AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo’s success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer — independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It’s not implausible to imagine that one day a “strategist in a box” could autonomously develop and execute a business strategy. I have spoken to several CXOs and leaders who express such a vision — and they would like to embed AI in the business strategy and their operating models

Business Processes – Increasing productivity by reducing disruptions

AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. Most leaders 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. Because they will happen. 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 organizations. Other large companies, such as BP, Wells Fargo, and Ping , an Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

Integrated Strategy Machine – The Implementation Scope of AI @ 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 competitive advantage. It must be embedded in what we call the integrated strategy machine. An integrated strategy machine is the collection of resources, both technological and human, 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 conceptualization, 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 optimization systems, an inventory forecasting system, a sales forecasting system, a profit optimization 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 optimize inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimization 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 An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, 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 million faces in various uses and now complements Ping An’s cognitive AI capabilities including voice and imaging recognition.

AI for Different Operational Strategy Models

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

1. Insights enabled intelligence

Now widely available, improves what people and organizations 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.

2. Recommendation based Intelligence

Recommendation based Intelligence, emerging today, enables organizations 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 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 personalization (and AI).

3. 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 percent 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 the deployment of artificial intelligence at scale , articulate what mix of the three approaches works best for you.

a) 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

b) Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an decision enabled intelligence approach, probably with more complex AI applications and robust infrastructure

c) Are you developing a genuinely new platform ? In that case, think of building first principles of AI led strategy across the functionalities and processes of the platform .

CXO’s need to create their own AI strategy playbook to reimagine their business strategies and operating models and derive accentuated business performance.  

AI adoption across industries is galloping at a rapid pace and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that AI can generate. Enterprises can get stuck trying to analyse all that’s possible and all that they could do through Ai, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees. Discovering real business opportunities and achieving desired outcomes can be elusive. To overcome this, enterprises should pursue a constant attempt to re-engineer their AI strategy to generate insights & intelligence that leads to real outcomes

Re-engineering Data Architecture & Infrastructure

To successfully derive value from data immediately, there is a need for faster data analysis than is currently available using traditional data management technology. With the explosion of digital analytics, social media, and the “Internet of things” (IoT) there is an opportunity to radically re-engineer data architecture to provide organizations with a tiered approach to data collection, with real-time and historical data analyses. Infrastructure-as-a-service for AI is the combination of components that enables architecture that delivers the right business outcomes. Developing this architecture involves aspects of design of the cluster computing power, networking, and innovations in software that enable advanced technology services and interconnectivity. Infrastructure is the foundation for optimal processing and storage of data and is an important which is also the foundation for any data farm.

The new era of AI led infrastructure is virtualized (analytics) environments also can be referred to as the next Big “V” of big data. The virtualization infrastructure approach has several advantages, such as scalability, ease of maintenance, elasticity, cost savings due better utilization of resources, and the abstraction of the external layer from the internal implementation (back-end) of a service or resource. Containers are the trending technology making headlines recently, which is an approach to virtualization and cloud-enabled data centres. Fortune 500 companies have begun to “containerize” their servers, data centre and cloud applications with Docker. Containerization excludes all of the problems of virtualization by eliminating hypervisor and its VMs. Each application is deployed in its own container, which runs on the “bare metal” of the server plus a single, shared instance of the operating system.

AI led Business Process Re-Engineering

The BPR methodologies of the past have significantly contributed to the development of today’s enterprises. However, today’s business landscape has become increasingly complex and fast-paced. The regulatory environment is also constantly changing. Consumers have become more sophisticated and have easy access to information, on-the-go. Staying competitive in the present business environment requires organizations to go beyond process efficiencies, incremental improvements and enhancing transactional flow. Now, organizations need to have a comprehensive understanding of its business model through an objective and realistic grasp of its business processes. This entails having organization-wide insights that show the interdependence of various internal functions while taking into consideration regulatory requirements and shifting consumer tastes.

Data is the basis on which fact-based analysis is performed to obtain objective insights of the organization. In order to obtain organization-wide insights, management needs to employ AI capabilities on data that resides both inside and outside its organization. However, an organization’s AI capabilities are primarily dependent on the type, amount and quality of data it possesses.

The integration of an organization’s three key dimensions of people, process and technology is also critical during process design. The people are the individuals responsible and accountable for the organization’s processes. The process is the chain of activities required to keep the organization running. The technology is the suite of tools that support, monitor and ensure consistency in the application of the process. The integration of all these, through the support of a clear governance structure, is critical in sustaining a fact-based driven organizational culture and the effective capture, movement and analysis of data. Designing processes would then be most effective if it is based on data-driven insights and when AI capabilities are embedded into the re-engineered processes. Data-driven insights are essential in gaining a concrete understanding of the current business environment and utilizing these insights is critical in designing business processes that are flexible, agile and dynamic.

Re-engineering Customer Experience (CX) – The new paradigm

It’s always of great interest to me to see new trends emerge in our space. One such trend gaining momentum is enterprise looking at solving customer needs & expectations with what I’d describe as re-engineering customer experience . Just like everything else in our industry, changes in consumer behaviour caused by mobile and social trends are disrupting the CX space. Just a few years ago, web analytics solutions gave brands the best view into performance of their digital business and user behaviours. Fast-forward to today, and this is often not the case. With the growth in volume and importance of new devices, digital channels and touch points, CX solutions are now just one of the many digital data silos that brands need to deal with and integrate into the full digital picture. While some vendors may now offer ways for their solutions to run in different channels and on a range of devices, these capabilities are often still a work in progress. Many enterprises today find their CX solution as another critical set of insights that must be downloaded daily into a omni-channel AI data store and then run visualization to provide cross-channel business reporting.

Re-shaping Talent Acquisition and Engagement with AI

AI s is causing disruption in virtually every function but talent acquisition t is one of the more recent to get a business refresh. A new data driven approach to talent management is reshaping the way organizations find and hire staff, while the power of talent analytics is also changing how HR tackles employee retention and engagement. The implications for anyone hoping to land a job, and for businesses that have traditionally relied on personal relationships are extreme, but robots and algorithms will not yet completely replace human interaction.AI will certainly help to identify talent in specific searches. rather than relying on a rigorous interview process and resume, employers are able to “mine” through deep reserves of information, including from your online footprint. The real value will be in identifying personality types, abilities, and other strengths to help create well-rounded teams. Also, companies are also using people analytics to understand the stress levels of their employees to ensure long-term productiveness and wellness.

 The Final Word

Based on my experiences with clients across enterprises , GCCs ,start-ups ; alignment among the three key dimensions of talent, process and AI led technology within a robust governance structure are critical to effectively utilize AI and remain competitive in the current business environment. AI is able to open doors to growth & scalability through insights & intelligence resulting in the identification of industry white spaces. It enhances operational efficiency through process improvements based on relevant and fact-based data. It is able to enrich human capital through workforce analysis resulting in more effective human capital management. It is able to mitigate risks by identifying areas of regulatory and company policy non-compliance before actual damage is done. AI led re-engineering approach unleashes the potential of an organization by putting the facts and the reality into the hands of the decision makers.

(AIQRATE, A bespoke global AI advisory and consulting firm. A first in its genre, AIQRATE provides strategic AI advisory services and consulting offerings across multiple business segments to enable clients on their AI powered transformation & innovation journey and accentuate their decision making and business performance.

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