AI’s commitments expand every day – from driving personal productivity improvements to enabling organizations to reveal powerful new business insights through data. While the potential of AI seems limitless and its impact is easy to imagine, the journey of a truly AI-powered ecosystem is both complex and challenging. This journey does not begin and end with implementation, adoption and even consistent use of AI. The full value of achieving AI solutions 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, organizational constant. Whether it is a husband-and-wife convenience store or a corporate organization, each company runs on data (financial records, inventory, security video, etc.). The management, accessibility and governance of this data are the cornerstones for realizing the full potential of AI within an organization. Gartner recently pointed out that 63% of organizations either lack confidence or are unsure whether their existing data practices or management structures are sufficient to successfully adopt AI. Enable organizations to unlock the full potential of AI requires thoughtful data practice. From collection, storage, synthesis, analytics, security, privacy, governance, and access control – there must be a framework and approach to properly leverage AI. In addition, mitigation of risks and unexpected consequences is crucial. Most importantly, data is the cornerstone of analysis and your AI fuel.
Accessing your AI solution must determine the potential for its delivery – so much we have seen the emergence of new features tailored specifically for it, namely the Chief Data Officer (CDO). In short, if an AI solution is introduced to an environment with “free floating” data that anyone can access, it will be prone to errors, biased, non-compliant, and likely to expose sensitive and private information. Instead, when the data environment is rich, structured, and accurate in the frameworks and methods of how an organization uses its data, AI can return instant benefits and save a lot of modeling, prediction and tendency to develop. Building around the data cornerstone are the access rights and governance policies of data that list their own concerns – human factors.
people: Underestimated Factors of AI Adoption
IDC recently shared that 45% of CEOs and 66% of CIOs were surveyed to convey hesitation about technology suppliers, which does not fully understand the downside risk potential of AI. These leaders’ caution is justified. It can be argued that the consequences of ancient IT risk are still similar to regulated AI (IE, downtime, operational seizures, expensive cyber insurance premiums, compliance fines, customer experience, data breaches, ransoms, ransoms, etc.). The concern is that there is a lack of understanding of the root causes of these consequences, or anxiety associated with AII AI enablement, which is a catalyst for these consequences.
A pressing question is: “I should invest in this expensive IT tool that greatly improves the performance of my business at every level of functionality because of the risk of IT implosion due to lack of staff preparation and enlightenment?” Dramatic? Absolutely – Business risk is always that we already know the answer to this question. With more complex technologies and improved operational potential, efforts must also be made to enable teams to use these tools legally, correctly, effectively and effectively.
Supplier Challenge
The lack of confidence in understanding technology vendors goes beyond the topic’s expertise and reflects a deeper problem: the inability to articulate clearly the specific risks that organizations may and will face incorrect implementation and unrealistic expectations.
The relationship between the organization and the technology provider is much like that between patients and healthcare practitioners. The patient consulted healthcare practitioners for diagnosis and wanted to make simple and cost-effective treatments. In preventive situations, healthcare practitioners will conduct dietary advice, lifestyle choices and specialized treatments with patients to achieve designated health goals. Likewise, organizations are expected to receive prescribed solutions from technology suppliers to resolve or plan technology implementation. However, it can exacerbate uncertainty about AI implementation when organizations are unable to provide normative risks for the IT environment.
Even if vendors effectively communicate the risks and potential impact of AI, many organizations are blocked by the true total cost of ownership (TCO) involved in laying the necessary foundations. There is growing awareness that successful AI implementation must begin in an existing environment – only when that environment becomes modern can organizations truly unlock the value of AI integration. This is similar to assuming that anyone can jump into the cockpit of the F1 supercar and win the race right away. Anyone with rationality knows that racing success is the result of skilled drivers and high-performance machines. Similarly, the benefits of AI can only be achieved if the organization is properly prepared, trained, and adopted and implemented.
Example: Microsoft 365 Copilot
Microsoft 365 Copilot is a great example of existing AI solutions should Do it, not understand it able Do. Today, more than 70% of Fortune 500 companies have taken advantage of Microsoft 365 Copilot. But when it comes to most real-world AI applications, it is a misunderstanding that AI will replace work. Although work displacement occurs in certain areas, such as fully automated “dark warehouses”, it is important to distinguish the entire AI and its uses in robotics. The latter has a more direct impact on the impact of replacement work.
In the context of modern work, the main value of AI is to enhance performance and expand expertise – not replace it. By saving time and increasing feature output, AI can enable more agile to market strategies and faster value delivery. However, these benefits depend on key enablers:
- Mature data practice
- Strong access management and governance
- Strong security measures to mitigate risks
- People revolve around responsible use of AI and best practices
Here are some examples of AI-driven feature improvements across business areas:
- Sales leaders can use customer lifecycle data to generate propensity models to drive cross-selling and sales strategies that improve customer retention and value.
- Corporate strategy and FP&A teams benefit from time-saving analytical business units, which gain deeper insights to better align with company goals.
- Accounts receivable teams can manage payment cycles more effectively by faster access to actionable data, improving outreach and customer engagement.
- Marketing leaders can build more effective, sales-consistent to market strategies by leveraging AI’s sales performance and opportunities.
- Operations teams can reduce the time to reconcile financial and sales data, minimizing chaos in the post-season or year-end process.
- Customer success and support teams can reduce response and resolution time by automating workflows and simplifying critical steps.
These examples only scratch the surface of AI-driven feature conversion and productivity gain potential. But realizing these benefits requires the right foundation – a system that allows AI to integrate, synthesize, analyze and ultimately fulfill its promises.
Final Thought: Artificial Intelligence Without Plugins
Implementing AI to unlock its full potential is not as simple as installing programs or applications. It is the integration of interconnected autonomous function networks that permeate your entire IT stack – providing insight and operational efficiency that would otherwise require a lot of manual effort, time and resources.
Realizing the value of AI solutions is based on building data practices, maintaining a strong access and governance framework and ensuring the foundation of the ecosystem – this topic requires its own in-depth dive.
The capabilities of technology vendors with valuable partners will depend on marketing and support, focusing on debunking myths and calibrating expectations about what AI potential really means.
