A recent Gartner report outlines seven critical workstreams that organizations should focus on when developing their AI roadmaps. These workstreams, spanning from strategy to data, guide companies in creating a roadmap that works for their specific needs and goals. They also address specific efforts and strategic initiatives to manage the risks associated with AI while promoting its beneficial uses.
An AI roadmap for software development is a strategic plan that outlines the steps, resources, and milestones required to integrate artificial intelligence into software solutions effectively. Various agencies, such as CISA and EASA, are developing roadmaps and comprehensive action plans to promote the beneficial uses of AI while ensuring safety and managing associated risks.
Here’s a closer look at how you can use these workstreams to unlock the full potential of AI in your organization.
Understanding AI Roadmaps?
An AI roadmap for software development is a strategic plan that outlines the steps, resources, and milestones required to integrate artificial intelligence into software solutions effectively. It provides a clear direction and framework for implementing AI solutions, ensuring that all stakeholders are aligned and working towards the same objectives.
A well-crafted AI roadmap considers the organization’s current state, identifies areas for improvement and outlines the necessary steps to achieve AI adoption. It serves as a guide for teams to align AI initiatives with business goals, ensuring a structured approach to leveraging AI technologies.
Why a Custom AI Roadmap is Essential
AI adoption is not a one-size-fits-all approach. Each organization has unique needs, goals, and challenges. That's why a carefully crafted, custom AI roadmap is essential for ensuring that your AI strategy aligns with your overall business objectives.
Instead of tackling every AI-related activity at once, it’s important to prioritize key initiatives and sequence them in a way that ensures the smooth and effective integration of AI technologies.
(Roadmaps are one of the key aspects for project success; learn how to build a product roadmap and which are the best product roadmap tools and software in our in-depth articles. )
Key Aspects of an AI Roadmap

1. Defining Your AI Strategy
The first step in any AI journey is defining what you want to achieve with AI. According to Gartner, this begins with clearly articulating your AI ambitions and aligning them with your broader business strategy. What impact do you want AI to have on your organization? Whether it's enhancing customer experiences, improving operational efficiency, or enabling new product innovations, having a clear vision is essential.
From there, you can set measurable goals and prioritize AI use cases that align with your strategy. As your AI initiatives evolve, it’s important to continuously refine your strategy and ensure that it remains aligned with your organizational objectives.
2. Driving AI Value through Use Cases
AI’s value is best realized through tangible business outcomes. Initially, organizations should focus on a few key AI use cases that offer the most immediate value. Running pilots and measuring their impact can help demonstrate AI's potential and build momentum for larger initiatives.
Gartner emphasizes the importance of creating an evolving portfolio of AI initiatives rather than focusing on one-off projects. This approach ensures that AI becomes an ongoing source of innovation and value, adaptable to shifting customer needs and market changes.
3. Building an AI-Ready Organization
For AI to scale effectively, your organization needs to evolve. This begins with establishing the right talent and resourcing to support your AI initiatives. Gartner suggests that organizations start by creating a community of practice or dedicated AI teams to focus on high-priority tasks. As AI adoption matures, you’ll need to refine your organizational structure, potentially developing an AI-focused operating model and formalizing external partnerships.
Having a dedicated AI team, supported by strong leadership and cross-functional collaboration, is key to driving AI initiatives across the organization.

4. Fostering an AI-Driven Culture
AI is more than just a technological shift. Start by assessing your current workforce’s AI readiness and identifying any talent gaps.
Gartner stresses the importance of creating a workforce plan that addresses these gaps, as well as implementing ongoing upskilling programs. Additionally, a strong change management strategy is essential to help employees adapt to new roles, workflows, and technologies as AI becomes integrated into everyday operations.
5. Establishing Strong AI Governance and Managing Risks
AI governance is crucial to managing the risks associated with AI deployment. Whether it's ensuring ethical AI practices, maintaining compliance, or safeguarding data privacy, effective governance frameworks are essential for AI's responsible use.
Gartner recommends organizations start by identifying key AI risks and establishing initial principles and policies to mitigate them. Over time, you can formalize your governance structure, define decision-making rights, and implement tools for ongoing monitoring and accountability.
AI governance ensures that AI projects align with your ethical standards and regulatory requirements, minimizing risks and protecting your organization.
6. Strengthening AI and Machine Learning Engineering Practices
A solid technical foundation is necessary to support reliable and scalable AI systems. Start by defining whether to build or buy the necessary AI tools for your organization’s use cases. Gartner advises that organizations set up a sandbox environment to experiment with different technologies and identify the best design patterns.
As your AI initiatives grow, you’ll need to implement best practices around ModelOps, AI observability, and platform engineering to ensure that AI models are consistently delivering value.
7. Optimizing Data for AI
The integration of AI and Data Analytics is revolutionizing how companies handle information. However, without high-quality, well-managed data, AI models won’t function effectively.
Gartner recommends starting by assessing your data readiness for initial use cases and developing a strategy to prepare your data for AI applications. Over time, organizations need to evolve their data practices, including improving data governance, ensuring data quality, and investing in tools for data observability.
As AI systems become more complex, having the right data infrastructure will be essential to supporting long-term success.

Benefits of Having an AI Roadmap
Having an AI roadmap offers numerous benefits, including:
Clarifies AI goals and objectives: A roadmap helps define what you aim to achieve with AI, ensuring everyone is on the same page.
Identifies potential risks and challenges: By planning ahead, you can foresee and mitigate risks associated with AI implementation.
Provides a clear direction for AI implementation: A structured plan ensures that AI initiatives are executed systematically.
Ensures stakeholder alignment and buy-in: With a clear roadmap, it’s easier to get support from all stakeholders.
Facilitates resource allocation and budgeting: Knowing the steps ahead allows for better planning of resources and budget.
Enhances collaboration and communication among teams: A roadmap fosters teamwork and ensures that all departments are working towards common goals.
Supports continuous learning and improvement: Regularly updating the roadmap helps in adapting to new challenges and opportunities.
Overcoming Challenges and Risks
The Path Forward: Creating Your Own AI Roadmap
While the Gartner AI roadmap provides a framework to guide your AI journey, it’s important to remember that there’s no one-size-fits-all approach. Each organization is unique, and your AI roadmap should reflect your specific goals, challenges, and resources.
Start by prioritizing the workstreams that are most relevant to your organization and sequence them in a way that allows you to build momentum and continuously improve. By following a structured approach, you can unlock the full potential of AI and drive lasting value for your business.

Jalasoft’s Commitment to AI Innovation
At Jalasoft, our approach to Generative AI is centered on staying at the cutting edge of technological innovation. Our journey with Generative AI is just beginning, and we are committed to further integrating these transformative technologies to enhance the value we deliver to our clients—all while upholding the highest standards of information security.
In initiatives like Cosmic Latte, our Research & Development team is actively exploring AI applications and developing proof-of-concept prototypes for AI integrations. These efforts demonstrate how AI tools can optimize processes, such as refining angular project structures, within our scalable products and high-quality services.