AI technology has advanced to a greater extent than one can imagine. Generative AI (GenAI) is creating huge excitement across all industries, and of course, for a good reason. It can generate highly realistic and customized content on demand, which has been transforming how businesses operate, increasing their productivity, and enhancing customer service experience.
However, GenAI is still in its early stages, and only a few companies have effectively used it to boost their business. As per BCG’s report, only 26% of companies have so far derived significant value from AI, while a significant number of organizations are still stuck at the proof-of-concept stage. And the other half believe their outdated IT infrastructure poses the biggest challenges to scaling, reuse, and data accessibility.
Optimizing IT Productivity
One of the many challenges business leaders face in implementing GenAI is knowing where the real value of technology lies and where they can drive efficiency and productivity.
Generative AI is a highly effective technology that can be used as a programming assistant, for knowledge management, legacy modernization, project management, cybersecurity, and so on.
Most importantly, all these areas also provide great opportunities for agentic AI as well. AI Agents use its highly advanced reasoning and iterative planning capabilities to solve complex and multistep problems easily.
As we move towards the future, we will see several leading companies using these agents for coding, customer support, enterprise workflow, and business intelligence as the next step in their GenAI endeavors.
Paths to Value
GenAI tools are definitely transformative for a wide range of business operations. However, the three use cases stand out because of their impact.
First, programming assistants like GitHub Copilot can help increase software development productivity by over 30% if implemented effectively.
Second, IT service desk tools powered by GenAI can streamline operations as they can provide self-service for various common issues and also automate several redundant tasks. They can do ticket resolution and manage incidents effectively. It is evident from the fact that one global insurer, BCG, helped estimate that Generative AI tools can be used to handle 75% of repetitive and low-complex service tickets.
Third, GenAI is also highly effective in enhancing knowledge management through its natural language interfaces, helping users to access different types of content like IT documentation, development updates, and product data effortlessly. If the generative AI is integrated with RAG, it can fetch current and external information beyond its training data, too, to improve its accuracy.
The Importance of GenAI in cybersecurity also cannot be neglected. It can analyze logs, emails, and other unstructured data to detect threats that are difficult to identify with traditional systems. But, we must also understand, GenAI can be a powerful weapon for attackers as well that can hinder gains in defense.
What Should CIOs Do?
CIOs and other decision makers looking after IT and technologies of their organizations now need to transform themselves into AI leaders to create proper value. Here are the important things CIOs should do:
- Focus on Value
CIOs must prioritize GenAI use cases according to their potential business value and not just cost savings. Using generative AI tools for a project with high productivity gains is much better than larger ones, but less effective. So, focus on opportunities with high impact that also align with business goals.
- Evaluate and Monitor Use Cases
There must be a clear system to assess and track GenAI initiatives. You may find it surprising that one of the Fortune 50 companies segmented 200+ ideas by impact, prioritized the top ones, routed mid-tier projects to existing teams, and used guardrails for low-impact trials by dropping underperformers. They ensured focused on investment and continuous value monitoring across the AI Portfolio.
- Rethink Processes
Just automating outdated processes won’t bring the real impact. Business leaders must rebuild workflows from scratch and eliminate inefficiencies and complexity. After that, apply GenAI to automate only the essential steps. You will be able to build true value when you remove limitations of the legacy system and apply automation to high-impact processes.
- Redefine Vendor Strategies
As GenAI allows vendors to operate more efficiently, it also creates value opportunities. CIOs must therefore explore new contract models such as outcome-based pricing for fixed deals and usage-based terms for managed services. Through such strategies, organizations can reduce IT costs and leverage vendor efficiencies effectively.
- Build and Attract the Right Talent
Most importantly, business leaders must focus on building the right AI skill workforce and GenAI team. New roles like AI engineers, prompt engineers, RAI leads, etc., are very important in AI transformation and must be hired and nurtured properly.
As easy it seems, implementing digital transformation can be quite a complex process. CIOs, CXOs, Presidents, Directors, and other such decision makers responsible for implementing AI tools and technologies in their organization’s business process must acquire the essential AI leadership skills to successfully lead digital transformation.
AI certifications for managers and leaders, such as CAITL™, provided by USAII®, can help business leaders and decision makers learn about the various steps involved in a successful AI transformation project. Through such AI leadership certification programs, these professionals can learn how to plan, strategize, build, and deploy AI solutions across their business operations.
Conclusion
The role of AI leaders and CIOs is very important and leading the digital transformation of an organization. They have to look after the entire AI transformation process from start to end. Thus, they need to have all the essential skills and knowledge required to implement AI projects and solutions properly. By enrolling in AI leadership programs, they will gain the required knowledge as well as practical experience and effectively lead their organizations towards creating real value with AI