The promise of AI expands daily – from driving individual productivity gains to enabling organizations to uncover powerful new business insights through data. While the potential of AI appears limitless and its impact easy to imagine, the journey to a truly AI-powered ecosystem is both complex and challenging. This journey doesn’t begin and end with implementing, adopting or even consistently using AI – it ends there. Realizing the full value of an AI solution ultimately depends on the quality of the data and the people who implement, manage and apply it to drive meaningful results.
Data: The Cornerstone of AI Success
Data, the organizational constant. Whether it’s a Mom-and-Pop convenience store or an enterprise organization, every business runs on data (financial records, inventory, security footage etc.) The management, accessibility and governance of this data is the cornerstone to realizing AI’s full potential within an organization. Gartner recently noted that 63% of organizations either lack confidence or are unsure about if their existing data practice or management structure is sufficient for successful adoption of AI. Enabling an organization to unlock the full potential of AI requires a well thought out Data Practice. From collection, storage, synthesis, analysis, security, privacy, governance, and access control – a framework and methodology must be in place to leverage AI properly. Additionally, it is essential to mitigate the risks and unintended consequences. Bottom line, data is the cornerstone of analytics and the fuel for your AI.
The access your AI solution has to your data determines its potential to deliver – so much so, we’re seeing the emergence of new functions tailored specifically to it, the Chief Data Officer (CDO). Simply put, if an AI solution is introduced to an environment with “free-floating” data accessible to anyone – it will be error-prone, biased, non-compliant, and very likely to expose sensitive and private information. Conversely, when the data environment is rich, structured, accurate, within a framework and methodology for how the organization uses its data – AI can return immediate benefits and save numerous hours on modeling, forecasting, and propensity development. Built around the data cornerstone are access rights and governance policies for data, which present its own concern – the human element.
People: The Underrated Factor in AI Adoption
IDC recently shared that 45% of CEOs and over 66% of CIOs surveyed conveyed a hesitancy around technology vendors not completely understanding the downside risk potential of AI. These leaders are justified in their caution. Arguably, the consequences of age-old IT risks remain similar with governed AI (i.e., downtime, operational seizures, costly cyber-insurance premiums, compliance fines, customer experience, data-breaches, ransomware, and more.) and are amplified by the integration of AI into IT. The concern comes from the lack of understanding around the root-causes for those consequences or for those that are not aware, the angst that comes with associate AI enablement serving as the catalyst for those consequences.
The pressing question is, “Should I invest in this costly IT tool that can vastly improve my business’s performance at every functional level at the risk of IT implosion due to lack of employee readiness and enablement?” Dramatic? Absolutely – business risk always is, and we already know the answer to that question. With more complex technologies and elevated operational potential, so too must the effort to enable teams to use these tools legally, properly, efficiently, and effectively.
The Vendor Challenge
The lack of confidence in technology vendors’ understanding goes beyond subject matter expertise and reflects a deeper issue: the inability to clearly articulate the specific risks that an organization can and will face with improper implementations and unrealistic expectations.
The relationship between an organization and technology vendors is much like that of a patient and a healthcare practitioner. The patient consults a healthcare practitioner with symptoms seeking a diagnosis and hoping for a simple and cost-effective remedy. In preventative situations, the healthcare practitioner will work with the patient on dietary recommendations, lifestyle choices, and specialized treatment to achieve specified health goals. Similarly, there’s an expectation that organizations will receive prescriptive solutions from technology vendors to solve or plan for technology implementations. However, when organizations are unable to provide prescriptive risks specific to given IT environments, it exacerbates the uncertainty of AI implementation.
Even when IT vendors effectively communicate the risks and potential impacts of AI, many organizations are deterred by the true total cost of ownership (TCO) involved in laying the necessary foundation. There’s a growing awareness that successful AI implementation must begin within the existing environment – and only when that environment is modernized can organizations truly unlock the value of AI integration. It’s similar to assuming that anyone can jump into the cockpit of an F1 supercar and instantly win races. Any reasonable person knows that success in racing is the result of both a skilled driver and a high-performance machine. Likewise, the benefits of AI can only be realized when an organization is properly prepared, trained, and equipped to adopt and implement it.
Case in Point: Microsoft 365 Copilot
Microsoft 365 Copilot is a great example of an existing AI solution whose potential impact and value have often been misunderstood or diluted due to customers’ misaligned expectations – in how AI should be implemented and what they believe it should do, rather than understanding what it can do. Today, more than 70% of Fortune 500 companies are already leveraging Microsoft 365 Copilot. However, the widespread fear that AI will replace jobs is largely a misconception when it comes to most real-world AI applications. While job displacement has occurred in some areas – such as fully automated “dark warehouses” – it’s important to distinguish between AI as a whole and its use in robotics. The latter has had a more direct impact on job replacement.
In the context of Modern Work, AI’s primary value lies in enhancing performance and amplifying expertise – not replacing it. By saving time and increasing functional output, AI enables more agile go-to-market strategies and faster value delivery. However, these benefits rely on critical enablers:
- A mature Data Practice
- Strong Access Management and Governance
- Robust Security measures to mitigate risks
- People enablement around responsible AI use and best practices
Here are a few examples of AI-driven functional improvements across business areas:
- Sales Leaders can generate propensity models using customer lifecycle data to drive cross-sell and upsell strategies, improving customer retention and value.
- Corporate Strategy & FP&A Teams gain deeper insights thanks to time saved analyzing business units, enabling better alignment with corporate goals.
- Accounts Receivable Teams can manage payment cycles more efficiently with faster access to actionable data, improving outreach and customer engagement.
- Marketing Leaders can build more effective, sales-aligned go-to-market strategies by leveraging AI insights on sales performance and opportunities.
- Operations Teams can reduce time spent reconciling Finance and Sales data, minimizing chaos during end-of-quarter or end-of-year processes.
- Customer Success & Support Teams can cut down response and resolution times by automating workflows and simplifying key steps.
These examples only scratch the surface of AI’s potential to drive functional transformation and productivity gains. Yet, realizing these benefits requires the right foundation – systems that allow AI to integrate, synthesize, analyze, and ultimately deliver on its promise.
Final Thought: No Plug-and-Play for AI
Implementing AI to unlock its full potential isn’t as simple as installing a program or application. It’s the integration of an interconnected web of autonomous functions that permeate your entire IT stack – delivering insights and operational efficiencies that would otherwise require significant manual effort, time and resources.
Realizing the value of an AI solution is grounded in building a data practice, maintaining a robust access and governance framework, and securing the ecosystem – a topic that requires its own deep dive.
The ability for technology vendors to a valued partner will be dependent on both marketing and enablement, focused on debunking myths and calibrating expectations on what harnessing the potential of AI truly means.
