and outcomes. Strategic AI implementation opens the door to deeper audience insights, predictive analytics, well-orchestrated customer journeys, creative innovation, and new methods for production, personalization, and more.

Pioneering: At the pinnacle of AI maturity, organizations are at the forefront of innovation, using AI to drive sustained competitive advantages. Leaders aren’t just adapting to external industry standards but actively shaping them through their actions. At the pioneering stage, the focus of the approach shifts from merely enhancing productivity and performance to using AI to create entirely new business models and value propositions. The use of AI at this stage is innovative, reshaping the competitive landscape, and setting new industry standards.

Technology

While AI maturity considers more than just technology itself, an organization’s approach to identifying, implementing, and integrating specific artificial intelligence models, systems, or solutions is foundational for its overall AI maturity. A set of key patterns emerge among typical organizations that successfully achieve a more mature approach to AI technologies:

AI Integration and Interoperability: A focus on AI integration and interoperability with existing marketing technologies becomes crucial as organizations move toward more advanced stages, ensuring seamless workflows and maximizing the value of AI investments.
Custom AI Solutions: At higher maturity levels, especially at the pioneering stage, organizations may invest in developing custom AI solutions tailored to their specific needs and objectives.
Technology Partnerships: As organizations progress, they may form strategic partnerships with AI technology providers, benefiting from advanced insights and capabilities.

Now, let’s consider the key technology benchmarks at each of the five stages in their AI Maturity pathway.

Ad Hoc: Organizations are at the very beginning of exploring AI technologies, with basic or no use characterized by ad hoc, manual processes and a lack of systematic technology adoption. Generative AI tools in particular may come into the organization as “Shadow AI” – unsanctioned, often consumer-grade technology that corporate employees use in ways unknown to or hidden from a departmental leadership or the organization’s central IT and risk management functions.

Experimental: There’s a limited and experimental use of AI tools focused on specific, often isolated, projects. The organization is testing the waters with available AI technologies, without a full commitment. While Shadow AI may still be a factor, organizations at the experimental stage are more likely to have selected a limited number of off-the-shelf AI point solutions (e.g., an AI writing assistant, an AI image generator), or may be exploring AI features offered in an existing enterprise technology system such as Microsoft Office 365, Salesforce.com, or the Adobe applications. The use of technologies may be limited to a subset of users and not yet operationalized across the entire organization.

Systematic: AI technologies are more consistently integrated into programs, processes, and operations. Decision-makers have established a clear build-vs-buy approach to ensure their team has access to the right tools for each job. The organization uses established, enterprise-ready AI tools systematically across various functions, such as recruitment, marketing, or analytics. At this stage, AI programs shift away from the deployment of standalone tools, toward the implementation of more integrated, interoperable and repeatable systems.

Strategic: The organization adopts advanced AI technologies that drive strategic initiatives and innovation. AI is a core component of all key workflows as the organization routinely leverages cutting-edge tools for predictive analytics, optimization, automation, and personalization. By the time an organization reaches the strategic stage of AI technology maturity, leadership is focused on integration and interoperability. Leaders incorporate AI across their entire technology stack, the larger revenue organization, or the entire enterprise, depending on the use case.

Pioneering: A leading organization innovates with AI technologies, potentially even raising the standard for how its industry thinks about and implements AI to support strategy, achieve objectives, and drive performance. At this stage, it may be likely that the organization develops and deploys their own proprietary AI models, applications, or vertically integrated solutuons.

Data

Organizations have heard the data drumbeat for more than a decade. However, the rise of AI has redoubled the need to focus on unifying customer and company data. Clean, properly tagged data, data-cleaning processes, and data frameworks are gold, fueling vital analytics insights, better generative AI outputs, and the potential to develop unique company or nonprofit-specific AI models and implementations. As the adage goes, “Garbage in, garbage out.”

Most executives understand the importance of aggregating data sources across digital platforms but need help unifying this information. A lack of comprehensive data governance policy combined with advancements in AI technologies and digital platforms has exacerbated the problems created by disaggregated data.

For example, in marketing the regulated movement away from third-party cookies has made unifying customer data even more complicated despite readily accessible API connections. In this department, maturity means moving through the steps that empower unified customer data platforms (CDPs), complete data-centric customer journeys, and mineable data lakes that can fuel new AI models.

Data-mature organizations approach their proprietary information as a valuable asset. Key patterns of data maturity include:

Strategic Value: Data takes an increased importance for companies and organizations that implement AI. In fact, data becomes the currency of AI. The more proprietary the data is — whether it is customer data, performance data or unstructured content — the higher the value of the data asset is. Some organizations even productize their data.

Protect Data Assets: Seeing their data sets as valuable assets, line-of-business leaders work hand-in-hand with their technology peers (the CTO, CIO, or their departments) to house and protect their AI systems, information, and content.
Process Discipline: It’s one thing to create processes, it’s another to ensure the processes are enforced, can evolve, and maintain value for the larger mission. Mature organizations enforce and maintain their data handling processes in a manner that matches the value they bestow upon it.

Here’s a look at how organizational data capabilities evolve through each stage of the AI Maturity Journey:

Ad Hoc: When beginning their data journey, most organizations have enterprise database systems like ERP and CRMs to manage information, but data may exist in silos with disparate and disaggregated data sources. While APIs may connect external digital platforms, internal dashboards lack clarity about key operations like logistics, supply chain, customer journeys, and recruitment pipelines. In general, data hygiene and clarity are afterthoughts. For these reasons, early AI initiatives tend to be limited to off-the-shelf applications that run on undifferentiated foundation models.

Experimental: Understanding the importance of a unified perspective and the value of proprietary data for fine-tuning models or training AI systems, executives make a concerted effort to unify their data into an intentional architecture. API integration allows enterprises to take their data into unify a single source-of-truth dashboard. Decision-makers recognize that content is unstructured data in its own right and use a rudimentary taxonomy to unify unstructured data in a content library.

Systematic: Given the importance of unstructured data – from internal content to client conversations to marketing and recruitment content — the enterprise puts governance processes in place to ensure clean data input, tagging, and handling processes. A unified data architecture provides a comprehensive home for all first-party information and the governance processes for integrating second and third-party data, allowing for complete digital transformation, AI functionality, and automated analytics. For example, the IT department keeps all structured and unstructured data housed in a secure data lakehouse with strong security protocols to manage access and use of resources.

Strategic: At this stage, the organization turns its data resources into a powerful tool to accelerate its activities, including full orchestration and optimization of a broad range of functions. LLM indexing allows internal users to use chatbots to access company information and content to provide company and customer–specific insights and outputs. AI-powered analytics improve decision-making and can even identify hidden patterns in data that deliver unforeseen business insights, identify unique efficiencies and opportunities, optimize digital experiences, and spot new product and service opportunities.

Pioneering: In the final phase, strategic executives leverage their proprietary data to maximize the benefits of AI across all programs and processes. Sophisticated scoring and predictive analytics allow leaders to activate even more precise, personalized customer journeys, optimize supply chains and de-risk business plans before they go to market. Personalization frameworks and retrieval augmented generation (RAG) databases empower businesses to leverage generative AI and create content that meets brand-specific micro-segmentation and one-on-one communication needs, all within strategic guidance. AI-assisted insights inform product/service development in a constant interactive process to strengthen offerings, fueling the enterprise to exceed competitor offerings.

Governance

The governance path addresses the critical role of structured oversight, ethical considerations, and stakeholder engagement in ensuring that AI technologies are used responsibly and effectively across all stages of maturity. The governance maturity path is marked by several key milestones:

Increasing Stakeholder Engagement: As governance matures, there’s an increase in engagement with external stakeholders, including regulators, customers, and partners, to ensure transparency and accountability in AI use.
Continuous Review and Adaptation: Governance structures and policies are continuously reviewed and adapted to keep pace with technological advancements and regulatory changes, ensuring ongoing relevance and effectiveness.
Integration with Broader Corporate Governance: AI governance becomes integrated with broader corporate governance frameworks, reflecting its strategic importance to the organization.

Here’s how AI governance evolves across each stage of the AI Maturity Journey:

Ad Hoc: Initially, there’s a lack of formal governance structures or policies related to AI, leading to uncoordinated efforts and potential corporate, customer, reputational, and regulatory risks. At this stage, decision-makers may lack a full awareness and understanding of key risks inherent in certain AI systems and/or the potential harms that may arise from using these systems. There may even be a sense of pessimism about the importance of responsible AI practices for their use cases.

Experimental: As organizations start experimenting with AI, basic policies are established, and leadership begins to take an active role in overseeing AI initiatives, possibly through the formation of an AI advisory council that includes a diverse group of internal stakeholders and external subject matter experts. CognitivePath strongly recommends convening AI advisory councils at this stage, if not sooner.

Systematic: A formal governance program provides effective oversight of AI projects. This includes clear guidelines, standards, and processes for ethical AI use, data management, and compliance. Organizations may implement an AI ‘control center’ platform for proactive management of AI governance for specific departments or across the entire enterprise.

Strategic: Organizations deploy a robust ethical AI framework that guides all initiatives, ensuring compliance with legal standards, responsible practices, and ethical considerations. This framework supports strategic decision-making and risk management in AI applications. A fully operational AI control center streamlines usage and adoption analytics, flags risks, facilitates remediation, supports ongoing audits, ensures regulatory compliance, and allows for consistent vendor risk assessments.

Pioneering: At the pioneering stage, the organization is recognized as a leader in ethical AI and responsible use, setting standards and best practices that others in the industry look to emulate. While not every leader aims to be a standard bearer for the entire profession, those that do will have an opportunity to champion AI that meets the highest levels of corporate responsibility, brand safety, and consumer centricity.

Expertise

Beyond the more technical aspects of AI maturity, leaders need to invest in upskilling and reskilling their workforce. The key to advancing from one stage of AI expertise to the next lies in continuously expanding knowledge and skills across the entire organization, fostering a culture that values learning and innovation, and aligning initiatives with strategic business goals. This progression involves not only technical upskilling but also developing learning-oriented structures and processes that support and enhance AI capabilities.

It’s here that alignment with talent management organizations is absolutely essential. There’s an inherent need for the human and cultural change necessary to make AI maturity a reality for organizations.

The organization with a more mature level of AI expertise focuses on:

Talent Development and Acquisition: There’s a growing emphasis on developing internal talent and acquiring external expertise to fill AI-specific roles, ensuring the organization has the skills and knowledge needed to leverage AI effectively.
Upskilling: Whether purchased from third parties or developed internally as part of a formal program, AI education and training facilitate upskilling for all players. Maintaining expertise at the speed of AI requires participation in industry conferences and events. Some organizations attend to mine market intelligence, and the best share their expertise with the larger sector.
Reskilling: As AI takes on more routine tasks, employees can reinvest their time and resources toward higher-value work that remains uniquely human. A well-coordinated change management program can help team members embrace evolution, encouraging a normal evolution of skills. Reskilling initiatives may also focus on “power skills” that are not easily automated, such as critical thinking, interpersonal communication, relationship management, creative thinking, emotional intelligence, and adaptability.

Here’s how an organization evolves its expertise through each stage of the AI Maturity Model:

Ad Hoc: The organization has limited understanding of AI capabilities and applications. At this initial stage, there might be isolated pockets of interest or experimentation, but knowledge and expertise in AI are generally low across the organization.

Experimental: Organizations begin to apply baseline knowledge unevenly, with some departments or teams experimenting with AI more actively than others. This stage involves prioritized use cases to explore AI’s potential in individual departments’ work, guided by selected champions who start to build expertise, sometimes through independent learning, sometimes through trial and error.

Systematic: As organizations move to a systematic approach to building expertise, there’s a working knowledge of AI tools, use cases, and best practices. This knowledge is aligned with the core strategy, with clear goals and metrics for measuring AI’s impact. Teams start to integrate AI into key programs and processes, supported by dedicated experts or a center of excellence.

Strategic: Organizations at the strategic level possess strong AI knowledge, with ongoing efforts in upskilling and reskilling the workforce to keep up with AI advancements. This organization may formalize its knowledge and approach with training and certification programs. AI expertise is now driving competitive advantage and differentiation, contributing to the broader business strategy.

Pioneering: Pioneering organizations have established a culture of learning and knowledge sharing that sets an example for others to follow. They have extensive expertise throughout the team, not just in technical AI applications but also in leveraging AI for strategic innovation. These organizations drive innovation and shape industry standards, recognized as leaders in ethical AI and responsible use.

Team

It would be safe to say the enterprise of the future may bear little resemblance to today’s organization. Ultimately, every job, policy, workflow, and measure of performance may be optimized around AI. While this won’t happen overnight, more mature AI-enabled organizations are already embracing new forms of expertise, previously unimaginable roles, innovative team structures, innovative ways of working, and new models.

This path reflects the department, functional or team-level transformation required to fully integrate and capitalize on AI technologies. As organizations mature, they shift from initial ad hoc efforts to a strategic, structured approach that maximizes the potential of AI for competitive advantage and innovation. Key factors shaping AI maturity include:

AI Becomes Part of Every Team Member’s Job: As AI tools become more fully embedded into the way a specific team, department, or function operates, AI adoption and initiatives shift from specific individuals to everyone.
Agile Team Design: Teams become more agile and flexible in their design, allowing them to quickly embrace new AI technologies and methodologies.
Cross-Functional Collaboration Increases: The organization’s structure becomes more conducive to cross-functional collaboration, facilitating the integration of AI initiatives across different departments, functions, and teams.

Let’s consider the key benchmarks for how a typical team inside an organization moves through the five stages in their AI Maturity Pathway.

Ad Hoc: AI adoption is driven by individual end-users or small groups might drive AI initiatives without formal leadership support or organizational structure. Department leaders with teams at this stage might sense “grassroots” momentum around the adoption of AI, and even recognize that certain individual contributors are working with AI but may not have an informed stance on larger scale implications.

Experimental: As teams recognize the potential of AI, selected champions or early adopters start leading AI projects without a formalized team structure but with organizational visibility. Here, the department leader begins making more deliberate choices about who on their team should have access to AI, for which tasks, and is looking for insights on AI’s impact on individual performance and productivity.

Systematic: Teams start formalizing their approach to AI with dedicated experts or establishing a department-specific Center of Excellence (CoE) or task force to centralize knowledge, best practices, and governance, indicating a more mature approach to AI. At (or leading into) this stage of maturity, an organization may choose to hire or appoint a head of AI to catalyze the strategic use of these technologies. The department leader will align their efforts and actions to the guidance issued by that new AI leader. Roles impacted by (or established for) AI result in updated job descriptions and job performance metrics.

Strategic: At this stage, there’s often a well-established AI team responsible for strategic AI initiatives, partnered with their peers across the enterprise. This is also when the organization more fully redesigns functions, roles, and team structures to better leverage AI capabilities.

Pioneering: In the final stage, AI is embedded in departmental processes, workflows, and decision-making. The team structure is fully optimized for AI-powered work. Roles and processes across the team intentionally leverage AI technologies for maximum impact. Roles that may have been necessary at earlier stages of maturity (from head of AI to dedicated AI experts) are absorbed into the existing teams and the larger organization as they are no longer required. Simply put, AI is part of the company culture.

Alignment

While evolving and advancing the marketing team itself is vital, it’s not sufficient for AI maturity. Simply put, marketing is not an island and marketing leaders need to build bridges to other parts of the enterprise to reach the highest levels of AI maturity. As noted earlier in this report, senior marketing leaders – notably, many Chief Marketing Officers – are already highly collaborative, working closely with peers across the organization to align marketing goals, strategies, and initiatives with overarching business strategies and priorities.

The same is true here. The alignment path highlights the evolution from initial, reactive engagement to strategic, influential leadership in AI initiatives, emphasizing the importance of cross-functional collaboration, communication, and strategic alignment as key drivers of AI maturity. To this end, the AI-savvy marketing leader champions organizational alignment, collaboration, and cooperation around AI, including:

Cross-Functional Teams: As the organization progresses, the marketing leader may champion the formation of cross-functional teams across the enterprise dedicated to AI, fostering a culture of collaboration and innovation.
Executive Sponsorship: Gaining C-suite sponsorship and board buy-in for AI initiatives becomes crucial, especially in the strategic and pioneering stages, to secure the necessary resources, foster cooperation across departments, and drive organizational change.
Communication Skills: Enhancing communication skills is critical for the marketing leader at all stages so they can articulate the value of AI initiatives to diverse stakeholders effectively.
Change Management: The marketing leader plays a key role in managing change as AI is integrated into the organization, ensuring teams are prepared for new ways of working and that there is alignment on the vision for AI’s role in the company.

Integrating these activities and competencies help ensure that alignment and collaboration are not only maintained but also deepened as organizations advance through the stages of AI maturity, ultimately leading to more effective and strategic use of AI across the enterprise. Here’s how the role of the marketing leader evolves to drive organizational alignment across each stage of the Marketing AI Maturity Journey:

Ad Hoc: At this stage, collaboration is minimal and often reactive. Key marketing decision-makers may engage with peers in IT, data, and legal on an as-needed basis. These conversations address immediate issues or opportunities pertaining to AI without a clear vision for how marketing-specific systems relate to the broader enterprise AI strategy. This may be exacerbated in organizations with a siloed structure or culture and may inadvertently create friction between marketing and other key corporate functions.

Experimental: The marketing leader starts to build more structured, productive, and proactive relationships with peers in IT, data, legal, and the rest of the C-suite. Collaboration is likely project-based, focusing on specific AI experiments or pilots, with efforts to align on goals and objectives. Marketing’s overall strategic approach may be under development and may not be fully aligned with enterprise-wide AI strategy, particularly in organizations where data science and AI are already priorities or operationalized.

Systematic: Collaboration between marketing leadership and other enterprise stakeholders becomes more integrated and proactive. The marketing decision-maker establishes or participates in regular cross-functional meetings and communications to ensure alignment with broader organizational goals and wider strategies for adopting and scaling AI. In this stage, a more strategic approach emerges to leverage common AI frameworks and systems across functions.

Strategic: Marketing leaders form strategic partnerships across the organization, working closely with IT, data, legal, and the C-suite to align AI initiatives with the company’s strategic objectives. They focus on leveraging AI for competitive advantage and ensuring organizational buy-in. While the marketing decision-maker remains focused on how AI supports marketing objectives specifically, they’re more likely to have a “seat at the table” for wider enterprise AI discussions. By this stage, the organization has a single “north star” for AI – a shared vision, strategy, objectives, and plan – and marketing leadership is either fully bought into that north star vision or (even better) has taken an active role in establishing it.

Pioneering: At this stage, senior marketing decision-makers help drive AI strategy across the organization. Their influence and authority are felt across the enterprise as they effectively partner with all departments, the C-suite, and the board of directors, influencing the organization’s overall direction and leveraging AI for industry leadership.

Lead the Way

This AI Maturity Model provides leaders with a comprehensive framework for understanding their own organization’s current state of AI adoption and charting a course toward a more AI-enabled future. The stages are distinct from one another, reflecting the progression of an organization’s understanding, capabilities, strategy, processes, people, culture, leadership, and technological sophistication as they move through the stages from Ad Hoc to Pioneering.

This maturity model aims to inspire leaders. It provides guideposts and encourages action, without being overly prescriptive. The model offers a working set of “promising practices” that can be customized and built upon to suit the unique challenges and opportunities, and work within the unique structure and circumstances of any organization.

It’s your time to lead the way. Use this maturity model to:

Benchmark AI readiness and assess progress
Create alignment among board members, in the C-suite, within a department, and/or across the enterprise
Establish a strategic “North Star” for AI that optimizes, accelerates, innovates, and transforms the organization
Develop, implement, and measure an AI strategy and roadmap
Create the future of the organization and thrive in an increasingly AI-powered world

Methodology

The frameworks and recommendations in this report are informed by desktop research, one-on-one executive interviews, and our experience working directly with clients. We’ve incorporated inputs from confidential conversations more than 100 executive-level decision-makers at mid- to large enterprises across a range of industries including: professional services, associations, consumer goods, education, financial services, healthcare, retail, technology, and others.

Disclaimer

ALTHOUGH THE INFORMATION AND DATA USED IN THIS REPORT HAVE BEEN PRODUCED AND PROCESSED FROM SOURCES BELIEVED TO BE RELIABLE, NO WARRANTY EXPRESSED OR IMPLIED IS MADE REGARDING THE COMPLETENESS, ACCURACY, ADEQUACY, OR USE OF THE INFORMATION. THE AUTHORS AND CONTRIBUTORS OF THE INFORMATION AND DATA SHALL HAVE NO LIABILITY FOR ERRORS OR OMISSIONS CONTAINED HEREIN OR FOR INTERPRETATIONS THEREOF. REFERENCE HEREIN TO ANY SPECIFIC PRODUCT OR VENDOR BY TRADE NAME, TRADEMARK, OR OTHERWISE DOES NOT CONSTITUTE OR IMPLY ITS ENDORSEMENT, RECOMMENDATION, OR FAVORING BY THE AUTHORS OR CONTRIBUTORS AND SHALL NOT BE USED FOR ADVERTISING OR PRODUCT ENDORSEMENT PURPOSES. THE OPINIONS EXPRESSED HEREIN ARE SUBJECT TO CHANGE WITHOUT NOTICE.