How to Get Started with Using AI

Welcome back to our newsletter series exploring the transformative power of artificial intelligence (AI). In our previous article, we discussed some key applications of AI that you can explore.

In this installment, we shift our focus to the practical steps necessary for navigating the exploratory process to realize AI solutions that can drive productivity and efficiency. We will discuss key considerations and the journey to AI adoption. By following these guidelines, you can lay a strong foundation for your AI initiatives, ensuring they are successful and sustainable.

Actions Before Adoption of AI

Successfully adopting AI within your organization begins long before implementing specific technologies or solutions. It requires a strategic approach that encompasses various preparatory activities. These activities ensure that your organization is ready to integrate AI seamlessly and derive maximum value from it.

Key areas to focus on include defining your AI and data strategy, establishing governance frameworks, setting up the necessary technology infrastructure, preparing your data, and fostering the right culture and skills among your personnel. Each of these steps is critical in creating a robust foundation that supports the effective use of AI.

Let’s delve into these preparatory steps in more detail.

I. Define AI and Data Strategy

Your organization may be exploring the adoption of an AI solution, perhaps investing heavily in critical AI infrastructure with high expectations. However, doing this without a strategy in place is akin to building a house without foundations.?

Clearly defining your AI and data strategy ensures that AI initiatives are well-integrated and support your organization's broader goals. This strategic alignment helps prevent common pitfalls such as unmet expectations, slow progress, or project fatigue.

A recent study by Gartner in 2023 found that 77% of organizations are currently in the process of deploying their first AI project. While many companies have initiated pilot programs and launched data-enabled products, the expected large-scale business transformation has yet to be fully realized. Data and AI activities remain specialized rather than core competencies for most businesses. Successful digital transformation in a data-driven organization requires fully committed leadership. Before embarking on any AI tool or pilot program, it is crucial to establish a robust AI and data strategy to ensure a strong foundation for your AI journey. Here’s how to get started:

Data and AI strategy framework

Defining an AI and data strategy is crucial. This involves setting up a strategic plan and establishing key pillars for AI within the organization. These pillars should be aligned with your overall strategy, talent pool, ambitions, and operating model. Your strategic plan should incorporate the following pillars:

• Alignment with Business Goals: Identify specific areas where AI can drive significant impact, such as improving customer service, optimizing operations, or developing new products. Ensure these areas are in line with your broader business objectives.
• Data Requirements: Determine the types of data you need, including its volume, and identify relevant data sources such as transactional databases, customer interactions, social media, IoT devices, and external datasets.
• AI Objectives: Set specific, measurable goals for your AI initiatives. These could include targets for process automation, cost reduction, revenue growth, or customer satisfaction. Organizations often experience fatigue when expectations are not met. Be a step ahead by setting achievable objectives.
• Resources and Skills: Assess your current talent pool to identify gaps in AI-related skills and knowledge. Develop a plan for hiring and training employees to build a team capable of executing your AI strategy effectively. Evaluate your existing technological infrastructure to determine what upgrades or additions are necessary to support AI initiatives.?
• Data Management: Establish robust data management practices to ensure that data is high-quality, well-organized, and accessible. This involves setting up data governance policies and procedures to manage data assets effectively.
• Ethical Considerations: Develop guidelines for the ethical use of AI, considering factors such as data privacy, security, and bias. Ensure that AI applications are designed and implemented responsibly.

II. Establish Data and AI Governance

Effective AI governance is crucial for mitigating risks and ensuring the responsible use of AI technologies. It encompasses robust frameworks that address trust, accuracy, fairness, privacy, security, and legal compliance. Trust and accuracy in AI models are maintained through rigorous validation of inputs and outputs, ensuring transparency and traceability in decision-making processes. Addressing fairness and bias involves managing inherent biases in training data and continually monitoring for unintended outcomes. Privacy and security measures safeguard against data misuse and cybersecurity threats, aligning with regulations such as the General Data Protection Regulation (GDPR).

Legal considerations encompass copyright, intellectual property rights, and liabilities associated with AI use. Robust model governance further ensures accountability through overlays, controls, and human-in-the-loop mechanisms. These measures enable ongoing monitoring, performance evaluation, and adherence to ethical guidelines for safe and constructive AI deployment.

Your organization's data is an asset, and having a high-quality, well-organized database is essential for a successful AI initiative. If the data is well-structured, legally collected, and compliant with recognized principles such as FAIR (Findable, Accessible, Interoperable, Reusable) by the European Commission or the guidelines and AI risk management framework provided by the National Institute of Standards and Technology (NIST) in the USA, it sets a strong foundation for success. These guidelines provide a comprehensive approach to data management, including aspects like security, privacy, and interoperability, which are crucial for supporting AI initiatives.

III. Set Up Requisite Technological Infrastructure

setting up the technological infrastructure to support your current and future needs is crucial. This step should encompass the end-to-end use case model, covering data collection methods, operating systems, data warehousing, appropriate cloud environments, analytical environments, and business-interfacing systems.

Considerations should include:

1. Computing Resources: Invest in high-performance computing resources, such as GPUs and cloud-based services, to handle the computational demands of AI. Cloud platforms like AWS, Azure, and Google Cloud offer scalable solutions tailored for AI workloads.
2. Data Storage: Establish scalable and secure data storage solutions to manage large datasets. Consider data lakes for unstructured data and data warehouses for structured data. Ensure that storage solutions provide fast data retrieval and support for real-time processing.
3. AI Platforms and Tools: Utilize AI platforms and tools that provide pre-built models, development environments, and deployment capabilities. Popular options include TensorFlow, PyTorch, and IBM Watson. These platforms offer extensive libraries, frameworks, and APIs that accelerate AI development.

IV. Prepare Your Data

AI relies on quality data to make informed decisions, learn from patterns, and improve over time. Therefore, it is crucial to have high-quality data to ensure your organization's AI models are trained on reliable information. This leads to more accurate predictions, better decision-making, and improved overall performance.

Quality data is perhaps the most critical aspect of AI model development, as it directly influences the intelligence of the AI solution. The better the data, the better the AI's outcomes and responses. To prepare your training data strategically, consider the following:

1. Ensuring Data Accuracy: Ensure your data is free from errors and inconsistencies, reflects the real-world phenomena it’s intended to represent, and is up-to-date and refreshed regularly.
2. Utilizing Relevant Data: Data must align with the specific problem or task at hand, include the necessary features and attributes, and exclude unnecessary or redundant data.
3. Keeping Data Well-Organized: Data must be structured logically and consistently, easy to navigate and understand, and appropriately formatted for analysis and modeling.
4. Making Data Easily Accessible: Data should be stored in a centralized and secure location, accessible to authorized personnel and systems, and easily retrievable and shareable when needed.

The separation of storage and computing

V. Hire and Train Personnel

The business use cases and technical needs for your organization determine your resource requirements. Often, organizations rely solely on their in-house data scientists and analysts, creating a significant expertise gap, particularly in the role of an AI strategist to assess the resource requirements for successful AI deployments.

To ensure effective AI implementation, prioritize upskilling your existing IT engineers, data analysts, and scientists. Provide training opportunities that enhance their capabilities in handling AI-related tasks, such as data processing and model development. Supplement these efforts with strategic hiring to fill specific gaps in expertise as needed.

Consider the following steps:

1. Assess Current Talent Pool: Evaluate your current team to identify gaps in AI-related skills and knowledge. Determine which areas require additional expertise and which skills can be developed internally.
2. Upskill Existing Employees: Provide training opportunities to your IT engineers, data analysts, and scientists to enhance their AI capabilities. Focus on areas such as data processing, machine learning, and model development.
3. Strategic Hiring: Fill specific gaps in expertise by hiring new talent. This might include roles such as AI strategists, data scientists, machine learning engineers, and AI ethicists.
4. Foster a Culture of Continuous Learning: Encourage a culture where continuous learning is valued. Provide ongoing education and professional development opportunities to keep your team up-to-date with the latest AI advancements.

Hiring the right talent before starting your AI initiative greatly increases the probability of success. By ensuring your personnel are well-trained and strategically positioned, you can effectively support your AI projects and drive them to successful outcomes.

VI. Foster an AI-First Culture

Introducing AI into the fabric of daily operations necessitates creating a culture where every employee sees AI as integral to their role. By familiarizing all team members with AI technologies and their practical applications, organizations can foster an environment that promotes curiosity, experimentation, and ongoing skill development.

How organizations have benefited from generative AI adoption

To achieve an AI-first culture, consider the following steps:

1. Leadership Advocacy: Leadership plays a pivotal role in advocating for AI initiatives and demonstrating their strategic importance. Leaders should actively promote AI adoption and integrate AI goals into the broader organizational vision.
2. Employee Training: Provide employees with the necessary training and resources to understand how AI can enhance their workflows and contribute to organizational goals. This includes both technical training and insights into practical applications of AI in various roles.
3. Cross-Departmental Collaboration: Encourage collaboration and knowledge-sharing across different departments. This helps embed AI capabilities throughout the organization and fosters a more integrated approach to AI initiatives.
4. Promote Curiosity and Experimentation: Create an environment that encourages curiosity and experimentation with AI technologies. Allow employees to explore new ideas and test AI solutions in a supportive setting.
5. Celebrate Successes and Learn from Challenges: Recognize and celebrate the successes of AI implementation projects. Equally, learn from any challenges encountered to continuously improve AI adoption strategies.

Fostering an AI-first culture, organizations can collectively harness AI's potential to drive innovation, efficiency, and competitive advantage. Ensuring that every employee understands the importance of AI and feels equipped to utilize it effectively is key to achieving this cultural transformation.

What Use Cases to Go For?

Determining what solutions to develop and when can feel overwhelming given that AI touches all aspects of the organization. applying a strategy to determine which use cases to pursue in the short-term, medium-term, and longer-term ensures alignment with organizational goals and maximizes the impact of AI initiatives

Identifying and prioritizing AI use cases is crucial for achieving your business goals and demonstrating the value of AI over time. You should:

Catalog and Qualify?

Start by thoroughly identifying and organizing all potential AI use cases within your organization. Conduct a comprehensive review to qualify each use case based on strategic fit, feasibility, and potential impact.

Engage Stakeholders

Engage stakeholders across different departments to prioritize and rank the identified use cases. This collaborative effort ensures alignment with organizational goals and enhances buy-in for AI initiatives.

Create a Roadmap

Focus on low-risk, high return-on-investment (ROI) options in the short term to demonstrate quick wins and build momentum for broader AI adoption. You must also create a vision for what you hope to achieve in the medium and long term with clearly defined milestones.

How to Explore Solutions

With the foundations in place and use cases roadmapped, you can begin to explore and validate AI solutions. Below are some recommendations on how to do so:

Build or Buy:?

Initially, consider purchasing off-the-shelf AI solutions rather than building custom ones. This approach often yields significant advantages:

1. Cost Savings: McKinsey estimates that buying pre-built solutions can reduce costs by up to 50% compared to developing custom solutions. Vendors often offer subscription models that provide flexibility and lower upfront costs. According to a report by Gartner, organizations that implement AI technologies can expect to reduce operational costs by 25% by 2025. Investing in purpose-built small language models (SLMs) and other AI technologies can offer even greater reductions in costs.
2. Speed to Market: Implementing off-the-shelf solutions can be up to 70% faster, allowing you to realize benefits sooner. Pre-built solutions come with tested and optimized functionalities, reducing the time needed for development and deployment.
3. Flexibility: Pre-built solutions are often flexible and can be customized or expanded later as your needs evolve. Many vendors provide APIs and integration capabilities that allow you to build on top of existing solutions.
4. Open Source Options: Explore open-source AI tools for cost-effective and flexible exploration. Tools such asTensorFlow, Keras, and Scikit-learn provide powerful capabilities for building and experimenting with AI models. LLMs such as LLaMA by Meta and Grok by X offer levels of transparency that closed-source solutions can’t match. According to Synopsys’ open source security and risk analysis, 96% of all commercial codebases contained open source components. Additionally, Stanford’s AI Index Report noted that 149 foundation models were released in 2023, with two-thirds of them being open source. These tools often have large, active communities that contribute to continuous improvement and support.

An emerging design pattern is to initially build a prototype or initial product that delivers strong performance using a large language model (LLM) like GPT-4. Later, for more cost-effective inference or improved performance on specific, narrowly scoped tasks, fine-tuning one of the many available open-source LLMs tailored to your needs can be advantageous.

Stakeholder Engagement

Involve stakeholders in the exploration phase to gather feedback and ensure that the AI solutions meet their requirements. Use iterative development practices, such as agile methodologies, to continuously refine the solutions based on stakeholder input.

Test and Refine

Conduct pilot projects to test the AI solutions in real-world scenarios. Use the results to refine the models and improve their performance. Pilot projects allow you to identify potential issues and address them before full-scale deployment.

Productionize and Automate?

Once the AI solutions have been validated, move them into production and automate their deployment to scale their impact. Establish monitoring and maintenance processes to ensure ongoing performance and reliability.

The minimum viable scale for AI deployment

Starting your AI journey can be daunting. Partnering with experts can ease this uncertainty, ensuring you leverage AI effectively while avoiding common pitfalls. It's crucial to define your AI strategy, establish governance, and set up the necessary infrastructure. Building a skilled AI team and fostering an AI-first culture are key to success. Prioritize use cases with high ROI and low risk to demonstrate AI's value early on. This approach lays a solid foundation for advancing AI initiatives in the future, driving productivity and efficiency across your organization.

As you embark on your AI journey, remember that the key to success lies in continuous learning and adaptation. AI is a rapidly evolving field, and staying updated with the latest advancements and best practices will help you stay ahead of the curve.

Author's Note: I am continuously learning, and that includes learning about what information is most useful to you the reader. Leave a comment or send me a message with suggestions of AI, data and advanced analytics topics that you would like me to cover in future articles.

Disclaimer: The views and opinions expressed in this thought piece are solely my own and do not reflect those of any other organizations with which I am associated. The information contained in this article is intended for informational purposes only and should not be relied upon as legal, financial, or professional advice. Always seek the advice of a qualified professional before acting based on the contents of this article.

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