Table of Contents
In a rush? Download the PDF for later
Download the e-BookI. The next chapter in digital transformation
Just a few years ago, it felt novel to say that every company was a software company. That meant that no matter what goods or services a company produced, it relied on software to get things done. From agriculture to aerospace, from manufacturing to medicine, employees used software to collaborate and execute while customers used it to engage with the business.
Fast forward to 2025, and now every company is an AI company. This transformative technology is not only upending the business models and operations of software companies, it’s made inroads into every industry imaginable, from restaurants to home improvement. A financial services company, for example, may not think of itself as an AI company. And yet as it leans into AI-powered workflows and agents to move money, engage with customers, and accomplish countless other tasks, the reality is undeniable.
Proof of AI’s holistic, lasting impact on the business world is in the numbers. AI spending is projected to reach $632 billion by 2028, more than double what it was in 2024. With this kind of spending, it’s no surprise businesses are expecting big results. A PwC study predicts that AI will boost global GDP by an additional 14.7% (that’s $15.7 trillion) by 2030.
When AI goes wrong in business
AI represents a tremendous opportunity for business. But that opportunity is not without risk. For all the stories of companies leaning into AI, tales of the tech going wrong have also made headlines. Last year, for example, a major airline’s AI agent gave a customer incorrect information about the company’s bereavement policy. The airline was forced to reimburse the customer after a court found it responsible for the agent’s behavior and misinformation.
Then there’s the global fast-food behemoth that briefly experimented with using AI to handle drive-thru orders in order to boost efficiency. The fast-food behemoth was forced to end its test program, however, after customers shared viral TikTok videos of the AI messing up food orders in various ways—including increasing the order count of chicken nuggets into the hundreds.
AI can also make mistakes in more novel ways (pun intended). This past May, several major U.S. newspapers came under fire for publishing a syndicated column of summer reading recommendations. The problem? Most of the titles were AI hallucinations—no such books existed.
How organizations can win in the age of AI
How can businesses avoid such outcomes and ensure that their AI initiatives are successful? It starts with cultivating the right organizational leadership. For reasons we’ll go into below, IT teams are uniquely suited to be an AI leader and cross-functional partner.
From there, businesses need to make sure they’re building AI tools and agents the right way—trained on the right data, behaving in compliance with key policies and regulations, and delivering real ROI and business value. With every company becoming an AI company, the organizations that get this right will be the ones who see the biggest wins from this breakthrough tech.
II. The natural leader for AI business transformation
As organizations race to adopt this powerful technology, one crucial question arises: Who will steer the AI ship?
The answer, sometimes lost in the initial enthusiasm, is clear: IT teams are perfectly positioned to take strategic leadership of AI initiatives within an organization.
Historically, IT departments have often been pigeonholed as problem-solvers, digital gatekeepers, or implementers of new technology. While these roles remain critical, the AI era demands that IT adopt a far more proactive and strategic stance.
With AI rapidly becoming central to business strategy, IT’s expertise in data management, system infrastructure, cybersecurity, and scalability is more than beneficial—it’s indispensable. Here’s why IT isn’t merely participating in the AI revolution but is the ideal departmental leader:
IT as the connector between business objectives and technology
Successful AI projects depend on more than sophisticated algorithms; they must solve tangible business challenges. Achieving this requires deep knowledge of business goals paired with technical expertise. IT is ideally positioned for:
Translating business needs to tech strategy: IT professionals excel at transforming broad business goals into specific technical requirements. They can help stakeholders clearly articulate AI objectives and then design effective technical solutions to further objectives such as boosting productivity.
Risk management: AI projects carry unique risks, from data privacy issues to potential algorithmic bias. IT has established processes to identify, assess, and mitigate these risks, ensuring responsible AI implementation.
Vendor and technology evaluation: The AI market is expansive and continuously evolving. IT leaders have the knowledge and insight necessary to evaluate and select the right AI platforms, tools, and vendors that align with long-term organizational strategies.
By bridging business units and technical teams, IT ensures AI initiatives are not just technically sound but strategically impactful.
IT as the builder of scalable AI infrastructure
Developing a pilot AI model is relatively straightforward, but scaling it across an entire organization is significantly more complex. Here’s where IT’s architectural expertise proves invaluable:
Infrastructure management: AI workloads are computationally demanding. IT teams understand the hardware, cloud solutions, and network requirements essential for deploying AI effectively—from initial development to enterprise-wide implementation. They optimize resources for performance, cost efficiency, and reliability.
System integration: AI tools rarely operate independently. Effective integration into existing business applications and workflows is crucial. IT’s experience with APIs, ERP systems, and CRM platforms ensures AI becomes seamlessly embedded into your operational framework.
Performance optimization: IT continuously monitors systems, identifies performance bottlenecks, and ensures AI applications operate reliably, delivering timely insights and meaningful outcomes.
Without IT’s comprehensive architectural vision, AI initiatives risk remaining isolated experiments rather than integrated solutions that drive meaningful business results.
IT as the custodian of AI data
At its heart, AI depends entirely on data. Accurate, well-managed, and readily accessible data is fundamental for any effective AI model, and this is precisely where IT excels. IT teams are unmatched experts in:
Data infrastructure and pipelines: IT designs, builds, and maintains the systems that gather, store, and process massive datasets, ensuring that data is reliable and AI-ready. Without robust data pipelines, AI initiatives are destined to fail.
Data governance and security: IT departments champion ethical data practices, uphold regulatory compliance, and protect sensitive information. With AI, this becomes even more critical as data biases or inadequacies can lead to flawed outcomes. IT is the essential first line of defense against these risks.
Data integration: Companies typically operate with numerous data sources. IT is skilled in integrating various systems to create a unified data landscape, essential for comprehensive AI insights.
To empower IT fully, companies must involve IT leaders in strategic discussions early, aligning AI initiatives with broader organizational goals. With the right resources and strategic support, IT can effectively develop and implement AI solutions that genuinely transform the business. The path to AI success is through a visionary and empowered IT department.
III. Building and managing effective AI agents
With the right organizational leadership in motion, businesses can move to building and managing AI agents the right way. In a recent survey, 96% of enterprise IT leaders said they anticipated expanding their use of AI agents in the coming year.
In many scenarios, these agents will take on roles traditionally held by humans in supporting users—whether they be customers seeking the right products and services or employees needing assistance with key tasks. Gartner, for example, forecasts that AI agents replacing humans will ultimately cut contact center expenses by $80 billion by 2026.
Yet, while AI agents have enormous potential, successfully developing, deploying, and managing them involves navigating several unique challenges.
Agent effectiveness depends entirely on training data quality
IT and operations teams are implementing AI agents for tasks ranging from automation of employee workflows and customer support to security oversight. But regardless of the intended use case, agents can only deliver meaningful outcomes if they accurately reflect user behaviors and real-world scenarios. This requires training AI agents on high-quality, comprehensive datasets.
Although the era of AI agents is still at its beginning, we're already seeing the consequences of deploying agents trained on flawed or incomplete data.
Agents must adhere to compliance policies and regulations
IT teams must also ensure AI agent behaviors comply with organizational policies, regulatory requirements, and ethical guidelines.
The autonomy and speed at which agents operate add layers of complexity to compliance and governance. Without robust data governance protocols, agents risk unintentionally accessing or exposing sensitive information, potentially violating privacy laws and harming the company's reputation.
To prevent these scenarios, IT teams require analytics capabilities that enable continuous monitoring and evaluation of agent behavior. Are the agents performing as expected? Are they beginning to display erratic or unintended behaviors (often called "hallucinations")? Real-time visibility into agent activities is essential for ensuring compliance, optimal performance, and risk mitigation.
Agents need to deliver measurable ROI
Beyond compliance monitoring, businesses need clear analytical insights to justify their significant investments in AI. With AI spending accelerating, IT teams can expect increased pressure to deliver concrete results in the face of growing budgets.
The right analytics solutions can provide critical insights into the performance and business impact of AI agents. Teams can answer essential questions such as:
- Are agents reducing time spent on routine tasks and workflows?
- Are they driving cost savings or boosting revenues?
- Are agents enabling teams to focus more on strategic, high-value work?
- Are they increasing productivity metrics, like task completions or conversions?
Having a clear line of sight into agent behaviors and the ROI they produce is crucial. With this transparency, IT and operations teams can take informed steps to refine agent performance and invest wisely to mitigate risk. This continuous improvement of agents will be what distinguishes successful AI initiatives from the rest.
There’s no escaping the new reality: Every company is now an AI company. If businesses want to solidify their competitive advantage in the market, they have to empower IT teams to be cross-functional leaders and form a strategy grounded in data.
