What's Next for AI: 3 Trends Shaping the Industry in 2025
In the world of artificial intelligence (AI), the quest to recognize value upside can be compared to the wide divide between elite athletes and basic fitness for the masses. Just as the most elite athletes work tirelessly to improve their physical strength, stamina, speed and mental game, the most elite AI researchers and data scientists are pushing the limits on super intelligence.
While the race for Artificial General Intelligence (AGI) might be akin to breaking world records in an Olympic event, the real transformation in AI is happening incrementally—much like the steady progress made by weekend warrior athletes striving to maintain their physical fitness. In 2025, applied AI – the kind that has the potential to return more value than is spent in achieving outcomes from it will resemble a regular fitness tune-up.
Key Trends Shaping the Future of AI
As we look ahead, the AI landscape is evolving rapidly, with several emerging trends that promise to reshape the industry. Much like fitness routines that adapt to new goals, making progress in the AI journey requires effort in data strategies, new business models, and evolving user experiences. Here are three key trends to watch for in 2025:
1. Data Structure Becoming Crucial
Much like how ‘great abs begin in the kitchen’ rather than through long, daily ab workouts, great AI results start with high-quality, well-structured data. With people, as with AI, it’s a matter of garbage in, garbage out. In 2025, the importance of data management, security, and governance will be more critical than ever. A key learning from listening to industry leaders at the recent AI Summit NYC is that customization to your business workflows using your data drives the specific results you need. Don’t be fearful of the tuning necessary to extract the best of what AI offers your enterprise – think of it as the normal process to integrate other more mature enterprise software platforms such as Salesforce.
This is no small challenge as there are no automatic tools that simply fix years or decades of neglect – whether on our eating habits or on enterprise data structures and silos. The upfront investment is an absolute must however. According to a blog from Pure Storage, data bloat, mismatched formatting and incompatible structures can result in both a reduced return from the investment in new AI platforms, and may balloon costs if a usage-based provider is used for AI training or inferencing.
2. Business Model Innovations – Freemium Tools on Open-Source Models
The biggest challenge I have heard from IT Directors trying to figure out how to offer AI platforms and services to their internal customers is that commercial off-the-shelf AI platforms with upfront fees come with the disclaimer “…you have to work with it to find the value to your enterprise.” Because a specific enterprise has its own data and data structures, AI platforms that rely on unique data require self-assembly, self-discovery and experimentation to find the nuggets of value for a specific business. While assembling a program requires significantly less time for an AI-partnered software expert, the majority of users may lack interest or incentive to go through the process of self-assembly just to discover if AI has value in a subset of their specific workflows. The upfront cost of entry for commercial AI platforms combined with the vague upsides a user must discover on their own through trial and error will limit the deployments to early adopters and enthusiasts if it continues.
As a result, the introduction of more freemium business models will ensure that there isn’t an adoption stall from the upfront cost of off the shelf tools combined with the treasure hunt required to find clear and specific business value.
3. The “Pixar effect”
There is a lot of debate about what creative industries AI could destroy and, at the same time, more content consumers are resisting and pushing back on AI-generated content. Whether videos, automated comments on social media platforms that reward ‘clicks’, or even the first two drafts of this blog – there is a spark missing and difficulty connecting to concepts that present like a chatbot reading a manual back to us.
When Pixar first began using computing technology to push the bounds of animation, many believed the art that previously underpinned animated film making was dead. Pixar’s end product and emphasis on story crafting, using technology to rapidly iterate on the art of what was visually possible changed animated film making disruptively. Like the technology Pixar relied on, AI amplifies what’s already there offering insight potential from underutilized data to do more, better.
Applying AI will only amplify what’s already there – great insights, great products, robust data. Don’t lose the importance of improving your differentiation, and weighing the potential of AI against staying true to your core mission in your business.
References:
- Pure Storage (2024). “Improve Data Hygiene, Overcome the AI “GIGO” Problem”
In the world of artificial intelligence (AI), the quest to recognize value upside can be compared to the wide divide between elite athletes and basic fitness for the masses. Just as the most elite athletes work tirelessly to improve their physical strength, stamina, speed and mental game, the most elite AI researchers and data scientists are pushing the limits on super intelligence.
While the race for Artificial General Intelligence (AGI) might be akin to breaking world records in an Olympic event, the real transformation in AI is happening incrementally—much like the steady progress made by weekend warrior athletes striving to maintain their physical fitness. In 2025, applied AI – the kind that has the potential to return more value than is spent in achieving outcomes from it will resemble a regular fitness tune-up.
Key Trends Shaping the Future of AI
As we look ahead, the AI landscape is evolving rapidly, with several emerging trends that promise to reshape the industry. Much like fitness routines that adapt to new goals, making progress in the AI journey requires effort in data strategies, new business models, and evolving user experiences. Here are three key trends to watch for in 2025:
1. Data Structure Becoming Crucial
Much like how ‘great abs begin in the kitchen’ rather than through long, daily ab workouts, great AI results start with high-quality, well-structured data. With people, as with AI, it’s a matter of garbage in, garbage out. In 2025, the importance of data management, security, and governance will be more critical than ever. A key learning from listening to industry leaders at the recent AI Summit NYC is that customization to your business workflows using your data drives the specific results you need. Don’t be fearful of the tuning necessary to extract the best of what AI offers your enterprise – think of it as the normal process to integrate other more mature enterprise software platforms such as Salesforce.
This is no small challenge as there are no automatic tools that simply fix years or decades of neglect – whether on our eating habits or on enterprise data structures and silos. The upfront investment is an absolute must however. According to a blog from Pure Storage, data bloat, mismatched formatting and incompatible structures can result in both a reduced return from the investment in new AI platforms, and may balloon costs if a usage-based provider is used for AI training or inferencing.
2. Business Model Innovations – Freemium Tools on Open-Source Models
The biggest challenge I have heard from IT Directors trying to figure out how to offer AI platforms and services to their internal customers is that commercial off-the-shelf AI platforms with upfront fees come with the disclaimer “…you have to work with it to find the value to your enterprise.” Because a specific enterprise has its own data and data structures, AI platforms that rely on unique data require self-assembly, self-discovery and experimentation to find the nuggets of value for a specific business. While assembling a program requires significantly less time for an AI-partnered software expert, the majority of users may lack interest or incentive to go through the process of self-assembly just to discover if AI has value in a subset of their specific workflows. The upfront cost of entry for commercial AI platforms combined with the vague upsides a user must discover on their own through trial and error will limit the deployments to early adopters and enthusiasts if it continues.
As a result, the introduction of more freemium business models will ensure that there isn’t an adoption stall from the upfront cost of off the shelf tools combined with the treasure hunt required to find clear and specific business value.
3. The “Pixar effect”
There is a lot of debate about what creative industries AI could destroy and, at the same time, more content consumers are resisting and pushing back on AI-generated content. Whether videos, automated comments on social media platforms that reward ‘clicks’, or even the first two drafts of this blog – there is a spark missing and difficulty connecting to concepts that present like a chatbot reading a manual back to us.
When Pixar first began using computing technology to push the bounds of animation, many believed the art that previously underpinned animated film making was dead. Pixar’s end product and emphasis on story crafting, using technology to rapidly iterate on the art of what was visually possible changed animated film making disruptively. Like the technology Pixar relied on, AI amplifies what’s already there offering insight potential from underutilized data to do more, better.
Applying AI will only amplify what’s already there – great insights, great products, robust data. Don’t lose the importance of improving your differentiation, and weighing the potential of AI against staying true to your core mission in your business.
References:
- Pure Storage (2024). “Improve Data Hygiene, Overcome the AI “GIGO” Problem”
