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2023 | Buch

Fundamental of AI Startups Kapitel lesen Erstes Kapitel lesen

AI Startup Strategy

A Blueprint to Building Successful Artificial Intelligence Products from Inception to Exit

verfasst von: Adhiguna Mahendra

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

Gain exclusive access to the secrets to building an enterprise AI start-up. AI innovation helps with every aspect of the business, from the supply chain, marketing, and advertising, customer service, risk management, operations to security. Industries from different verticals have been adopting AI and get real business values out of it.
This book guides you through each step, from defining the business need and business model, all the way to registering IP and calculating your AI start-up valuation. You see how to perform market and technology validation, perform lean AI R&D, design AI architecture, AI product development and operationalization. The book also cover building and managing an AI team, along with attracting and keeping business and developer users,
Building an Enterprise AI start-up is hard because Enterprise AI is an effort to build applications to mimic human intelligence to solve business problems. Hence it has a different challenge from building traditional non-AI applications, such as scouting, recruiting and managing AI talents; designing the most cost-efficient and scalable Enterprise AI; or establishing the best practice to operationalize AI in production
As we are in the dawn of the AI-first product wave, AI-powered products for enterprises will be created for many years to come and AI Startup Strategy is the one-stop guide for it.

What You'll LearnMatch customer’s expectation VS technical feasibilityJustify business values and ROI for customersReview the best business models for high valuation enterprise AI start-upsDesign an AI product that gives a satisfactory experience for the user
Register and value AI IP

Who This Book is For
Startup Founders, Product Managers, Software Architects/Lead Engineers, Executives

Inhaltsverzeichnis

Frontmatter
Chapter 1. Fundamental of AI Startups
Abstract
Artificial intelligence is a machine that can reproduce human cognitive capabilities and perform better than humans in scale, speed, endurance, and accuracy. Such capabilities include vision, speech recognition, natural language processing (NLP), learning, planning, and strategy. They are made possible by the availability of large amounts of data, growing computing power, and improved learning algorithms.
Adhiguna Mahendra
Chapter 2. AI Startup Landscape
Abstract
In the previous chapter, we built a conceptual understanding of the fundamental of AI techniques and their applications, the foundation of AI startups, and how AI helps enterprises become AI-first companies.
Adhiguna Mahendra
Chapter 3. Product-Market Validation for AI-First SaaS
Abstract
Successful AI startups need to understand the nature of AI technology and how it is bought and used. AI is an enabling technology – a set of tools and technologies that can be applied to solve numerous use cases. Therefore, AI will enable many use cases for enterprises – AI can solve substantial inefficiencies in the corporates and even create a whole new business. Netflix, Amazon, and Uber are examples of companies where AI enables business models.
Adhiguna Mahendra
Chapter 4. Product-Market Validation for AI as a Service (AIaaS)
Abstract
The previous chapter taught us how to validate AI-first SaaS with the business user as the target market. In this chapter, we will learn more about AI as a Service (AIaaS), which targets the developers' market, and how to validate its market.
Adhiguna Mahendra
Chapter 5. AI Product Strategy
Abstract
In the previous chapters, we learned how to validate AI products for B2B and B2D markets. After we validate the market, the next step is to build a sound product strategy essential to the overall process. The product strategy guides decisions regarding the vision and mission of the product, the outcome expected for the customers, the stakeholders involved in their development and marketing efforts, and other crucial aspects necessary for success, like desired feature sets.
Adhiguna Mahendra
Chapter 6. Human-Centered AI Experience Design
Abstract
In the previous chapter, we learned how to create an AI product roadmap. This is a tool that product managers and engineers use to plan the features and functionality of AI-based products based on user needs and demands. The roadmap helps ensure that the products align with the company's strategy and vision. It can also be used to communicate with stakeholders about the planned features and their impact on the business.
Adhiguna Mahendra
Chapter 7. Human-Centered AI Developer Experience Design
Abstract
In the previous chapter, we learned how to build a human-centered AI experience design oriented toward the end user. In this chapter, we will learn how to design AI developer experience (DX), a methodology aimed at making the development process of AI-powered applications more straightforward and intuitive for developers. By focusing on the needs and preferences of developers, this approach can help streamline the creation of AI-based products and services. In addition, by providing a framework for incorporating AI into applications, AI developer experience design can help reduce the risk of errors and glitches during development. In building a developer experience for AI products, design thinking is essential to create an intuitive and user-friendly experience that contributes to the AI product's robustness and trustworthiness. By empathizing with the needs and preferences of developers and using design thinking methodologies to guide the development process, teams can create an experience tailored to their target audience's needs. This allows for a more efficient and effective development process and an end product more likely to succeed. In this chapter, we will explore the role of design thinking in creating a successful developer experience for AI products and provide two case studies that illustrate its application in practice.
Adhiguna Mahendra
Chapter 8. Building an AI Platform
Abstract
As an AI startup, it is essential to have a clear strategy for designing, developing, and operating an AI platform that can meet market demands. In the previous chapters, we learned about validating AI products from various perspectives. This chapter will explore the details of building a successful AI platform that involves designing, developing, and operating the AI system. Building an AI platform is a complex process that requires careful consideration of many factors. We will explore the importance of the three pillars of AI platform design: system design, process design, and team design. We will provide a comprehensive framework that unifies these pillars into a single approach to building an effective AI platform. Measuring the maturity of an AI platform is also an essential aspect of the building process. We will discuss how to assess the platform at different stages, from initial development to optimized operation. This chapter will also discuss the challenges and best practices of building an AI platform. We will explore common issues and provide practical solutions to ensure success. To illustrate how the framework and best practices discussed in this chapter can be applied in practice, we will provide a case study of designing, developing, and operating an eKYC AI as a Service platform. This case study will provide real-world examples of the key considerations and decisions in building an effective AI platform. By the end of this chapter, you will have a comprehensive understanding of what it takes to build a successful AI platform and the steps involved in designing, developing, and operating such a system.
Adhiguna Mahendra
Chapter 9. Go-to-Market Strategy for an AI Startup
Abstract
The previous chapters explored the essential steps in validating, developing, and operationalizing an AI product. This chapter will delve into the crucial topic of the go-to-market (GTM) strategy for an AI startup. Similar to any other product, launching an AI product requires a well-defined strategy to make it a successful and sustainable business. We will discuss the significance of the GTM strategy for businesses and the unique challenges that AI startups face while devising their GTM strategy.
Adhiguna Mahendra
Chapter 10. AI Startup Exit Strategy
Abstract
As discussed in the previous chapters, building a successful AI startup is no easy feat. It requires a deep understanding of complex technology, its operationalization, a sound business model, and the ability to navigate complex market dynamics. But once a startup has gained traction and is on the path to success, the founders need to start thinking about their exit strategy.
Adhiguna Mahendra
Backmatter
Metadaten
Titel
AI Startup Strategy
verfasst von
Adhiguna Mahendra
Copyright-Jahr
2023
Verlag
Apress
Electronic ISBN
978-1-4842-9502-1
Print ISBN
978-1-4842-9501-4
DOI
https://doi.org/10.1007/978-1-4842-9502-1

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