Essential Steps to Mastering Your AI Strategy for Business Success

Artificial intelligence (AI) has been a powerful motive for innovating and reinventing business operations. This technology has benefited various industries by granting machines the capabilities to understand, interpret, and make decisions based on trained data, releasing human agents from routine tasks.

However, implementing AI technology without a detailed strategy causes businesses to skip their goals and current resources to maximize the potential benefits. An AI strategy allows organizations to align the AI solution with business issues, ensuring they achieve proposed goals and objectives. Moreover, it will help strategize the implementation roadmap to prepare needed resources and expertise at every step of the process.

To help businesses adapt to the increasing trend of adopting artificial intelligence technology, in this article, we’ll outline the steps to building an AI strategy and tips to choose a strategic service for long-term success.

What Is An AI Strategy?

An AI strategy is a plan that specifies the roadmap to implement artificial intelligence technologies that align with businesses’ goals. It should define clear objectives, indicate needed tools, clarify the data-employing process, and establish the framework for monitoring performance.

A well-planned AI strategy helps organizations choose the right algorithms, manage their data effectively, and address their skill gaps by hiring AI experts. It also enables businesses to adapt to updated technology and industry shifts during implementation.

The responsible data use will be guaranteed under a specific framework that mitigates ethical issues such as transparency, data biases, and regulatory concerns. As artificial intelligence continues to impact various sectors virtually, a strategic AI approach empowers organizations to maximize capabilities and outperform competitors in the evolving digital age.

Benefits of Having an AI Strategy

Overall, a well-designed AI approach can prevent businesses from significant delays and wasted resources while deploying the solutions. Regarding different sizes of AI projects, detailed AI strategies will identify readiness gaps and opportunities to scale businesses effectively.

Product-Level AI Strategy’s Benefits

An AI strategy grants product-level projects foundational building blocks to emerge as helpful business tools from raw ideas. The plan’s detailed level will depend on the type of solution, whether businesses want to buy an off-the-shelf tool, build from scratch, or customize an existing one.

A detailed AI product strategy will help the company indicate data used for training regarding its volume, quality, and format. Besides, it can assign the number of personnel required to build models and data pipelines and perform integration.

After the initial preparation, you can install proper IT infrastructure, establish performance metrics, and manage ethical considerations.

At the Business Unit Level

For the business unit level, an AI strategy will help align all enterprises’ resources with businesses’ objectives. By ensuring businesses choose the right initiatives regarding data and infrastructure readiness, the AI strategy creates a logical implementation roadmap for maximizing results.

Plus, a comprehensive AI strategy can help businesses address new problems that greatly affect the operational process. Meanwhile, human agents can provide data for A/B testing and leverage their domain expertise to enhance the AI model’s performance.

At the Organizational Level

An AI strategy can prepare the organization for the implementation roadmap by creating seamless collaboration between business units. Indeed, with an in-depth AI strategy, each department can utilize the necessary resources and comprehend AI technology at a basic level.

Planning an AI strategy will help businesses identify potential gaps in budget, culture, data, infrastructure, and skills needed before implementing the solutions. A lack of cultural readiness for AI can hinder its adoption, impair employee collaboration on AI projects, and increase the risk of unforeseen challenges.

Key Pillars of an AI Strategy

The AI strategy undergoes a holistic process focusing on four key elements: Visions, Values, Risks, and Adoption. This structured approach will establish a business-oriented vision and then select and justify specific initiatives.

The following sections will clarify considerations of each pillar to help businesses conduct an in-depth strategy for AI projects.

AI Vision

Visualizing the potential of AI technology to drive significant results for businesses enables them to adjust their plans based on appropriate performance metrics. Each business goal regarding increased revenue, better customer engagement, or more optimized costs requires different metrics to measure the AI projects’ results accurately.

Business metrics that an organization should focus on when planning an AI strategy include:

• Business growth: includes cross-selling, price adjustments, demand forecasting, and monetizing new assets.
• Customer success: track retention rates, satisfaction metrics, and increased shares of customer wallet.
• Cost-efficiency: inventory optimization, production cost reduction, employee productivity enhancement, and asset utilization.

When establishing metrics to make the AI vision realistic, the AI team should gather opinions from related departments, domain experts, and data scientists to mitigate implementation risks.

AI Values

Maximizing AI value requires business readiness to change the organizational structure and employee skill sets to capture the identified opportunities. Companies can identify barriers that slow adoption by analyzing internal staff’s skills and current operational systems and investing more in necessary solutions to optimize AI value.

Moreover, they can come up with strategic actions to adapt to changes in the organizational structure. For example, businesses will prioritize executive-level training to address organizational data literacy deficiencies to ensure alignment with AI project goals.   

AI Risks

The increasing ethical concerns in AI system development could lead to new regulatory frameworks in response to public opinion. Along with challenges in monitoring ethical uses, businesses must consider talents’ competencies and AI services’ reputations to gain the best results from every AI initiative.

AI Adoption

Business stakeholders can identify the most valuable use cases by evaluating technical, internal, and external factors. Indeed, companies should explore the portfolio of use cases in which AI solutions can support scaling quickly with measurable performances. The AI use case should also match the organizations’ orientation by solving problems for companies and their target customers.  

Steps for Building a Successful AI Strategy

Following is the step-by-step approach to map out an effective AI strategy tailored to business goals regardless of the project’s size and complexity:

1. Understand AI technologies: There are various terms of AI technologies across diverse applications, such as machine learning, natural language processing, computer vision, and generative AI. Businesses must understand its advantages for specific departments, implementation methods, and potential roadblocks.

2. Define clear objectives: AI solutions’ performance differs depending on the types of business issues. Therefore, businesses should clarify their long-term objectives before implementing AI technology.

3. Select suitable partners and vendors: Working with trustworthy AI and ML consulting services lets businesses manage their resources accordingly in each phase of the AI integration project. Companies should thoroughly assess potential tools, vendors, and partnerships to find a suitable solution.

4. Build a Roadmap: The AI strategy should clarify the implementation roadmap, indicating the tools and support needed to ensure the project’s success. In detail, the plan must be deployed thoroughly based on the following elements:

• Data: Manage data quality and volume for training AI models. Companies should also establish a comprehensive data governance framework to optimize data management.
• Algorithms: A quality machine learning algorithm can generate trusted outcomes to support data-driven decisions. It requires skilled AI experts to design, develop, and validate models.
• Infrastructure: An appropriate platform can impact an AI project’s scalability significantly. Practically, organizations can consider different options, whether customized or ready-to-use platforms.
• Talent and outsourcing: Evaluate the skillset and knowledge of the internal team before integrating AI initiatives. Afterward, businesses can outsource specialized tasks or build an internal talent pipeline with advanced skills for required roles, such as developers and data scientists.

5. Present the AI strategy: Introduce the plan to stakeholders for final approval, explaining the costs, potential value, and expected results. Communicate early to ensure the strategy aligns with the businesses’ objectives.   

6. Train and encourage internal staff to learn: Encourage the internal team to explore cutting-edge AI advancements and creative approaches to solve problems, fostering a culture of innovation and adaptability.

7. Establish ethical guidelines: Encourage responsible use of AI with an actionable guideline based on government regulation. Indeed, the strict framework will allow for fair and transparent practices to mitigate data biases.

8. Adapt to future trends: Monitor the AI model’s performance regularly and adjust the strategy based on new insights and opportunities.

Challenges of Building an AI Strategy

Many issues might prevent AI projects from scaling enormously, requiring early access to minimize the impact. Common challenges of conducting an AI strategy include:

• Insufficient data: As mentioned, training the AI model with inadequate quality and relevant data leads to inaccurate output results, affecting data-driven strategic decisions. A detailed data strategy is necessary to gather large volumes of data from various sources.
• Lack of AI knowledge: The complexity of AI projects raises the need to equip comprehensive AI knowledge to ensure effectiveness. Indeed, understanding how the ML algorithm processes data helps businesses evaluate performance and optimize the operational system.
• Talent deficiency: Limitations in upskilling for current employees trigger businesses to seek outsourced options. However, the market shortage and high costs of recruiting experienced ML experts make it difficult for organizations to manage human resources.
• Inconsistent objectives: Unallignment between AI initiatives and the organization’s visions leads to inappropriate assets and wasted efforts when deploying AI solutions.
• Legal risks: Businesses must follow current AI regulations to avoid deploying copyrighted and protected data.

How to Choose The Right AI Strategy Consultant

A competent AI consulting partner indicates a high adoption success rate. However, the differences in business use demands and vendors’ expertise make it challenging to choose the right consultants.

The decision to partner with an AI consulting service should be based on the following considerations:

• Domain knowledge: The AI consulting service needs to design the AI strategy based on an in-depth understanding of the business industries. For instance, AI applications for healthcare sectors demand consultants to have expertise in managing patient data and improving diagnostics. Similarly, the third-party service should explain how finance businesses can manage financial risks and detect fraud using AI. Certifications and affiliations with industry associations can further validate a consulting service’s credentials.
• Proven successful portfolio: A track record of previous projects can help AI firms convince that they can deliver expected results. Companies should evaluate consultant’s case studies thoroughly from conception to execution. Besides, AI services can prove themselves as reliable partners when establishing long-term relationships with their current customers.
• Technological expertise: The consultant service must show extensive knowledge about different aspects of AI technology, covering NLP, ML, computer vision, and robotics. Simultaneously, consultants’ proven experience handling large-scale data systems and standard certification can qualify them as reliable industry experts.
• Ethical AI commitment: Managing data in a transparent framework enables the generation of unbiased and fair outputs for data-driven decisions. Industries like finance, government, and healthcare will require consultants to conduct an AI strategy to protect sensitive data and cover all user groups.

Implement Successful AI Strategy with Neurond AI Service

Neurond’s AI services contribute our expertise in establishing comprehensive AI strategies for various industries, from finance or education to marketing, logistics, and healthcare. We ensure to help businesses understand the current platform holistically, prepare relevant data for training, and deploy the models after a series of testing algorithms.

Contact us now to receive consultancies of an optimized data-driven AI strategy for your company.