Take One: AI as the Fix for U.S.-Africa Relations
A student recently asked: “How can AI transform the relationships between the U.S. and African countries?” The premise is compelling—AI has the potential to drive transparency, trade, and governance reform, resetting relationships on healthier grounds.
But we’ve seen this movie before.
Foreign aid, trade agreements, governance reforms—each iteration promises a breakthrough. Yet, like many enterprises struggling to implement AI-first strategies, governments often deploy AI as a static tool, locked into rigid, one-time solutions rather than dynamic, evolving intelligence systems.
Much like businesses stuck in pre-determined KPIs, static dashboards, and one-off AI models, the U.S.-Africa relationship risks repeating history—deploying AI reinforcing old cycles instead of breaking them.
It’s time to change the script.
The Real Challenge: AI’s Success Hinges on Living Intelligence
Governments and enterprises both fall into the same trap: they treat AI as a tool to automate decisions rather than as an intelligence system to co-create value.
But AI isn’t just an optimization mechanism—it’s a force multiplier. Whether in diplomacy, governance, or business, AI can break the cycle of reactive decision-making and shift us toward real-time adaptation, foresight, and strategic co-creation.
The problem isn’t AI. The problem is how we think about AI.
Breaking the Loop: Living Intelligence in Action
From Static Data to Evolving Governance
The Challenge: Bad Data, Poor AI Adoption
In Africa: Many governments lack structured digital records, leading to unreliable economic, social, and governance data. In its current form, AI is often used retrospectively—analyzing past corruption cases and producing reports that do little to prevent future fraud.
In Enterprises: Companies struggle with siloed, unstructured, and poor-quality data, making AI adoption inconsistent and ineffective. AI models fail because they aren’t connected to real-time business shifts.
The Living Intelligence Approach
Instead of just delivering insights, AI should co-create value by continuously:
Monitoring government transactions and business operations in real-time.
Identifying fraud patterns before they escalate.
Adapting its models dynamically as new corruption tactics or market conditions emerge.
AI should not simply report on past failures—it should actively intervene, detect, and evolve alongside the systems it’s meant to improve.
From Aid-Based Relations to Dynamic Trade Partnerships
The Challenge: Outdated Economic Engagement
In Africa: The U.S. has historically engaged with Africa through pre-planned development projects that fail to adapt to shifting economic realities.
In Enterprises: Many companies treat AI as a one-time investment, building predictive models based on yesterday’s data instead of continuously evolving with new market conditions.
The Living Intelligence Approach
AI-driven trade intelligence can track economic shifts, supply chain disruptions, and investment opportunities in real-time, enabling agile policy-making and business decisions.
AI should constantly adjust trade recommendations based on real-world events rather than being confined to a static five-year strategy.
The U.S.-Africa relationship must evolve from a fixed aid model to an adaptive, AI-driven trade model—just as businesses must shift from rigid AI deployments to continuous learning systems.
The Role of AI in Fighting Corruption and Strengthening Governance
AI can be a powerful tool for transparency and accountability in governance—just as it is for improving corporate decision-making. If properly implemented, AI can:
Prevent Corruption: AI can analyze financial transactions, flag fraud, and detect embezzlement before it escalates.
Improve Decision-Making: AI-powered analytics can provide policymakers with real-time economic insights rather than backward-looking reports.
Enhance Electoral Integrity: AI can support voter roll verification and detect election fraud before it happens.
Optimize Public Service Delivery: AI can track and verify whether government programs (health, education, infrastructure) reach the intended populations.
Support Trade & Investment: AI-driven market intelligence can strengthen U.S.-Africa trade relations by identifying high-growth sectors and optimizing investment strategies.
However, none of these benefits can be realized without data governance and a mature digital ecosystem—the same challenge businesses face when adopting AI-first strategies.
Government Policy vs. Enterprise Leadership: Why Culture Makes AI Stick
One key difference between government and enterprise AI strategies is how change endures beyond leadership transitions.
Government AI Policy: The Influence of Strategic Interests
While individual administrations shape priorities (e.g., USAID funding, AI-driven diplomacy), foreign policy is influenced by broader strategic interests—not just the whims of one leader. This ensures greater continuity in AI engagement, even if policies change or evolve.
Enterprise AI Strategy: The Challenge of Leadership-Driven Pivots
In contrast, many companies pivot AI strategies based on leadership changes, leading to inconsistencies and stalled momentum. Without a strong data culture, AI initiatives often die when a new CEO or CIO takes over.
In both cases, the real challenge is not technology—it’s cultural adoption. Organizations (whether governments or businesses) that embed data-driven thinking into their DNA will sustain AI transformation beyond leadership changes.
The Takeaway: AI Must Co-Create Value, Not Just Deliver Insights
AI can reset U.S.-Africa relations and revolutionize business strategy, but it won’t unless we change how we think about intelligence.
The biggest mistake we make in AI-driven governance, diplomacy, and business is assuming that value is fixed— that once AI is implemented, its purpose and impact are set, as if intelligence can be predefined rather than continuously shaped. We think AI is a product to be deployed, a solution we can lock in and expect consistent results from, rather than an evolving force that demands engagement, adaptation, and refinement over time. But humans struggle with context switching, defaulting to rigid structures that make AI conform to old ways of thinking rather than letting it reshape how we think.
For governments, this means breaking free from the outdated cycles of aid and rigid policymaking—AI should not be used to reinforce bureaucratic inertia but to dismantle the very structures that prevent dynamic, real-time governance. It’s not just about detecting corruption—it’s about rendering corruption irrelevant by building systems that refuse to accommodate it in the first place.
For businesses, this means abandoning the fantasy of AI as a one-time optimization tool and accepting that intelligence—whether human or artificial—is only as good as the culture that sustains it. AI will not save companies that refuse to challenge their own decision-making paradigms. It will not deliver "insights" to organizations that treat knowledge as a static commodity rather than a continuous dialogue between data, models, and human intuition.
In both cases, AI’s success depends on cultural maturity, not technical sophistication. The real work isn’t in building better AI models—it’s in rewiring how institutions, governments, and businesses perceive the relationship between intelligence and power.
Just as the U.S.-Africa relationship must evolve from aid to AI-driven trade, enterprises must stop fetishizing AI as a “solution” and start embracing it as a living intelligence system—one that demands participation, reinvention, and friction to create lasting value.
We don’t need AI to confirm our knowledge; we need it to challenge our assumptions.
Governments, businesses, and policymakers must abandon the idea of AI as a linear journey and instead embrace it as a continuous, unpredictable negotiation between data, governance, and human agency.
Only then will AI break the loop, and only then will it truly transform the world.
Source:
https://carnegieendowment.org/research/2024/09/africa-ai-us-development?lang=en
https://www.linkedin.com/pulse/values-behind-data-technology-christine-haskell-ph-d--3f7qc/?trackingId=RyfeMnuvSTuApSk2xfSy%2Bw%3D%3D
CHRISTINE HASKELL, PhD, is a collaborative advisor, educator, and author with 30 years in technology, driving data-driven innovation and teaching graduate courses in executive MBA programs at Washington State University’s Carson School of Business and is a visiting lecturer at the University of Washington’s iSchool. She lives in Seattle.
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