The adoption of artificial intelligence has become a strategic priority for companies, but most organizations continue to find great difficulties in transforming interest in AI into real business results. According to the new AI Self-Diagnosis Guide prepared by the technology consultancy h&k, one of the main problems is not in the technology, but in the lack of reliable data, governance, prioritization and, especially, effective adoption within companies. Without real adoption by teams, AI initiatives lose impact.
The guide identifies six major stages of maturity in the adoption of AI, from organizations with messy or dispersed data to companies that already have solutions deployed, but that have not yet achieved full and integrated adoption in the daily lives of their professionals.
“The market is full of promising initiatives that remain in pilot and never reach real production. Many companies believe that the challenge is to implement AI, when in reality the real challenge is to achieve its adoption as a sustainable capability, connected to the business and used regularly by people,” explains Javier Tejada, co-president and head of technology at h&k.
From enthusiasm for AI to a real strategy
The h&k AI Self-Diagnosis Guide highlights that many organizations are entering a phase of accelerated enthusiasm for Artificial Intelligence without having yet resolved fundamental aspects that determine adoption, such as data quality, governance or the prioritization of use cases.
Among the main symptoms detected by h&k are:
- Data spread across multiple tools and systems.
- Lack of a single and reliable source of information.
- AI projects without clear prioritization criteria.
- Isolated pilots who fail to climb.
- Solutions deployed without real adoption by teams.
Risks associated with the uncontrolled use of AI tools that hinder safe and consistent adoption within the organization.
According to Javier Tejada, “moving forward by skipping phases usually results in solutions that do not scale, generate frustration or fail to be adopted, which prevents achieving a tangible impact on the business.”
“AI cannot be approached as a sum of disconnected tests. A roadmap is needed that combines strategy, data, governance, operation and cultural change to guarantee adoption. Order matters,” adds Tejada.
Six stages to understand the real point of maturity
The AI Self-Diagnosis Guide proposes a practical model that allows organizations to identify their current situation and define the priority steps to advance judiciously in the adoption of AI.
The six stages identified by h&k are: messy or scattered data, data without governance or quality, interest in AI but without focus or prioritization, clear use cases ready for implementation, pilots that do not scale and technology without real adoption.
Each stage incorporates self-diagnostic indicators and specific recommendations to evolve realistically and sustainably towards effective adoption.
AI with a focus on business and real adoption
Through this guide, h&k reinforces its positioning as a strategic partner in Data & AI projects, accompanying organizations from data strategy and governance to the industrialization and adoption of Artificial Intelligence solutions.
Each stage incorporates self-diagnosis indicators and specific recommendations
The consulting firm’s offering includes services such as data strategy and architecture, data governance and AI governance, AI master plans, identification and prioritization of use cases, development and implementation of AI solutions, industrialization and scaling of initiatives, as well as training, leadership and change management to drive adoption.
h&k’s ultimate goal is to help companies turn opportunities into measurable results, promoting real adoption of AI, without hype and with discipline. The AI Self-Diagnosis Guide can be consulted on the consultancy’s website.
