3 Key Factors for Effective AI Adoption

3 Key Factors for Effective AI Adoption

Technology progresses at a breakneck pace. In order to keep up, we must speak its language. Or, more accurately, we need to program technology to speak our language. Artificial intelligence (AI) is a subset of computer science that approximates human thought by automating certain intuitions and building on the data as it is accrued. AI is itself an act of adaptation. When a digital system adopts our ways of perceiving the world, the least we can do is meet it halfway.

To understand the best ways to incorporate AI into our business life or personal realm, let’s explore three major facets of the adoption process.


The digital revolution is on the march. Between the years 2017 and 2018, surveys indicate that the number of businesses using AI increased from 20 percent to 47 percent. In order to transition smoothly to a brave new world of digital enlightenment, it requires a deeper understanding of the technology itself.

When we discuss machine learning, we often fixate on the word “machine” and forget about the “learning” part. In order to effectively implement AI into business infrastructure, it requires comprehensive education on various levels. Clients must train their staff on how to interact with the new programs. After all, teamwork makes the proverbial dream work, and AI is the newest member of team YOU.

Managers are not only teaching their workers how best to interface with the new software but also how to pass that newfound understanding on to the consumers. If the public gets frustrated with subpar AI, the support staff must know how to intervene. Hence, AI adoption is essentially teaching clients how to teach their target demo. It is a cascading, multi-tiered process that requires expertise and patience at all levels.

In order to facilitate the painstaking process of transitioning to an AI landscape, you want your developer to be a trusted partner. Instead of viewing new technology as a challenge, it should be seen as an opportunity. 

Customer service, for example, is on the frontline of the user/business relationship. When automation is successfully implemented, it can enhance the dynamic between the consumer and their chosen brand. Responsive AI can field phone calls, categorize them according to the emotions being expressed by the caller, and proceed accordingly. By involving the AI provider in the implementation process, you pave the way to a successful symbiosis. The developer understands its own software intimately and can advise the best ways to use AI. No more dropped calls only elevated communication.


Teaching consumers how to love their new AI partner is no small feat, but convincing your own shareholders may be an even bigger ask. In order to properly integrate AI into your business model, you need to prove that it will yield a positive return on investment (ROI). Before we can discuss the return, however, we must assess the actual investment.

The cost of an AI program involves more than merely what you see on a price tag. Sure, you can most likely afford a digital upgrade; in fact, most of us have AI technology sitting in our pockets right now, in the form of a smartphone. But as the old saying goes: time is money. The amount of resources required to jumpstart your AI in a timely manner will determine if it is worth the trouble. In other words: will the return on investment be significant enough for the transition process?

You cannot merely foist a new program onto the public without testing it first. This entails creating a beta version, running it through its paces in-house, and making sure all of the bugs are exterminated before progressing to alpha. 

Once the testing phase is complete, the AI is ready to meet its adoring public… but is it advanced enough to meet the demands of today’s consumers? Beta testing can be time-consuming, especially if there are significant issues to resolve. You run the risk of spending too many resources perfecting a program that, once it’s ready for use, is now suddenly obsolete.

Data should be at the forefront of your AI integration plan. Study the needs of the consumers and propose how machine learning can address those needs. You must not only meet the demands of the client in the short term but also consider long-term analytics. Every industry has complex concerns that factor into its overall return on investment.

Case in point: banking. Consumers want the convenience of AI assistance without the threat of fraud or other unexpected, unwelcome incidents. Voice mapping can reduce the risk of unauthorized users by verifying account holders by sound waves. Incidents of fraud are avoided, which lowers the overall security costs for a given bank. These savings are passed on to the consumer, increasing both customer satisfaction and ROI. By solving a problem before it even happens, you yield benefits that can never accurately be measured. 

But ROI involves more than consumer satisfaction. Increasing revenue requires foresight. Businesses need to predict the worst and hope for the best. If they have an AI system that warns against a flood of non-performing loans (NPLs), for example, they can fend off disaster. Avoiding financial calamity is the ultimate return on investment. AI to the rescue!


Sometimes, the best way to get personal is to be anonymous. You can express yourself in a brutally honest way behind a mask because you don’t fear the repercussions of identification. AI can embrace your desire for role-playing by anonymizing data. Simply put: a program can collect and assess human input as any emotionally sensitive technology can, but in an anonymous way. The collective information gives a client a macro impression of consumers’ attitudes without storing personal information or “outing” an individual user.

The larger theory behind anonymized data is called federated analytics. An AI system can localize input, keeping it isolated to a store location or finite region. For example, your program can store consumer impressions in New England regarding the rollout of a given product. This does not violate Karen’s right, but rather includes Karen in the overall landscape of data pertaining to a specific campaign.

To design and implement a localized approach to AI, you need a partner who respects users’ security and privacy. Behavioral Signals has mastered the art of defining your goals, whether you are an entertainment provider or an investment bank. In either case, we can engineer a secure server, equipped with the AI you need to connect with your clientele in profound new ways. When consumers trust your technology, they trust you. AI has thus facilitated a handshake between privacy and discovery. Welcome to the future, circa now.