3 things to expect when investing in enterprise AI software


3 things to expect when investing in enterprise AI software


If your organization is thinking about implementing analytic solutions powered by artificial intelligence (AI) and machine learning (ML), you’re on the right track—and you’re not alone. A recent Forbes article highlighted findings that place machine learning and advanced analytics as an important priority for at least 65% of respondents across industries. But what is often left out of most discussions are the possible consequences for work culture that AI adoption could have for your organization. Here are some things to consider before moving forward with any AI plans.

More confidence in decisions and less lag in implementation due to data backing

Frozen productivity from second guessing decisions is far from a new phenomenon. However, in recent years business ecosystems have accelerated and ramped up the sense of gravity and impact when it comes to making the right choice at the right time. This can pose a problem. And if it doesn’t affect you, it will, by nature, affect a proportion of your organization. In 2015, the Harvard Business Review thought it was an important enough problem to address, citing a clear negative impact on productivity at all org levels, as well as the perceptions employees have of leadership.

With advanced (and democratized, for any skill level) predictive analytics like those found in new AI platforms, comes a new standard for insights that leads to better practices. These practices include trust and confidence when it comes to decision-making. When trusted analytics are providing the data needed to make the call, you can act much faster and without uncertainty.

Improved collaboration between management and their direct reports

Trust is a key aspect to workplace health. Far more than a “wellness” issue, there are correlations to productivity and, further down the line, profitability of the organization. A runoff effect of implementing predictive analytics across many different functions of an organization is that as the accuracy of augmented decision-making is recognized, frictions are reduced between those who are tasked to make important calls, and those who oversee them. 

This isn’t a small factor. A study about digital transformation from Capgemini’s Digital Transformation Institute unearthed some damning adjacent findings: “85% of top executives believe that their organizations promote collaboration internally, while only 41% of employees agreed with this premise.” The study in general indicates that when it comes to modern collaborative practices, there is already a rift to span. This only increases when a lack of confidence is added to the mix. The augmentation to human decision-making that AI platforms bring puts everything on more sound footing. This means better decisions lead to more trust, which in turn leads to more efficient approval and overall better perceived collaboration.

Highly technical talent spending more time on high-impact projects and less on day-to-day operations

Let’s face it: normally AI projects are stewarded by highly technical data scientists. When those projects finally move into the production phase, and are actively being used in the organization, they are generally being used by the same people who’ve had active roles in building them. The era of the self-serve AI business platform has changed most of that, ushering in a much more equitable outlook on AI.

What this means is that AI and ML technology can be democratized across an organization. Every project, big and small, is important in its own way. But with this new outlook, the technical talent doesn’t need to be involved in every application of AI and ML in the company. This frees them up to dedicate their time uncovering new use cases and putting their time and effort into other innovative projects that require their expertise. This is good for the entire organization.

Augmented human decision-making for the entire organization

Many industries are rightly turning towards planning out AI/ML projects to help them take on challenges of needing to produce more with less, faster. They recognize that the stakes are high and the speed in which crucial day-to-day decisions involving customer behavior, planning, process automation, and more, need to be carried out. It’s not a decision to take lightly, or to put off for that matter. As we’ve seen in aggregate, the types of augmentations to human decision-making that come from adopting a new outlook towards platform AI can only help foster confidence, accountability, and better business for all involved.

Many industries are turning towards AI/ML projects to help them take on challenges of needing to produce more with less, faster.

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