{"id":10640,"date":"2026-06-02T10:50:18","date_gmt":"2026-06-02T08:50:18","guid":{"rendered":"https:\/\/mio.one\/blog\/integrating-ai-and-genai-into-your-marketing-strategy-where-to-start-without-losing-focus\/"},"modified":"2026-07-09T10:17:53","modified_gmt":"2026-07-09T08:17:53","slug":"integrating-ai-and-genai-into-your-marketing-strategy-where-to-start-without-losing-focus","status":"publish","type":"post","link":"https:\/\/mio.one\/en\/blog\/integrating-ai-and-genai-into-your-marketing-strategy-where-to-start-without-losing-focus\/","title":{"rendered":"Integrating AI (and GenAI) into Your Marketing Strategy: Where to Start Without Losing Focus"},"content":{"rendered":"\n

Reading time:<\/strong> 5 minutes <\/sub><\/p>\n

\n
\"\"
David Segura
Global Strategy Director
MIO One
<\/figcaption><\/figure>\n<\/div>\n

Integrating AI into Your Marketing Strategy: Where to Start Without Losing Focus<\/h1>\n\n

Most companies don’t have a problem accessing AI; their problem is knowing where to start. They’ve seen demos, read reports, and heard that “the competition is already doing this”\u2026 yet they still aren’t clear on what to change first or how to demonstrate real impact. <\/p>\n\n

I’d like to offer a series of recommendations for those who want to incorporate AI without losing their minds\u2014doing so in a way that aligns with business objectives, leverages their own data, and supports a scalable way of working.<\/p>\n\n

The advantage isn’t in “using AI,” but in knowing where and for what purpose<\/h2>\n\n

The competitive advantage of using AI is not just technological\u2014it\u2019s strategic. Integrating AI effectively means aligning it with business objectives<\/strong>, but always combining it with human judgment to create relevant and consistent experiences. AI speeds up execution and improves the customer experience, but if it\u2019s deployed without reliable data or governance, the only thing that scales is error. <\/p>\n\n

Where It Really Makes a Difference: The “P” for Promotion<\/h2>\n\n

When it comes to the 4Ps of marketing, the most immediate focus for teams is on Promotion<\/strong><\/em>. To reach their audience, brands operate in two ways: <\/p>\n\n

PULL:<\/strong> when the user searches or asks a question<\/a> (search engines, LLMs, comparison sites).
PUSH:<\/strong> when the brand sends messages and creative content through various media channels.<\/p>\n\n

And what about AI? It’s reshaping both of them. <\/p>\n\n

PULL: If the model doesn’t know you, it won’t choose you<\/h3>\n\n

Visibility no longer depends solely on SEO or content, because visibility in generative AI is based on frequency of mention, not on ranking. What determines whether a brand appears in the responses from ChatGPT, Perplexity, or Gemini is whether there are clear, verifiable, and consistent signals about its value proposition in the models\u2019 decision<\/a> inputs. <\/p>\n\n

This requires us to consider assets that are not only aimed at humans but also at machines, featuring an up-to-date knowledge base, structured and consistent information, trust signals (case studies, guarantees, reviews), and a conversational experience aligned with the brand.<\/p>\n\n

Practical example (B2C):<\/strong> When there\u2019s a high volume of repetitive questions (\u201cWhat\u2019s included?\u201d, \u201cHow do I cancel?\u201d, \u201cWhat isn\u2019t covered?\u201d), conversion rates drop if the answers are scattered across PDFs and outdated landing pages. An AI layer connected to a well-managed knowledge base isn\u2019t \u201ca bot\u201d\u2014it\u2019s a way to reduce friction and increase the resolution rate<\/strong>. <\/p>\n\n

PUSH: It’s not about creating more, but about operating differently<\/h3>\n\n

Here, AI shouldn’t be understood as simply “more output.” What matters is that it changes planning (more scenarios, faster pace), operations (more test-and-learn<\/em>, more creative variations<\/a>), and measurement (greater emphasis on attribution, fewer vanity metrics). <\/p>\n\n

Practical example (retail\/e-commerce):<\/strong> Testing three ad copies per campaign is not the same as testing 30 controlled variations and learning each week which messages drive conversions for each audience. The value lies not in the volume of creative work, but in the speed of learning. <\/p>\n\n

Beyond PUSH and PULL, AI is having a major impact in two additional areas: ongoing customer relationships<\/strong>, making them more frequent and personalized; and brands\u2019 digital assets<\/strong>, which are becoming increasingly conversational and are geared toward both human users and AI systems.<\/p>\n\n

Practical example (travel website):<\/strong> Traditional filter navigation is replaced by a dialogue, similar to the one that takes place at a travel agency with a human agent. That dialogue can be generated either on your own website or via an LLM, so the content must be structured in a way that allows the models to access and process it correctly. <\/p>\n\n

The most common mistake is to start with the tools<\/h2>\n\n

Before evaluating platforms or off-the-shelf solutions, there are four questions<\/strong> that will guide the way:<\/p>\n\n

Efficient processes.<\/strong> Which process takes the most time without adding any unique value? That\u2019s usually where the first use case lies: lead classification<\/a>, summaries and reporting, and feedback analysis. These are simple, low-risk cases that have a real impact on focus and speed. <\/p>\n\n

Differential processes.<\/strong> Which of these processes can provide a real competitive advantage? AI can significantly improve the customer experience and set a company apart from competitors who haven’t yet taken the plunge. <\/p>\n\n

A solid foundation.<\/strong> What data do we actually have, and what is its quality? Self-optimizing systems require reliable input data; if tracking is incomplete or identity is fragmented, optimization is based on an unstable foundation, regardless of how powerful the model<\/a> is. <\/p>\n\n

People.<\/strong> Who on the team can handle QA? AI isn’t something you just deploy and forget about; it’s essential to keep it under supervision and subject to iterative refinement, and to have someone who can tell when a response is plausible but incorrect. <\/p>\n\n

What Changes and What Doesn’t<\/h2>\n\n

Today’s models are very powerful, but to be useful in marketing, they need context\u2014and the more, the better: proprietary data, customer insights, and the team’s judgment.<\/p>\n\n

What AI won’t replace:<\/strong> the value proposition, the brand vision, and qualitative market analysis.<\/p>\n\n

What can be substantially improved:<\/strong> execution speed, large-scale customization, and the detection of patterns that humans would not be able to spot in time.<\/p>\n\n

With all of this on the table, we can only ask ourselves whether we want to use AI to increase production or to make better marketing decisions; whether or not to use it is no longer an option.<\/p>\n\n

<\/p>\n\n

\n","protected":false},"excerpt":{"rendered":"

Reading time: 5 minutes Integrating AI into Your Marketing Strategy: Where to Start Without Losing Focus Most companies don’t have a problem accessing AI; their problem is knowing where to start. 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