Harmonizing natural and artificial intelligence for positive impact
The critical question isn't "What can AI do?" but "What value will it deliver?"
Bridging the gap between technical capability and business utility creates measurable results.
The Industry Reality: From Hypothesis to Value
Most organizations are exploring this disruptive era by piloting AI technology to discover potential value. This is the right instinct: experiments validate assumptions and test hypotheses upon which value depends.
An experiment succeeds when it answers the questions for which it was designed. If the probability of realizing value is low, pausing progression is a strategic discipline. However, if it demonstrates clear utility but the organization lacks the capability to capture it (Operational Stalling), then this is a true failure.
Industry data from the AWS Generative AI Innovation Center reveals that nearly 70% of proofs of concept never reach production. As noted in recent insights shared with McKinsey, providing the technology alone is "insufficient." The hurdles often appear operational—data readiness and governance—but the root causes are strategic: a lack of clear problem definition, strategic alignment, or a defined business case articulating ROI. Providing clarity and addressing these often ambiguous areas can bridge the gap between the POC and the scale of a successful enterprise deployment.
The Methodology: Business Value Flow
Bridging the gap between a Proof of Concept and production requires a structured approach to value realization. The Business Value Flow aligns technical implementation directly with business opportunity, ensuring that every line of code serves a measurable outcome.
This flow moves the conversation from "What technology should we use?" to "What value are we creating?" It transforms isolated experiments into integrated business capabilities.
The Execution: Crawl, Walk, Run, Fly
The Business Value Flow is the constant engine of success. By consistently applying this methodology to every initiative, organizations safely advance through the maturity curve—scaling from simple, low-risk pilots ("Crawl") to autonomous, enterprise-wide ecosystems ("Fly").
Establishing the Mental Model
Effective governance and execution require a clear understanding of the tools. Before building the roadmap, it is critical to distinguish signal from noise and understand the fundamental capabilities of modern AI.
Understanding AI →
Leadership & Expertise
Axonics AI is led by Dan Longacre, an Enterprise Transformation Leader and PgMP. Holding an MS in Computer Science from Purdue and an MBA from Ohio State, he combines deep technical rigor with the strategic business acumen needed to deliver outcomes, not just experiments.
Meet the Principal →