Yesterday, while perusing the garden, I couldn’t help but reflect on the similarities, between plants and AI. Weird comparison I know. But, stay with me.

Plants needs three things, i.e. sun, water and soil. AI needs three things too…Data, processing power and algorithms.

But, just like plants, each industry needs a specific mix of the essentials to thrive. Too much or too little and your project is doomed.

So, it’s got me thinking, as AI evolves, so does the need to deeply think on the industry specific solutions that blend human and artificial intelligence, and it comes with a rethink of the GTM models needs to succeed.

Top of mind questions for enterprises might be:

·         How has AI transformed industry specific models that have been around for many years?

·         What roles do major players and dynamic starts play in this transition?

·         What are the million-dollar decisions businesses need to consider?

·         How do we balance AI benefits with human intelligence (relevant for any AI decision)

The Evolution of Industry-Specific AI

Industry specific models are not new, I’ve personally been involved in industry specific models, involving data and tech for many years. What is new is the ability to take the personalization of data to new levels.

The data is less generic and more highly tuned to the sectors. It’s being designed to address the specific challenges. Traditionally, organizations might have had to go with a single flavour of ice-cream, i.e. vanilla and re-package this for multiple industries. But, as we all know, this just leaves businesses wanting chocolate or strawberry flavours.

Now, businesses can offer a gelato shop worth of ice-creams to cater to the needs of each business use case. Consistent elements i.e. the ice-cream cone will be present in each solution, but the business can be responsive to the customer needs, without prohibitive development costs.

If of course your business looks after one industry, the AI technology will enable you to go deeper and to craft richer, more complex flavors that satisfy the nuanced needs of that sector—think of it as offering not just chocolate but triple-chocolate fudge with caramel swirls, customized to fit the unique tastes and requirements of your industry.

Moving off ice-cream…

AI use cases in targeted industries

Leading technology companies are at the forefront of industry-specific AI.

·         Microsoft, for example, collaborates with Bayer on a Crop Protection model that uses AI to provide insights into agronomy, enabling farmers to optimize yields.

·         In agriculture, Blue River Technology’s AI-powered system uses computer vision to target weeds selectively, reducing herbicide use and promoting sustainable practices.

·         In healthcare, Viz.ai’s platform leverages deep learning to analyze scans in real time, providing faster diagnoses for conditions like strokes.

 By integrating AI directly into existing workflows, these companies help organizations enhance their current operations without a costly overhaul.

Strategic Choices for Businesses

For organizations considering industry-specific AI, a few critical decisions lie ahead:

1. Identifying High-Value Use Cases: The first task is identifying where AI will deliver the most impact, whether automating repetitive tasks or providing customers with personalized experiences.

2. Choosing the Right Tools: AI tools need to fit seamlessly with a company’s goals and existing systems, aligning with both business and operational needs.

3. Investing in Training: As teams begin using these advanced tools, they need adequate training to fully leverage AI's capabilities, ensuring the organization makes the most of these technologies.

Balancing AI Capabilities with Human Oversight

While industry-specific AI opens up exciting possibilities, challenges remain. In sales-focused AI, data quality is essential: inaccurate data can produce misleading insights and unreliable recommendations.

For example, biased data can skew customer profiles, inflate lead scores, or distort pipeline forecasts.

While AI offers powerful analytics, it can’t replace the human touch needed to interpret subtle client cues or intuitively grasp market shifts. Without trust, the sales team’s reliance on AI will decline.

Rethinking Organizational Structures

With industry-specific AI driving more targeted insights, traditional hierarchies are giving way to more agile, cross-functional teams. These adaptable structures empower teams to leverage AI insights while collaborating across departments.

This collaborative model promotes flexibility, rapid adaptation, and team empowerment, creating a culture where innovation can thrive. As a result, teams can respond faster to industry shifts and bring value to the organization by turning data into action.

Moving Forward: Thoughtful Leadership and Strategic AI Adoption

As industry-specific AI takes root in business, companies will need to rethink their structures and strategies. The ones who thrive will be those who align AI with their goals while keeping a strong sense of human oversight. It’s a moment for thoughtful leadership—leaders who not only see AI’s potential but understand when human intuition brings the most value.

Exciting times are ahead, and with the right balance, companies can turn these tools into real advantages