Article discusses financial strategies, pricing models, and IP protection for AI commercialisation, indicating essential steps for success.
The successful commercialisation of artificial intelligence (AI) solutions requires a comprehensive business strategy that extends far beyond technical development. To transition a proof of concept into a market-ready entity, innovators must adopt a multidisciplinary approach that integrates robust financial architecture, strategic pricing models, and rigorous intellectual property (IP) protection. A foundational element for securing investment is developing a detailed financial framework, articulated through an Income Statement for profitability forecasting, a Cash Flow Statement to manage operational runway, and a projected Balance Sheet for long-term valuation. This financial planning must also incorporate risk management, often through a contingency fund. Pricing strategy is equally critical, advocating for a shift from simple cost-based models to more lucrative but complex value-based methodologies. The article cautions against arbitrary discounting and instead promotes a tiered architecture that employs psychological price anchoring and modular value extraction to protect profit margins, especially in aggressive negotiations. This commercial framework must be supported by forensic customer intelligence and a tailored market presentation, differentiating pitches to clients from those to investors. Finally, securing the core asset is paramount, with a detailed focus on copyright law as the primary means of protecting software and AI-related works. It outlines how software is classified as a "literary work" under Indian law, the extensive global protection afforded by international treaties, and the procedural mechanics of registration in India. The article concludes that the fusion of technical innovation with disciplined financial planning, value-conscious pricing, and proactive legal protection is essential for transforming AI concepts into durable, competitive, and economically sustainable business assets.
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P. K. Majumdar (2026) studied this question.
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