Devising a Supply-side Strategy to Win in the AI Market

To capture value in this market, suppliers need to clearly understand the key trends.

Developments in algorithms, data, and hardware have increased the opportunities in AI tremendously. To capture value in this market, suppliers need to clearly understand the key trends in the industry and devise a go-to-market strategy accordingly.

Introduction

With better algorithms, increased stores of data and improved hardware performance, AI is finally beginning to achieve its potential. Sophisticated technologies like Deep Learning further increase the need for investments in leading-edge hardware and software. While several companies have taken early steps to win in this market, the industry is still nascent, leaving the field open for supply-side companies to capture value and see a return on their huge AI investments.

The supply-side landscape

 

Before moving on to a strategy, it is important to clearly understand the chaotic supply landscape in AI. The offerings in AI can be seen as a nine-layered technology stack:

Positioning to win in AI

To capture value in the growing AI market, companies on the supply side need to heed the following six points:

  1. Value capture in the consumer sector will be limited initially – Early AI offerings are product and service enhancements that appeal to consumers and increase their engagement level, but do not contribute to the bottom line, e.g. online translation, digital voice assistants. These are typically offered by large tech companies, who have bigger pockets and access to more data, leaving little opportunities for smaller companies initially.
    But fee-based offerings, like home assistants and self-driving cars, will create more opportunities in the next wave of innovations.

  2. Winners will focus on micro verticals in promising industries – Instead of getting confused by the hundreds of opportunities available, suppliers should focus on specific industries.
    To select the right industries, following criteria can be helpful:

  • Size of the industry

  • Potential for disruption within the industry

  • Maturity (or readiness to embrace new solutions)

Based on these criteria, the strongest opportunities appear to be available in the public sector, banking, retail and automotive industry.
Once an industry is identified, suppliers should identify specific verticals where solutions can result in high ROI. The value proposition is not compelling for broad, horizontal, industry-wide solutions.

  1. Suppliers will need to provide end-to-end solutions – Customers look for solutions across all nine layers of the technology stack from the same supplier. This saves effort spent in getting different components to work together, and also provides suppliers with strategic foothold with customers. Such wide-ranging capabilities could be completely in-house, through acquisitions or through collaborations. Nvidia, for example, offers its Drive PX platform for cars as a module, not just a chip, combining processors, software, cameras, sensors and other components.

 

  1. Most value will come from services and hardware – Bucking the general trend of commoditization of hardware, about 40-50% of value to AI vendors will come from this part of the technology stack. This is because, in AI, every use case has slightly different requirements, needing partially customized hardware (head nodes, inference accelerators, training accelerators).
    Another 40-50% value will come from services (solutions and use cases). System integrators, who often have direct access to customers, will capture most of these gains by bringing solutions together across all layers of the stack.
    Software, surprisingly, is unlikely to be a differentiator. Since data – another important component of AI – comes from the customer itself in most cases, it will not provide much value either. Though a market for third-party data might emerge in the future.

 

  1. Specific hardware architectures will be critical differentiatorsCloud will continue to be the favored option for many applications, given its scale advantage. Within cloud hardware, customers and suppliers vary in their preference for application-specific integrated circuit (ASIC) technology over graphics processing units (GPUs), and the market is likely to remain fragmented.
    There will be a growing role for inference at the edge, where low latency or privacy concerns are critical, or when connectivity is problematic. At the edge, ASICs will win in the consumer space because they provide a more optimized user experience, including lower power consumption and higher processing, for many applications. Enterprise edge will see healthy competition among field programmable gate arrays, GPUs, and ASIC technology. However, ASICs may have an advantage because of their superior performance per watt, which is critical on the edge. 

 

  1. The need to act is now – To win in AI, reliance on status quo won’t work. Strategy revision and big bets to develop solid offerings right now is the key, instead of waiting two to three years. Unconventional strategies are already seeing high returns. For instance, Nvidia is increasing its AI R&D expenditure and creating an end-to-end product ecosystem, gaining market share and seeing exceptional returns from AI offerings.

 

Keeping in mind the above points, these three questions could be useful in devising a strategy for the AI market:

 

  • Where to compete – Look at industries and micro-verticals, select a use case that suits your capabilities and address the customers’ most pressing needs

  • How to compete – Look for partners to provide end-to-end solutions; Be open to creative pricing options

  • When to compete – Instead of striving for perfection, focus on solutions that help you establish a presence now


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