Tag: AI

The #IoT + #IIoT Megatrend is in Danger of Stalling

Many of you who are involved in one of the #IoT segments (industrial, healthcare, consumer, etc.) are currently living in PoC hell. Your pilots, trials and proof of concepts are not making the jump to production for a variety of reasons. I think it’s time to push the reset button on how we convey the value of IoT and how we deliver solutions. The best place to start is by listening to customers.

As it turns out, customers aren’t interested in hearing how smart you are or which esoteric technologies you’re using to build IoT solutions. The only reason they’re talking to you is because they’ve heard Internet of Things solutions can save them money, reduce unplanned downtime/non-productive time, optimize operations, improve worker safety, boost product quality, lower risk and many other compelling value props. Here’s a quick list problems and solutions to get you started:

1. Customers are finding all the pieces to the IoT puzzle to be too complex. You need to focus on extreme simplicity and reduce friction at every tier of an IoT solution. Hundreds of pages of code examples isn’t working.
2. Customers don’t have the skill sets needed to work with IoT solutions. Good enough has to be good enough, so stop using technologies and protocols that no one has ever heard of and embrace pervasively adopted tech that everyone already understands. If the tech you’re using isn’t familiar to customers, they’ll be uncomfortable about using your solution.
3. Customers have heard about large-scale, IoT hack attacks and are reluctant to move forward due to security concerns. Security and privacy must be baked-in to your IoT solution from the get-go and defense in depth must be practiced at every tier of the solution. You must also respect a customers data governance and sovereignty requirements even if it means delivering a 100% air-gapped solution.
4. Customers struggle to achieve an acceptable return on investment on their IoT solutions. Despite lower costs for all the components required to build an IoT solution, when a customer strings together sensors, microcontrollers, communications networks, storage, middleware, servers, analytics, and integration software, it’s possible that the combined cost could exceed the expected ROI. It’s critically important to beat-up on those costs to stay well-within the ROI envelope.
5. Customers don’t want another data silo. Too many IoT solutions are focused solely on capturing data from machines and keeping it within their respective systems. It’s important to integrate with a customer’s existing databases, CRM, ERP and other systems no only to add context to machine data but to take actions on insights. Telling a customer they can write code to call APIs on their backend systems is the wrong answer. Make it easy.
6. Customers keep hearing you must combine Artificial Intelligence with IoT in order to derive value. The tech industry must stop sending this message because it’s dead wrong and it’s scaring customers away. The average person doesn’t know anything about AI except that they think SkyNet is going to take over the planet and robots will be our overlords. There’s tremendous value in connecting your people and machines to gain real-time visibility and situational awareness over your operations. There’s additional value in layering even the simplest analytics to drive decisions and automation. None of this is rocket science and it’s stuff your customers can easily wrap their head around.
7. Customers who are pitched horizontal IoT platforms quickly become paralyzed. Stop leading with generic, horizontal IoT platforms that try to be all things to all people because it doesn’t work. Customers are not interested in writing code to implement one of many millions of IoT use cases on the platform you’re selling. Your sales motion should include knowing your customer’s business and always leading with vertical solutions to problems they already want to solve.
8. Customers often find the tech needed to create a smart, connected product eats too much into product profit margins. IoT-enabling products is a super-important way to provide better, ongoing customer service. Especially when those products come with warranties or SLAs that must be met, companies absolutely require IoT capabilities to reduce their risk and eliminate service calls that eat into profits. The sensors, microcontroller, power source, and connectivity for an individual product must always represent the smallest percentage of the total product cost to ensure mainstream adoption. Otherwise, only early adopters will use your smart, connected product.
9. Customers are unsatisfied with the results they expected from analytics applied to IoT data. This often points to poor data quality and/or unlabeled data. Garbage in, garbage out. Ensure your IoT system is labeling incoming data points as well as mapping unintelligible items like PLC registers to something a human can understand. It’s also super-helpful if your IoT systems knows the data types and units of measure of the incoming data points inside captured data sets to help both simple and advanced analytic systems make sense of the data. Don’t overwhelm customers by delivering 100% of data communicated by endpoints into an IoT system. For the most part, de-duplicate incoming data and only send anomalous data values that stray outside acceptable limits.
10. Customers have grown tiresome of IoT projects that take too long. I’ve heard of managers who’ve green-lighted IoT projects being asked to leave after 3 years of boiling the ocean to drive value at an organization. Don’t try to boil the ocean anymore. Find small, targeted use cases that can be tackled in just a few months to get tangible, quick wins. When everyone can see the value, move on to the next small project while continuing to build confidence and grow support across the organization. Remember to eat the IoT elephant just one bite at a time.

Keep it simple to achieve success!