Why is it that some people and companies so quickly enhance their productivity and speed with new A.I. tools, while others do not even know there is more than just ChatGPT?
It is because not everyone is at the same maturity level when it comes to using A.I.
There are in fact 7 levels of increasing maturity for how individuals and companies use A.I. in their work.
For team leaders, company directors, project managers, innovators and senior leadership, understanding the stages of AI maturity is essential for harnessing its power to drive innovation and efficiency.
This post outlines a roadmap that takes you from a baseline state of no A.I. adoption to a fully autonomous A.I. workforce.
Here are the 7 key levels of AI usage maturity:
- Level 0: No A.I.: AI is not used at all, and most processes are still performed manually (whether digitally or physically)
- Level 1: Unaware A.I.: AI is embedded in everyday tools without strategic intent.
- Level 2: Basic A.I.: Organizations experiment with generative AI for simple, high-impact tasks.
- Level 3: In-App A.I.: Built-in AI features within existing software enhance daily workflows.
- Level 4: Advanced Prompt Engineering: Teams craft detailed prompts to tailor AI outputs for precision.
- Level 5: Workflow Automation: AI agents and low/no-code platforms automate routine manual tasks.
- Level 6: A.I.-driven Innovation: AI is used to develop new products, services, or business models.
- Level 7: Autonomous A.I. Workforce: AI systems operate independently, handling tasks traditionally done by humans.
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We will now explore each level in more detail, examining what it entails, the steps needed to progress, and real-world examples to illustrate the journey.
Level 0: No A.I.
At the most basic level, the organization does not use any AI-enabled technology. Traditional processes dominate, and decision-making relies on manual methods. While this approach may have served well in the past, it leaves significant potential for improvement on the table. It is also where you see some of the most frustrated employees, who know there is a better way to do work but feel like they are stuck in the status quo.
What It Entails
Organizations at this stage rely on established workflows without exploring the benefits of digital transformation. There is little to no awareness of how AI can improve efficiency, reduce errors, or provide strategic insights.
Examples
- Example: A traditional law firm managing all case files manually without digital document processing.
- Real-World Example: Many regional manufacturing companies continue to rely on paper-based systems and manual quality checks, missing opportunities for automation and efficiency gains.
Level 1: Unaware A.I.
At this stage, AI is present in everyday tools and devices without the people or company knowing that it is even using AI. They think there are just standard features. Therefore, the benefits are incidental rather than a result of strategic planning.
What It Entails
Unaware A.I. means that while there is no direct investment in AI technology, employees are exposed to AI capabilities through the tools they use daily. This stage often serves as the initial exposure for many organizations, where the advantages of AI become apparent through consumer-grade applications. Often, the users do not even know there is AI happening in the background, and these uses are not described as AI, so the companies and employees do not talk about AI much.
What It Takes to Get There
- Adoption of consumer-grade technology that incorporates AI
- Exposure to AI through everyday interactions
- No significant investment or dedicated resources for AI projects
Examples
- Phone voice dictation that converts spoken words into text
- virtual assistants such as Siri or Google Assistant integrated into smartphones
- Translation application
- Automatic image / photo enhancement
Level 2: Basic A.I.
At Level 2, organizations begin to experiment with AI tools for straightforward tasks. Often, this involves individuals starting to try out new AI systems themselves, only later rolling out AI systems company-wide once enough people have tested them. The focus is on low-risk, high-impact applications such as generative AI for writing text or content creation. This is the stage where AI is treated as a helpful tool rather than a strategic asset.
What It Entails
People often sign up for a free account of AI tools such as ChatGPT and begin experimenting with it. Companies use off-the-shelf AI applications to handle routine tasks. The adoption is typically ad hoc, and the organization is in the early stages of assessing return on investment. The aim is to save time on repetitive tasks while testing the benefits of AI.
What It Takes to Get There
Examples
- ChatGPT is used to draft standard email responses, saving time on routine communication
- MidJourney generates creative images for internal presentations or social media content
- Real-World Example: Many marketing agencies and small businesses have incorporated ChatGPT to help draft social media posts or customer emails, leading to improved communication consistency and time savings.
Level 3: In-App A.I.
As organizations advance, they begin to leverage AI features built into the software they already use. AI becomes a seamless part of daily operations rather than an external tool accessed sporadically. This integration enhances productivity and decision-making.
What It Entails
In-App A.I. means that companies take advantage of software that embeds AI capabilities directly into everyday applications. This integration improves efficiency and reduces the learning curve because employees do not need to switch between multiple tools. It also improves data security and consistent use of tools, since often enterprise-level AI tools have options to only use uploaded data within the organisation, reducing the risk of it being spread outside agreed locations or used to train the AI models themselves.
What It Takes to Get There
- Understanding the tasks where AI could enhance productivity
- Evaluating and selecting the right software with built-in AI features, which may be systems the company already uses
- Providing training to ensure employees fully utilize these capabilities
- Adjusting workflow processes to incorporate AI-generated insights
Examples
- Automatic meeting transcription and real-time captioning in Microsoft Teams, or using additonal tools like Otter or Zoom
- AI-driven writing assistance in tools such as Microsoft Word, Google Docs or Grammarly
- task prioritization suggestions in project management platforms like Asana, Wrike, Monday.com or Trello
Level 4: Advanced Prompt Engineering
At this level, organizations take a more active role in shaping the output of AI systems. Advanced prompt engineering involves crafting detailed and strategic prompts that lead to high-quality, relevant outputs. Importantly, users also know the value of continuing a conversation with the AI beyond the initial prompt, to provide further context and refinement of the output, or even produce additional more value-adding results. This level reflects a deeper understanding of how AI works and how to best utilize it.
What It Entails
Organizations and individuals move beyond simple use cases and develop expertise in guiding AI systems. By learning the strengths, weaknesses, and limitations of various AI tools, teams can create detailed prompts that produce outputs tailored to specific needs. This is particularly important in industries where precision and context is critical, and goes beyond the data which is already trained within current AI systems.
What It Takes to Get There
- Investment in training and development on advanced AI techniques
- Creation of best practice guidelines for prompt formulation
- Understand which information, data and context is permissible to be shared with an AI to improve their output (this is often related to knowing which AI systems have enterprise agreements with the company already, or which have risks of data being shared externally)
- Ongoing experimentation with few-shot learning techniques and iterative prompt refinement
Examples
- using few-shot prompting techniques to train an AI on industry-specific terminology
- crafting detailed prompts to generate precise market summaries or business reports
- uploading additional documents or text context to allow the system to understand the nuance of what you are asking for
- iteratively refining prompts in a conversation with the AI, based on feedback and output of what the AI previously produced
- using “deep research” features within search-based AI systems, by understanding exactly how to formulate the prompt to get what you are looking for
- Real-World Example: Financial institutions have embraced advanced prompt engineering with tools like ChatGPT-4 to produce detailed market analyses that align with the specific analytical needs and language of the finance industry
Level 5: Workflow Automation
At Level 5, the focus shifts to automating routine and repetitive tasks. Organizations integrate AI agents and low/no-code platforms into their operations, significantly enhancing efficiency and reducing manual workload. The aim is to let AI handle tasks that do not require human judgment, yet go beyond simple automation processes (such as “send a status update email when someone sets a task a complete”) since they need an AI to process data at one of the steps and provide a new output.
What It Entails
Workflow automation involves the use of AI to streamline digital processes, such as customer service, lead generation, and project management. This goes beyond other ways of optimising digital workflows, like robotic process automation (RPA), which simply automate one step in a process being completed in a set sequence the same way every time. RPA can be thought of like a pre-programmed algorithm, where the same inputs will result in a known output. With AI, at some point the data in the process must be manipulated using an AI system, making the output more random and nuanced to the additional context available.
This stage not only reduces human error but also frees up staff to focus on higher-value activities that require critical thinking and creativity. One key consideration here is to what degree humans still need to be involved in the work, to check the results of what the AI is producing before it is finally released to the end-user or public.
What It Takes to Get There
- Identifying repetitive tasks that are prime candidates for automation
- Implementing AI agents or low/no-code platforms to manage these tasks
- Establishing continuous monitoring to ensure that the automated processes meet performance standards
Examples
- deploying AI-powered chatbots to handle initial customer support inquiries
- using automated tools for lead generation and follow-up communications
- having an AI automatically analyse form input and make a recommended decision to a human (such as for a mortgage approval)
- implementing AI systems that synthesize project reports and update dashboards automatically
- Real-World Example:
Zendesk utilizes AI chatbots to handle common customer queries, allowing human agents to focus on more complex issues. Similarly, HubSpot offers automation tools that streamline the lead management process from initial contact to follow-up.
Level 6: A.I.-driven Innovation
At this stage, AI becomes a catalyst for innovation rather than just a tool for efficiency. Organizations leverage AI to develop new products, services, and business models. The technology is used to reimagine traditional processes and create competitive advantages in the market.
What It Entails
A.I.-driven innovation involves using AI not only for optimization and incremental improvements to the existing business, but also for radical transformational innovation. The focus shifts to exploring new possibilities that were previously unimaginable, driven by data insights and rapid prototyping. This stage requires a willingness to invest in research and development and to embrace change.
This is likely to be the level where a lot of companies get excited by all of the potential applications of A.I., but struggle to effectively evaluate and prioritise their efforts into a coherent portfolio of projects to execute on innovation. In my experience, innovation usually fails at the handover from the team developing the ideas, to the next team which should focus on execution.
If you want an expert to help you consider the opportunities AI is presenting you to innovate, and then effectively planning these into projects along with the right frameworks to execute them, this is a speciality of mine. Contact me, and let us see how I could help your company innovate with and execute your A.I. projects
What It Takes to Get There
Examples
- Using AI simulations to rapidly prototype new product ideas and assess market potential
- leveraging predictive analytics and deep market research to identify emerging trends and design innovative services
- integrating AI into backend systems to automate decision-making and drive operational excellence
- develop entirely new product categories which use AI systems as their backbone
- Real-World Examples:
- Jasper developed a new tool to create corporate social media content built on generative AI systems
- Harvey uses AI to speed up lawyers reviewing legal documents
- Almost all Generative AI Chat clients allow anyone to develop simple code and applications using natural language, especially Anthropic’s Claude.ai
- Replit, Cursor and other AI coding tools allow people to develop entire applications using a chat interface, without needing to know how to code
- Medical Imaging AI is replacing the need for Doctors and Radiologists to manually review Xrays, MRI scans and other medical images
- Khan Academy is using AI tools to develop tailored teaching assistants for individual students
Level 7: Autonomous A.I. Workforce
At Level 7, the final stage represents the pinnacle of AI maturity. Organizations deploy an autonomous A.I. workforce capable of handling tasks traditionally performed by humans. These AI systems are currently sometimes referred to as AI Agents or AI Assistants, Although human oversight may still be present for critical decisions, routine functions are managed independently by AI systems.
In the near future, it may even be that after enough training, in some companies these AI Agents and Assistants will no longer require human oversight to complete their tasks, with them completing tasks autonomously without requiring approval from a human.
These AI workforce systems will likely also enable individual entrepreneurs to build companies with larger revenue than would ever be possible without human employees. In a recent interview, Sam Altman (Leader at OpenAI) suggested that we would soon see the world’s first “solo unicorn”, a company valued at a billion dollars with only one employee. The rest of the workforce would be a large team of specialised AI Agents doing the majority of the work.
What It Entails
An autonomous A.I. workforce signifies that AI systems are highly sophisticated and self-sufficient. They can learn from their environment, adapt to new challenges, and operate with minimal human intervention. This transformation redefines the organization’s operational landscape, allowing human employees to focus on strategic decision-making and creative endeavors.
There is already a selection of AI Agent development platforms like n8n and Make.com, which can create AI Agents to automatically handle tasks of intermediary complexity. These tools will only continue to improve as the underlying LLMs evolve, and along with that will come new tools to create AI Agents with even more advanced capabilities.
What It Takes to Get There
- Significant investment in advanced AI research and development if developing own AI Agent systems
- Investment in understanding how low/no-code AI Agent creation tools can create simple AI Agents
- Implementation of robust data governance and cybersecurity measures
- A cultural shift toward trusting AI systems to manage core operations
- Continuous feedback and adaptive learning to ensure that AI models remain up to date and effective
- Setup of dedicated sandbox environment / company where AI Agent-based business models can be tested
Examples
- deploying AI-managed systems for content production that generate and schedule social media posts automatically
- utilizing fully autonomous AI agents for voice-based sales outreach and customer engagement
- implementing decision-making systems that optimize supply chain management with little to no human intervention
- Real-World Example:
Companies such as IBM are experimenting with autonomous AI agents in various sectors, including customer service and IT operations. In some banking and retail settings, AI-driven systems are already handling routine tasks like transaction processing and customer inquiries, demonstrating the early stages of a fully autonomous workforce.
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Charting the Journey from Awareness to Autonomy
Advancing through the AI maturity spectrum is a gradual and iterative process.
For organizations that start at Level 0 or Level 1, the journey begins with understanding the potential of existing AI tools and experimenting with low-risk applications.
As companies move to higher levels, the focus shifts from basic usage to integrating AI into the strategic core operations of the business.
Key Steps for Progression
- Awareness and Education: Build a foundational understanding of AI and its benefits through workshops, seminars, and pilot projects. Contact me if you would like me to help your teams
- Strategic Investment: Allocate resources to explore and integrate AI into core processes. This involves both financial commitment and a readiness to change existing workflows. Vitally, a company and its leadership should understand what aspects of their strategy these innovation projects should help address.
- Cross-Department Collaboration: Successful AI adoption requires input from IT, operations, marketing, leadership and several other internal departments. Cross-functional teams can drive innovative applications and ensure a smooth transition.
- Mindset: It is tempting to get excited by all the opportunities which the increasing levels provide. However, risk also needs to be considered. A mindset which is open to experimentation, with the expectation that many will fail, is key to finding the right implementations which bring the largest impact.
- Feedback and Iteration: At every stage, it is crucial to test and refine AI applications. Regular feedback helps fine-tune implementations and prepares the organization for further advancements.
- Clarity in expectations and project management frameworks: Most projects fail, but they do not need to. By setting expectations early, there will be clarity on how success can be validated, and this will help decide on the right frameworks to achieve this change.
- A step by step approach: Companies which try to jump directly from lower levels to the highest levels will likely meet the highest levels of internal resistance to change (often called corporate antibodies). Instead, it is possible to slow down the progress into more intermediary steps to allow people to adjust and get used to the changes, then adding additional functionality over the following quarters.
Each stage of maturity demands not only the right technology but also a willingness to adapt. Leaders must balance the potential gains of AI with careful risk management and ethical considerations. The journey from awareness to autonomy is about creating a partnership between human talent and AI capabilities that drives smarter decision-making and operational excellence.
The Benefits of Advancing Through the Maturity Spectrum
Organizations that progress through these stages can expect to see tangible benefits along the way. At the lower levels, the focus is on efficiency gains and time savings. As maturity increases, the potential for innovation and competitive advantage grows exponentially.
Efficiency and Productivity Gains
- Reducing manual tasks through automation frees up valuable time for employees
- Integrated AI tools help eliminate human error in routine processes, especially those involving data manipulation
- Rapid content creation and data analysis lead to faster decision-making
Innovative Breakthroughs
- AI-driven insights can reveal new market opportunities and trends
- Advanced prompt engineering and automation lead to more precise outcomes
- A culture of innovation, supported by AI, enables new business models and efficiencies
Competitive Advantage
- Organizations that effectively use AI can adapt more quickly to market changes
- Data-driven decision-making creates more resilient and agile operations
- An autonomous AI workforce can reduce operational costs and increase scalability
Preparing Your Organization for the Future
Embracing AI is not about replacing human ingenuity. It is about augmenting it and creating a synergy that allows both to excel. As your organization progresses through these levels, it is essential to ensure that change management, ethical considerations, and continuous learning are at the forefront of your strategy.
Steps to Get Started
- Conduct an AI Readiness Assessment: Evaluate your current processes, technologies, and culture to determine where you stand on the AI maturity spectrum.
- Set Clear Goals: Define what you want to achieve with AI, whether it is improved efficiency, innovative products, or an autonomous workforce.
- Invest in Talent: Equip your team with the skills needed to leverage AI effectively. This may involve training programs, hiring specialists, or collaborating with technology partners.
- Implement Pilot Projects: Start with small, manageable projects that allow you to test AI applications without significant risk. Use these projects as learning experiences to refine your approach.
- Measure Progress: Establish metrics to track the impact of AI on your organization. Regularly assess improvements in productivity, cost savings, and innovation.
To get from levels 0 – 2, there are a multitude of excellent free resources online. The best by far are just creating a free account with these tools and starting to experiment, and then using YouTube to find more advanced ways to use these tools.
To get above level 3, it often helps to bring in an external perspective to help plan and guide the opportunities. This is where I focus.
I focus on helping clients move between levels 3-6. If that sounds like somewhere I could help you add value, then contact me and let us chat.
Embracing Change
Transitioning through the maturity spectrum requires a cultural shift. Leaders must communicate the benefits of AI and foster an environment that embraces experimentation and learning. Trust in technology is built gradually, and early wins can help build momentum for larger initiatives.
Conclusion
The AI maturity spectrum is more than a theoretical model. It is a practical roadmap that guides organizations from the early stages of AI exposure to a future where an autonomous A.I. workforce drives operations.
Each level, from having No A.I. to reaching full autonomy, presents unique challenges and opportunities.
For team leaders, company directors, project managers, innovators and senior leadership, the journey toward full AI integration demands a commitment to continuous learning, strategic investment, and open-minded experimentation. The key is to move step by step, starting with building awareness and progressing toward a future where AI and human creativity work together seamlessly.
Organizations that embrace this transformation will not only improve efficiency but also unlock new avenues for innovation and growth. By carefully planning each step and fostering a culture of collaboration and adaptation, companies can navigate the complexities of AI adoption and emerge as leaders in their industries.
Take the first step today by assessing your current maturity level and identifying quick wins. Whether you are just beginning to explore AI or are ready to implement an autonomous workforce, the roadmap provided here offers actionable insights for every stage of the journey. The future is here, and those who adapt will be best positioned to lead in the digital age.
Embrace the change, invest in your team, and let AI be the partner that drives your organization toward a smarter, more innovative future.
And if you want me to help you get there, get in touch and let us talk.
Creativity & Innovation expert: I help individuals and companies build their creativity and innovation capabilities, so you can develop the next breakthrough idea which customers love. Chief Editor of Ideatovalue.com and Founder / CEO of Improvides Innovation Consulting. Coach / Speaker / Author / TEDx Speaker / Voted as one of the most influential innovation bloggers.