With the heavy emphasis on digital transformation, software buyers are actively seeking out products that have artificial intelligence software embedded within the applications for a number of reasons. Those buyers who can grasp the realities of the rapid modernization of business technologies know that this embedded AI will help to automate business processes and tasks, provide users and businesses with actionable insights via advanced analytics, help guide decision making, and improve overall customer experience. Inevitably, buyers will not have to seek out products that have AI in them, AI will just be there, and that will be the focus for software companies in 2018.
I’m sure that product marketers will push the notion that their software has powerful AI capabilities, but this might be the only time you hear about it. Most AI applications within software will go unnoticed by everyday users of the tools, and that is the point. It will simply augment everyday tasks or give users access to functionalities they never had before, that ultimately make their jobs easier. These augmentations will be both predictive and reactionary in nature, but both will provide a great deal of value.
The development of embedded AI will be made increasingly simpler with the advancements of enterprise AI microservices.
Because of the imperceptible functionality of embedded AI, software companies will need to become even more service oriented in their sales and customer success approaches. They will need to understand the needs of the business and how their applications’ use of AI can help digitally transform the company they are selling to. Consulting companies and value-added resellers will have a major opportunity in this area as well, especially if they can concisely prove the impact of embedded AI to companies.
The development of embedded AI will be made increasingly simpler with the advancements of enterprise AI microservices. Software companies will not have to seek out the few software engineers with the skills to create advanced machine learning models; instead, they can utilize machine learning offerings from the likes of AWS, Microsoft Azure and Google Cloud Engine, among others. This will save them time, effort and a lot of money in yearly salaries.
Additionally, the internet of things (IoT) will benefit from the use of embedded intelligence. By creating intelligent things, businesses can be predictive and proactive in spaces like manufacturing, supply chain and field service management, to name a few. The amount of sensor data and the use of IoT analytics will only help fuel machine learning and increase the intelligence of physical things. While such breakthroughs might still be a few years away from mass utilization, they are being taken advantage of by companies like GE.
The path of AI is similar to that of mobile devices or cloud computing. A decade ago (or less) there was huge push to advance towards those trends, and now we hardly notice them. We just expect that there is a mobile application for our business tools and that our products are run in the cloud. Embedded AI will progress towards that level of normalcy in the coming year.
The impact of embedded AI on business modernization
The business impact of digital transformation is wide-reaching, but buyers should look for embedded AI to have the greatest influence in the following areas:
When buyers are searching to purchase software that contains AI, they should look into the way the solution will automate everyday tasks for employees. Embedded AI should be saving employees’ time and energy so that they can reallocate it towards more important work. CRM has been an area that has taken advantage of automating processes with AI. Solutions in this space are beginning to make jobs easier for sales development representatives by providing intelligent lead scoring and optimized email content, so the rep can quickly choose which accounts to prioritize and how to grab their attention. These tools can even recommend the optimal time of the day to send email, based on open and click-through rates, and inform the rep the proper follow-up method. Each of these tasks is automated, so the representative can drive more meetings and ultimately help increase revenue.
The big data boom is the real catalyst for all AI advancements, but as machine learning consumes and learns from the data, it can begin to provide digestible insights for users. This helps augment the need for highly skilled data scientists and analysts. Advanced and predictive analytics can be useful for those in traditionally analytics-free departments such as human resources. HR coordinators can utilize data-driven performance reviews or track employee engagement based on internal surveys to predictively determine churn. This would help a company plan their hiring needs before employees actually leave to ensure that backfilling takes place quickly and efficiently, proactively attempt to retain employees, and avoid performance gaps.
Businesses will be able to take advantage of recommendations from embedded AI to make more informed decisions. Much of the guesswork can be taken out with predictive machine learning features so that decision-makers know that they are making the most optimal moves to grow their business. A great example of this is in the enterprise resource planning (ERP) space, where embedded AI will be able to assist with traditionally manual processes such as budgeting and forecasting, inventory management and pricing. With machine learning, ERP systems will be able to hone in on more exact budgeting and forecasting numbers to allow companies to determine things such as manufacturing numbers or how much inventory is optimal to keep at a time. Embedded AI will also be able to assist with optimized pricing figures based on market data and inventory numbers. This is just one minor example of how embedded AI will help decision making, but the impact will be felt in every department.
Customer success in both B2B and B2C companies is so crucial in today’s business world that it would be seemingly irresponsible if embedded AI wasn’t able to improve it in some fashion. Chatbots have already worked themselves into customer service platforms and are almost always the first line of defense for B2C companies, but B2B businesses are finding ways to utilize intelligent knowledge bases as a method of streamlined customer experience. By taking advantage of natural language processing (NLP), businesses do not always need to have a paid employee on call to speak with a customer; instead, they can augment those tasks with a bot.
Predictive vs. reactionary AI
Something important to recognize is that each of these business processes optimized and streamlined by digital transformation take either a predictive or reactionary approach to embedded AI. While predictive is more beneficial to a company, it is nearly impossible to always be one step ahead. The best example of this may be the use of embedded AI in cybersecurity trends.
Companies hope to be as predictive as possible when it comes to protecting their devices and data from malware and cyberattacks. The hope is that AI can predict cyber threats before they do any damage to a business; however, there are so many new forms of malicious cyberattacks that it is impossible to predict them all. Therefore, security applications need to be reactionary as well. If a piece of malware does get through a threat intelligence solution, the embedded AI needs to be able to immediately take the appropriate steps to mitigate any damage or potential loss of data.
It would be an ideal world if we had the solutions to all of our business problems before we knew what they were, but that is just not a realistic expectation, so reactionary AI is still necessary. Spaces such as ERP, where the embedded AI provides insights based on historical data, are providing a predictive service based off of the reaction of prior performance, inexact human projections, and unknown or outside catalysts. Same goes for the people analytics provided by AI in HR solutions, along with a plethora of other business software.
Software vendors will need to adapt or third parties will dominate
It is already becoming standard practice to take a service-oriented approach to selling, but with embedded AI becoming the foundation of products, vendors will have to truly understand their prospects to deliver. There will need to be a greater emphasis on how the vendor’s solution can take advantage of a company’s data and use that data to fuel and empower the AI-based software. Often this will mean figuring out how the solution can consume a company’s unstructured and uncleaned data, which will be a major challenge for software vendors. Sales teams will need to be able to define the possibilities of their tool and not oversell, because not living up to expectations can be more costly than a lost sale (just ask IBM).
Truthfully, it is just challenging for sales reps to keep up with and do intense due diligence into each of their accounts, so there will be a major opportunity for consulting firms, vendor partners and value-added resellers to capitalize on the sale of solutions containing AI. These companies may have more opportunities to learn what a company needs before beginning the sales process, so they will be more apt to provide them with the exact AI-based tool they need to solve their problems. However, if a sales representative understands the needs of the company and the nuances of how their product solves that problem using AI, they will have a chance to far exceed their quotas. Buyers will want to know how the embedded AI will contribute to their company’s digital transformation, so proving and selling business impact is everything.
Machine learning as a service will help embedded AI growth
There are only so many software developers with the knowledge and skills to build the machine learning models necessary for embedded AI products, but the need for those employees is being lessened by the cloud enterprise vendors. Amazon Web Services (AWS), Microsoft’s Azure, and Google Cloud Platform, are the three leading digital platform vendors capitalizing on microservices, including machine learning as a service.
Advancements in machine learning range from Google DeepMind’s AlphaGo to Amazon’s ability to add Alexa into its products.
In the last decade, these behemoths have moved all of their enormous infrastructures into the cloud, took what they learned from those processes, and provided outside companies datacenter space with infrastructure as a service. This public cloud storage is a major reason for the rapid migration of legacy products to the cloud: it’s just so easy. Setup time is significantly shortened with these microservices, and businesses only pay for what they use. It’s simple, seemingly cost effective and convenient.
Similar things are happening in the machine learning space. Because of the data accessible to the enterprise vendors, they are able to build and train machine learning models of their own, and are responsible for some of the most rapid advancements in AI. Such advancements range from Google DeepMind’s AlphaGo, which is able to beat world champion “Go” players, to Amazon’s ability to add Alexa into its products to provide a conversational user interface. Enterprises are taking these machine learning tools and providing them to other businesses for a monthly fee, but they are quick and easy to deploy and can make immediate impacts on a company’s digital transformation.
For example, by feeding a company’s image data to products like Amazon Rekognition, Google Cloud Vision API, IBM Watson Visual Recognition, or Microsoft Computer Vision API, a business can train its solutions to recognize and classify images. The ease and speed of plugging these machine learning algorithms into software products will increase the number of solutions using embedded AI in the coming year, and ‘machine learning as a service’ will become a common term as more and more software developers take advantage of the services.
The internet of things will utilize embedded AI to create intelligent things
IoT and AI have been on technology trends lists for more than a few years now, but by embedding AI within internet-connected devices, you get something even more powerful and beneficial, intelligent things. This concept was on G2 Crowd’s trends list for 2017, but it will continue to be talked about in the upcoming year as businesses continue to embrace digital transformation. Embedded AI will be able to analyze IoT data, which will be an enormous amount of consumable data moving forward, to gain better actionable insights. This is what will allow for the machines to be predictive as opposed to reactionary.
The industries that this could impact the most are seemingly more traditional, such as manufacturing, agriculture, aviation, healthcare and shipping. B2B companies in these spaces can take advantage of embedded intelligence by providing products and machinery that are predictive in nature by utilizing machine learning. The AI embedded within the physical product, let’s say a tractor, can alert owners or businesses of maintenance issues before they actually happen. This would save the farmer time and resources while ensuring maximum up time, so that they could maintain their harvest to the best of their abilities.
I keep reluctantly coming back to this IBM Watson commercial (because who wants a YouTube video in the middle of a technology trends piece?), but it is a really good example of embedding AI into machinery.
Embedded AI will continue to make its way into software applications in 2018, whether users know it or not. Marketing teams will make the machine learning capabilities of their solutions the go-to piece of collateral, while sales teams will have to adjust their selling methods by digging further into the data of a company and how they can benefit from the embedded AI. If sales departments do not adapt, third-party consultants will have a massive opportunity to help modernize businesses by implementing intelligent applications.
Machine learning development will prosper in 2018 due to the use of machine learning as a service, provided by enterprise cloud computing vendors. Nearly all aspects of business will be impacted by the implementation of applications containing embedded AI, including non-software spaces when the embedded intelligence is combined with the internet of things.
None of these predictions may be revolutionary, or even come true, over the next year. However, if software buyers and businesses are serious about digital transformation, they will make sure to inquire about a product’s AI capabilities prior to purchasing.