The past three years have been filled with promises that AI will reinvent how companies do business. Countless AI innovations have entered the marketplace from simple tools that automate tasks like research, data management, and reporting, to platforms that help companies aggregate data in preparation for AI applications. Expectations for higher margins, reduced labor, and increased shareholder value led many companies to ride the wave and invest in a wide range of AI solutions.
In some cases, expectations have been met, but only on a narrow or departmental scale, or as part of testing. In others, expectations have fallen short. According to researchers with MIT’s Project NANDA, only 5% of companies report seeing a return on AI investments that collectively total $30–$40 billion in enterprise spending on GenAI. For many companies, AI investments improve specific workflows in departments but have yet to deliver strategic advantages across the entire organization.
During AI testing phases, it’s perfectly acceptable to allow technology and department teams to evaluate and test AI solutions. In fact, this step is key to understanding where the tools may fit in the larger technology stack. But as company objectives for AI adoption become clearer, C-suite leaders are becoming more involved, and the very nature of the AI buying and selling process is changing.
This gap between hype and tangible outcomes now requires greater C-suite involvement in decision-making for future AI investments. As companies assess AI strategies for the coming years, a new evaluation and assessment process has emerged to better incorporate quantitative KPIs based on an enterprise view of how AI should support the broader organization.
Many of the AI tools developed over the last few years have not been vertically aligned, meaning they can be applied across many industries. But through testing, buyers have determined that generic tools often don’t provide the industry-level context, accuracy, and usability needed to meet their specific enterprise goals. During initial scoping meetings, ask suppliers to demonstrate industry alignment through integrations with vertical platforms, custom AI tools or agents, compliance with industry standards or regulations, and customizable reporting on industry-specific KPIs.
In retail, CPG brands continue to face supply chain challenges exacerbated by tariffs, geopolitical conflicts, and climate change. Purpose-built AI for retail is equipped to deliver accurate, connected industry data to overcome supply chain challenges. It’s designed to promote agility, to quickly adjust orders, assortments, promotions, and display placements when needed to prevent out-of-stock items, reduce waste, and improve collaboration with retail partners.
For example, we helped one large CPG brand detect an early risk in its distribution network that would have impacted 6,200 cases for five SKUs. The combination of deep, industry-specific data and strategically selected AI tools used to leverage large retail data sets helped the company avoid a costly error. Once industry expertise is confirmed, the next area to explore is customization. Off-the-shelf AI tools may work as point solutions, but to achieve strategic advantages across the entire organization, buyers still want customization, especially when it comes to AI agents, connectivity, and reporting.
Buyers should start the technology discussion by clearly outlining business goals and KPIs, existing data and platforms, and where current systems fall short or where knowledge gaps exist. This sets the foundation for expectations of AI deliverables. A good supplier will understand that today’s sales engagement requires a “build as you buy” approach, where demonstrating customization during the sales meeting may be a baseline requirement.
A goal of 100% accuracy is also becoming a new standard for AI solutions. AI-generated data and analysis should precisely align with buyer KPIs, and buyers should ask for evidence of success. For example, if a CPG brand knows that a product is being phased out, it can build an AI agent to automatically manufacturing, logistics, and marketing for the product and develop a phase-out and discount plan to move remaining product off the shelf. This scenario requires complete accuracy to ensure that each step of the process is followed, data is delivered for reporting, and humans can step in to adjust along the way.
Rather than requiring internal team members to search for data, develop reports to solve a problem, and manage workflows, cross-functional AI agents can quickly analyze data across departments, recommend a solution, and with human guidance, automatically take action to solve the problem. Importantly, AI agents can overcome silos common in large organizations and work efficiently across departments toward specific outcomes. In this way, teams can overcome the inefficiencies of organizations structured by functions and limited by insights.
As C-suite leaders take a more active role in AI buying decisions, it’s important to ask the right questions, request examples of customization, require proof of data accuracy, and illustrate how AI can reduce workloads while addressing your specific industry, company, and team requirements in mind, based on your enterprise KPIs. By applying a smarter evaluation process to AI solutions, leaders can unlock strategic value from AI investments across the organization while delivering better outcomes for end users.
Are Traasdahl is the founder and CEO of Crisp.