:root {
–bg: #f7f8fa;
–surface: #ffffff;
–text: #1f2937;
–muted: #4b5563;
–line: #d1d5db;
–accent: #123a63;
–accent-hover: #0f2f50;
–radius: 16px;
}
.ai-wrap {
max-width: 980px;
margin: 0 auto;
padding: 48px 20px 72px;
font-family: Arial, Helvetica, sans-serif;
color: var(–text);
line-height: 1.6;
}
.ai-hero,
.ai-card,
.ai-cta {
background: var(–surface);
border: 1px solid var(–line);
border-radius: var(–radius);
box-shadow: 0 8px 24px rgba(0, 0, 0, 0.04);
}
.ai-hero {
padding: 48px 32px;
margin-bottom: 28px;
}
.ai-grid {
display: grid;
gap: 24px;
}
.ai-card {
padding: 28px;
}
.ai-cta {
margin-top: 28px;
padding: 32px;
}
.ai-wrap h1 {
font-size: clamp(2rem, 4vw, 3rem);
margin: 0 0 16px;
line-height: 1.2;
}
.ai-wrap h2 {
font-size: 1.5rem;
margin: 0 0 12px;
line-height: 1.2;
}
.ai-wrap h3 {
margin-top: 0;
line-height: 1.2;
}
.ai-wrap p {
margin: 0 0 16px;
color: var(–muted);
font-size: 1.05rem;
}
.ai-eyebrow {
display: inline-block;
margin-bottom: 12px;
font-size: 0.9rem;
text-transform: uppercase;
letter-spacing: 0.08em;
color: var(–accent);
font-weight: 700;
}
.ai-steps {
padding-left: 22px;
margin: 0 0 16px;
color: var(–muted);
}
.ai-steps li {
margin-bottom: 12px;
}
.ai-article-list {
list-style: none;
padding: 0;
margin: 0;
}
.ai-article-list li + li {
margin-top: 14px;
padding-top: 14px;
border-top: 1px solid var(–line);
}
.ai-article-list a {
color: var(–accent);
text-decoration: none;
font-weight: 600;
}
.ai-article-list a:hover {
text-decoration: underline;
}
.ai-button-row {
margin-top: 20px;
display: flex;
gap: 12px;
flex-wrap: wrap;
}
.ai-btn {
display: inline-block;
padding: 12px 18px;
border-radius: 10px;
text-decoration: none;
font-weight: 600;
transition: background-color 0.2s ease, transform 0.2s ease;
}
.ai-btn-primary {
background: var(–accent);
color: #ffffff !important;
}
.ai-btn-primary:hover {
background: var(–accent-hover);
transform: translateY(-1px);
}
@media (max-width: 640px) {
.ai-hero,
.ai-card,
.ai-cta {
padding: 24px;
}
}
More On
AI Strategy & Implementation
AI is not limited by capability as much as it is by how it is applied. The challenge is not access, but structuring its use so that outputs are reliable, decision-ready, and aligned with organizational goals.
How It Works
To address this, the approach focuses on three areas that move AI from general interest to practical use within real workflows.
- Identifying where AI adds measurable value.
- Designing workflows and prompts aligned with real decisions.
- Refining outputs through structured iteration.
This process helps ensure that AI supports execution rather than creating additional complexity.
Featured Case Study
Prompt Engineering for Organizational Decision-Making
This approach was applied in evaluating how AI can support organizational decision-making. A structured prompt iteration process was used to improve the clarity and usefulness of AI-generated outputs when assessing which roles may be suitable for AI integration.
Insights & Applications
The following insights expand on the principles used in the case study and show how prompt structure influences the quality of AI-generated outputs.
Next Step
If AI is being considered within your organization, the next step is not adoption alone, but structured implementation.
Consulting by Henry M. Suazo 