Digital transformation goes beyond merely implementing the latest technologies, resolving technical debt, or enhancing infrastructure agility. It centers on how these strategic initiatives foster growth, elevate customer experiences, streamline workflows, improve quality, and achieve other essential business outcomes. Transformational CIOs consistently invest in their operating models by embracing practices such as product management, design thinking, agile methodologies, DevOps, change management, and data-driven decision-making.
In 2024, three key factors are poised to reshape CIOs’ operating models and digital strategies:
- The integration of embedded AI agents in enterprise SaaS solutions is transforming workflows, prompting leading organizations to develop their own AI agents.
- The incoming US President-elect is expected to implement significant changes affecting enterprises, including new import tariffs, immigration policies, energy regulations, and relaxation of certain business constraints, all of which will influence supply chains and labor markets.
- While fostering a culture of curiosity, collaboration, and experimentation is vital for effective change management, there has been pushback against diversity, equity, and inclusion (DEI) initiatives, alongside a trend of large companies ending remote work arrangements over the past two years.
In: Cultivating transformational leadership and employees prepared for AI
In 2024, one of my top digital transformation priorities for CIOs is fostering transformational leadership to enhance the support for strategic initiatives, experimentation, and change management within IT. As we move into 2025, AI will significantly boost productivity in areas such as coding, content generation, and workflow orchestration, influencing the necessary staffing and skill sets for agile innovation teams. To navigate this landscape, CIOs must enhance the business acumen of their digital transformation leaders, ensuring that initiatives are prioritized effectively, vision statements align with business goals, and AI model accuracy is rigorously validated.
Additionally, knowledge management, training, and change management programs will be essential for helping employees adjust to AI-driven workflows. According to Ed Macosky, chief product and technology officer at Boomi, the surge of AI agents and the C-suite’s eagerness to stay ahead in technology will heighten the pressure on IT leaders. He emphasizes that democratizing access to AI and upskilling employees will be critical. Organizations that invest in upskilling will leverage AI for a competitive advantage, while those that hastily adopt new technologies without adequate preparation risk stalling their innovation efforts.
Read more: IT Outsourcing Costs in 2025: Costs Breakdown & Cost Savings Analysis
Out: Supporting ambitious AI innovations that lack clear business justification
How much patience will boards and executives maintain for ongoing AI experimentation and long-term investments? Gartner recently indicated that AI might be entering the trough of disillusionment, supported by two reports suggesting the end of the AI honeymoon. Deloitte’s “State of Generative AI in the Enterprise” reveals that nearly 70% of organizations have moved only 30% or fewer of their generative AI experiments into production. Meanwhile, Wharton’s “Navigating Gen AI’s Early Year” report shows that 57% of respondents expect slower increases in AI spending, signaling a continued search for ROI on initial investments.
Louis Landry, CTO at Teradata, emphasizes that “2025 will be the year when generative AI needs to deliver value.” He notes that investment in generative AI will slow as companies shift their focus from mere adoption to identifying real-world opportunities that create tangible benefits. Given that many early AI successes have enhanced productivity and efficiency, CIOs should seek avenues where significant cost savings can further drive innovation and infrastructure development.
Self-funding AI-led business reinvention is becoming essential by reducing technology, data, and process debt while adopting AIOps and AI-enabled software development. Realizing business value and effectively tracking capabilities will allow organizations to reinvest savings into innovation, transform customer experiences, reimagine cost structures, and enhance enterprise agility.
In 2025, successful organizations will focus on developing an integrated IT roadmap that combines generative AI with more established AI strategies. As the initial excitement around generative AI diminishes, it will be crucial to identify and implement point-specific applications of AI that drive measurable business success.
In: Strengthening data and AI governance efforts.
Historically, getting business leaders to understand, invest in, and collaborate on data governance has been a challenge for CIOs and chief data officers. Many organizations have prioritized defining policies for AI governance to guide employee use of copilots while safeguarding sensitive data from exposure to public large language models (LLMs). In 2025, it will be essential for CIOs to integrate data and AI governance efforts, emphasizing data security to mitigate risks and enhance business outcomes through improved data quality.
A synergistic approach to data and AI governance is crucial, with data serving as the fuel and AI as the engine. When random data types are introduced into a high-performance AI system, the results can be unpredictable. For AI to yield safe and reliable results, data teams must accurately classify data before supplying it to these demanding models.
Focusing on data classification and quality improvement is an offensive strategy that can enhance AI model accuracy and drive business results. CIOs who find it difficult to justify investments based solely on this approach should also highlight defensive strategies, including lessons learned from AI-related failures in 2024, to motivate stakeholders.
As poor AI implementations become more publicized, organizations may reassess their data strategies. Many brands face challenges in effectively utilizing AI due to unstructured, incomplete, and biased data accumulated over time from their websites and applications.
To address these concerns, CIOs need to raise awareness among employees and managers about how dark data and other data debt issues can impact business competitiveness in the AI era. Dark data, often lurking in emails, spreadsheets, and outdated systems, can contain sensitive intellectual property or personal information, making it susceptible to breaches. AI tools can further exacerbate these issues by revealing hidden data pockets and creating additional security risks.
Out: Lift and shift, app migrations, and dumb automations
A positive outcome of AI is that business leaders are increasingly recognizing the need to transform operations rather than simply updating existing processes with newer technologies. This shift may encourage CIOs to move away from merely lifting and shifting workloads to the cloud, modernizing applications without enhancing user experiences, and implementing robotic process automation without a broader transformative strategy.
For instance, migrating workloads to the cloud does not always lead to cost reductions and often requires refactoring to enhance scalability. Organizations are beginning to realize that static workloads may actually be more cost-effective when run on-premises. The mindset of indiscriminately moving all operations to the cloud is shifting as businesses focus on cost management, acknowledging the complexities that AI workloads introduce, which can significantly inflate cloud expenses.
App modernization efforts that solely address technological issues are also becoming outdated. Emphasizing AI copilots, upgrading to generative AI capabilities, and adopting platform engineering can streamline the development of superior applications. The emphasis will increasingly be on improving user experiences, embedding AI functionalities, and iteratively enhancing business outcomes.
AI-enhanced development will enable user experience designers to implement their visions as intended, rather than compromising due to increased development demands. AI will facilitate the collection and analysis of user feedback, informing future improvements and creating a beneficial cycle of enhancement.
Additionally, improvements in coding efficiency and developer experience will provide CIOs with new motivations to strengthen software development practices and reduce technical debt. This upskilling is essential as organizations aim to leverage proprietary data in large language models, develop AI agents, and integrate capabilities securely within their partner ecosystems.
The future of applications is moving toward a composable architecture, where APIs serve as the backbone for AI integration. AI enhances these APIs by adding intelligence that improves their functionality and efficiency. This new generation of composable applications, powered by advanced development tools and techniques, will enable organizations to accelerate the delivery of innovative products and solutions, driving growth and enhancing user experiences.
Read more: 12 Key Digital Transformation Technologies Transforming Industries
In: Enhanced training on security, safety, and trust
CIOs and CISOs face growing challenges in educating employees to recognize malicious emails and avoid clicking on links from unknown sources, particularly as new AI threats emerge. In 2025, the rise of sophisticated deepfake technology will significantly undermine trust in digital content and online interactions. Atticus Tysen, CISO and CIO at Intuit, emphasizes the need for organizations to develop new strategies for verifying information and ensuring authenticity, including enhancing workforce education and awareness.
As AI advances, it will improve at data attribution, complicating the ability of organizations to differentiate between legitimate and malicious personas. Mike Arrowsmith, chief trust officer at NinjaOne, notes that there will be a renewed emphasis on training employees to identify AI-related risks, ensuring organizations are equipped to address the security vulnerabilities that AI may introduce.
Additionally, Law Floyd, chief of security operations at Telos, highlights the ongoing challenge of insider threats. He asserts that effective training programs are crucial in combating these risks, reinforcing the importance of comprehensive security, safety, and training initiatives within organizations.
Out: Expecting employees will keep up with AI
A common misstep in digital transformation is underestimating the importance of change management or delaying its planning until it’s nearly too late. At the recent Spark Executive Forum, I moderated a panel highlighting how a business-as-usual mindset hinders CIOs from fully leveraging AI’s potential to boost productivity. As AI capabilities evolve, the pace of change can outstrip many employees’ ability to adopt new technologies and adapt to AI-driven workflows.
To keep up, organizations must foster a culture of lifelong learning that extends beyond basic skill training to include experimentation, teaching, and continuous improvement. In 2025, CIOs will need to significantly transform their organizational cultures to navigate the impacts of generative AI, political shifts, regulatory changes, and cultural challenges, which can either hinder or enhance a business’s competitive edge.
