Introduction to Private AI in Business
One of the ways in which organisations are using the latest AI algorithms to help them grow and thrive is the adoption of privately-held AI models in aligning their business strategies. The differentiation between private and public AI is important in this context – most organisations are rightly wary of allowing public AIs access to what are sensitive data sets, such as HR information, financial data, and details of operational history.
The Benefits of Private AI
It stands to reason that if an AI is given specific data on which to base its responses, its output will be more relevant, and be therefore more effective in helping decision-makers to judge how to strategise. Using private reasoning engines is the logical way that companies can get the best results from AI and keep their intellectual property safe. Enterprise-specific data and the ability to fine-tune a local AI model give organisations the ability to provide bespoke forecasting and operational tuning that are more grounded in the day-to-day reality of a company’s work. A Deloitte Strategy Insight paper calls private AI a “bespoke compass”, and places the use of internal data as a competitive advantage, and Accenture describes AIs as “poised to provide the most significant economic uplift and change to work since the agricultural and industrial revolutions.”
Potential Drawbacks of Private AI
There is the possibility, however, that like traditional business intelligence, using historical data drawn from several years of operations across the enterprise, can entrench decision-making in patterns from the past. McKinsey says companies are in danger of “mirroring their institutional past in algorithmic amber.” The Harvard Business Review picks up on some of the technical complexity, stating that the act of customising a model so that it’s activities are more relevant to the company is difficult, and perhaps, therefore, not a task to be taken on by any but the most AI-literate at a level of data science and programming.
Finding a Balance with Private AI
MIT Sloane strikes a balance between the fervent advocates and the conservative voices for private AI in business strategising. It advises that AI be regarded as a co-pilot, and urges continual questioning and verification of AI output, especially when the stakes are high.
Believe in the Revolution
However, decision-makers considering pursuing this course of action (getting on the AI wave, but doing so in a private, safety-conscious way) may wish to consider the motivations of those sources of advice that advocate strongly for AI enablement in this way. Deloitte, for example, builds and manages AI solutions for clients using custom infrastructure such as its factory-as-a-service offerings, while Accenture has practices dedicated to its clients’ AI strategy, such as Accenture Applied Intelligence.
Asking the Right Questions
Additionally, AI models can interpret queries couched in normal language, and make predictions based on empirical information, which, in the context of private AIs, is highly-relevant to the organisation. Relatively unskilled personnel can query data without having skills in statistical analysis or database query languages, and get answers that otherwise would have involved multiple teams and skill-sets drawn from across the enterprise. That time-saving alone is considerable, letting organisations focus on strategy, rather than forming the necessary data points and manually querying the information they’ve managed to gather.
Software of Experience
Organisations might pursue private AI solutions alongside mature, existing business intelligence platforms. SAP Business Organisations is nearly 30 years old, yet a youngster compared to SAS Business Intelligence that’s been around since before the internet became mainstream in the 1990s. Even relative newcomers such as Microsoft Power BI represents at least a decade of development, iteration, customer feedback, and real-world use in business analysis. It seems sensible, therefore, that private AI’s deployment on business data should be regarded as an addition to the strategiser’s toolkit, rather than a silver bullet that replaces “traditional” tools.
Conclusion
For users of private AI that have the capacity to audit and tweak their model’s inputs and inner algorithms, retaining human control and oversight is important – just as it is with tools like Oracle’s Business Intelligence suite. There are some scenarios where the intelligent processing of and acting on real-time data (online retail pricing mechanisms, for example) gives AI analysis a competitive edge over the incumbent BI platforms. But AI has yet to develop into a magical Swiss Army Knife for business strategy. Until AI purposed for business data analysis is as developed, iterated on, battle-hardened, and mature as some of the market’s go-to BI platforms, early adopters might temper the enthusiasm of AI and AI service vendors with practical experience and a critical eye. AI is a new tool, and one with a great deal of potential. However, it remains first-generation in its current guises, public and private.
FAQs
- Q: What is private AI and how does it differ from public AI?
A: Private AI refers to AI models that are specifically designed and trained for a particular organisation, using their own data and tailored to their specific needs. Public AI, on the other hand, is more general and can be used by anyone. - Q: What are the benefits of using private AI in business?
A: Private AI can provide more relevant and effective results, as it is trained on the organisation’s own data and can be fine-tuned to their specific needs. It can also help to keep intellectual property safe. - Q: What are some potential drawbacks of private AI?
A: One potential drawback is that it can entrench decision-making in patterns from the past, and it can be difficult to customise a model to be relevant to the company. Additionally, there is a risk of overconfidence in AI responses and data obsolescence. - Q: How can organisations balance the use of private AI with traditional business intelligence platforms?
A: Organisations can use private AI as an addition to their traditional business intelligence toolkit, rather than replacing it. It’s also important to retain human control and oversight, and to continually question and verify AI output.
