Editor’s note: The following is a guest post from Bern Elliot, distinguished VP analyst at Gartner. The topic was discussed at Gartner’s IT Symposium/Xpo 2025 in Orlando, Florida.
From chatbots and virtual assistants, to AI-driven agents capable of automating complex workflows, conversational AI is rapidly transforming how organizations interact with customers, employees and partners.
Yet, as the market explodes with new offerings, spurred by advances in generative AI and agent-based architectures, CIOs struggle to identify and implement the right tool for their unique needs.
The provider landscape is crowded, confusing and constantly shifting. Vendors are pivoting to generative AI-native products, layering conversational features into enterprise platforms and touting AI agent capabilities. Meanwhile, the distinction between build and buy options is blurring — and the technical, operational and strategic stakes are rising.
For CIOs, the core problem is not just technical selection. It’s strategic alignment and, too often, organizations underestimate the ongoing effort required to build, maintain and scale conversational AI solutions. They also overlook the features already available in their existing enterprise applications and make tactical, short-term decisions that drive up long-term costs and technical debt.
To cut through the noise, CIOs must adopt a structured approach; one that begins with a clear-eyed assessment of business needs and ends with a tool that is robust, cost-effective and future-proof.
1. Map the provider landscape
CIOs must start by understanding the four primary offering types in the market. Each has distinct trade-offs:
- Generative AI-native applications: Standalone, SaaS-based tools that offer rapid deployment and ease of use, but limited customization and scalability.
- Conversational AI extensions within enterprise apps: Add-ons or features embedded within existing enterprise platforms, offering seamless integration but sometimes lacking in depth.
- Dedicated platforms: Comprehensive platforms designed for sophisticated, multi-channel conversational experiences. These offer flexibility and scalability but require greater technical investment and expertise.
- Custom environments for AI solution development: Highly customizable, often open-source toolkits for building bespoke tools. These are best suited for organizations with unique requirements and strong internal AI capabilities.
2. Define use case scope
Not all conversational AI use cases are created equal.
Is the goal a simple FAQ chatbot, a transactional virtual assistant or a fully autonomous AI agent? The broader and more sophisticated the use case, the more important it is to choose a platform that can scale, integrate and comply with the organization’s risk and security requirements.
3. Document risk, security and compliance needs
Conversational AI platforms often process sensitive data and interact with critical business systems. CIOs must rigorously assess the sensitivity and confidentiality of data handled, exposure to cyber threats and regulatory requirements and the need for audit trails, governance and responsible AI practices.
If a CIOs use case involves stringent compliance or unique security demands, a custom development environment might be necessary, even if it means a longer path to production.
4. Take stock of existing capabilities
Before investing in new technology, CIOs should inventory their current conversational AI assets.
Many enterprise applications now include conversational features or can integrate with generative AI models. Leveraging these can accelerate time to value and reduce redundancy.
5. Assess internal skills and resources
The best conversational AI tool is only as good as the team that implements and maintains it.
CIOs must evaluate the technical proficiency of their teams, availability of resources for ongoing support and the learning curve associated with more advanced platforms.
No-code and low-code tools can empower business users, but complex, feature-rich platforms might require deeper expertise.
6. Prioritize time to value and scalability
If rapid deployment is critical and technical resources are limited, targeted extensions within existing platforms might be the best fit.
However, CIOs must be wary of locking themselves into solutions that can’t scale as needs evolve. For organizations with ambitious, multi-departmental strategies for conversational AI, investing in a dedicated platform capable of orchestrating diverse use cases and supporting future innovation is essential.
7. Plan for the future
Adoption rarely stops at a single use case. As conversational AI proliferates across business units, the risk of fragmentation grows. CIOs should prioritize platforms with strong orchestration capabilities, enabling centralized management, governance and a consistent user experience.
The market will only become more complex as generative AI matures and business demands grow. By taking a structured, requirements-driven approach and resisting the urge to chase every new trend, CIOs can ensure their organizations adopt conversational AI in a way that delivers lasting value, resilience and strategic advantage.
