Both consumer and business technologies tend to be oriented toward efficiency, from the explosion of on-demand everything to discourse around artificial intelligence (AI). But when it comes to the potential impact of AI, efficiency barely scratches the surface of the technology’s potential. Existing jobs will be re-skilled, employees will be empowered, and end users will have a better experience.
With that in mind, let’s dig a little deeper into what improved efficiency with AI means and what other benefits are tied to its implementation. More specifically, let’s look at how automation, data collection and the promise of an improved user experience can result from AI solutions.
AI is so commonly associated with efficiency because it breeds automation. And the benefit of automation shouldn’t be downplayed. Many industries are held back by manual processes. Automation can empower people, sparing them time-consuming tasks and allowing them to focus on activities that can propel their businesses forward.
In the context of corporate legal departments, for instance, AI can help automate invoice reviews, rate reviews, compliance and other administrative items on the to-do list, thus saving money, alleviating frustration and letting employees focus more time on the complex thinking required of law.
To that end, rather than replace existing jobs, AI is going to augment them. Even automated workflows must continue to be managed by humans. While the human brain simply cannot hold and process all the information that a machine can, technology lacks the context and domain expertise of an actual employee. In turn, it’s not an either-or between AI and humans; it’s an opportunity for synergy.
Managers must communicate this distinction to their employees. It’s incumbent upon them to explain how their day-to-day tasks will both change and continue to make an impact. Then, employees working with AI can replace fear with buy-in.
Data is essential for optimized decision-making. For example, AI can comb through years of data pertaining to which law firms have handled which cases in which jurisdictions and compare outcomes and costs. Corporate law departments can then use that information to choose which external law firm is the best fit for a particular case. This type of analysis is only feasible with the speed and data capacity of machines.
But in order for AI to be able to automate such decisions, wide-scale and ongoing data collection by humans is required. AI is not rule-based, but model-based; that model can only be matured and its output improved if data continues to be fed in. At the end of the day, AI is not a one-point solution but the beginning of an ongoing feedback loop.
Of course, data and AI don’t just live behind-the-scenes. Just like AI can help organizations internally leverage data for better decision-making, it can also pass that perk along to end users by tailoring the end product to particular personas. “Users” is a broad community, but AI can look at users’ past actions to better serve up relevant information. C-level users, for example, may want insights into top performers, which requires a vastly different data set and user experience than, say, a sales or customer service team.
Processing and understanding the historic actions of particular users can also help shed light on what additional insights would be useful and take the user experience to the next level. Beyond fueling the day-to-day workflow of employees and impacting internal decisions, AI and data can also be leveraged as part of actual tech products. The data insights can be passed along so customers can make better decisions themselves.
Despite the widespread potential of AI, the reality is that a move to incorporate the technology should be evolutionary, not revolutionary. Small gains in efficiency are a good first step for organizations looking to apply the potential of AI. But as we’ve demonstrated here, this is just the tip of the iceberg -- the beginning of a long and transformative organizational journey.
By incorporating some aspects of AI into things employees are already doing, incremental progress can be made, and buy-in from employees and customers alike can begin. If companies are willing to do this and experiment a bit with AI, they have the chance to truly transform their businesses.