5 Priority Areas That Will Define the Developer’s AI to-do List in 2024

AI adoption continues at an unstoppable pace across countless industry sectors, and for good reason.   AI applications can transform the enterprise with stronger decision support, predictive capabilities, and automation that drastically improve production quality and scale business processes. There are more and more AI products on the market to choose from that streamline this adoption path. Yet, in the rush to implement, organizations can’t afford to skimp on the DevSecOps priorities that will ensure this adoption doesn’t come at the expense of accuracy, accountability, and compliance.   

More than any other role in the org chart, the developer is in the hot seat to reconcile this dilemma of taking AI as an investment priority and ensuring the implementation lives up to that investment as an effective, secure, and compliant operation around the use of AI.   With that in mind, here are five priority areas that will define the developer’s to-do list in 2024 when it comes to AI: 

1. Doubling Down on Data-Layer Integrity

It’s the developer’s job to ensure data ingestion, engineering, and validation are optimized for AI to work properly. These may sound like trivial steps, but they are table stakes for a data management and optimization strategy that helps avoid the modern AI version of “garbage in, garbage out.” The key to sharpening the accuracy and value of search results, predictions, and the extraction and classification of data is to ensure a unified view of data across the organization. Data fabric and other advanced data architectures can eliminate information silos and gaps so that AI applications can effectively connect and analyze all data for valuable patterns and insights.  

2. More Repeatability of AI Processes

Developer-focused tooling is increasingly available, allowing for repeatability by standardizing and simplifying AI steps around modeling and algorithm training that would otherwise require coding from the ground up. These tools help ensure not just repeatability but also increase developer productivity. For example, platforms exist that allow a team to set up repeatable processes for AI modeling – from data preparation and feature extraction to model training, selection, and tuning. Great use cases for this include document classification, document extraction, email classification, and other core business functions that lend themselves easily to user-defined model development. 

3. Facilitating Iterative Experimentation

Especially in the age of generative AI, where issues around AI hallucinations and accuracy of results can stymie the reliability of AI outputs, developers need to ensure the validity of AI work product through experimentation and iterative testing for bias and provenance of results. This is made easier by the stronger data layer mentioned above, and developers can also take advantage of private AI platforms that train exclusively on data that is specific to one user or company. Private AI has the additional benefit of ensuring that this experimentation and model development doesn’t get shared outside the organization, protecting valuable insights and sensitive business data from potentially being appropriated by other companies, including possible competitors.

4. Enhancing the Developer’s Role in Compliance

This point is especially critical in highly-regulated sectors like finance, healthcare or critical infrastructure operations. Whether something was done manually or is the work product of an AI algorithm, all operations must remain compliant with rules for data privacy, cybersecurity preparedness, and other regulatory priorities. Because of this, developers need to be fully looped into the compliance and risk management operation so they can ensure metadata, tagging, and other cyber asset management tasks align with compliance contingencies and privacy implications for all regulated data and systems.

5. Aligning AI Models Closely With Business Processes and Workflows

Developers need to make sure the AI models and tools they create actually make sense for the processes and workflows they are meant to address for business users. This is especially critical given that what you code is, more likely than not, going to get automated and scaled – meaning that if you scale a bad AI tool, you're also scaling the dysfunction. Fortunately, developer-friendly tools exist that bundle up the complexities and put AI-driven automation and process optimization within reach of transformation teams made up of business analysts and staff developers who may not have advanced data science skills or experience.

Conclusion: Strengthening the Developer’s Role in AI-Driven Transformation 

As AI continues to be the chief driver of digital transformation, the developer’s role will only grow in importance as organizations seek to adopt stronger and more powerful AI tools that take advantage of an ever-expanding range of data sources and advanced technologies like IoT, edge computing, hyper-converged networks, and 5G connectivity. The five priorities above will help position developers for success in implementing and managing AI to run effectively, securely, and compliantly in the enterprise.