The IoT and AI markets are both booming, which means companies around the globe are looking for ways to capitalize on these technology trends. Accenture forecasted the IoT industry could add $14.2 trillion to the global economy by 2030. Intel said there will be 200 billion connected objects by 2020, which translates to 26 connected objects per human being on earth.
The AI market has astounding numbers as well. Studies say the AI market will reach $70 billion by 2020, and 72% of business leaders think AI is a “business advantage.” With all of the hype, one of most significant barriers to both IoT and AI innovation is talent. A global survey of executives found that while business leaders think IoT will advance their companies, 31% said their organizations face a “major skills gap” in IoT. Similarly, a poll found that 56% of senior AI professionals say “a lack of talent is the greatest barrier to implementation within business operations.”
Given the incredible potential of IoT and AI, and due to the dramatic skills gap, many companies are turning to outsourcing. Global outsourcing firms have the talent to tackle IoT and AI projects, which comes as a relief to organizations ready to invest in this technology.
Why outsource IoT and AI?
It’s worth noting that as these technologies grow, humans are still needed to advance them. A report by McKinsey and Company noted that roughly 49% of activities that require human interaction and management — ranging from processing data to communicating with stakeholders — will be difficult to automate by 2030. While the robotic revolution would solve a lot of the skills gap problems, it’s unlikely to occur anytime soon. Therefore, companies are looking to outsourcing.
Whit Andrews, research vice president and distinguished analyst at Gartner, said, “Despite huge levels of interest in AI technologies, current implementations remain at quite low levels. However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts.”
The cost advantage is one of the more significant benefits of outsourcing emerging technology, but even more advantageous is the numerous skill sets on hand in an outsourced team. Creating an IoT product, for example, requires more than just a developer. Teams need skills such as microprocessor programming, chip design experts and so forth. Hiring in-house is not always an option because these skills are hard to find and costly to bring on full-time. Outsourcing, therefore, becomes an easier way to create the product. However, it is important to note that more outsourcing of emerging technologies means necessary changes to service-level agreements (SLAs) and contracts.
Emerging technologies such as IoT and AI are designed to detect problems, which means less likelihood of downtime. However, it’s likely that liability caps will become higher, because if malfunctioning does occur, the damages will probably be greater. A possible dispute could arise when negotiating liability caps.
SLA metrics will change as well. Depending on the SLA type and service, common metrics include service availability, security, technical quality and defect rate. With new technologies comes new metrics. IoT SLAs, for example, will consist of measurements related to the network and data, as well as performance-based metrics.
The rise of IoT and AI projects
Outsourcing firms are likely in the process of reworking SLAs for emerging technologies because of the benefits companies see in this space. Ninety-six percent of companies that invested in IoT have already seen an ROI, and $33 trillion of annual economic growth will be added to the worldwide economy by 2025 because of AI developments. Outsourcing has clear benefits and may be the only option as the global skills gap continues. Both outsourcing firms and clients, however, will need to revise SLA protocols to come to a fair agreement on these projects.
SLA changes: Dos and don’ts
SLAs for IoT and AI products and services have to take a different approach. Instead of adding more metrics, companies should choose different metrics, or they run the risk of high overhead. Metrics should not overlap; instead, each metric should measure and report on one aspect of the development with clear outputs. The same advice goes for service levels. SLAs for IoT and AI products should have comprehensive service levels, maybe even fewer than other development products.
SLAs for IoT and AI projects will have to be more precise and may require extra work on the outsourcing and client side. These agreements will look different than standard SLAs often used for less technical development projects. Given that, it’s worth taking the time to craft a comprehensive, clear SLA that both teams are comfortable signing.
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