If putting AI on your 2025 predictions bingo card is a safe bet, leave room for IT consolidation. This is borne out by a recent survey of more than 1,000 technology professionals: nine in ten IT pros identified software consolidation as a priority over the coming months, as 73% predict their organization will continue to grow their software investment and add new technologies in the next year.
See also: Report Uncovers the Realities of AI Adoption
That same survey showed that the tech consolidation urgency is spread across the business, with the biggest proponents of consolidation being IT with 48% saying it is time to focus on it, followed by 38% of executives and 28% of boards of directors. The pressure is on to tame the “tech sprawl” of too many tools and disconnected systems.
There are several factors driving the IT consolidation urgency, with AI among the most prominent catalysts. AI models demand high quality, accurate data to be useful; if an organization has data silos, likely a result of maintaining a variety of disconnected digital tools and apps, AI efforts will fall short of expectations or stall out.
Yet the need to rein in tech sprawl has been brewing long before AI hit the mainstream. Here is why: The proliferation of digital tools has been growing for years because it is easier than ever for anybody, regardless of technical skills, to download or quickly build an app for their individual project or team needs. That ease, however, comes with an unintended consequence, where shortcuts promising to streamline business processes for individuals and teams put an unnecessary strain on the infrastructure.
All that strain has IT pros feeling challenged in overseeing all these software applications, with 80% feeling frustrated when trying to tame that tech sprawl, managing and maintaining inconsistent data sources, information silos, and hidden consequences. These include higher costs, according to 48% of respondents; integration problems (39%); and security or compliance risks (31%), all major challenges associated with tech sprawl. Additionally, 90% believe that tech sprawl can hinder their plans to implement AI tools.
The Black & White of Gray Work
Gray Work is another lurking issue that can derail AI plans and one that organizations often overlook. The term refers to the time and resources lost when employees have to dig through all those apps and digital tools searching for the information they need just to do their job.
If you don’t think Gray Work impacts your workforce, consider how many applications you currently use to do your job a daily basis: Email, project management and tracking, CRM, ERP, specialized apps, and more. Now consider all of the apps colleagues, partners, and customers use that are not on your radar. The IT consolidation survey found that over 75% of participants say their organizations use more than 10 software applications, with nearly two-thirds relying on at least five project management tools.
When employees are forced to spend most of their day toggling between apps just trying to track down information, it is easy to see how Gray Work piles up. The most recent Gray Work Index, an annual look at productivity and how work gets done, found 45% of employees spend 11 hours or more each week chasing information from different people across multiple systems.
As a result, nine out of 10 employees surveyed reported feeling overwhelmed by the sheer amount of software solutions they use every day. Ironically, these are products that tout their ability to increase productivity. This squandered time impacts an employee’s ability to finish meaningful work within a reasonable amount of time, causing productivity leaks throughout the company.
Looking closer at the issues driving IT consolidation and impacting workforce productivity often prompts a company to more carefully assess its readiness for the widespread deployment of AI.
A Reality Check: AI Adoption and Governance Framework
As noted above, despite IT voicing frustrations with managing so many technology tools, nearly three-quarters of IT pros say they expect their companies to invest in new software solutions in the coming year. This underscores the disconnect between what tools people want to use to complete work and the challenges all that tech sprawl presents to true productivity.
The disconnect can lead to stalled AI efforts, which require businesses first get their tech stacks in order. The foundational data supporting AI models must be solid, and ensuring this often requires IT consolidation, streamlining and centralizing digital tools and information sources. This ensures that the business will have fewer risks of bad data being fed into the AI models.
Avoiding those unnecessary delays calls for a strong governance framework. This allows IT departments to maintain control, avoid tech sprawl, ensure compliance, and mitigate risks.
Ideally, an effective AI governance framework should be based on the following five principles.
- Establish accountability: Engage stakeholders from across the organization, including IT, security, compliance, and operations teams, to build and extend buy in for that framework.
- Conduct regular risk assessments: Schedule routine evaluations to identify vulnerabilities including biased data sets, compliance risks, and potential security threats.
- Keep a human in the loop: Ensure transparency and accountability through detailed audits and human oversight of AI decision-making processes.
- Integrate AI governance into IT systems: This supports the ability to scale AI across the organization without causing disruptions or incurring unnecessary costs.
- Institute continuous monitoring: Ongoing testing and monitoring are essential for safety and compliance.
By following a structured governance framework, businesses can safely deploy and accelerate the use of AI throughout the organization and extend it to customers and partners, confident that they have the right guardrails in place to manage and protect the data so critical to an effective AI strategy.
The Inextricable Link between AI and IT Consolidation
With IT consolidation viewed as a priority over the coming months, business and IT leaders are looking to improve operational efficiency, support better data integration, and simplify IT management. Let’s not overlook costs, either. Nearly all survey participants – 93% – say that a cost reduction of 10% or more makes IT consolidation worthwhile.
This doesn’t mean the path to effective IT consolidation will be easy for every organization. Employee frustration over losing existing tools and having to learn new ones is a valid concern.
To avoid slow user adoption, an effective consolidation strategy should provide IT professionals with training and change management support, integration options, and a detailed cost-benefit analysis. These elements will make the business case clear while supporting a more collaborative transition.
Ultimately, this shared knowledge will drive a successful IT consolidation strategy and accelerate AI adoption and implementation. In turn, each should become a forcing function of the other, making future consolidation easier by identifying redundant tools and improving decision-making as the AI models get smarter.
Dalan Winbush is the CIO of Quickbase.