AI and business just might be a match made in heaven. With the emergence of accessible, affordable and effective AI technologies, even non-technical ‘civilian’ users are getting in on the act. The difference, it seems, has been generative AI.
‘Traditional’ or ‘narrow’ AI systems rely on structured data and rules. They understand how a system or a ruleset (e.g. chess) works. They can analyse events and data changes within that system (e.g. an opponent’s moves) to generate an ‘intelligent’ response (e.g. the AI system’s moves).
Generative AI systems also rely on data and rules but function differently. They can analyse unstructured data and, instead of responding in known ways, they can create new responses based on their training data. That’s how, for example, ChatGPT debugs code or writes stories and how DrawGPT creates new drawings.
Simply put, narrow AI offers pattern recognition (e.g. data analysis), while generative AI offers pattern creation (e.g. new data).
AI and business
What does this mean for business? Currently, companies are using AI to enhance many functions and operations. A recent Forbes survey of US businesses found that the most common uses for AI were:
- Customer service (56% of respondents)
- Cybersecurity/fraud management (51% of respondents)
- Digital personal assistants (47% of respondents)
- Customer relationship management (46% of respondents)
- Inventory management (40%)
Internally, they’re using AI to analyse and improve business and production processes, SEO, automation, internal communications and business data integration.
What about generative AI?
Generative AI systems offer new possibilities. They are well-placed to generate business value beyond the analytics-based benefits of narrow AI systems. MIT Technology Review cites a McKinsey survey estimating they’ll add US$2.6–US$4.4 trillion in value to the global economy. Similarly, Goldman Sachs predicts a 7% (~US$7 trillion) increase in global GDP. MIT cited use cases including:
- Forecasting complex scenarios
- Analysing complex and unstructured data sets
- Aggregating production system metrics
- Creating text, charts and graphs for reports
- Analysing operational concerns (e.g. inventory and staffing)
- Optimising pricing strategies
- Auto-generating and adapting critical documents (e.g. contracts, purchase orders and invoices)
- Understanding user preferences, behaviours and contextual cues
- Automating and personalising customer service
- Learning from support tickets
- Providing scripts for agent interactions
- Developing personalised marketing
- Increasing ad targeting effectiveness
- Detecting security threats (e.g. account takeover attempts)
- Analysing communications (e.g. for attempted phishing and social engineering)
Any business operating in these domains or with these concerns can benefit from generative AI systems.
AI and strategy: The final frontier?
One area of business on which AI has only had a limited impact so far is strategy. Narrow AI systems have provided boards, executives, senior managers and other decision-makers with analytics and insights for some time. But they’re unable to provide direct decision-making assistance.
The critical limitation has been their inability to understand broader business, social and economic contexts. As generative systems become more sophisticated, we can expect to see their strategic usefulness increase.
In a recent interview, Yuval Atsmon, leader of McKinsey’s Center for Strategic Innovation, noted that there are six levels of AI development:
- Simple analytics (‘descriptive intelligence [and] … dashboards for competitive analysis’)
- Diagnostic intelligence (‘understand root causes and drivers of performance’)
- Predictive AI (‘provides another systematic viewpoint in the room’)
- AI advice (‘value-creating based on the analysis)
- AI decision authority (‘with constraints and supervision)
- Autonomous AI (‘analyses and decisions with no human interaction)
Current systems only provide the first three; AI’s ability to add strategic value will increase as the remaining three become practical.
We should not overlook some legitimate concerns regarding AI and its use. Although the Forbes survey found most business owners expect positive business outcomes from using AI, there were also concerns about technology dependency (43%), workforce reduction (33%) and misinformation (30%).
However, as our systems become more sophisticated, we anticipate these concerns will recede. Historically, new technologies create more jobs than they abolish (though some workers must re-train). As we improve the training data we use to ‘educate’ our AI systems, we can eliminate the misinformation and biases that plagued earlier iterations.
Microsoft, AI and you
As a Microsoft provider, we’re pleased that Redmond is a strong player in the AI space, already providing practical, easy-to-use AI solutions. Specifically, Microsoft Copilot is now an integral part of its product suite, from the Microsoft 365 Office suite to Dynamics 365.
Copilot is an AI capability that enhances CRM and ERP, Sales, Insights and Marketing, Customer Service, supply chain and more.
We’ve already begun using it internally and would love to explore how it can help your business reach new heights, so why not contact us today for a chat?