Artificial Intelligence (AI) is fundamentally transforming business operations, offering immense opportunities alongside new responsibilities. For organizations of all sizes, the central question is no longer “if” but “how” to implement AI responsibly, securely, and with measurable business impact.
This AI Success Kit, compliments of ATC provider, Expedient, leverages the Crawl-Walk-Run framework to deliver a practical guide to implementing enterprise AI, with step-by-step advice for integrating AI into complex enterprise environments, with a focus on sustainability, robust data governance, and real business value.
Access your complimentary AI Success Kit today!
This AI Success Kit includes actionable resources, newsletters, guides, articles, and reports. It will provide you with the knowledge and tools you need to begin crafting your AI strategy by using the technology reposnsibly and with the most impact on your business.
Why Enterprise AI?
AI’s true impact extends far beyond automation or riding the next tech wave. Top-performing enterprises utilize AI to address real business challenges, accelerate strategic objectives, and derive actionable insights from vast datasets. Successful adoption requires tight alignment between IT leadership, business stakeholders, and governance teams. Enterprise AI isn’t about chasing hype; it’s about aligning technology to business drivers, policies, and operational standards.
Gartner predicts that by 2025, 90% of enterprise strategies will explicitly mention information as a critical enterprise asset—and analytics/AI as an essential competency.
The Crawl-Walk-Run Framework for Enterprise AI
Crawl — Establish Foundations and Safeguards
Define Strategy and Governance
- Executive Sponsorship & Ownership: Engage C-level and board sponsors early—AI projects with executive backing are 2.6x more likely to succeed (McKinsey).
- AI Policy Drafting: Build cross-functional policy teams (IT, legal, compliance) to define responsible AI use, data ownership, and privacy controls, ensuring a secure and compliant approach from day one.
Inventory and Prepare Data
- Data Discovery: Catalog all structured and unstructured data assets, identifying sensitive or regulated sources.
- Access & Authorization: Leverage enterprise identity systems (e.g., Active Directory) for robust, auditable access control.
- Data Readiness: Consolidate, cleanse, and pipeline data for AI readiness—a critical step, as data quality issues undermine 47% of AI projects (Gartner).
Walk — Pilot AI Use Cases and Build Capabilities
Select Meaningful, Low-Risk Use Cases
- Align with Business Objectives: Choose pilot projects that have a clear connection to business goals (e.g., use AI copilots to make development teams up to 30% more productive or enhance support desk performance).
- Parallel Innovation: Dedicate part of your AI team to exploring transformative use cases (such as automated knowledge assistants or real-time sentiment analysis).
Choose Model Deployment Pathways
- Private vs. Public Models: Analyze data sensitivity and compliance needs to select appropriate AI deployment options.
- (Private models for IP-sensitive data; public models for agility and speed).
- Model Flexibility: Design architectures that enable model portability, future-proofing your investment as technologies evolve.
Pilot and Iterate
- Start Small: Limit initial deployment scope (e.g., single department or workflow), and rigorously track key success metrics.
- Engage & Iterate: Collect user and stakeholder feedback; iterate governance and operational procedures as you expand.
- Document Everything: Maintain detailed records of decisions, use cases, and results to streamline scaling.
Run — Operationalize, Scale, and Future-Proof AI
Operationalize Across the Organization
- Dedicated Teams & Champions: Establish cross-functional AI centers of excellence and business unit liaisons to drive adoption and monitor progress.
- Enterprise Training: Build AI competency into professional development to ensure every employee can confidently use and support AI tools.
Integrate Data Governance and Security
- Continuous Policy Review: Regularly refresh AI and data governance as technology and regulations evolve.
- Guardrails & Tooling: Invest in platforms that enforce enterprise data boundaries and ensure compliance.
Scale Use Cases and Architect for Change
- Modular Architecture: Future-proof your infrastructure to support multiple, evolving AI models without vendor lock-in.
- Shared Learning: Use pilot wins and lessons learned to justify further investment and guide high-impact expansion.
Enterprise AI in Practice: Practical Use Cases
- Development Accelerators: AI copilots to boost code quality and engineering productivity.
- Enhanced Customer Support: Automation to flag tickets, summarize responses, and enhance user satisfaction.
- Operational Insights: AI-powered analytics to detect risks and business opportunities from big data.
- Sales and Service Augmentation: AI-generated CRM briefings and insights for empowered sales conversations.
Final Thoughts
Implementing enterprise AI is a journey—one best navigated by crawling first, walking next, and only running when your foundation is secure. With this AI Success Kit, organizations that act with responsibility and vision today will set tomorrow’s standard.
Curious how ATC and Expedient help partners and clients implement AI with confidence?
