Artificial intelligence energized enterprise software and has become a ubiquitous part of the modern workplace. Yet, the nuance of how these systems become incorporated and, ultimately, used on a general basis remains muddled.
Zoho's recent survey, Unveiling Trends in Digital Workplace Transformation, analyzed patterns across demographics and company sizes to determine trends in 'digital maturity', or how efficient and standardized companies were in various work-related domains. Operating with the latest technology that has been fully integrated into a company's workflow? High digital maturity. Reliance on manual, analog, and/or idiosyncratic systems? Low digital maturity.
The survey identified some key areas where enterprise organizations were particularly lagging, the most pronounced of which were process inefficiency and security vulnerabilities. This is partially due to these companies' complexity of scale, reliance on legacy systems, and propensity for siloed operations. The study also discovered that even when advanced tools are introduced to an enterprise setting, they often fail due to:
To make matters worse, the survey further found that the process for individual service requests within these organizations continues to rely on informal processes, slowing down opportunities for change and increasing the risk of security issues perpetuating throughout the company.
Upgrading an organization's digital maturity can be a significant undertaking, and for best results, these companies must ensure these new systems are calibrated for the proper outcome: maximizing decision intelligence.
Not all intelligence is created equal
Companies can be forgiven if they expect to solve all their problems by incorporating the latest AI technology into workflows and hoping for the best. The hype cycle has come full circle, from massive over-hype to backlash, to a return to hype with the release of DeepSeek in early 2025.
When AI stopped short of solving all of the tech industry's problems, AI detractors and pragmatists pointed to other variables that affect AI's usability within an organization. Rather than try to sell employees on the value of upending their workflow for the sake of new software, AI had to run mostly in the background so users weren't aware of its role, yet found value in the output itself.
To ensure the results are accurate, tailored, and actionable, a company's AI has to be trained using proprietary data to place everything within the proper business context. Analytics software remains central to this effort, as it can produce data from which AI can iterate its training as well as guide AI's human counterparts in making informed business decisions. This is the business intelligence (BI) piece of the puzzle.
Together, the meeting of AI and BI can be referred to as Contextual Intelligence (CI), and it lays the groundwork for better enterprise-level decision making alongside AI. This functionality goes beyond making decisions on behalf of employees, as well -- internal search is greatly enhanced when the system can navigate data, relations, and permissions to produce only the most important and relevant results. High levels of CI are a result of effective AI anchored by the weight of comprehensive BI. Together, they form a data-driven ecosystem that helps users make better decisions.
As AI use cases have become commonplace among enterprise organizations, it's becoming clear that CI is only yet another piece of the puzzle. For example, it shouldn't take the full weight of a company's AI and BI ecosystem to submit invoices to clients automatically or add action items to an employee's calendar after a meeting. Similarly, companies benefit tremendously from both BI honed by an AI model trained on company information and BI in a vacuum -- one that weighs industry data and trends to form the basis of its decisions -- so as not to miss out on insights competitors may be gleaning from an out-of-the-box consumer LLM.
The concept of CI does not represent the end of an AI decision tree, but rather part of a larger framework within which modern companies will find the most success.
Intelligent decisions
Wise AI implementation ensures the technology serves the employee, not the other way around. The end goal of corporate AI should be to unlock an employee's ability to do their best work by handling mundane tasks, streamlining large-scale operations, and analyzing organizational and industry trends.
The synthesis between AI, BI, and CI can be referred to as Decision Intelligence (DI), and it represents a holistic approach to enterprise change management and efficiency. Essentially, DI is the concept that the best AI-driven business decisions are a result of rote task-completion, industry-specific analysis, and deep, personalized company knowledge -- all three in concert, not just one.
DI also remains far stickier than most out-of-the-box LLMs that may be capable of handling repeated operational functions and standardizing admin functions (AI and a bit of BI) but would require a deep well of company data to truly level up to CI. At the other end, a system presenting even the most sophisticated, context-soaked LLM might start applying context in the wrong places, like scanning a receipt for employee reimbursement and attempting to extract meaning where there may be none. It's also far less efficient to, for example, pull a wagon with a tow truck when a bicycle would suffice -- in a business setting, especially at the enterprise level, inefficiencies with technology accumulate.
DI encourages allotment of a level of computing power to match the complexity of the task. Some custom LLMs, tailored by vendors for each individual customer, are built to maximize DI by imposing wise limitations. Smaller tasks are accomplished by a more narrow LLMs that snap to attention, while bigger-picture initiatives are served by more complicated LLMs trained on a company's data for better service. This produces precise, actionable data within the proper context and within a way that allows a company's technology ecosystem to thrive.
By controlling the entire tech stack, or hiring a vendor who does, companies can ensure they are set up for whatever else is coming their way in the world of AI by maximizing DI -- equipping employees with the strongest, most accurate tools for their jobs, while empowering them to steer the ship in the most accurate and efficient way possible.
A new project management paradigm
The best example of DI at work can be seen in the modern practice of Data Driven Project Management (DDPM), representing a foundational shift from intuition-based decisions to a focus on data and analytics. The idea is to pull data from across multiple tasks -- including budget, time tracking, task management, and deliverability metrics -- and synthesize everything into actionable insights for intelligent decision making.
Consider the journey of Virtuoso, a luxury travel advisory. In 2020, the company migrated away from Microsoft Dynamics, whose multiple functions didn't coalesce in a way that worked for Virtuoso, to drive down operating costs while increasing efficiencies. Steve Wooster, Vice President of Operations at Virtuoso, says the company decided on a unified system for project management supported by DDPM and included capabilities for analytics and online scrum oversight so teams could maintain a complete view of priorities and cross-department data, maximizing DI.
Virtuoso's software switch enabled improved coordination between divisions, more robust collaborative work features, and a higher level of visibility into higher quality, more contextual data. The concert of routine task management and data-driven, targeted decision-making led to more successful project delivery -- so much so that the company adopted the vendor's upgraded project solution that pulled these previously disparate products into a single piece of software.
What can be done?
DI isn't something that can necessarily be objectively measured; much of its capability is measured by the ways in which it serves the particulars of an organization both in the moment and moving forward.
Some features of modern DDPM-enabled software are worth prioritizing, as they can directly contribute to an increase in DI and, eventually, company growth.
Primary among them is an increased emphasis on predictive analysis. This partially boils down to leveraging historical company data to predict risks, estimate timelines, and anticipate resource needs, but this process can't tell the full story. The software must continually refine its DI when new data becomes available, and for the best and most time-sensitive results, a unified system of apps must be present. This furthers DI by identifying project bottlenecks and lapses in quality control so they can be corrected immediately, ensuring employees are always operating with the most up-to-date information available and working as efficiently as possible.
It's also essential that DI-driven software be fully supportive of asynchronous work. Put aside the debate on in-person versus hybrid versus fully remote work for a second and consider how drastically the COVID-19 pandemic shifted the paradigm in such a short amount of time. Even if five-days-a-week in the office becomes the norm again, there's no guarantee that work will only occur between 9am-5pm, Monday thru Friday, particularly across time zones. Asynchronous work ensures decisions can happen quickly and under any circumstances so as not to impede others' efforts.
These days, the world of enterprise technology seemingly reinvents itself overnight, every night. The more digitally mature organizations will emerge as the winners because they are always operating at the highest levels of intelligence, ensuring heightened security in the present and a more accurate level of future-casting moving forward.
