Opinion: Effective AI Hinges on Meaningful Strategic Vision

As artificial intelligence reshapes industries and redefines global competition, leaders across academia, government and industry must unite around a clear, strategic vision. The future of AI lies in its power to transform systems, revolutionize workflows and empower human creativity—unlocking unprecedented opportunities while addressing critical challenges on a global scale.

A successful AI vision rests on five interconnected pillars.

AI strategy and tactics

AI strategy is the structured path by which an organization defines how it will achieve and sustain competitive advantage. In the RFP-driven realms of government contracting and agency operations, however, tactical tasks tend to dominate the agenda, which creates barriers to developing a strategic AI vision.

In a world where geopolitical adversaries are amassing unprecedented power, maintaining competitive advantage is a national imperative in both warfare and deterrence. Our greatest asset will be a strategically guided, AI-enabled edge.

Building a true AI strategy requires a deliberate, top-down approach that recognizes AI as a paradigm shift, not merely a tool, use case or application. This shift redefines how all forms of work are accomplished, from creating new knowledge to executing physical tasks to managing back-office operations. At its core, an AI strategy is about fundamentally reimagining and transforming the nature of work itself.

AI operationalization

To operationalize AI, leaders must bridge the gap between strategic vision and actionable tasks, creating an essential link that transforms theoretical plans into practical outcomes.

Success lies in the construction of a layered, integrated operational framework. This framework serves as a scalable platform, allowing use cases, applications and automation to coalesce into a cohesive whole.

AI operationalization builds a dynamic infrastructure, purposefully designed to accommodate shifting priorities and new developments. By constructing an adaptable operational foundation, organizations can respond to emerging needs while preserving the integrity of their overarching strategy.

In short, operationalization is about establishing a robust yet agile base from which AI’s potential can be fully realized, ensuring that strategy and execution remain in constant alignment.

AI organizational dynamics

AI organizational dynamics recognize that AI is far more than a technology. It represents a new class of “workers” integrated into workflow, alongside humans and other automated systems.

These AI “workers” bring distinct attributes and operate in unique configurations, leading to an emerging type of organization that demands a shift in organizational structure and behavior.

To adapt to the future workforce, leaders must model AI’s impact on the organization comprehensively, identifying which roles and skill sets may be phased out by automation and which new roles AI will create.

This requires an understanding of AI’s place within the organization, including how it reshapes work, reallocates human effort and introduces new competencies. An organization’s success in an AI-driven future depends on its ability to understand and embrace these organizational dynamics, viewing AI as a true, integral part of its workforce.

AI relativeness

While AI strategy charts the course to gain an edge, AI relativeness defines what that edge means in a constantly shifting landscape.

Relativeness acknowledges that competitive advantage in AI is not static; it is a moving target shaped by the actions of adversaries, the current technological frontier, available resources, the effectiveness of investments and the capacity to set and recalibrate vision.

AI relativeness requires organizations to continuously assess and redefine their competitive standing, not just relative to internal goals but in response to an evolving global and technological context.

To navigate this dynamic, organizations must embrace a multifactor optimization approach, blending technological capabilities, strategic foresight, resource allocation and talent development in a constant recalibration process.

Success demands not only technical acuity but a disciplined, responsible vision that keeps pace with emerging threats, seizes technological opportunities and aligns AI efforts with core values and long-term goals.

This relentless pursuit of relevance and advantage is no simple task; it requires sustained human insight, ethical responsibility and an unwavering commitment to adaptive, forward-looking strategies that ensure AI remains a powerful, purposeful tool in the organization’s arsenal.

AI technological trajectory

AI technological trajectory reflects the evolution of AI from traditional machine learning to today’s advanced architectures and beyond.

Where once we had isolated algorithms, AI now boasts a sophisticated ecosystem. Machine learning gave way to deep learning, which ushered in transformer architecture, revolutionizing multimodal and agent-based AI capabilities.

Innovations like synaptic intelligence have enhanced explainability, reasoning engines now amplify the power of large language models, and efficient small language models (SLMs) free up hardware, allowing AI to push further boundaries. This progress isn’t just technical; it begs for a new structure, a human-led order, to harness its vast potential.

Despite these advancements, many organizations still rely on technologies like robotic process automation (RPA), which present could create barriers to embrace true AI capabilities.

As we transition beyond the RPA era, leaders must adopt a fresh perspective to truly advance their approach to AI – one that recognizes the art of the possible and refuses to settle for outdated solutions.

Moving forward, the AI trajectory should embody the full spectrum of its power, shifting away from superficial rebranding of existing technology tools to a deep, strategic commitment that propels us into a genuinely transformative AI future.

ARFL’s strategy reimagines government’s approach to AI

During GovCIO Media & Research’s AI Summit in Tysons Corner, Virginia last month, Chief Information Officer and Director of the Digital Capabilities Directorate of the Air Force Research Laboratory (AFRL) Alexis Bonnell outlined a vision of what AI can and should be: a force that redefines the nature of work, augments human potential and drives sustainable, strategic advantage.

Bonnell’s vision establishes an AI strategy where humans develop a profound relationship with knowledge, powered by Retrieval-Augmented Generation (RAG)-centered intelligent automations.

RAG combines an organization’s proprietary data with large language models (LLMs) to create dynamic and tailored AI applications. These RAG-based applications form a network of capabilities designed and driven by innovators across the organization.

Bonnell’s framework encourages anyone with a passion for knowledge-sharing to create their own “knowledge center,” fostering a culture of discovery and growth.

The essence of her vision lies in making this process not only purposeful but also enjoyable, while serving real, tangible needs. To build this “society of knowledge centers,” she identified four foundational pillars: empathy, intimacy, curiosity and learning.

• Empathy: At the core of this framework is a profound respect for others and a commitment to understanding their needs.
• Intimacy: This involves recognizing shared needs and developing a true understanding of what is required to meet them effectively.
• Curiosity: This includes heightened awareness that drives not only internal exploration but also an outward understanding of adversarial strategies, plans and capabilities.
• Learning: This entails a state of constant growth, driven by an insatiable desire to acquire new skills and refine existing ones.

Bonnell’s vision moves beyond the tools or technologies to transform how knowledge is created, shared and applied. In her future, the fusion of innovation, security and human-centric design forms the foundation for AI’s transformational potential, incorporating seven key principles:

• Top-down vision: Bonnell outlined an enterprise-wide transformation, where AI serves as a unifying force across all levels. This top-down perspective ensures that every piece of the organization contributes to a cohesive, strategic whole, rather than becoming siloed efforts with limited scope and impact.
• Integration of technology, developers and users: Bonnell’s vision fosters seamless integration. She emphasizes a collaborative ecosystem where developers and users are co-creators to ensure technology evolves with users, driven by real-world needs and ideas. Her approach transforms users into innovators, aligning technical capabilities with human creativity and practicality.
• Unleashing creativity: Empowerment of individuals to think, create and solve is central to Bonnell’s vision. By enabling people to envision solutions and solve problems autonomously, she democratizes AI, allowing innovation to emerge from any corner of the organization. Empowerment is the hallmark of a future-focused strategy that values human ingenuity as much as technological capability.
• Mission-driven standards: Bonnell set clear standards that align with mission goals, ensuring that innovation serves a larger purpose. By grounding her vision in mission objectives, she creates a guiding north star that ensures every AI initiative contributes to measurable, impactful outcomes.
• A society of automation and human partners: Bonnell’s vision moves past isolated systems and individual tools to create a dynamic ecosystem of automation and human collaboration. This “society” recognizes the interdependence of humans and intelligent systems, fostering partnerships where automation enhances human capabilities, rather than replacing them.
• Demand-driven accessibility: Bonnell’s democratic approach ensures applications are not hoarded by select groups but made available to all legitimate users who need them. This demand-driven model ensures that AI is accessible and actionable, empowering individuals across the organization to leverage its power responsibly and effectively.
• Anticipating phenological and evolutionary dynamics: Bonnell’s model anticipates the dynamic nature of AI systems. By incorporating the principles of evolution and adaptation, she ensures that automations remain relevant, flexible and capable of growing with changing needs. This forward-thinking approach boosts resiliency in the face of technological advancements and shifts.

By combining strategic depth, human empowerment, operational accessibility and evolutionary foresight, Bonnell crafted a model that transcends the limitations of today’s AI implementations.

If executed properly, this vision holds the potential to give the United States a profound competitive advantage over its adversaries, redefining how we harness AI for strategic, operational and national progress.

Note: This article is a summarized version of Dr. Naqvi’s original article titled “Alexis Bonnell: Formulating a True AI Vision” which is written in first person and can be read at the American Institute of Artificial blog here.