To get busy teams to adopt AI, implement a "forcing function" like a mandatory workshop. The goal is for each person to leave with a built agent or use case that saves them time, demonstrating immediate ROI and breaking the inertia of being "too busy."
While consultants offer a short-term fix, companies must own AI development internally. Create a "Labs" unit with trained knowledge workers, not just engineers, who act as "forward deployed engineers" to build solutions. This retains IP and allows for cross-departmental sharing of innovations.
While AI leadership can come from various roles (CFO, CIO), the most successful transformations are led from the very top. When AI is a top three priority for the CEO and they are actively involved, it grants the entire organization permission and urgency to adopt it.
In complex enterprises, avoid bottlenecks where every team requests the same AI tool approval. Instead, establish "horizontal approvals" by vetting a key integration (e.g., ChatGPT-to-HubSpot) once. This creates a pre-approved connection that multiple teams can use under defined permissions, accelerating adoption safely.
Properly funding AI isn't just about software licenses. A comprehensive budget should address four layers: 1) People to lead the strategy, 2) Platforms and their token usage, 3) Infrastructure like specialized hardware (e.g., GPUs), and 4) long-term investment in training proprietary models.
Budgeting for AI is difficult because the utility-based, per-token pricing model is not viable or scalable for business departments like marketing and sales. This system is a temporary phase; expect AI providers to shift toward more predictable, outcome-based pricing models as the technology matures.
As AI automates task-based work historically done by entry-level employees, companies risk decimating their future leadership pipeline. The solution is to create modern apprenticeship programs where junior staff learn by shadowing senior leaders, supported by AI-driven personalized learning paths, to accelerate expertise development.
Building your business operations on a single AI platform like ChatGPT is risky, akin to building on "rented land." To ensure redundancy, document all agents and system instructions, then create and maintain mirrored versions on a competing platform (e.g., Claude) to ensure business continuity if one provider goes down.
Before sending a new AI vendor's terms to your legal team, use an LLM to accelerate the review. Feed your company's AI policy and the vendor's contract into a chat session and ask it to flag discrepancies. This allows you to quickly identify non-starters and highlight key issues for your attorneys.