Reimagining Business Strategies And Operating Models Through Lens Of GenAI

Generative AI is toppling the business strategy & operational models of the enterprises and startups. In addition to executing well-defined tasks, GenAI is starting to address broader, more ambiguous problems.

It’s not implausible to imagine that one day, a “GenAI strategist in a box” could autonomously develop and execute a business strategy. During my multiple conversations with several CXOs and startup founders who express such a vision — and they would like to embed GenAI in the business strategy and their operating models; here’s a sneak peek of the observations emanating from the conversations and the ensuing impact of GenAI on business strategy & operational models:

Business Processes – Increasing Productivity By Reducing Disruptions

AI algorithms are not natively “intelligent.” They learn inductively by analysing data. Most leaders and startup founders are investing in AI talent and have built robust information infrastructures. 

Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge.The plan was to increase the production rate of that aircraft faster than ever before. To do that, they needed to address issues like responding quickly to disruptions in the factory.

Airbus turned to AI. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems. AI led to rectification of about 70% of the production disruptions for Airbus, by matching to solutions used previously — in near real time.

Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organisations. Other large companies, such as BP, Wells Fargo, and Ping , an insurance company, are already solving important business problems with AI. Many others, however, have yet to get started.

Integrated Strategy Machine – The Implementation Scope Of GenAI At Scale

The integrated strategy machine is the AI analogy of what new factory designs were for electricity. In other words, the increasing intelligence of machines could be wasted unless businesses reshape the way they develop and execute their strategies. No matter how advanced technology is, it needs human partners to enhance its competitive advantage. It must be embedded in what we call the integrated strategy machine.

An integrated strategy machine is a collection of technological and human resources that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualisation, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights.

Amazon represents the state-of-the-art in deploying an integrated strategy machine. It has at least 21 AI systems, which include several supply chain optimisation systems, an inventory forecasting system, a sales forecasting system, a profit optimisation system, a recommendation engine, and many others.

These systems are closely intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that the popularity of an item is increasing, it starts a cascade of changes throughout the system: The inventory forecast is updated, causing the supply chain system to optimise inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimisation system adjusts pricing, again updating the sales forecast.

Manufacturing Operations – An AI Assistant On The Floor

CXOs at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. They have something called the BP well advisor that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. 

They are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.

Customer-Facing Activities – Near Real-Time Scoring

Ping, the second-largest insurer in China, with a market capitalisation of $120 Bn, is improving customer service across its insurance and financial services portfolio with AI. 

For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 Mn faces in various uses and now complements Ping An’s cognitive AI capabilities including voice and imaging recognition.

Generative AI For Different Operational Strategy Models

To make the most of the GenAI implementation in various business operations in your enterprise, consider the three main ways that businesses can or will use GenAI:

Insights Enabled Intelligence

Now widely available, it improves what people and organisations are already doing. For example, Google’s Gmail sorts incoming email into “Primary,” “Social,” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides. Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks.

Insights-based intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. 

For example, one auto manufacturer has developed a simulation of consumer behaviour, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles, and the variations in those patterns for different city topologies, marketing approaches, and vehicle price ranges.

The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. 

As the automaker introduces new cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.

Recommendation Based Intelligence

Recommendation based Intelligence, emerging today, enables organisations and people to do things they couldn’t otherwise do. Unlike insights enabled intelligence, it fundamentally alters the nature of the task, and business models change accordingly.

Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behaviour, but on those of the audience at large. 

A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos that are more tailored to the way they feel at any given moment.

Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalisation (and AI).

Decision Enabled Intelligence

Being developed for the future, decision enabled intelligence creates and deploys machines that act on their own. Very few intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75 per cent of Nasdaq trading is conducted autonomously) and facial recognition. 

In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations, and perform other tasks inherently unsafe for people.

As you contemplate deploying generative artificial intelligence at scale, articulate what combination of the three approaches works best for you.

1. Are you primarily interested in upgrading your existing processes, reducing costs, and improving productivity? If so, then start with insights-enabled intelligence with a clear AI strategy roadmap
2. Do you seek to build your business around something new — responsive and self-driven products or services and experiences that incorporate AI? Then pursue a decision-enabled intelligence approach, probably with more complex AI applications and robust infrastructure
3. Are you developing a genuinely new platform? In that case, consider building the first principles of an AI-led strategy across the platform’s functionalities and processes.

CXOs and startup founders need to create their own AI strategy playbook to reimagine their business strategies and operating models and derive accentuated business performance.

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