Enterprise artificial intelligence (AI) adoption continues to surge in the wake of the pandemic. According to IDC, the AI solutions market is on track to break the $500 billion mark in 2024 – a significant jump from $341.8 billion in 2021. At the same time, the ROI on AI initiatives is quite low. More than three out of four organizations are barely breaking even on their AI investments, which means business leaders are struggling to find the most effective ways to operationalize AI in their business.

As companies continue to incorporate AI into their business operations, it’s becoming even more critical that they avoid relying solely on new AI tools while overlooking the business case. Instead, they need to determine how to use AI in a pragmatic manner and streamline its output into user workflows. 

The Need for Purpose-Built AI Initiatives

When it comes to AI, many often quickly think of algorithms. 

Algorithms have become increasingly commoditized and easily accessible, but what is far from commoditized is how the algorithms are used to solve real business problems. No matter how good an algorithm is, it is bound to have blind spots. This is especially true in the case of business-to-business (B2B), which is marked by its rapid change of pace and the complexity of commercial decisions.

Many companies fail to realize that to find true value in AI, it must be pragmatically applied, adaptable, and used in conjunction with business expertise to solve the challenges that directly impact growth and profitability. When done correctly, teams can find themselves with access to actionable customer insights that support strategies such as real-time pricing that reflects current market conditions, reducing churn, increasing cross-selling, winning back lost business, enforcing contract compliance, and more effective prospecting. Additionally, companies must consider how to operationalize AI across different go-to-market channels to ensure a seamless buyer experience, in instances where AI is impacting commercial decisions. 

With the guidance of industry experts, business leaders must prioritize developing purpose-built AI initiatives rather than simply chase the algorithmic arms race.

A Greater Resilience to Disruption

A key benefit of AI is the ability to become more resilient to disruption from outside forces. Practically, the most common source of business intelligence within enterprises is analytical reporting that takes a significant amount of time to generate before action can be taken. 

For example, inflation and cost volatility have necessitated more frequent price updates. The process of updating and disseminating prices manually can be as long as four months – meaning by the time new prices are live in your commercial systems, costs have changed again. A greater embrace of AI technology can enable manufacturers, distributors, and services companies to further bolster their commercial decision-making process as market conditions change. Whether it’s inflation, supply chain disruption, or any other unforeseen disruptions, AI can offer companies the insights they need to respond effectively. 

Embracing AI to bolster commercial decision-making significantly speeds up an organization’s ability to keep pricing, sales, and other commercial guidance aligned to the market. Let’s look at the most common type of business-oriented AI: optimization. In a fraction of the time of the manual approach, large data sets can be processed to determine which commercial actions are optimal to achieve margin, revenue, or other strategic goals. Even more specifically, price optimization can use price elasticity in conjunction with a constraint-based mathematical solver to set prices with a level of transparency that makes it easy to understand, interact with, and tailor pricing strategies. 

In times of significant disruption, it’s critical for companies to be able to understand how price changes will affect margin or volume at a more granular level – as such AI enables pricing to become a strategic lever within businesses. 

The Next Stage of Data Democratization

The best AI model is not necessarily the most complex or predictive, but one that provides transparency as to how insights were generated as well as one that can be successfully integrated into business processes and commercial decisions. When AI models are developed within the silos of data science or business intelligence departments, they often fail to realize a return on investment. 

There are two key reasons for this lack of ROI on AI initiatives: They fail to solve a problem that impacts business performance, and/or they aren’t integrated into existing commercial processes in a manner that makes it straightforward to act on the insights of the AI models. By contrast, a pragmatic approach to AI should not only generate intelligent insights that make a significant, measurable impact to the business, the output of the model should also be highly accessible to business users and customer-facing roles. 

For example, a pragmatic approach to AI allows pricing teams to easily conduct what-if scenarios on various pricing strategies to understand how margin and volume will be impacted before publishing. The AI model should also be highly explainable, making it easy for sales reps to understand and act on the optimized guidance. The output of AI should also be easily operationalized, meaning the guidance is seamlessly integrated into commercial systems like CRM, eCommerce, ERP, CPQ, order entry systems, or otherwise. Essentially, pragmatic AI should leverage superior science while remaining highly interactive, actionable, and understandable within many levels of your organization. 

Conclusion

There’s always a tendency to go overboard on developing complicated models. Unfortunately, the more “black box” something is – resulting in a lack of complete understanding of its inner workings – the less likely a businessperson will be compelled to rely on it. 

For companies looking to fully realize the benefits of AI, a pragmatic approach is vital to solving business challenges that can directly impact a company’s profit and loss statement. Companies with a pragmatic approach can generate a higher ROI on their AI investments than those that employ a more siloed approach to AI and data science.