Modern business requires that artificial intelligence (AI) be integrated into processes to create more efficient systems. But with all of the opportunities and efficiencies, there is a glaring pitfall: We have generated data in tremendous amounts, and it’s becoming impossible to make sense of it all.
If you gathered all of the data created in just one day and burned it onto DVDs, you could stack those disks on top of each other and reach the moon -- twice. Data needs to be distilled into something coherent and relevant for it to be useful. That’s why incorporating AI is a must, not a need. It allows us to distill massive amounts of data into digestible information. The insights gained can differentiate a successful company from the competition.
Experience, Intuition And New Insights
The amount of data has far exceeded our capacity as humans to process the wealth of information it can provide us. A typical data scientist may use experience and intuition to determine relevant correlations between various inputs that cause a specific output. But with the sheer volume of data around us, it has become simply impossible to correlate every possible input. AI can help.
Right now, there are a lot of rule-based algorithms that tell you what the relevant information is. But this is sub-optimal at best. This needs to be driven more from the customization of information that fits your needs. AI and machine learning are being heavily leveraged successfully in social media. For instance, consider how Facebook interprets a user's likes, connections and activity to determine content to place in that user's news feeds. The longer the user remains active on Facebook, the more data the platform consumes and ultimately learns from, using deep learning to get continuous feedback on its algorithms as users interact.
While Facebook has made the greatest strides in AI versus the competition, others are not far behind. For instance, Twitter uses AI to rank and recommend tweets based on the relevance to a particular user.
Automation And Real-Time Decisions
AI systems have moved on from augmentation to automation to become a strategic differentiator. It can make decisions and provide one with relevant insights while enabling efficiencies to drive strategy. And this is how corporations will start moving toward an insight-driven world and make better and more real-time decisions.
Some vendors such as SalesForce have already started making the move to big machine-based AI algorithms that present relevant information and help workers perform daily tasks. The key here is closed-loop systems and a cross-departmental alignment of data for the machine to plow through and figure out the right information.
One of the key benefits of AI is the democratization of insights available when utilizing the correct platform. By creating interfaces for business users, every level in an organization can gain from the insights provided that are relevant to them -- which is vastly different from the canned reports that they are currently provided.
The AI Learning Curve
AI adoption in an enterprise is not necessarily a “Big Bang,” but it is created from the bottom up. And in its creation, it’s vital that you repeatedly deploy tests that allow for the findings and learnings as they apply to your business objectives. The goal is to solve problems based on accuracy, transparency and increasing conversions.
You can do this by finding use cases where AI boosts accuracy (i.e., reducing false positives in fraud detection or using look-alike models to infer preferences to a customer). You can also find use cases that were not possible before you implemented AI. In doing so, you’d be building data over time, which can be leveraged with data-hungry AI and ML algorithms.
Before investing in AI, objectives should be clearly defined, with metrics for how results will be measured. Prioritize user stories based on that which provides the greatest business value to ensure buy-in from the rest of the organization. Also, transparency is key. Emphasize consensus-building efforts, having other departments sit through "hack days." Know that there are going to be mistakes and missteps in the name of learning. Tracking and communicating in a highly visible manner will ensure the things your teams learn will advance your overall competency.
An example of how the move to AI comes with its own enterprise learnings: Let’s say an airline’s system computes that a certain product was recommended for a certain customer because they were bumped from a previous flight. But the frontline employee dealing with that customer doesn’t have the proper information and can’t explain why they are receiving that recommendation! This points to the need for organizations learning to live in an AI-driven world to train their employees differently. An AI-driven enterprise will need to re-think its customer-facing interactions.
Lastly, it’s important to avoid the common misconceptions of AI that lead to wrong expectations when implementing. Don’t assume that you can dump data into an “AI box” and the machine figures it out all on its own without any human intervention. You need to spend time cleaning and organizing data to do analytics. Finally, there’s the incorrect notion that “citizen” data scientists (business users) can do analytics now. Even with the must-have integration of AI, you will still very much need actual professional AI and ML data scientists.
