The Roadmap to Successful AI Implementation
Identifying AI opportunities, assessing use cases, and ensuring organisational readiness for successful AI integration.
Have you ever had a great idea only to discover that someone else had already thought of it?
Actually, when it comes to AI use cases, this is a positive sign because it means we can learn from others' mistakes and progress more quickly.
In previous articles, I introduced various approaches for identifying use cases, such as Brainstorming and SWOT analysis.
In today’s article, I go deeper into the first three steps to take before formulating your AI Change Management Strategy to implement an AI Use Case:
Let’s dive in. 🤿
Step 1: Generation of ideas
It’s important to note that the types of use cases will range from:
Agnostic use cases, such as those in HR or Project Management, which often provide quick wins and substantial benefits when promptly implemented in your organisation.
Industry-specific use cases which might include, for instance, the optimisation of Resource Allocation for Maintenance Plans in the aerospace sector.
Both types of use cases offer valuable paths toward AI integration, but they require different approaches and considerations such as IP, limitations, security, data used, etc.
A good starting point to identify opportunities for AI are and to generate ideas are:
Pinpoint High-Potential Areas: Begin by identifying roles or tasks within your organisation that hold the highest potential for AI integration. Focus on repetitive tasks that could be significantly enhanced through automation and improve cognitive offloading.
Create a Wish List: Based on the focus areas, compile a wish list of potential AI applications. Consider how automation and AI could streamline operations, improve safety, and enhance decision-making processes.
Effective Brainstorming: Referencing the newsletter "The Art of Effective Brainstorming" you can facilitate group sessions to generate and prioritise your wish list. This prioritisation could be based on various factors such as cost, quality, time, and safety.
SWOT Analysis for AI Strategy: For organisations new to AI, starting with a SWOT analysis can provide a structured framework to shape your initial AI strategy, identifying strengths, weaknesses, opportunities, and threats related to AI implementation.
My tips for success are:
Seek Quick Wins: Initially focus on agnostic use cases where off-the-shelf products can be quickly integrated into your operations for immediate benefits.
Implement or Build AI Solutions: Consider the use of existing AI assistants or the development of customised platforms utilising existing Large Language Models (LLMs) so you can start testing in smaller groups of people and grow from there, cultivating an AI Culture.
Caution! Security, intellectual property, and the inherent limitations of AI technologies should be at the forefront of your strategic considerations. These factors are critical in ensuring that your AI integration efforts are both effective and sustainable.
Step 2: Selection of use cases
Once you've brainstormed a list of potential AI use cases, the crucial next step is to decide which top ones promise the most significant benefit for your organisation.
Here’s a deeper dive into what constitutes a good versus a bad use case:
1. Problem-Solving Capability
Good: Targets a specific, recognised problem within the organisation, offering a solution that directly addresses and mitigates the issue.
Bad: Aims at perceived problems without validating the need, resulting in solutions searching for a problem rather than the other way around.
2. AI need
Good: Utilises AI to bring about improvements that wouldn't be possible, or would be significantly less efficient, with traditional methods. The application of AI is integral to solving the problem more effectively.
Bad: Incorporates AI as a buzzword or afterthought, where its use does not contribute meaningfully to the solution, potentially adding unnecessary complexity.
3. Complexity versus Usability
Good: It's sophisticated enough to be effective but simple enough for end-users to adopt and use without extensive training.
Bad: So complicated that it becomes inaccessible to its intended users, requiring significant time for adaptation and possibly leading to rejection or underutilisation.
4. Measurable Success
Good: Provides specific, quantifiable metrics for success, allowing for clear measurement of ROI, performance improvements, or other key outcomes.
Bad: Lacks concrete success metrics, making it difficult to evaluate its effectiveness or justify its continuation.
5. Timeliness of Impact
Good: Designed to produce early wins or short-term benefits that demonstrate value and support broader long-term goals.
Bad: Delivers value only in the long term, risking loss of stakeholder interest and support due to the delayed realization of benefits.
6. Ethical and Regulatory Alignment
Good: Anticipates and addresses potential ethical issues and regulatory requirements, facilitating smoother adoption and integration into existing workflows.
Bad: Raises ethical concerns or regulatory red flags, potentially leading to resistance, legal challenges, or reputational damage.
7. Scalability and Flexibility
Good: Built with scalability in mind, allowing for adaptation to growing or changing organisational needs without requiring a complete overhaul.
Bad: Has a fixed scope with limited adaptability, potentially becoming obsolete as the organisation evolves.
8. Clarity and Focus
Good: Clearly defined in terms of objectives, scope, and expected outcomes, ensuring efforts are targeted and aligned with organisational goals.
Bad: Broad or poorly defined, leading to scope creep, resource wastage, and diluted results.
When assessing AI use cases, consider creating a scoring system based on these criteria to objectively compare and prioritize projects.
This structured approach can help ensure that your organization invests in AI applications that are practical, have impact and are aligned with strategic objectives. Additionally, engaging stakeholders from across the organization in the evaluation process can provide diverse perspectives, further refining your selection of AI initiatives.
Step 3: Organisational Impact and Readiness Assessment
Now that you have identified your top use cases, before you even embark in the formulation of your Change Management Strategy to develop or implement the use case, you need to assess the Organisational Impact and Readiness.
In my article Assessing AI Change Impact and Readiness I make an overview of three blocks I have divided as follows:
Block 1: AI Change Impact Assessment
Block 2: AI Organisational Readiness
Block 3: AI Adoption Framework
Build a multidisciplinary team depending on the complexity of the AI initiative.
I cannot emphasise more how important is you spend time assessing the Organisational Impact and Readiness prior embarking in the AI implementation, even in the development of the use case.
Between 50% and 70% of most major organisational changes fail due to lack of preparation.
Conclusions
Embarking on the AI journey begins with sparking new ideas, selecting high-impact use cases, and assessing organisational impact and readiness.
Main highlights:
Shared Learning: Identifying that your idea has precedents is beneficial. It enables you to learn from others' experiences.
Strategic Focus: Discipline selecting use cases is key to address real organisational needs with clear benefits, demonstrating the importance of strategic alignment.
Assessing Organizational Impact and Readiness: Preparing your organisation for AI is as crucial as the technology itself.
This is 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.