Rounding the Corner: Three Things Businesses Have Learned About Implementing AI-Driven IoT
While the interplay between these closely related technologies is not yet fully understood, our survey finds that forward-looking companies are getting a handle on the major challenges (privacy and security) and the potential for both monetization and an enhanced customer experience.
The automated power of artificial intelligence (AI) is fueling widespread adoption of intelligent Internet of Things (IoT) technologies, as companies derive greater insight from massive data sets collected by these devices. With this added insight, organizations get a clearer view of operations and business performance.
To assess the business expectations of IoT and how AI creates new opportunities, we partnered with Informa Engage to survey executives with IoT implementations planned or underway. The survey also explored the challenges faced and found that organizations across a range of industries, including the industrial sector, are mostly in the early stages of understanding the interplay between IoT and AI.
Companies are incorporating AI into their IoT applications to glean insights into the mountain of data that IoT collects. Of particular use is machine learning (ML), the AI subset that gains intelligence without being explicitly programmed. ML performs real-time analytics on massive IoT data streams to automate exception handling and take informed actions without human intervention.
More than half (56%) of our respondents are well along with their implementations, indicating they have either integrated IoT across their ecosystem or are now optimized for business value. The remaining respondents are in the early stages leading up to an initial proof of concept. (For more on the study, including its methodology, see “How AI-Driven IoT Forges New Opportunities.”)
Here are three key findings from the survey. We believe that enterprises earlier on the AI-IoT adoption curve should study these results, learning from the hard-won experience of others.
Potential security threats and data privacy requirements must be addressed early.
IoT-connected devices and the data that they produce are a potential threat vector that malefactors might seek to exploit if vulnerabilities exist. It’s impossible to state it too many times: Survey respondents, along with experts in general, say that safeguarding both company intellectual property and customer data must be a cornerstone of any AI/IoT project.
Note, however, that AI can and should also be part of the security solution; organizations are using ML to analyze event data and detect threats before they become critical. A security strategy called security incident event management (SIEM) employs ML to process event logs, looking for suspicious combinations of events. IoT devices participate by collecting sensor data, including that from access control systems, video surveillance and presence monitors. By feeding this data into an AI-driven SIEM system, businesses gain a force multiplier in the fight against fraud and data loss.
There’s another role, too, for AI in the battle to tighten down security: ongoing penetration testing, in which IoT networks are continuously exposed to known security threats in order to measure their vulnerability. By automating penetration testing and vulnerability assessment, AI algorithms and ML can make these processes more consistent and scalable, culling false-positives and establishing a business’s baseline security conditions. Vulnerabilities introduced through human error or software changes are thus quickly identified and remediated.
Identify opportunities to achieve value and monetization.
As with any emerging technology, identifying ways and means to monetize services and products can be a significant challenge. Our research shows that more than half of early adopters aim to monetize their AI/IoT transformation by introducing new services, and most of those adopters plan to create new multi-platform (mobile, online or in-person) customer experiences. This provides a path to new business models, as well as more innovative ways to create customer and partner loyalty.
Even industries with more mature AI/IoT experience, such as manufacturing, are using IoT as more than a means to control budgets. For instance, manufacturers use data-driven insight about production bottlenecks to support the design of plants that are more efficient and more effectively IoT-instrumented. Other sectors exploit new connected products that are unique to their market segment. In the utilities industry, energy suppliers monetize IoT data by providing homeowners with rebates and rewards to incent reduced consumption during peak-usage times.
When asked how they monetize their IoT programs, respondents identified several beneficial applications, most of which they apply simultaneously:
52% are introducing services around connected products.
47% are developing new business processes to optimize operations.
47% are developing entirely new connected products.
42% are creating multi-platform customer experiences.
Although introducing new services and connected products ranks among the top monetization strategies, only 23% are now using these offerings to identify new customers. When asked how they expect to create value from their analytics, 46% said they will create even more service lines and business models. This shows us that organizations feel a need to focus first on internal processes and their existing customer base before setting their sights on acquiring new customers.
How companies create new revenue streams from their IoT programs heavily depends on how they use their data. Much work is needed in this area: only 29% are getting alerts and notifications from sensors regarding preventive maintenance, service reminders, performance deviations, and the like. Even fewer (23%) have implemented advanced analytics applications or predictive models, which can offer insights into possible pending failures and suggest courses of action.
Clearly businesses must first embrace, rather than discard, the data that their IoT collects before they can benefit from it — a process that AI can facilitate. A closer look at the factors that influence the customer experience will show some of the benefits from deep IoT data analysis. Businesses must then use a top-down assessment to fine-tune IoT sensors needed for intended business outcomes.
Deploy IoT for an enhanced customer experience.
As shown in Figure 1, enhancing the customer experience is a core IoT benefit, because it can help secure revenue streams and boost customer retention. How an organization senses and deploys data collected through its IoT platforms — being mindful of data privacy — can have a significant impact on its ability to fuel growth and monetize data-driven insights.
Connected products and services keep customers engaged, making customer interactions more rewarding through such devices as virtual assistants, mobile apps and “smart” services and appliances. Augmented reality (AR), which superimposes computer-generated imagery over a user’s view of their real-world surroundings, is the next big thing in AI-driven connected products, our study found, with devices already in the market and delivering completely new types of experiences. AR goes far beyond consumer applications to industrial training, troubleshooting and virtual models, called digital twins.
In an era when customers are won or lost based on the experience delivered, 57% of respondents said that if they could further monetize IoT, they would use it to create faster, more efficient customer service. And 48% cited improved customer communication and information sharing as potential revenue enhancers.
Moreover, 40% believe connected products can tighten customer relationships, while 39% envision the value from providing multi-platform experiences. With only 37% of respondents indicating two or more years of experience with their IoT implementation, it might be too early in the lifecycle for many organizations to project financial benefits through improved IoT-supported customer experiences.
Businesses also must not overlook the security aspects of their new augmented customer experiences. Data breaches are not the only vulnerabilities to guard against; customers are already wary of how much data is being collected — and how many conversations are monitored via these devices. As a result, IoT implementers should be security-proactive, with transparent privacy policies and a clear plan for protecting their approach through appropriate network partitioning and monitoring.
As AI and IoT become more commonplace, they introduce implementation challenges. If companies instate AI and IoT without proper preparation, then they will likely not realize the technologies’ full potential. The best guidance? Organizations should keep their eye on the goal of achieving new value. As noted previously, companies are right to be concerned about security; it must be an upfront business plan component, rather than an afterthought.
Businesses that are just getting started with IoT should take guidance from the lessons learned by early adopters:
Start small, fail fast and scale quickly — which requires knowing what data to access. Then establish a tech foundation to store data, making data complete and clean, and generating intelligence based on what you want from it.
Identify areas in which implementing AI/IoT can improve business processes and provide a sustainable return on investment.
Implement multiple AI/IoT use cases to demonstrate value in solving complex challenges across the business.
Consider security requirements at the start of every project. Whether your organization is ready for AI and IoT integration depends entirely on how well it has planned. Only by first understanding how the technologies and approaches can be put to work will organizations generate optimal returns for recognizable value.