AI Strategy for Your Business: From Concept to Implementation

Understanding the AI Strategy

Before you’re faced with the dilemma of the right steps for the AI integration process, you need to understand what AI strategy is and what is the importance of it.

According to McKinsey’s 2023 global survey on GenAI, about one-third of business leaders responded that they have adopted the technology for at least one functional area, including product development, manufacturing, supply chains, marketing and sales, service operations, corporate finance, and human resources. This means that about 60% of organizations that claim to have adopted AI are using generative AI.

So what is an artificial intelligence strategy? It is a plan for integrating AI into an organization so that it aligns with and supports the goals of the business. A successful AI strategy should act as a roadmap for this plan. Depending on the organization’s goals, the AI strategy might outline the steps to effectively use AI to extract deeper insights from data, enhance efficiency, and build a better delivery process.

A well-planned AI strategy should also guide the tech infrastructure, ensuring that the business is equipped with the hardware, software, and other resources needed for effective AI implementation. Since technology evolves so fast, the strategy should allow the organization to adapt to new tools, frameworks, and shifts in the industry. Ethical considerations such as bias, transparency, and regulatory concerns should also be addressed to support responsible deployment.

Benefits from the AI Implementation

An AI strategy outlines the steps that will help your AI projects smoothly transform ideas into impactful solutions. A well-designed strategy will provide a clear plan for managing, analyzing, and leveraging data for AI initiatives. It will also determine the talent the organization needs to develop, attract, or retain with relevant skills in data science, ML, business intelligence services, AI development, etc. It will also guide the procurement of the necessary hardware, software, and cloud computing resources to ensure effective AI implementation.

Here are a few of the possible benefits that AI can bring to your organization.

Revenue Growth

All implementations into your business should lead to automation of the processes and cost reduction, therefore the revenue will increase.

The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools.

The 4 AI Strategy Pillars

Implementing a successful AI strategy requires to have a certain foundation. Let’s discuss what this foundation is.

Decide How to Measure the Success of AI

To measure the success of any project business KPIs (key performance indicators) are commonly used. However, what is a reliable KPI for AI? Reliable KPIs are measurable and transferable. So they can be directly recorded and then used indirectly to inform other metrics. In healthcare, for example, the speed and precision of a model’s clinical diagnosis could be measured as direct metrics, which could then influence an indirect metric like patient satisfaction.

For example, KPIs could be used to gauge the performance and ROI of several business initiatives. Similar to other projects, the KPIs your organization measures for AI should be based on your goals and overall automation strategy.

KPIs are frequently set to measure factors like data quality or security. Some may also track a hallmark of Trusted AI, like privacy (with a metric like location entropy) or fairness (with proportional parity).
Commonly used KPIs for AI include:

• Mean time to repair (MTTR). Measures the duration of error repair in an AI.
• Mean absolute error (MAE). Determines the average difference of predictions made by a regression model to the true value.
• First contact resolution rate (FCRR). Reveals the percentage of problems solved via basic support.
• True or false positive rate (T/FPR). A measure of fairness that helps determine the sensitivity and specificity of predicted classifications.

Ways To Implement AI In Your Business Strategy

There are many ways/approaches to integrate AI into your business strategy. In any case, the first step is to identify areas of your business that could benefit from AI implementation. It may include tasks that are repetitive or time-consuming, such as data analysis or customer service. It could also include business aspects where precision and speed are critical, such as manufacturing or financial services. Here is the universal guide.

AI Implementation Strategy: How Can Companies Select the Right AI Model?

Here are some tips for choosing the right AI Model for your business:

1. Select the right data type
   Different AI models excel at handling specific types of data, such as images, text, or time-series information. Identify the nature of your data to determine which model is the most suitable for your project.
2. Evaluate the complexity of the problem
   Evaluate the complexity of the problem you’re trying to solve. Some tasks may be well-suited to pre-trained models, while others require custom-built solutions. Tailor your choice to match the intricacy of your specific problem.
3. Assess the availability of labeled data for your project
   Deep learning models often require a substantial amount of labeled data for effective training. Assess the availability of labeled data for your project and consider techniques like transfer learning if data is limited.
4. Consider the available resources
   Consider the computational resources available to your organization. Large models like GPT-3 demand significant computing power, which may not be feasible for all companies. Ensure that your infrastructure can support the computational requirements of the chosen model.
5. Consider the level of interpretability required for your model/project
   Think about the level of interpretability required for your model, especially in domains like healthcare or finance where transparency is crucial for regulatory compliance. Choose models that align with your interpretability needs and provide the necessary level of transparency.

What Should be the AI Implementation Plan?

To successfully implement AI in your business, begin by defining clear objectives aligned with your strategic goals. Identify the specific challenges AI can address, such as enhancing customer experiences or optimizing supply chain management.

Next, assess your data quality and availability, as AI relies on robust data. Ensure your data is accurate, relevant, and comprehensive. If necessary, invest in data cleaning and preprocessing to improve its quality.

Select the appropriate AI models that align with your objectives and data type. Train these models using your prepared data, and integrate them seamlessly into your existing systems and workflows.

Prioritize ethical considerations to ensure fairness, transparency, and lack of bias in AI systems. Thoroughly test and validate your AI models, and provide training for your staff to effectively use the new AI tools.

Plan for scalability and ongoing monitoring while staying compliant with data privacy regulations. Continuously measure ROI and the impact of AI on your business objectives, making necessary adjustments along the way.

Consider partnering with AI experts or service providers to streamline the implementation process. With a well-structured plan, AI can transform your business operations, decision-making, and customer experiences, driving growth and innovation.

Implementation Roadmap

An AI business roadmap is a strategic plan that outlines how your organization will incorporate AI technologies into its operations to achieve specific business objectives. Also, an effective AI roadmap helps in creating a clear path to meet your strategic goals while driving big business impact and ROI.

1. Assessment and Preparation
   You should begin by assessing your current business processes and identifying where AI could make a significant impact. Consider how AI can streamline operations, enhance customer experiences, or even create entirely new revenue streams. In the tech world, we can call this step a “discovery phase”.
2. Defining Clear Objectives
   Whether it’s through automation, predictive analytics, AI chatbots, or innovative product development, AI has versatile applications.
3. Technology Selection
   Once you have defined goals and found a skilled team, identify the AI technologies that align with your objectives. From ML and NLP to robotic process automation, and computer vision, there’s a myriad of AI directions to choose from. Each has its strengths, and the choice depends on your business needs.
4. Data Collection and Analysis
   AI relies on high-quality data to generate accurate insights and predictions. Your data may need to be cleaned, processed, and labeled to ensure it is suitable for AI model training. Read more about data collection for ml.
5. Data Integration
   You may consider investing in data integration tools and platforms that facilitate the seamless integration of data from disparate sources. Choose tools that support data extraction, data transformation, data loading (ETL), and data synchronization to streamline the data integration process.
6. Primary Testing
   It’s recommended to try implementation with the pilot programs before full-scale integration. You may start small by initiating a pilot project to tackle specific challenges. Use this opportunity to refine AI models based on their performance.
7. Integration with the Existing Processes
   Determine what you aim to achieve through AI integration, whether it’s reducing costs, increasing productivity, improving quality, or enhancing customer experiences. Integrate AI with your existing data sources and systems to ensure access to relevant data for AI algorithms.
8. Scalability and Flexibility
   They are crucial considerations when implementing AI in your business roadmap to ensure that your AI initiatives can grow with your organization and adapt to evolving needs. For example, you can design AI solutions with a modular architecture, leverage cloud computing services to build scalable and flexible AI solutions, or build a scalable data infrastructure that can handle large volumes of data generated by AI applications.
9. Monitoring and Improvement
   This step ensures the smooth strategic implementation of AI in your business. As we mentioned, tools like KPIs, automated alerts/notifications, user feedback, or data quality monitoring are needed to successfully evaluate and measure the impact of AI on your business and operations.
10. Documentation & Knowledge Transfer
    By documenting AI processes, methodologies, and best practices, and facilitating knowledge transfer through training, workshops, and mentorship, you can ensure that your organization effectively leverages AI to drive innovation. For example, work on establishing documentation standards and guidelines to ensure consistency and clarity in documenting AI-related processes, methodologies, and best practices.

Overcoming Challenges

It’s very important to acknowledge the common roadblocks to building a successful AI strategy. For example, insufficient data, misalignment of strategy, etc. Realizing and preventing these challenges early is a must in your AI strategy planning.

Common challenges are:

1. Insufficient data
   AI models rely heavily on robust datasets, so insufficient access to relevant and high-quality data can undermine the strategy and the effectiveness of AI applications.
2. Lack of AI knowledge
   A lack of awareness about AI’s capabilities and potential applications may lead to skepticism, resistance, or misinformed decision-making. This will drain any value from the strategy and block the successful integration of AI into the organization’s processes.
3. Misalignment of Strategy
   If the AI initiatives are not closely tied to the organization’s goals, priorities, and vision, it may result in wasted efforts, a lack of support from leadership, and an inability to demonstrate meaningful value.
4. Scarcity of Talent
   Professionals are needed to effectively develop, implement, and manage AI initiatives. A shortage of AI talent, such as data scientists or ML experts, or resistance from current employees to upskill, could impact the viability of the strategy.

Case Studies and Examples

Let’s check in detail the real-life examples of AI implementation in organizations.

Final thoughts

Even though artificial intelligence and machine learning open many doors for businesses in 2024, we believe that at this point we can not realize AI’s full potential just yet. For any company, the success of AI initiatives depends on many factors such as creating a well-designed AI strategic plan, coming up with approaches to implement this plan, and being aware of possible challenges. These are the key pillars of the AI integration process. Nowadays we can observe many cases of how AI benefits businesses and Waverley is doing what’s in our power to help streamline that technological revolution, working with companies and organizations worldwide as their AI transformation partner.