Embrace AI-driven innovation, it is the future.

It has been amazing how AI generative thinking (GenAI) has taken hold. It has only been one year since the launch of ChatGPT, then with a follow-up of GPT-4. In a really fascinating routine or guide to how Generative AI developed, then you should read Bernard Marr’s post It is well worth the read.

As he points out, “Today, Generative AI stands as a testament to the power of human imagination and technological innovation. It has grown from humble beginnings into a sophisticated technology capable of producing remarkable output.”

As Barnard Marr opens his post “Generative AI has the unique ability to create. It can generate new content like audio, art, and text, all by learning from a set of data without explicit instructions.” I feel “explicit instructions” need to be carefully managed.

For me, the last six months or so I have been working with ChatGPT to learn different ways to look at focus areas I spend in advising and mentoring and where innovation links into my different work.

This is rethinking the innovation process, how ecosystem thinking and design can shape our collaborative worlds differently, looking much harder at innovation ecosystems and applying different triggers of thought in how AI generative thinking will influence and shape much of the Energy Transition, as my endpoints.

Recently, I have been looking specifically at the way the (traditional) innovation management process will change. The deployment of AI-driven thinking utterly alters my perspective of “delivering” innovation.

I constantly recognise the fundamental shifts working in AI-driven projects will bring. This is my ‘push’ to get onto the wave.

So often, I have been reading about the ‘fantastic’ end results of combining human and AI ingenuity, but so many articles or posts miss the structure of how this can be achieved. Unstructured AI is not a silver bullet. It can lead to a real ‘roadcrash’ of innovation outputs.

We need to correct this ‘magic wand waving’ and put the order into AI-driven innovation work and recognize it will be a very different way to manage innovation in the future, to be more successful than today, and that’s regarded as a low benchmark. Arthur D Little offer a well-stated position of innovation today in this detailed review. They open with Over the last ten years, returns on and satisfaction with innovation have been in decline.

Harnessing AI-driven innovation might resolve today’s innovation results

So, recognizing similar outcomes of innovation, I have been working through a structured way to harness AI-driven innovation to realize the potential that can occur with many of the principles of design thinking, agile development and AI integration.

In earlier investigative work last year before ChatGPT, I wrote about some initial AI work building unique data sets relating to how and where ecosystem thinking triggers innovation. The two posts are the introductory one of ” Innovation Ecosystem Understanding through an AI-driven approach” and the outcome results and understanding I gained, summarized in this post “Business Innovation Ecosystems.” This initial work took me into this world of AI’s value, allowing me to quickly search for connected areas in innovation that “make” ecosystems.

The value in GenAI is the adaptive nature it can bring to the understanding of the problems you need to resolve, the continuous learning and ways to incorporate AI into currently accepted innovation process principles.

My initial work with ChatGPT was to provide the first workable structured process for deploying AI-driven generative thinking into innovation.

I am going into new versions of PAH-1, but let me share this opening design for AI-driven innovation.

Building an AI-driven innovation process (PAH-1)

A repeatable, structured process for deploying AI-driven generative thinking in innovation can be designed as follows, that takes gradual steps away from what is an accepted innovation process today and fuse it with AI generative approaches:

1. Define Objectives and Problem Scoping:

• Clearly define the innovation objectives and the specific problem or challenge you want to address.
• Ensure alignment with the organization’s overall strategy and goals.

2. Establish an Innovation Team:

• Assemble a multidisciplinary team consisting of data scientists, domain experts, designers, and business strategists.
• Assign roles and responsibilities within the team.

3. Data Collection and Preprocessing:

• Identify and gather relevant data sources that can be used for generative thinking.
• Clean and preprocess the data to ensure it’s suitable for AI model training.

4. Select and Train AI Models:

• Choose appropriate AI models, such as GPT-4 or other generative models, for the specific problem.
• Train the models using the preprocessed data and fine-tune them as needed.

5. Scenario Selection and Generation:

• Generate a range of innovation scenarios based on the problem scope.
• Utilize the AI models to brainstorm and develop innovative ideas and solutions.

6. Validation and Feasibility Testing:

• Evaluate the generated scenarios for feasibility, technical viability, and alignment with objectives.
• Conduct initial tests and analyses to refine and validate the concepts.

7. Iterative Refinement:

• Gather feedback from stakeholders and experts.
• Iterate on the generated ideas, making improvements and adjustments.

8. Prototype Development:

• Create prototypes or minimum viable products (MVPs) for the most promising concepts.
• Test these prototypes with potential users or stakeholders.

9. Verification and Validation:

• Conduct thorough testing, both in controlled environments and real-world settings.
• Validate the solutions against key performance indicators (KPIs) and user feedback.

10. Scaling and Deployment:

• Scale up the successful concepts for full deployment within the organization.
• Ensure seamless integration with existing processes and systems.

11. Continuous Learning and Improvement:

• Establish mechanisms for continuous monitoring and feedback.
• Analyze the outcomes and adapt the generative thinking process as needed.

12. Ethical and Regulatory Compliance:

• Regularly assess and address ethical considerations, biases, and compliance with relevant regulations.

13. Documentation and Knowledge Transfer:

• Document the entire process, including lessons learned, best practices, and model details.
• Share knowledge within the organization to facilitate future projects.

14. Communication and Change Management:

• Effectively communicate the benefits and results of the AI-driven innovation to all stakeholders.
• Implement change management strategies to ensure a smooth transition.

So, my suggested opening structured process.

By following this structured process, your organization can establish a repeatable and scalable approach to leveraging AI generative thinking for innovation.

This process encourages a balance between creativity and feasibility, emphasizes continuous improvement, and ensures ethical and responsible AI use. It also allows for flexibility and adaptation to various innovation challenges.

Rocket science is built in stages of experimentation, breakthroughs and piecing ‘something‘ together.

This process above is not rocket science but it gives (me) a good basis to build on this foundation.

As I have stated, this is PAH-1. I am already looking well beyond this as any change that merges radically different thinking leaves the willing, able, and curious to ask more questions in a continued iterative, experimental way.

 Adapting Your AI Generative Thinking Process for Ongoing Improvement:

Adapting the AI generative thinking process for ongoing improvement is a crucial aspect of innovation. Here’s how I am setting about doing this:

• Regular Reviews: Schedule regular reviews of your AI models and generative thinking process.
• Feedback Loops: Establish feedback loops with both AI models and human experts to make iterative improvements.
• Monitoring Key Metrics: Define and monitor key performance indicators (KPIs) related to your innovation projects. If you notice that specific metrics are not meeting expectations, it’s a signal to adapt your process.
• Continuous Training: AI models should undergo constant training and updates to stay relevant, practical and effective, including upgrading to more advanced AI technologies as they become available.
• Knowledge Sharing: Encourage knowledge sharing so everyone benefits from the lessons learned. This post is a building block to this need.
• Experimentation: Use controlled experiments to test different variations of your AI-driven generative thinking process.
• Iterative Process Design: Don’t be afraid to iterate on the generative thinking process itself, working through bottlenecks or areas where you lack a good understanding of this shift in innovation design. I am hitting a number of those.
• External Benchmarking: Look at what other organizations are doing in the field of AI-driven innovation. Question rigorously those that offer just rhetoric and identify ones that help you figure out all or enough of it to keep pushing forward.

I am presently in the investigative stage of searching for actionable insights to learn and build out my understanding, process iterations to give informed improvements to the present AI generative thinking process, and, thirdly, continuous learning, constantly having a feedback loop to sustain momentum.

The aim of this work is the need for a specific context of combining human and GenAI, making the result of practising and working with innovation more relevant, effective and impactful in building a comprehensive and effective AI-driven innovation process.

Working in combination with ChatGPT to evolve this.