Four strategies to address the US$148 billion edge, IoT, and AI market opportunity

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Drawing on insights from interviews with 30+ solution providers and STL Partners’ market research, this article reveals four strategies to help edge, IoT, and AI solution providers identify their role and capitalise on market opportunities. These strategies are based on a series of reports sponsored by Volt Active Data.

To capture the US$148 billion opportunity, solution providers must navigate a fragmented ecosystem

The market for converged edge computing, IoT, and AI solutions is projected to reach $148 billion by 2030 according to STL Partners’ edge market forecast. Together, these technologies are reshaping enterprise operations: edge processes data close to its source, IoT connects devices to collect and share that data, and AI turns it into actionable intelligence. This powerful combination enables real-time automation, drives efficiency, and can unlock new revenue streams.

However, the ecosystem remains fragmented. Enterprises face disconnected technologies, incompatible platforms, and uncertainty about where to invest, slowing adoption and limiting progress. This article provides practical guidance to help solution providers address these challenges, meet enterprise needs, and seize the opportunities ahead.

Four strategies to maximise success in the market for converged edge, IoT and AI solutions

Source: STL Partners

1. Focus on verticals that offer the greatest chance of success

In a fragmented market, solution providers that spread their efforts too widely risk losing focus and falling behind. To succeed, providers must prioritise verticals based on adoption maturity and demand readiness. Our research identifies three categories:

Source: STL Partners

• Early IoT adopters: These verticals have already embraced IoT and are now primed for edge and AI solutions to enhance performance and efficiency. For established providers, the manufacturing industry is the most lucrative example, and is projected to drive 44% of demand for converged edge, IoT and AI solutions between now and 2030 (STL’s edge market forecast).
• Up and coming innovators: These verticals are scaling IoT solutions beyond PoCs, creating openings for edge and AI in greenfield deployments. Transport, retail, and healthcare lead this group, with use cases such as fleet optimisation, real-time inventory management, and patient monitoring. Solution providers at earlier stages can capitalise on this momentum by riding a timely wave of adoption. The market for transport solutions, in particular, is expected to grow at a CAGR of 24% and almost match manufacturing in size by 20301.
• Speculative explorers: Adoption here is typically still in the R&D or pilot phase, but these verticals represent a strong opportunity for niche players, or those looking to significantly disrupt industries. Emerging use cases like real-time financial risk analysis, hospitality automation, and digital twins in construction point to future demand.

In addition to identifying verticals with the most potential, identifying use cases driving demand should also be a priority. For instance, use cases that rely on video analytics such as in security and production and maintenance are expected to drive much demand in the short-term. More information on such use cases (and other verticals) is available in report 1 from our series.

2. Address enterprise needs by converging edge, IoT and AI solutions

Adopting converged platforms helps enterprises face mounting challenges as fragmented edge, IoT, and AI systems create complexity, higher costs, and slow deployment. Evidence from an STL Partners survey of 590 enterprises shows that 70% prefer solutions that are pre-integrated or come with provider support, highlighting the opportunity for solution providers to meet this demand with enterprise ready platforms. Designing solutions with enterprise needs as the focal point maximises providers’ chance of succeeding in the market.

Converged platforms can benefit enterprises through:

• Offering an effortless user experience: A unified platform provides a seamless, user-friendly interface to manage distributed systems, increasing the chance of adoption and effective use at scale.
• Managing complexity: Integrating edge, IoT, and AI capabilities helps enterprises avoid siloed solutions and mitigates the unnecessary burden of operating and maintaining multiple separate interconnected platforms, which can worsen with scale.
• Allowing enterprise-wide visibility: Converged solutions enable a consistent, enterprise-wide view of operations, improving oversight and decision making to enable fast changes that support enterprises in achieving their business objectives.

Converged platforms also benefit solution providers by:

• Enabling differentiation: Converged, pre-integrated solutions are not yet widely available in the currently fragmented market despite a preference from enterprises. Therefore, providers capable of delivering on this, stand the best chance of winning a commercially significant share of their target market.
• Minimising integration costs: A converged platform avoids duplication and reduces the need for custom integration work, particularly if platforms are designed to seamlessly integrate into enterprise IT/OT systems with varied environments and standards. This saves providers time and resources during deployments
• Maximising market access: Well rounded, converged platform solutions accelerate time-to-value for enterprises, enhancing adaptability while reducing time-to-value. If they are designed to be open in nature, they can also foster ecosystems with third-party integrations that enables enterprises to balance lean operations and costs with additional, enhanced functionality.

To learn more about how to approach edge, IoT and AI platform/solution convergence, see report 2 from our series.

3. Shape partnership, go-to-market, and monetisation strategies to highlight strengths

Building a sustainable business model requires providers to focus on practical strategies that align with their strengths. As outlined below, providers should shape their partnerships, refine their go-to-market approaches, and adopt flexible monetisation models to deliver value while maintaining a strong competitive position.

As highlighted above, designing business models strategically requires careful consideration of a provider’s strengths. A good way of understanding the different factors and approaches to consider, is to learn from early movers in the market. For key learnings from solution providers including Vodafone, Atos, and Trilogy Networks, read report 3 from our series.

4. Remain competitive with AI by focusing on specialisation and open partnerships

Recent hype has increased pressure on technology companies to integrate AI into solutions and demonstrate an ability to remain ahead of technology trends. Meanwhile, scaling AI amplifies a host of challenges across commercial, organisational, technical, and legal and ethical domains. To succeed, providers must specialise in areas where they have a competitive advantage, while collaborating openly with best-in-breed partners.

A collaborative partnership approach enables providers to harness deep specialisation across the various dimensions that drive success. Providers can address their capability gaps, better manage complexity and mitigate risks by combining their strengths. Ultimately, this approach, which is already being taken by the most successful techcos (e.g., Microsoft and OpenAI) will be fundamental to capitalising on the benefits of AI now and in the future.

To gain insights into which edge and IoT use cases will benefit most from AI and to build a nuanced understanding of how to approach the challenges of deploying AI at scale, download report 4 from our series.

You can explore our recommendations in more detail by following the links below:

1. Use cases and verticals driving demand for edge and IoT

• Webinar
• Report

2. Is there such a thing as an edge IoT platform?

• Webinar
• Report

3. Horizontal or vertical? Winning business models in edge and IoT

• Webinar
• Report

4. Edge IoT: Closing the enterprise AI and automation capability gap

• Webinar
• Report