How to create an appropriate AI strategy for your company

Ten steps to success for your company's AI strategy

In this article, we’ll delve into how to build the right AI strategy for your company through practical experiences from AI projects. We’ll look at how to set goals, what the most important elements of the strategy are, and how to ensure that AI supports the company’s long-term goals.

This is how you create an AI strategy for your company:

1. Establish goals and metrics
2. Ensure skills and expertise
3. Identify and prioritize AI use cases
4. Ensure data governance
5. Choose the right AI technology for your business
6. Develop guidelines for responsible and ethical use of AI
7. Plan the implementations of an AI project
8. Map AI risks
9. Monitor and evaluate performance
10. Ensure scalability and sustainability

Our approach to planning an AI strategy is based on solid experience:

• We have already completed a large amount of AI projects within Twoday, which has deepened our understanding of the application of AI in various industries.
• Our team consists of AI experts who are dedicated to exploring and utilizing the latest technologies.
• Our developed application Twoday AI Agent, which supports multiple language models such as OpenAI GPT, Llama and SiloAI Poro, is an example of our technical expertise.

Do you want to make sure you're ready to take advantage of AI in your business?

Now you can download our checklist that our experts have developed to help you create an effective AI strategy.

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1. Establish goals and metrics

Setting clear and measurable goals for AI projects means defining the outcomes your organization wants to achieve. For example, such goals might include improving efficiency or automating processes.

You should also set clear metrics for your goals, such as improved customer satisfaction or faster processes. Keep your goals flexible and update them to reflect changing business conditions and technological advances.

This includes regular evaluations and, if necessary, updating goals and metrics to keep them relevant and achievable.

AI project success metrics

1. Change in customer satisfaction:
   Compare the results of feedback surveys before and after the project.
2. Reduced response time:
   For example, compare the speed of answering customer service questions before and after AI implementation.
3. Increase in automation:
   Estimate the degree of automation of processes using artificial intelligence.
4. Increase in processing volumes:
   Measures the change in customer service processing volume without additional resources.
5. Campaign Performance Improvement:
   Measures the increase in leads or sales generated by marketing campaigns using AI.
6. Cost Savings (across business lines):
   Compare cost savings that the project brings with the old operating model.
7. Customer Attrition Reduction:
   Use AI to track improvements in customer retention.
8. Employee Satisfaction Improvement:
   Evaluate the improvement in the quality of employee work thanks to artificial intelligence.
9. Market Share Growth:
   Measures the increase in the company's market share as a result of an AI project.
10. New Customer Acquisition:
    Track the growth of new customers as the project progresses.

2. Ensure competence and expertise

For a smooth implementation of AI in your company, it is crucial to identify the skills and roles required for the project. You also need to ensure that your organization has the necessary expertise available.

A broad team of experts helps maximize the value of AI and a smooth transition to AI-based processes.

Key competencies include, for example:

• Prompt engineering, which involves designing and optimizing instructions or "prompts" that communicate with artificial intelligence.
• Data management
• Transformational project management
• Information security expertise
• Collaboration expertise

Managing these areas ensures not only that technical requirements are met, but also that AI solutions are seamlessly integrated into your company's daily operations. This enables you to maximize the value of AI and contributes to a smooth transition to AI-based processes.

3. Identify and prioritize AI use cases

Whether it’s automating customer service, accelerating content creation, or any other process, identifying and prioritizing AI use cases is a cornerstone of your AI strategy. It’s important to define goals and metrics for each use case that drive your business towards tangible results.

Analyze how different AI applications can support and improve your business areas. Set clear, measurable goals and track their performance, such as through customer satisfaction, employee satisfaction, cost savings, or improved performance. This ensures that AI adoption delivers real value and advances your organization’s goals.

Examples of AI applications that may be of interest to businesses include:

• Efficiency and automation of customer service.
• Create personalized shopping experiences in your online store.
• Optimization of production processes in industry.
• Forecasting and optimizing energy consumption.
• Analysis and optimization of traffic flows in cities.
• Fraud detection and risk management in the financial sector.
• Human resource management and optimization.
• Forecasting and analyzing market trends.
• Analysis of health data and personalized treatment recommendations.
• Data analysis to develop and optimize marketing strategies.

4. Ensure data management and continuous quality assurance

Data management is the foundation for effective use of AI models. To use AI models effectively, build systems that enable the collection, storage, processing, and maintenance of high-quality data, which is critical to the accuracy and reliability of AI models.

To achieve this, your organization needs to invest in data infrastructure that supports the collection and analysis of large amounts of data in real time, and ensure that data governance and protection comply with GDPR and other data protection regulations.

Continuously ensuring data quality and regularly updating it are also critical factors. By developing and maintaining processes that ensure data quality and timeliness, you can maximize the effectiveness of your AI models. This includes methods for correcting incorrect or missing data, enriching data, and ensuring that you have access to a diverse and comprehensive collection of data that represents different scenarios and situations.

5. Choose the right AI technology for your business

For a company to remain competitive, it is crucial to choose AI technologies and platforms that best meet the needs and goals of the organization.

Here are some things to consider when choosing an AI solution:

• Technical quality and capabilities of the solution
• Security in use
• Compatibility with current system environment
• Compatibility with long-term strategy
• Cost-effectiveness
• Technical scalability
• Technical upgradeability
• Training and support needs

When assessing technical quality and capabilities, you need to determine whether the solution offers the necessary features and performance for your specific business needs. The technology must be flexible enough to handle current and future tasks and robust enough to support complex analytics and data management processes.

Security in use is also a critical factor. It must be ensured that the technology complies with industry security standards and legislation, especially when handling sensitive or personal data. This includes both physical and digital security, including data encryption and access control.

Compatibility between the technology and the current system environment is also important. Effective integration with existing systems and software reduces implementation complexity and costs. It also enables a smooth transition to new technologies, reducing downtime and improving employee onboarding.

Integration of business processes and technical support for long-term business strategy are critical factors to consider. Evaluate how the selected AI technology integrates with existing business processes and supports long-term business strategy. It is also important to ensure that users receive the necessary training and support to effectively leverage the technology.

Finally, it is crucial to evaluate costs, resources, scalability, and upgradeability. The technology should be cost-effective in relation to the value it adds, and it should grow and evolve with your business. Scalability ensures that the technology can handle the increasing data volumes and user numbers generated as your business expands, while upgradeability ensures that you stay up to date with the latest technological innovations.

6. Develop guidelines for responsible and ethical use of AI

Develop clear guidelines for how AI should be used responsibly and ethically within your organization. These guidelines should cover ethical aspects such as data management, bias management, user privacy and data security.

In practice, this means creating concrete principles of action and documenting them. Ensure that the guidelines are effectively applied by regularly training staff so that they understand these principles and can apply them in their daily work.

You can also use, for example, the European Commission’s Principles for Responsible Artificial Intelligence, which cover seven key areas, to help organizations identify and mitigate risks when using AI.

European Commission Principles for Responsible Artificial Intelligence:

• Human agency and oversight
• Technical robustness and security
• Integrity and data protection
• Transparency
• Diversity, non-discrimination and fairness
• Environmental and social well-being
• Accountability
  
  

7. Plan the implementation of an AI project

Creating a roadmap for implementing AI projects is a key part of project management and the success of your AI strategy. This roadmap covers the entire project lifecycle, from initial design and prototype development to final deployment and continuous improvement processes.

Key elements include scheduling, resource allocation, and integration with existing systems, as well as planning for required skills and identifying gaps.

The roadmap should include flexibility for changing circumstances and technological developments, including regular checkpoints and evaluation methods. These measures ensure that projects stay on schedule and budget, and effectively achieve set goals.

8. Map AI risks

To identify potential risks with AI projects, it is important to conduct a comprehensive risk analysis that includes:

• Technical risks, such as system failures or security issues.
• Operational risks, such as inefficient processes or incorrect decisions.
• Reputational risks, such as loss of customer trust.
• Ethical risks, such as lack of transparency in AI decision-making and potential biases.
• Legal risks, such as lack of compliance and possible legal consequences.
• Financial risks, such as unexpected costs and lack of return on investment.

Based on this analysis, create concrete strategies to manage and minimize these risks. Risk management strategies can include training programs, technology upgrades, backup and recovery plans, and regular stakeholder engagement.

💡 To facilitate risk analysis, you can use public databases such as the Partnership on AI case database, where you can read about past AI case studies. This helps identify potential risks in advance. For example, the Partnership on AI collects an AI Incident Database with over 1,200 reports of problems caused by AI systems.

9. Monitor and evaluate performance

It is important to define metrics and processes to monitor the effectiveness of AI solutions and set goals, as well as make adjustments. Create systems for collecting feedback and checkpoints that allow for real-time monitoring of AI solutions and quick action in case of deviations from planned results.

The role of specialists in this process is invaluable. Their expertise helps you interpret data more deeply, identify hidden performance challenges and suggest effective adjustments.

In addition, their expertise ensures that collecting feedback and checkpoints not only supports real-time monitoring of AI solutions, but also long-term development and learning. This ensures that AI solutions not only remain relevant, but also constantly evolve to meet the changing demands and opportunities of the business.

Involving an expert in the process ensures that the monitoring and evaluation of your AI solutions is targeted, accurate and provides timely information to support decision-making.

10. Ensure scalability and sustainability

An essential part of the AI ​​strategy is to design long-term scalability and sustainability for AI solutions, so that they can evolve in line with changing business needs and technological advances.

This design should include modular and flexible architectures that allow AI systems to be easily upgraded and expanded without significant disruption to their operations. It is also important to plan resources and infrastructure so that AI solutions can scale effectively as the organization grows and the market changes.

By following an AI strategy, you can better leverage AI effectively and responsibly, maximizing its benefits while minimizing risks and challenges.