AI Transformation:
Human Investment is Key to ROI

Artificial intelligence is everywhere, promising a revolution in the world of work. Yet, the transformation necessary to realize this potential is lagging. Most organizations have adopted AI tools, but few have truly transformed how their people work to unlock the most valuable pools: those areas of the business where AI offers the fastest and most significant returns.

The Gap Between Adoption and Value

Recent studies show a striking gap: while AI experimentation is widespread, generating value at scale remains the exception. Many companies see this gap and react by doubling down on tools, rather than building the human capabilities that translate this adoption into business results.

Traditional training is not enough to meet the scale and urgency of the GenAI (Generative AI) moment. Winning companies build integrated enablement systems that embed AI into how people think, work, and lead.

From Lab to Reality: Bridging the Industrialization Gap

A statistic often cited in industry studies (notably MIT) is staggering: nearly 95% of AI initiatives fail to scale beyond the experimental stage. This "POC graveyard" (Proof of Concept) is rarely due to the technology itself, but rather a lack of maturity in fundamentals.

At VOID, we identify four missing pillars in stalled projects:

• Data Foundation ("Garbage In, Garbage Out"): AI is not magic. If your historical data is siloed, unstructured, or poor quality, even the most sophisticated model will produce noise. Data governance is the absolute prerequisite.
• Industrialization vs. Tinkering: Moving from a ChatGPT prompt to an enterprise application requires robust software engineering: regression testing, security management, cost control (tokens), and hallucination monitoring. This is the shift from "Prompt Engineering" to "LLM Ops".
• Use Case Relevance: Too many projects try to "shoehorn AI" where a standard script would suffice. Technology must answer a real business friction, not the other way around.
• The Trust Factor: Model opacity ("Black Box") is the primary barrier to adoption. If teams don't understand how a decision is made, they will bypass the tool. Explainability is a feature, not an option.

The VOID Approach: Anchor, Apply, Embed

At VOID, we observe that capability building only generates impact when organizations move beyond standard training programs. What works best is a three-part progression that produces lasting behavioral change:

Define a Clear Context for AI Enablement

To generate impact, the context of the effort must be clearly defined by identifying high-potential value pools and rethinking associated workflows.

Take the example of a financial institution looking to rethink its credit operations. By integrating generative AI into end-to-end workflows (document validation, consistency checks), results can be transformative: massive productivity gains, drastic reduction in manual processing time, and significant acceleration of approval cycles.

The key? Don't sprinkle AI everywhere, but focus on a clear value pool (here, credit operations) and rebuild processes around the opportunity.

A Role-Based Approach

AI transformation is everyone's business, but not in the same way. To optimize learning, we recommend a targeted approach according to four archetypes:

• ShapersExecutives and leaders who define the organization's AI vision.
• LeadersManagers who create the necessary conditions for AI to scale.
• TransformersTeam leads who reconfigure workflows to integrate new tools.
• ContributorsCollaborators who use AI tools in their daily work.

Trust and Motivation: Keys to Transformation

AI deployments elicit emotional responses. Some are deeply skeptical, others fear for their job security. To succeed, organizations must face these concerns head-on.

"The real challenge with AI isn't the technology, it's getting people's trust. If you don't build trust first, no AI initiative will succeed."

It is crucial to upskill managers not only on AI "fluency" but also on change management, so they can address concerns with empathy while reinforcing the business case.

Measure What Really Matters: Value

Measuring adoption rate is a starting point, not an end. True success comes from measuring how new capabilities unlock value.

Traditional training metrics (completion rates, quizzes) fail to capture the real value of transformation. Instead, companies should track:

• Depth and variety of tool usage.
• Experimentation rates.
• Observed behavior changes (e.g., better prompting quality).
• Tangible business results (time saved, reduced errors, accelerated decisions).