The Taco Bell Problem: Why Your AI Strategy Needs All Three Levels

Remember the story of Taco Bell's AI drive-thru? Customers trolled the system by ordering absurd things, like "18,000 cups of water," which the AI dutifully added before a human had to intervene. In other videos, the AI got stuck in frustrating loops, repeatedly asking customers what they wanted to drink even after they had already ordered a drink.

This is what happens when we focus on the efficiency of the system and forget about the real-world, sometimes absurd, human element. As the company's CTO later admitted, they're now considering a hybrid approach where humans and AI work together, especially at busy locations.

Many tool implementations are built on the assumption that if the tool works, the people will follow. But as the Taco Bell example shows, real change happens at multiple levels. When it comes to AI, particularly in the nonprofit world, introducing new approaches happens at three distinct levels: Organization, individual employee, and client. Ignoring any of these is like trying to drive a car with only two wheels. 🚗

From what I’ve seen, the strategic approach to AI is different in nonprofits than for-profits. While for-profits often lead with a systems-first approach to maximize their bottom line, nonprofits in the economic mobility and workforce development space have a client-first imperative. This mission-driven choice creates a unique set of challenges and opportunities.

The Three Levels of AI Integration in Nonprofits

1. The Systems Level

Opportunity: Using AI to automate back-end operations can free up resources, allowing nonprofits to scale their impact and better demonstrate it to funders.

Risk: Nonprofits' reluctance to invest in back-end AI can cripple their ability to scale. This creates an unsustainable operational model where manual, slow, and error-prone processes hinder the very mission they're trying to achieve.

Economic mobility programs operate on tight budgets and are under constant pressure to show a direct return on investment for every dollar. As a result, they are often reluctant to invest in tools that streamline internal operations like grant management or impact reporting. This is a missed opportunity. The organizations that thrive will be the ones that recognize that internal efficiency is not a distraction from their mission but a prerequisite for scaling it. This is especially true in the current funding climate.

2. The Individual Employee Level

Opportunity: AI can be a powerful equalizer for nonprofit staff, freeing them from mundane tasks to focus on the high-touch, human-centric work that is core to their mission.

Risk: "Shadow users" and a lack of clear AI policies can expose organizations to data privacy risks and bias.

This level where many economic mobility nonprofits see immediate value. These organizations are often at the forefront of "doing more with less," and AI tools can be a force multiplier. For example, AI can summarize lengthy client intake forms, draft initial emails to potential employers, or help a career coach prepare a personalized development plan. 

This level of AI use is a double-edged sword. Without proper training, these models can perpetuate existing biases present in their training data, leading to skewed decisions in hiring, resource allocation, or program design. 

To make the most of this opportunity, leaders must foster a culture of transparency and shared learning. Instead of cracking down, they should offer level-setting training, empowering staff to become advocates for responsible AI use.

3. The Client Level

Opportunity: Nonprofits are leading the charge in using client-facing AI to address the skills gap and ensure equitable access to the future of work.

Risk: Failing to consider the digital divide or the ethical implications of AI can create new forms of exclusion, leaving the most vulnerable behind.

This is the level where nonprofits are most comfortable and, in many cases, are innovating faster than their for-profit counterparts. In the economic mobility space, the mission is to prepare clients for an evolving job market. This means AI isn't just an internal tool; it's part of the program offering.

Nonprofits are leveraging AI to help clients with:

• Resume and cover letter generation: AI-powered tools can tailor documents to specific job descriptions, a task that was once time-consuming and often intimidating for job seekers.

• Interview practice: AI chatbots can simulate job interviews, providing real-time feedback on a client's answers and body language.

• Skills gap analysis: AI can analyze a client's existing skills, match them to in-demand jobs, and recommend specific training or credentialing programs.

But the risk here is creating a new digital divide. If AI tools are only accessible to those with a smartphone, internet access, or a certain level of tech savviness, they may unintentionally leave behind the most vulnerable. Nonprofits must also be vigilant about the "black box" nature of AI, i.e. the lack of transparency in how decisions are made. A client needs to understand why a certain job was recommended or why their resume was flagged. Without this transparency, organizations risk undermining the trust they've worked so hard to build.

Making It Real in Your Organization

The broader application here extends beyond AI. Any major organizational change, be it new software, team restructuring, or new program launches, benefits from mapping its impact across the system, team, and end-user levels.

When planning your next major initiative, are you considering how it will affect operational efficiency, team dynamics, and client outcomes simultaneously? The organizations that thrive in the next decade understand how to orchestrate change across all three levels, creating comprehensive rather than piecemeal implementations.