Since the launch of ChatGPT in 2022, AI tools have been widely adopted by businesses, with a U.S. Chamber of Commerce survey showing that 98% of small businesses are using them. However, despite the success of AI in areas such as data analysis, aggregation, personalization and more, a recent survey of 2,500 employees in the US, UK, Australia and Canada found that three-quarters of employees said AI has actually increased reduced their workload. So the promise of AI remains high, but the reality so far seems a bit underwhelming.
This disparity highlights a key challenge: bridging the gap between AI’s huge promise and its current limited practical impact on business operations. Closing this gap is critical for organizations to realize the full value of their AI investments and increase employee and stakeholder adoption.
Product Vision of Artificial Intelligence Investment
Despite significant advances in artificial intelligence, many business solutions are still in the experimental proof-of-concept stage and are not fully suitable for day-to-day operations. In a cross-country and industry survey of 1,000 CxOs and senior executives, BCG found that 74% of companies struggle to realize and scale the value of their AI investments. Part of the reason for this is that today’s most prominent AI user interfaces are based on natural language delivered through the chatbot paradigm. While these patterns are undoubtedly useful when it comes to tasks like summaries and other text-based context, they don’t match the way most businesses actually work.
To maximize impact, AI tools must be designed to move beyond siled, text-based interfaces to integrated, workflow-enhancing applications that better meet the operational needs of large organizations. The next phase of AI development will increasingly automate operations, integrating seamlessly into the context of enterprise operations and allowing teams to focus on high-level ideation and strategy, thus automating operations, bypassing manual execution but still keeping humans in the loop. Controls that rely on non-automated human judgment.
This shift from “experimental” to “necessary” requires a productized approach to AI development, deployment, and operations, just as Apple revolutionized the technology industry with the introduction of the iPhone—a well-designed, A user-friendly product that integrates state-of-the-art technology and combines it with a world-class user experience from day one.
Close data gaps and ensure cost efficiency
To move toward this more sophisticated productized version of AI, addressing gaps in enterprise data assets is critical. Growing interest in deploying AI in the enterprise has exposed widespread data silos that prevent organizations from scaling AI beyond prototypes.
Of course, it’s worth noting that financial barriers can also prevent organizations from scaling their use of AI from pilots to enterprise-wide adoption. The infrastructure required to train and maintain advanced AI models, including computing power, data storage and ongoing operating costs, can escalate rapidly. Without careful oversight, the costs of these projects may become unsustainable, reflecting early challenges in cloud technology adoption.
Focusing first on ensuring the integrity, cleanliness and quality of your data can help reduce costs in the long run. Too often, companies focus on AI first and then address data challenges, resulting in inefficiencies and missed opportunities.
Cost efficiency is closely tied to investments in data and core infrastructure layers. Investing in this part of the stack is key to ensuring the LLM can operate at scale. In practice, this means standardizing data collection, ensuring accessibility, and implementing a strong data governance framework.
Responsible Artificial Intelligence
Companies that embed responsible AI principles into a strong, well-governed data foundation will be better able to scale their applications efficiently and ethically. Principles such as fairness, transparency and accountability in AI inputs and outputs are no longer optional for businesses – they are strategic imperatives to maintain employee and customer trust and comply with emerging regulations.
One of the key frameworks is the EU Artificial Intelligence Act, which requires clear documentation, transparency and governance of high-risk AI systems. Complying with such a framework requires companies to implement processes that not only validate their AI models, but also make them explainable and accountable, which is particularly important in high-risk applications such as credit scoring, fraud detection, and investment advice. Companies that prioritize these practices can stay ahead of regulatory requirements and avoid costly legal or reputational risks.
Additionally, as the industry evolves and agent AI systems capable of making autonomous decisions become more common, the risks of responsible implementation become greater. Delegating actions to an AI tool requires confidence in its reliability and ethical behavior. To achieve this, organizations must invest in ongoing auditing and monitoring frameworks to ensure that AI systems are functioning as intended and intelligently preventing outcome bias and persistent unfair outcomes.
Looking to the future
The transformative potential of AI in business operations is undeniable, but realizing its full value will require organizations to change the way they develop and deploy it. Moving from experimental applications to scalable, workflow-integrated tools requires a focus on solving fundamental issues such as data quality, governance, and accessibility, and the adoption of a product mindset.
Closing the data gap and making responsible AI central to strategy will be key to maintaining trust with stakeholders, continuing to meet strategic compliance requirements, and ensuring AI systems are not only scalable but also reliable and effective. In this way, the promise of AI can be realized and organizations of all sizes can overcome current AI adoption difficulties.
