Continuing our journey into the world of Artificial Intelligence (AI), this article expands on previously presented information, delving into the process of designing AI products and services for businesses in 2024. Offering a detailed guide based on MIT recommendations, we will explore the four essential stages and eight key decisions in developing AI solutions. From defining metrics and scope, through strategy and operations, to choosing technology and managing specific AI challenges, each phase is detailed to transform ideas into impactful innovations. We will review the decisions of each phase, such as choosing tools, intellectual property strategy, and managing operations and data, which are crucial for product success.
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Stage 1 – Identifying Behaviors and Process
This stage is crucial for determining the direction and goals of the project. It focuses on:
- Identifying behaviors and desired outcomes of the AI system, understanding its capabilities and limitations. Experimentation is recommended to find successful use cases and gather potential applications of generative AI for each specific company.
- Setting realistic goals. Key questions to consider include the specific problem to be solved with AI and how to measure the project’s success. SMART metrics should be defined to effectively evaluate project progress and impact, such as:
- Improved process efficiency
- Increased task accuracy
- Increased staff productivity
- Reduced processing time.
This phase also includes defining, along with the project scope, the areas of application, technological limitations, and available resources, which helps keep the project focused and aligned with business objectives.
Stage 2 – Strategy and Operations
This phase is important for integrating artificial intelligence into a company’s existing structure and workflows. This stage focuses on how AI will be incorporated into daily operations to improve efficiency and achieve specific business goals.
In this phase, key decisions are made about strategy and operational focus:
- Type of Tools. The decision must be made whether to use third-party AI tools, develop proprietary solutions, or a combination of both. This choice depends on factors such as costs, resources, internal expertise, and specific project goals. Using third-party tools offers speed and efficiency with proven technologies, while in-house development allows for customization and full control. A mixed strategy leverages both options, adapting existing solutions and developing key components internally.
- Operations. Daily operations around AI are also planned. This includes identifying the business processes that will be affected, determining operational objectives, and considering necessary changes to infrastructure, staff training, and alignment with overall company strategies.
This phase is a bridge between the conceptual vision of AI and its practical application, ensuring that the technology is not only advanced but also relevant and applicable in the specific business context.
Stage 3 – Choice of IP Strategy and Data
This phase involves selecting the appropriate Intellectual Property (IP) strategy and formulating an effective data strategy.
- Choice of Intellectual Property (IP): The right AI technology is selected, considering the specifications derived from previous stages. The choice is challenging due to the wide range of options and the rapid obsolescence of some technologies. It is suggested to consider patenting if the AI application is novel and useful.
- Data Strategy: The source of the data, how it will be stored, protected, and processed to optimize the performance of the AI model to be obtained must be determined. This decision directly impacts the quality and effectiveness of the AI product.
Stage 4 – Development with an Applied AI Focus
Stage 4 is a critical process in implementing artificial intelligence solutions. In this stage, the effective development of AI software is carried out, and unique technology-related challenges are addressed. In this phase, two fundamental decisions are faced:
- Software Development Strategy: This choice involves defining the approach and methodology for developing software for your AI project. Agility and adaptability are crucial, as AI is a constantly evolving field. Therefore, the development plan must be flexible to accommodate changes and discoveries during the process.
- Addressing AI Challenges: Here we must address how you will tackle specific AI problems, such as biases and unwanted behaviors, lack of generalization, and other “AI cancers.” It is important to develop strategies to detect and mitigate these issues, ensuring the safety, efficiency, and ethics of your AI solution. This involves constant monitoring and adjustment of the model to ensure its proper operation and avoid unexpected negative impacts.
Stage 4, therefore, not only focuses on building the AI system but also on its continuous refinement to optimize its performance and reliability.
In conclusion of this journey on the critical decisions of implementing artificial intelligence (AI) in the business environment, we recommend innovators who will develop products and services to give relevance to an integrated and continuous approach in acquiring knowledge and advice on AI. This process is not static but must evolve to adapt to changes in the technological and business environment. It is crucial for entrepreneurs to invest in both technology and their AI training. A deep understanding of AI and staying up to date with innovations allows for strategic decision-making and effectively leading the integration of AI solutions. The AI consultant acts as a complementary guide, offering not only guidance and understanding of AI principles but also managing risks and maintaining competitive advantages. This approach ensures an effective and adaptive use of AI throughout its lifecycle in business, key to sustained success in the modern market.
This article was created with the support of AI technology, using reliable information sources, and was reviewed by an expert in information technologies to ensure its accuracy.
