Automation and AI 

Automation and AI are obsolete without data, so as the use of these tools have grown, the imperative to capture and mine data sets has expanded dramatically as well. Business leaders are making a renewed commitment to good data hygiene to facilitate sharing and transforming data companywide. Trustworthy data can be analyzed for patterns that can provide worthwhile insights.

Many companies have accumulated disparate streams of data across various departments and divisions. Executives need to look across silos, thinking broadly about all of the data streams they have access to and how those might be incorporated into AI and automation initiatives. These disparate data streams might also be housed within different legacy systems, so leaders also need to explore opportunities to update and consolidate systems.

Going through the exercise of cataloging data streams and systems can help leaders see where data lives, how much can live together, and the data security concerns to be aware of. If a company is managing huge data sets with poor data hygiene or clunky systems that can’t communicate with each other, they will struggle to pursue the free sharing and transformation of data.

Companies are also on the hunt for new forms of data to evaluate, such as using text as data: AI is used in customer service applications to do things such as parse free text or make observations about how people are feeling based on the language they use.

Given the proliferation of automation and AI – and the parallel need for data – every business leader should be concerned with master data management, an overarching strategy that can drive decisions about business outcomes and vision.

For executives who haven’t yet ventured into AI but want to learn more, the first step is to identify what automation or AI might accomplish for the business. Starting with customer relationships and engagement, leaders should identify challenges they’re currently facing and think about how AI could play a role in addressing those challenges.

Some questions to consider related to customer engagement are: How do we want to customers to interact with our business online? How do we want to engage them? What are customers’ shopping behaviors and how can we streamline their experience? Leaders need to listen to customers and then take steps to create an experience that delights them. Automation and AI are two impactful tools in the experience toolbox that allow technology, in real time, to determine and advance customer engagement opportunities in alignment with business expectations.

It’s also important for leaders to consider the implications of automation and AI on a more holistic industry level, including where it’s headed and how their own business fits in. Data-driven insights can be game changing, but companies should have a clear plan for how to approach opportunities and deliver on them. A third-party adviser might be worth engaging to help companies pinpoint areas in which the use of AI has the greatest potential to drive value creation.

Data and the information learned from data can accumulate rapidly, so it’s important to analyze data regularly and make frequent strategic adjustments according to the data. For example, in an online shopping context, a company might suggest a second item based on a customer adding a first item to their shopping cart. How do customers respond? Similarly, a company could test whether customer behavior changes if a shopper is offered a discount after they add the first item to their shopping cart. By collecting and analyzing the data on how these initiatives affect shopping behavior, companies can continually shift their approach according to what is most effective.

Additionally, once an automation or AI program has been deployed, companies need to track its effectiveness. From a strategic process perspective, the first step is to create initial benchmarks that can be a point of comparison. Leaders should identify a set of concrete business outcomes and success criteria and then track progress toward those outcomes.

For example, in a customer service context, companies can compare first-call resolution pre- and post-AI deployment, or they can compare, over time, the number of interactions that required a live agent to achieve resolution versus those resolved by a bot. An AI program targeted at revenue enhancement for a retailer might look at the success rate of recommendations or the customer response to live discount offers.

Cloud-based platforms such as Salesforce can facilitate this type of tracking with metrics, including open and conversion rates of marketing campaigns. These platforms can predict business outcomes based on gathered data or identify patterns in the data to drive insights and then make recommendations.

In addition to benchmarking and outcome-identifying exercises, another crucial measure of success is the return on investment of an AI implementation. The right implementation can help workforces become more efficient, and it can lead to increased revenue and improved customer retention and loyalty.

To make the most of automation and AI, executives need to embrace what’s possible with data and be agile and forward-thinking. If the data and resulting insights suggest a shift in strategy, executives should be prepared to move quickly.