Generative AI isn't the only option CIOs can make a difference in their organizations in the coming year. Addressing operational gaps and developing new digital leaders are also key considerations for CIOs.
Generative AI remains a priority
2023 is a year of great ups and downs for large language models. There is no doubt that generative AI will be at the top of the list for CIOs in their digital transformation in the coming year. Not only that, but the company's CEO and board of directors are certainly reluctant to be left behind by generative AI, and many employees are also looking to try out the latest generative AI capabilities to optimize workflows.
Boards often expect CIOs to update their AI transformation strategies frequently, and many companies are expanding their budgets to do so.
According to the survey results of Ernst & Young Technology, 94% of executives plan to increase their investment in IT or emerging technologies, and 80% plan to increase their investment in artificial intelligence in the coming year.
Therefore, CIOs should continue to include generative AI measures in their priority lists. After all, every department is under pressure to increase efficiency, automate, access data, and improve the employee experience, some of which can be solved with generative AI. At the same time, CIOs still need to reduce technical debt, modernize applications, and control cloud costs.
The massive demand for IT and data capabilities is putting tremendous pressure on CIOs and their IT teams. Therefore, before committing to too many priorities, CIOs should reset and review what constitutes transformational measures, take steps to avoid overstating goals, and work with the executive team to set reasonable priorities for the upcoming year.
Identify priorities that will lead to change
The huge need for technical capabilities should be met with a keen understanding of the differences between digital transformation and transitional technology investments. Isaac Sacolick, President of Digital Transformation Training Firm Stacie, believes that digital transformation is not just about technology and its implementation, but about looking at business strategy through technical capabilities and how this will change the way operations are done and business models.
In other words, CIOs should look for investments that will impact business strategy and strengthen, with an impact on revenue generation and operational efficiency. Many technology investments are only transitory, just to do what is done better today, not necessarily to transform the business or operating model.
At the same time, CIOs must identify the gap between business leaders' high expectations of IT and reality. Many CIOs have promised their capabilities to their stakeholders, but the implementation and business impact have been much lower than expected. In many cases, such gaps can be anticipated and addressed before commitments are made.
Three digital transformation priorities pair:
As part of the 2024 Digital Transformation Priorities, Isaac Sacolick made three recommendations:
Recommendationsone: Create six generative AI workflows
CIOs should document their AI strategies to achieve short-term productivity improvements while planning for visionary future impact. Productivity improvements could come from experimenting with platforms and tools that embed prompts and other natural language capabilities, while long-term effects could come from embedding a company's intellectual property into privately managed large language models.
CIOs have prioritized generative AI workflows such as:
1.Experiment by working with ChatGPT, Copilots, and other tools.
2.Evaluate large language models, embedded, and vector databases.
3.Define a regulatory framework for generative AI for your organization, review risks, and translate new regulations.
However, CIOs should also add three execution gaps to these AI workflows.
First, businesses have a long history of trying to improve the customer, employee, and other search experiences. Improving search capabilities and addressing unstructured data processing challenges is a key gap for CIOs looking to deliver generative AI capabilities. The 2023 Enterprise Search: Overlooked Heroes report found that 98% of organizations say they are improving search capabilities across portals, CRM tools, e-commerce, and communities, but 99% also have technical challenges, with integration (68%), data volume and cleansing (59%), and managing unstructured data (55%) being the top three challenges. Addressing these gaps while improving the search experience is the building block of intelligence that facilitates generative AI's prompting and natural language query capabilities.
The second gap that CIOs must address is critical to both results and credibility. In the 2023 State of Data Science and Machine Learning Report, 18% of respondents said they have at least half of their machine learning models in production. If CIOs fail to improve the conversion rate from pilot to actual production, investors may lose patience.
The third gap is data quality. In the 2024 Chief Data Officer Agenda: Navigating the Data and Generative AI Frontier, 57% of respondents have yet to change their data landscape to support generative AI. Nearly 50% of respondents see data quality as the biggest challenge to realizing the potential of generative AI, which requires broadening the scope of data operations and data governance initiatives to include unstructured data sources and clarifying how to apply them in large language model experiments.
These workflows require setting a vision, appointing an owner, and empowering the team to experiment. CIOs should set realistic goals and communicate priorities for those workflows.
Recommendation 2: Close the gap between operations and security
Digital transformation is often achieved through new digital products, improved customer experiences, and data-driven decision-making to gain a competitive advantage. Depending on the industry and business strategy, the specific measures will also vary, and may include smart manufacturing, digital health, electronics**, sustainability projects, digital twins, etc.
These measures are underpinned by core competencies for digital transformation, including design thinking, product management, agile**, DevOps practices, citizen development, and data governance. These DevOps practices include leadership and delivery practices supported by operational and risk management capabilities. Research shows that many organizations lag behind in these capabilities. CIOs should consider how to close these gaps in their digital transformation priorities.
For example, over the past decade, many CIOs have driven DevOps practices such as CI CD, infrastructure as a rule, data observability, and bridging the cultural gap between development teams and IT operations. The results of the 2023 DevOps Benchmark Study show that the top-performing organizations deliver significant business value. Of these, 67% achieved on-demand deployment frequency, and 94% had a change failure rate of less than 15%.
However, according to other surveys, there is a lag in implementing security best practices for DevSecOps (an R&D model that seamlessly integrates security into one of them, fully following the idea of DevOps). According to the SANS 2023 DevSecOps survey, less than 22% of respondents patched and resolved critical security risks and vulnerabilities within two days. While SAST (Static Application Security Testing) was the highest-rated tool among 82% of respondents, only 28% of respondents reported that these tools were used by more than 75% of their library.
The 2023 State of DevSecOps report points to some of the security gaps that should be highlighted, including insufficient security training, a shortage of application security staff, and a lack of transparency in development and operations workflows. CIOs should look for other operational and risk management practices to refine their transformation plans. For example, McKinsey estimates that companies can save 15 to 20 percent on cloud costs through optimization, and implementing cloud financial operations practices is an opportunity. Another area to focus on is continuous testing, especially as the risks posed by generators increase as generative AI and collaborative models accelerate development.
Recommendation 3: Introduce more measures to develop transformational leaders
Today's increasing demands for innovation, enterprise-wide transformation, and department-wide technology, data, and automation capabilities are a major challenge for CIOs. It's not uncommon for technology investments to outnumber the number of project managers, architects, and others who play leadership roles in transformation initiatives. Add to that the backlog of technical debt, security improvements, and transforming shadow IT, and IT departments are often overburdened.
At this time, being able to oversee more initiatives, standardize multi-purpose platforms, and deliver regulated citizen data science programs can make a big difference. This transformation is driven by the development of more digital transformation leaders, or digital pioneers.
According to Gartner, 76% of managers have been overwhelmed by increased responsibilities, taking on more responsibilities than they can handle by 51%. Many CIOs may also face this problem.
To address this, CIOs should work with HR to make the most of their training and development budgets and initiate programs to develop leaders in transformation and change management. This will help CIOs increase the number of IT initiatives they can initiate, deliver results faster, and reduce resistance to organizational change.
Finally, Isaac Sacolick cautioned that over-promising and under-delivering can lead to IT project failure, so it's critical for CIOs who need IT technology to achieve greater and faster impact and invest in learning programs.