Alejandro Oses, CEO and cofounder at Rootstack. I lead digital accelerations for companies across all industries.
getty
As organizations race to integrate AI into their operations, having access to the right technical expertise has become a competitive necessity rather than a future consideration.
While dedicated software development teams have long provided organizations with specialized technical resources, AI projects require an expanded set of skills that go beyond traditional software development. Companies that successfully deploy AI solutions are increasingly combining experienced developers with specialists in machine learning, data science, automation and AI governance.
A traditional software development team typically includes back-end and front-end developers, QA specialists, DevOps engineers and security professionals. While these roles remain essential, AI initiatives require additional expertise.
One of the most important additions is an AI engineer or machine learning specialist with experience in technologies such as machine learning, data processing, natural language processing (NLP), generative AI, speech recognition, intelligent automation and data analytics. These professionals help organizations select, implement and optimize AI solutions that align with business objectives.
Prompt engineering has also emerged as a valuable discipline, particularly as generative AI tools become integrated into everyday workflows. Effective prompt design can significantly improve the quality, consistency and reliability of AI-generated outputs.
Security and compliance expertise are equally important. AI systems often process large volumes of sensitive information, creating new risks related to data privacy, cybersecurity and regulatory compliance. Organizations operating in regulated industries must ensure that AI implementations meet both security requirements and industry-specific standards.
Once the necessary expertise is in place, AI-focused development teams can support organizations through a variety of services.
Artificial intelligence is increasingly changing how software is built. The rise of "vibe coding" has brought greater attention to the use of AI-assisted development tools that help generate, test and refine code.
However, successful AI-assisted development still requires experienced engineers who can validate outputs, identify errors, ensure security and maintain scalability. Rather than replacing developers, AI tools accelerate development while allowing teams to focus on higher-value engineering decisions.
Before implementing AI solutions, organizations must determine where AI can deliver meaningful business value. AI consulting services help companies evaluate opportunities, identify use cases, assess technical readiness and design implementation road maps.
Many organizations realize immediate value by applying AI to operational processes. AI can automate repetitive tasks across departments such as sales, marketing, finance, customer service and human resources.
Beyond these services, dedicated AI teams may also develop AI-powered applications, build custom chatbots and virtual agents, implement Model Context Protocol (MCP) integrations, and create industry-specific automation solutions.
For companies looking to expand their AI capabilities, the first step is identifying where AI can create the greatest business impact. Organizations should evaluate existing processes, determine which workflows would benefit most from automation or intelligence, and prioritize initiatives with clear return-on-investment potential.
Once these needs are identified, leaders typically have three options:
This approach allows organizations to retain employees who already understand their systems, products and business processes while developing new AI expertise internally. The trade-off is that training requires time, investment and ongoing learning.
Bringing in external specialists can accelerate implementation, but recruiting qualified talent remains highly competitive and often expensive.
Outsourcing provides immediate access to established AI expertise, making it well-suited for organizations looking to quickly launch an AI project without building an entire internal capability from scratch. However, it's important to note that onboarding and consultation are still necessary.
Artificial intelligence is no longer a future initiative. It is increasingly becoming part of everyday business operations, helping organizations automate repetitive work, improve decision-making and increase productivity.
Whether through internal hiring, employee development or external partnerships, companies that invest in AI expertise today will be better positioned to scale their initiatives tomorrow. A dedicated team with the right mix of software engineering, AI, security and operational expertise can help organizations move from experimentation to measurable business outcomes.
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