Introduction
Organisations are increasingly leveraging the power of Artificial Intelligence (AI) to drive growth and refine their business strategies. A significant trend in this space is the adoption of privately-held AI models, designed to align closely with a company’s specific strategic goals.
Private vs. Public AI: Why the Distinction Matters
In the context of business strategy, the distinction between private and public AI is crucial. Most organisations hold sensitive datasets – including HR, financial, and operational history – that they are understandably wary of exposing to public AI models. Private AI models offer a secure alternative, processing data internally to ensure confidentiality and intellectual property protection.
The “Bespoke Compass”: Benefits of Private AI
Giving an AI specific, internal data allows it to produce more relevant and effective outputs, directly supporting decision-makers in shaping strategy. Private reasoning engines are seen as a logical approach for companies seeking optimal results from AI while safeguarding their valuable intellectual property. The use of enterprise-specific data and the ability to fine-tune local AI models empower organisations to generate bespoke forecasts and operational adjustments grounded in their day-to-day reality. A Deloitte Strategy Insight paper aptly describes private AI as a “bespoke compass,” highlighting how internal data use can become a significant competitive advantage. Accenture goes further, suggesting AIs are “poised to provide the most significant economic uplift and change to work since the agricultural and industrial revolutions.” It’s worth noting, however, that major consulting firms advocating strongly for AI enablement, such as Deloitte and Accenture, also offer AI implementation services, suggesting their perspectives may be influenced by their business models.
Advocates for AI in general correctly point to its superior ability to identify trends and statistical undercurrents compared to humans. With the sheer volume of internal and external data available to modern enterprises, software capable of parsing data at scale provides an incredible advantage. Instead of time-consuming and error-prone manual analysis, AI can cut through the noise to surface actionable insights.
Furthermore, AI models can interpret queries posed in natural language, making predictions based on empirical, organisation-relevant data. This allows personnel without deep skills in statistical analysis or database languages to access valuable answers that previously would have required multiple teams and specialized skill-sets, saving considerable time and allowing teams to focus on strategy development.
Navigating the Pitfalls
Despite the immense potential, there are significant risks to consider. Both McKinsey and Gartner warn against overconfidence and data obsolescence. Relying solely on historical data, especially if it spans many years, can lead to decisions based on outdated patterns, potentially “mirroring their institutional past in algorithmic amber,” as McKinsey puts it. Overconfidence manifests when operators unquestioningly trust AI responses without delving into details or questioning the validity of responses to poorly-phrased queries. The Harvard Business Review also highlights the technical complexity involved in customising AI models to a company’s specific needs, a task best suited for those with significant data science and programming expertise.
AI as a Co-Pilot, Not a Replacement
MIT Sloane strikes a balanced view, suggesting AI be regarded as a co-pilot. This perspective emphasizes the need for continual questioning and verification of AI output, particularly when the stakes are high. Human oversight and a critical eye are essential to ensure AI recommendations are sound and relevant.
Integrating Private AI with Existing BI
Organisations should consider private AI solutions as complements to their mature, existing business intelligence platforms. Tools like SAP Business Objects, SAS Business Intelligence, and Microsoft Power BI represent decades of development and real-world application. Private AI should be seen as an addition to the strategist’s toolkit, not a silver bullet replacing traditional tools. While AI excels at processing real-time data for scenarios like online retail pricing, it has not yet evolved into a universal tool for business strategy.
The Road Ahead
Until AI specifically designed for business data analysis reaches the same level of maturity and battle-hardened reliability as established BI platforms, early adopters should temper enthusiasm with practical experience and critical evaluation. AI is a powerful new tool with substantial potential, but in its current form, both public and private, it remains a first-generation technology that requires careful implementation and a clear understanding of its capabilities and limitations.
Leave a Reply