Generative AI in the Real World: Lessons From Early Enterprise Winners
Evangelos Simoudis occupies a valuable vantage point at the intersection of AI innovation and enterprise adoption. Because he engages directly with both corporations navigating AI implementation and the startups building new solutions, I always appreciate checking in with him. His insights are grounded in a unique triangulation of data streams, including firsthand information from his AI-focused portfolio companies and their clients, confidential advisory work with large corporations, and discussions with market analysts. Below is a heavily edited excerpt from our recent conversation about the current state of AI adoption.
Become a premium member: Support us & get extras!
Current State of AI Adoption
What is the current state of AI adoption in enterprises, particularly regarding generative AI versus traditional AI approaches?
There’s growing interest in AI broadly, but it’s important to distinguish between generative AI and discriminative AI (also called traditional AI). Discriminative AI adoption is progressing well, with many experimental projects now moving to deployment with allocated budgets.
For generative AI, there’s still a lot of experimentation happening, but fewer projects are moving from POCs to actual deployment. We expect more generative AI projects to move toward deployment by the end of the year, but we’re still in the hype stage rather than broad adoption.
As for agentic systems, we’re seeing even fewer pilots. Enterprises face a “bandwidth bottleneck” similar to what we see in cybersecurity – there are so many AI systems being presented to executives that they only have limited capacity to evaluate them all.
In which business functions are generative AI projects successfully moving from pilots to production?
Three major use cases stand out:
Customer support – various types of customer support applications where generative AI can enhance service
Programming functions – automating software production, testing, and related development activities
Intelligent documents – using generative AI to automate form-filling or extract data from documents
These three areas are where we see the most significant movement from experimentation to production, both in solutions from private companies and internal corporate efforts.
Which industries are leading the adoption of generative AI?
Financial services and technology-driven companies are at the forefront. For example:
Intuit is applying generative AI for customer support with measurable improvements in customer satisfaction and productivity, reporting 4-5× developer-productivity gains
JP Morgan and Morgan Stanley are seeing productivity improvements in their private client divisions, where associates can prepare for client meetings more efficiently by using generative AI to compile and summarize research
ServiceNow is having success in IT customer support, reporting over $10 million in revenue directly attributed to AI implementations and dramatic improvements in handling problem tickets more efficiently
Interestingly, automotive is not among the leading industries in generative AI adoption. They’re facing more immediate challenges like tariff issues that are taking priority over AI initiatives.
Keys to Successful Implementation
What are the key characteristics of companies that successfully move from AI experimentation to production?
Three main characteristics stand out:
They are long-term experimenters. These companies haven’t just jumped into AI in the last few months. They’ve been experimenting for years with significant funding and resources, both human and financial.
They are early technology adopters. These organizations have been monitoring the evolution of large language models, understanding the differences between versions, and making informed decisions about which models to use (open vs. closed, etc.). Importantly, they have the right talent who can make these assessments.
They are willing to change business processes. Perhaps the most expensive and challenging characteristic is their willingness to either incorporate AI into existing business processes or completely redesign processes to be AI-first. This willingness to change processes is perhaps the biggest differentiator between companies that successfully deploy AI and those that remain in the experimental phase.
A good example is Klarna (the financial services company from Sweden), which initially tried using AI-only customer support but had to modify their approach after discovering issues with customer experience. What’s notable is both their initial willingness to completely change their business process and their flexibility to adjust when the original approach didn’t work optimally.
How important is data strategy when implementing generative AI, and what mistakes do companies make?
Data strategy is critically important but often underestimated. One of the biggest mistakes companies make is assuming they can simply point generative AI at their existing data without making changes to their data strategy or platform.
When implementing generative AI, companies need to understand what they’re trying to accomplish. Different approaches – whether using off-the-shelf closed models, fine-tuning open-source models, or building their own language models – each require an associated data strategy. This means not only having the appropriate type of data but also performing the appropriate pre-processing.
Unfortunately, this necessity isn’t always well communicated by vendors to their clients, leading to confusion and resistance. Many executives push back when told they need to reconfigure, clean, or label their data beyond what they’ve already done.
Model Selection & Operational Considerations
How should companies approach AI model selection, particularly regarding open weights versus proprietary models?
There’s significant confusion about what models companies need. Key considerations include:
Do you need a single model or multiple models for your application?
How much fine-tuning is required?
Do you need the largest model, or can you get by with a smaller one?
Do you need specialized capabilities like reasoning?
The pace at which new models are released adds to this confusion. The hyperscalers (large cloud providers like Microsoft Azure, Google Cloud, AWS) are making strong inroads as one-stop solutions.
Regarding open weights versus proprietary models, the decision depends on what you’re trying to accomplish, along with considerations of cost, latency, and the talent you have available. The ideal strategy is to architect your application to be model-agnostic or even use multiple models.
There are also concerns about using models from certain geographies, such as Chinese models, due to security considerations, but this is just one factor in the complex decision-making process.
For large corporations already on the cloud, what seems to be the easiest path for sourcing generative AI models and solutions?
The typical hierarchy seems to be:
Hyperscalers: (Microsoft Azure, Google Cloud, AWS) are often the first stop, leveraging existing relationships and infrastructure
Application Companies: (ServiceNow, Salesforce, Databricks) who embed AI into their existing enterprise applications
Pure-Play AI Vendors: (OpenAI, Anthropic) both large and small
Enterprises are weighing whether to pursue a best-of-breed strategy or an all-in-one solution, and hyperscalers are making strong inroads offering the latter, integrating various capabilities including risk detection.
How do operational considerations affect AI adoption?
The lack of robust tooling around ML Ops (Machine Learning operations) and LLM Ops (Large Language Model operations) is one reason why many companies struggle to move from experimentation to production.
We’re seeing strong interest in the continuum between data ops, model ops (including ML ops and LLM ops), and DevOps. The hyperscalers don’t have the strongest solutions for these operational challenges, creating an opportunity for startups.
Are there common architectural patterns emerging for production generative AI systems?
Retrieval-Augmented Generation (RAG) is definitely the dominant pattern moving into production. Corporations seem most comfortable with it, likely because it requires the least amount of fundamental change and investment compared to fine-tuning or building models from scratch.
Regarding knowledge graphs and neuro-symbolic systems (combining neural networks with symbolic reasoning, often via graphs), we see the underlying technologies becoming more important in system architecture. However, we’re not seeing significant inbound demand for GraphRAG and graph-based solutions from corporations yet; it’s more of an educational effort currently. Palantir is another company notably pushing a knowledge graph-based approach.
Agentic Systems & Future Outlook
What’s the state of adoption for agentic systems, and what can we expect in the near future?
Currently, we’re seeing individuals working with at most one agent (often called a co-pilot). However, there’s confusion about terminology – we need to distinguish between chatbots, co-pilots, and true agents.
A true agent needs reasoning ability, memory, the ability to learn, perceive the environment, reason about it, remember past actions, and learn from experiences. Most systems promoted as agents today don’t have all these capabilities.
What we have today is mostly single human-single agent interactions. The progression would be to single human-multiple agents before we can advance to multiple agents interacting among themselves. While there’s interest and experimentation with agents, I haven’t seen examples of true agents working independently that enterprises can rely on.
What’s your six-to-twelve-month outlook for enterprise generative AI and agents?
In the next 6-12 months, I expect to see more generative AI applications moving to production across more industries, starting with the three primary use cases mentioned earlier (customer support, programming, intelligent documents).
Success will be judged on CFO-friendly metrics: productivity lift, cost reduction, higher customer satisfaction, and revenue generation. If these implementations prove successful with measurable business impacts, then moving toward agent-driven systems will become easier.
However, a major concern is that the pace of adoption might not be as fast as technology providers hope. The willingness and ability of organizations to change their underlying business processes remains a significant hurdle.
Autonomous Vehicles Case Study
What’s your perspective on camera-only versus multi-sensor approaches for self-driving cars?
I don’t believe in camera-only systems for self-driving cars. While camera-only systems might work in certain idealized environments without rain or fog, deploying one platform across a variety of complex environments with different weather conditions requires a multi-sensor approach (including LiDAR, radar, cameras).
The cost of sensors is decreasing, making it more feasible for companies to incorporate multiple sensors. The key question is determining the optimal number of each type of sensor needed to operate safely in various environments. Fleet operators like Waymo or Zoox have an advantage here because they work with a single type of vehicle with defined geometry and sensor stack.
How important is teleoperations for autonomous vehicles?
Teleoperations are a critical, yet often undiscussed, aspect of current autonomous vehicle deployments. What’s not widely discussed is the ratio of teleoperators to vehicles, which significantly impacts the economics of these systems. Having one teleoperator per 40 vehicles is very different from having one per four vehicles.
Until there’s transparency around these numbers, it’s very difficult to accurately assess which companies have the most efficient and scalable autonomous driving systems. In essence, many current autonomous vehicle systems are multi-agent systems with humans in the loop.
