Innovation is Deciding (Part 2): AI-Powered Decision-Making

How can AI help us in deciding for innovation?

In the previous article in the Innovation series, I said,

If innovation requires discipline to nurture the spark and divergence to fan the flames, then deciding is the crucial act of shaping the fire. It's where the rubber meets the road, turning ideas into action.

Let’s take two aspects of deciding and see how AI can help us.

AI for Group Divergence and Idea Evaluation

One, group challenges around pushing the boundary, divergence, relevance of the idea and how we go beyond the highest-paid person or the loudest in the room to a more logical space.

Based on my prompts, here are AI created ideas. I like them.

1. Idea Evaluation:

• Originality: Do the ideas challenge existing assumptions? Are they truly novel or merely variations of existing solutions?

• Relevance: Are the ideas aligned with the problem or opportunity at hand? Do they address the core issues?

• Feasibility: Are the ideas technically and practically possible? Do they consider available resources and constraints?

• Value: What impact could these ideas have? Are they likely to create value for users, customers, or the organisation?

2. Process Analysis:

• Exploration: Did the group actively seek out diverse perspectives and information sources?

• Challenge: Were existing assumptions and biases actively questioned and challenged?

• Experimentation: Did the group explore various approaches and "what-ifs"?

• Curiosity: Did the group demonstrate a genuine desire to understand the problem and explore new possibilities?

3. Measuring Divergence:

• Idea Variety: Do the ideas span across different categories, perspectives, and functionalities?

• Idea Depth: Are there ideas that go beyond superficial solutions and delve into deeper, more nuanced solutions?

• Out-of-the-Box Thinking: Are there ideas that are radical, unexpected, and potentially disruptive?

We can take these and build into something we like, or you can iterate with the AI and create an approach that you are comfortable with. Ofcourse, the answers will still be subjective, but provide a good starting point for a conversation.

AI for Organisational Decision-Making and Governance

The second is the deciding as an organisation. This goes into governance, strategy, funding etc. Again prompting and some too and frothing with the AI.

1. Governance Structure:

• Innovation Committee: Establish a dedicated committee within the board responsible for overseeing the innovation portfolio. This committee should have diverse expertise (including external experts), a good understanding of the company's strategic direction, and a focus on long-term impact.

• Innovation Council: Create a smaller, more agile council comprising representatives from various departments (R&D, marketing, finance, etc.) to advise the Innovation Committee. This council provides a more operational perspective on feasibility, risk, and potential roadblocks.

• Innovation Pipeline: Develop a structured pipeline that outlines the stages of innovation, from initial idea generation through to implementation and scaling. This pipeline should include clear milestones, criteria for advancement, and resource allocation guidelines.

2. Innovation Metrics:

• Beyond Financial Metrics: While financial returns are important, use metrics that measure the innovative impact of the portfolio. These can include:

  • Number of New Product/Service Launches: Reflects the company's ability to bring innovative solutions to market.

  • Customer Acquisition Cost (CAC) Reduction: Measures the impact of innovation on cost efficiency and customer acquisition.

  • Customer Satisfaction/Net Promoter Score (NPS) Improvement: Indicates the positive impact of innovation on customer experience and brand perception.

  • Employee Engagement: Tracks how innovation inspires and motivates employees.

  • Industry Recognition: Acknowledges the company's leadership and thought-leadership in innovation.

  • Intellectual Property Generation: Measures the value created through patents, trademarks, and other IP assets.

• Balanced Scorecard: Develop a balanced scorecard that integrates financial metrics with these innovation-focused metrics to provide a comprehensive view of the company's performance.

3. Decision Process:

• Gatekeeping: Define clear gatekeeping stages for innovation projects. Each gate represents a key decision point where the project is evaluated based on its progress, feasibility, and alignment with strategic objectives.

• Investment Criteria: Establish clear investment criteria to guide resource allocation. This could include factors such as:

  • Alignment with Strategic Objectives: Ensuring the project aligns with the company's overall strategy and long-term vision.

  • Market Potential: Assessing the size and growth potential of the target market.

  • Competitive Advantage: Analysing the project's potential to differentiate the company from its rivals.

  • Risk Mitigation: Identifying and addressing potential risks associated with the project.

The Importance of the Human Element in AI-Assisted Decision-Making

We should not forget the human in the loop in any of this. Ultimately, this provides a starting point and guidance, but not answers. Remember, AI is not really thinking!

The value is always in the discussion. We can evaluate the responses on these criteria and have another comparison. Or we can use a specifically trained AI model that matches our organisation's values, principles, strategy, goals, and context to evaluate this alongside humans.

The possibilities are endless once we decide to go human + Ai.

Reach out to me if you would like to figure out how to use AI or create a structure of innovation for your organisation.

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Reader Pointers:

• How can AI help your team overcome common challenges in group decision-making?

• What are the ethical considerations of using AI in decision-making processes?

• How can you ensure that AI-generated insights are used responsibly and ethically in your organisation?