AI has seen a monumental rise in visibility and applicability for business in recent years:ﾠ The IBM Global AI Adoption Index 2022, for example, showed that 35% of businesses are deploying AI, with another 42% exploring its use. These numbers suggest that there is plenty of opportunity for most organizations to integrate and scale the technology. While data complexity, cost, and scalability are significant barriers to AI adoption, the leading roadblock is a longstanding problem: the AI talent crunch.

IBM also reports that 34% of companies surveyed cite limited AI skills, expertise, or knowledge as the primary reason for lack of AI implementation. For over a decade, the demand for this talent has far outpaced supply, and even enterprise-level organizations struggle to attract and retain the specialized individuals needed to create AI solutions in-house. The New York Times recently reported, for example, that many AI specialists command salaries from $300,000 to $500,000 a year in addition to equity or company shares. Launching a pilot AI project (as opposed to an enterprise-scale ready-to-use tool) requires full-time data architects, scientists, and engineers, a range of specialized developers, as well as project managers, product managers, and other operations support staff.ﾠ

The takeaway: the cost of a from-scratch, in-house AI team can be well into millions of dollars on salaries alone per annum.

Companies are exploring many options for hiring and retaining the best talent, including increasing pay when possible, partnering with universities and bootcamps by sponsoring research projects and providing scholarships and internships, and, perhaps most popular, upskilling existing employees to learn necessary skills. But unless an organization’s strategy is oriented around releasing new AI technology, the time and expense involved in building talent pools is not well spent. 

This is one reason why companies are increasingly interested inﾠ packaged AI solutions. These commercial AI platforms solve specific business problems at a fraction of the cost of developing in-house technology, and are increasingly easy to implement and scale.ﾠ

NobleAI creates commercial Science-Based AI solutions for chemical and material product development. Read on to learn how off-the-shelf AI tools can make AI adoption faster and cheaper for chemical and materials companies. We discuss:

• How the accessibility, data-efficiency, and scalability of commercial AI solutions drive business values; and
• How our unique approach to Science-Based AI makes it fast and easy for chemical and material product teams to digitally transform their workflows. 

Off-the-Shelf AI Unlocks Business Value At a Fraction of the Time and Expense

Commercial AI solutions are smart investments for organizations seeking to integrate artificial intelligence within their digital transformation strategies. Many off-the-shelf AI solutions enable sales and cost savings by making it easy to integrate this cutting-edge technology into the existing IT infrastructure, reducing the time and expense of training models and managing data sets, while making it possible to try out the best solutions that work for your specific organization. “Starting with a pre-built foundation gets you to market faster,” says Rob Thomas, general manager of IBM Data and Watson AI, “and with the additional benefit of future-proofing the application by keeping the underlying technology decoupled and modular.” 

Buying an AI tool also eliminates the need to hire a complete team to integrate AI into a digital transformation strategy. Many companies offering packaged AI solutions also sell customized services that support clients’ use of the product. This model is designed to supplement teams’ ability to work with data, for example, or maintain the technology, saving businesses the expense of carrying multiple salaries for these services. For our customer, this means that scientists are free to focus on conducting experiments rather than cleaning data or learning how to get complex data science working for their specific problem. 

If the most successful AI adopters typically invest 10% of their AI budget to algorithms, 20% to technologies, and 70% to integrating AI into business processes (like product development or R&D), as BCG has recently reported, then an off-the-shelf platform makes the good sense: companies get a high-powered technology that is easily embedded into existing processes at a fraction of the cost of in-house development.ﾠﾠ

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In my experience in the technology industry, many companies, even very large and capable organizations, eventually transition from creating the tools they need for innovation in-house to purchasing those tools from third-party partners. Semiconductor companies, for example, now spend billions of dollars annually on tools they used to make themselves. So take comfort in buying AI solutions–the market pendulum will – swing from build to buy. 

-Sunil Sanghavi, CEO

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NobleAI: Technology and Teammates Empowering Chemical and Material Product Development

Our unique approach to commercial Science-Based AI pairs a powerful, cloud-native platform, The NobleAI Reactor Platform, with ongoing support from our expert team of data scientists and AI specialists. When you work with NobleAI, you get superior technology as well as superior AI talent. 

The NobleAI Reactor Platform drastically reduces product development’s time-to-market while minimizing operational costs–including the expense associated with finding, hiring, and retaining top AI talent. We are proud to have helped our customers realize unprecedented improvements in productivity, enabled sales, and cost reductions. 

Don’t let the lab bench be the last part of your business to undergo digital transformation. Schedule a demo with our team to start empowering product development with best-in-class Science-Based AI today.