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.
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.