Step-by-Step guide to Crafting Your First AI Strategy
Step-by-Step guide to Crafting Your First AI Strategy
Unlock AI's potential for Aerospace CEOs and Business Leaders — simplify strategy, overcome tech inundation. Today, six steps integrate AI with a Safety Department use case.
Everyone discusses AI's potential for rapid business value, but for Aerospace CEOs and Business Leaders, the path forward may seem elusive.
Far from reality…
The inundation of technology can be overwhelming.
My approach is to simplify and be pragmatic—starting easy and evolving progressively.
Today, I'll outline six crucial steps for integrating AI into your aerospace business strategy, exemplified through a use case in the Safety Department.
A strategy plan that is both respectful and utilises the full potential of AI technology and it’s aligned with your overall business strategy.
Let’s dive in. 🤿
Step 1: Understand the basics of AI
In order to understand how AI can help your business to achieve your goals, you need to understand the technology.
Start by getting introduced to the core concepts, principles and use cases.
There are multiple free courses available to acquire the foundational and technical understanding of AI.
Example: Just as a pilot needs to understand autopilot functionality, business leaders should grasp AI basics to navigate its potential in enhancing safety protocols.
Step 2: Identify Business Areas
Pinpoint areas with the highest AI potential in your aerospace organisation.
Consider Product Development, Logistics, Procurement, Quality, Finance, etc.
Example: Bringing AI into the Safety Department as a vital business area, that can significantly benefit from AI applications,
Step 3: Identify Core Processes
Focus on the core processes within these areas, with a particular emphasis on repetitive tasks that could be streamlined through automation and create your wish list.
In the newsletter “The Art of effective Brainstorming” I introduced the technique to facilitate group sessions and to help you to prioritise your list that could be based on cost, quality, time, safety, etc.
The identification of the use case should be lead by the real need rather than what AI can do and needs to help you to advance your business goals.
Example: By employing AI to analyse safety reports, the Safety Department can automate the trending process, quickly identifying recurring patterns or emerging trends. This not only reduces the manual workload but also enables timely interventions to address safety concerns, ensuring a proactive approach to aviation safety.
Step 4: Evaluate AI Suitability
Create an in-house Multi-Skilled Team. Combine business insight, subject matter experts on the process, legal and compliance experts, security experts, IT professionals and AI advisors/experts (internal or external to your organisation).
Pose critical questions:
About AI-Readiness Culture
Open dialogue about fears, concerns and expectations. If AI could take over or assist in this task, could the team be more focused in strategic tasks?
Have you built an AI-ready culture? Do you have Ethical Guidelines within the organisation?
About the Use Case
Do you need a Time Study to get an accurate picture of how the time is being used for the specific task?
Is the task complex or with high-risk/high-impact? Are you aware of AI limitations? Is the expectation for Human augmentation or to go fully automated? If so, do you expect to have additional measures in place?
Do you need a low or high-latency AI solution?
What's the potential Return of Investment (ROI)?
About the Data
Do you have enough data to train the model? What's the data quality and accuracy?
Do you have Infrastructure to store the data? Any storage limitations?
About the Change
Have you risk assessed both positive and negative long term impacts within your organisation after adopting the AI solution? Do you have mitigations in place?
Do you have a Management of Change process to ensure you are ready for the change, to monitor regularly the impacts o AI or new emerging risks and to assist the teams adapting and understanding the AI solution?
Example: The complexity of analysing vast safety datasets becomes manageable with AI algorithms, presenting a high potential for positive ROI in the Safety Department. The organisation might face challenges though regards AI-readiness culture and Management of Change along with data security and privacy implications. This is why you might need to seek for advice if you don’t have specialists in-house.
Step 5: Identify any Legal and Compliance Requirements
It is possible you might need to seek Legal advise to answer a few questions:
Data security or privacy implications.
Any potential issues with intellectual property or licensing AI solutions.
Accountability, responsibility and liability / ownership of AI risks across all actors (from the AI provider, organisation and end-user).
How assurance and oversight is going to be integrated in the AI governance processes.
Also, the Aerospace industry is highly regulated. Are you aware of what regulators are saying?
In the article “AeroStudio: The Regulatory Landscape of AI in Aerospace” I dived into the aerospace regulatory implications of AI and discover the EASA roadmap for trust in AI technology.
EASA's "Artificial Intelligence Roadmap 2.0" and the proposed "EASA Concept Paper: First usable guidance for Level 1 & 2 machine learning applications" are setting the stage for AI's role in safety and the environment.
Example: The application of proportionality for the regulatory requirements of an AI-based system employed to analyse safety reports for trending process could depend on the level of oversight and authority of the end user along with the criticality of the AI system, which could be limited by the Assurance level.
Step 6: Engage with AI Specialists
Look for AI suppliers to develop the solution.
They will guide you to select the most suitable AI available tools tailored to your needs and aligned with your Ethical Guidelines and let AI specialists guide you through the necessary steps and requirements.
Example: Consider partnering with renowned AI suppliers or start-ups for a tailored approach to your unique needs. Begin with small-scale pilot projects focused on specific areas. As you witness the positive impact and gain confidence in the technology, gradually escalate and optimize your AI implementation across various business functions.
Conclusions
This strategic approach to crafting an AI strategy isn't confined to aerospace; its principles can be seamlessly applied to any industry.
Here are my four key pieces of advice:
Build the Right Foundations: Culture and Change Management Process
Establishing a solid foundation is pivotal. Cultivate a culture that embraces innovation and change within your organisation. Simultaneously, implement a robust change management process to navigate the transition smoothly. The success of your AI strategy relies on the collective buy-in and adaptability of your teams.
Start Small and Grow
My mantra is "start small and then grow". Initiating with manageable pilot projects allows for controlled experimentation, valuable learning, and the identification of what truly works for your specific business needs. This incremental approach minimizes risks while maximizing the potential benefits of AI integration.
Seek Expert Guidance, Empower Your Teams
Building a multi-skilled team is crucial, but empowering your internal teams is equally important. Collaborate with AI specialists when needed, drawing on their expertise to tailor solutions to your unique requirements. Simultaneously, cultivate a culture of continuous learning within your teams. This dual approach ensures a harmonious blend of external insights and internal capabilities, fostering sustainable AI adoption.
Identify Legal and Compliance requirements
In the ever-evolving landscape of AI, legal and compliance considerations are paramount. Identify and thoroughly understand the legal and regulatory landscape that governs AI applications in your industry. Ensuring that your AI strategy aligns with these requirements not only mitigates risks but also enhances the ethical standing of your implementation.
In summary, building an AI strategy is a dynamic journey that requires a careful blend of cultural transformation, strategic planning, and the judicious application of technology. By adhering to these principles, businesses, whether in aerospace or any other industry, can navigate the complexities of AI implementation with confidence and foresight.
That’s all for today.
See you next week! 👋
Disclaimer: The information provided in the newsletter and related resources is intended for informational and educational purposes only. It does not constitute professional advice, and any actions taken based on the content are at the reader's discretion.