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Next wave of AI ripples through digital

Artificial Intelligence (AI) is poised to unleash the next wave of digital disruption, and companies must get prepared now. We already see real-life benefits from early adoption and building the capabilities, making it more urgent than ever for others to accelerate their digital transformations using AI. Pursuit of AI-based solutions cannot happen in a vacuum, when digital transformation is in full swing across most of the businesses. Introducing AI-based solutions will have significant implications on how customer experience can and must be enhanced, how data will be used as advantage, how innovative processes will be used in the value-chain and business models will be innovated. We need to be very clear that AI will not completely take over tasks from humans, but will augment their existing activities. Undoubtedly, AI based solutions have the high potential to drive top-line growth for businesses into the future.

Is your business ready for AI?

Finally, evidence of AI starting to deliver real-life business benefit is growing. As a result, investments in AI are growing too and are increasingly coming from organizations outside the tech space. AI has been through a long history of booms and busts, extravagant promises and frustrating disappointments for businesses. As is typical with all emerging technologies, their potential is propped up with a lot of hype in the beginning. It has not been any different for AI. Obviously, mainstream businesses are being cautious about dipping their feet into the water until they can see real value. But, this time around, the potential claimed for AI is different, due to several reasons. Computer power has grown and continues to grow significantly, algorithms are becoming more sophisticated, and connectivity has multiplied exponentially. Perhaps the most catalytic reason for AI’s success this time is the generation & access of data propelled by technologies such internet of things (IoT) and big data from other hand-held and wearable devices with exponential growth of digital solutions. Digital engagement is spewing out a lot more data that is overwhelming the businesses. AI can help significantly in this arena. But capturing the relevant unstructured big-data, cleansing it and feeding it to AI or machine-learning or deep learning modules has continued to remain a challenge.

Expectations from AI are high. The potential is high. Enthusiasm among early adopters, led by technology giants who have made huge investments, is high. But mainstream businesses have been cautious and slow to readily adopt and jump on the band-wagon. Leading sectors adopting AI are high-tech, telecom, financial services, transportation, logistics, automotive, energy, media and entertainment. Right at the heels of these are healthcare, retail, consumer packaged goods, professional services & education sectors. Businesses across all sectors need to consciously decide how they will leverage AI to achieve the expected levels of automation and free up capacity for value-adding growth.

While AI has the potential to fundamentally reshape your businesses and even industries, significant uncertainty remains about how the technology will develop with time. Unfortunately, to many this might suggest a “wait and see” approach. However, there is a need for urgent but clearheaded action to respond to the opportunities and risks that are already apparent. For many firms, this will mean accelerating their digital journey to ensure that they can effectively deploy AI tools. Most organizations foresee sizable effects on IT, operations and manufacturing, supply chain management, and customer-facing activities. AI becomes impactful when it has access to large amounts of high-quality data and is integrated into automated work processes. AI is not a shortcut to these digital foundations. Rather, it is a powerful extension of them.

Get a good grasp of AI

Executives need to know the fundamental capabilities of AI and have an intuitive understanding of what is possible. Instead of simply reading accounts in the media of every new wonder, they could start to experiment. Briefly, AI encompasses every aspect of cognitive computing and other modules that enable its interaction with humans. It helps in decision making using case-based reasoning and expert systems. But at the core of AI is its machine learning capabilities using advanced analytics and algorithms. Machine learning includes data mining, reinforcement learning, supervised learning, unsupervised learning and deep learning based on neural networks. Computer visioning and listening comes from speech recognitions, handwriting recognition, optical character recognition, image & video recognition, and facial recognition. Much of the human interaction with AI is enabled by natural language processing that includes natural language understanding, machine translation and sentiment analysis. AI responds back using speech synthesis, natural language generation, robotic process automation and control of other systems through API’s. Fundamentally, it tries to mimic human intelligence using advanced analytics, rules, and algorithms that learns through pattern recognition. AI as a process takes in vast amounts of data as input in various forms to get trained and build domain-specific knowledge-base to deliver its output of actionable insights.

AI-based solutions could help self-disrupt

Adopting an offensive digital strategy is the most important factor in enabling incumbent companies to reverse the curse of digital disruption. An organization with an offensive strategy radically adapts its portfolio of businesses, developing new business models to build a growth path that is more robust than before digitization. So far, the same seems to hold true for AI. Early AI adopters with a very proactive, strictly offensive strategy report a much better profit outlook than those without one. There is no organization that shouldn’t be thinking about leveraging AI, because either you do—in which case you’ll probably surpass the competition—or somebody else will. And by the time the competition has learned to leverage data really effectively, it’s probably going to be too late for you to try to catch up. Your competitors will be on the exponential path, and you’ll still be on that linear path.

Automation with intelligence

Many companies are starting to realize the benefits of combining robotic process automation (RPA) and AI. They can achieve both the rapid payback of RPA and the advanced potential of AI. This combination is especially attractive for companies with large legacy systems—such as in the financial services and telecom industries or in HR and finance functions. Employees can work together with both RPA and AI to optimize service processes. A natural transition from automation to intelligence occurs when a human intervenes in a rules-based process. For example, a bot routes text, digitized via optical-character recognition, to a human to classify items such as date, address, and topic. Over time, an AI system can take over this classification. As it improves, the system can gradually replace additional human interactions. But companies are only scratching the surface of what is possible. Tomorrow’s winners are those that embrace these capabilities as part of a next-generation operating model and move quickly to capture the value from them.

Approach to build AI-based solutions

Where is the value for the business?

The first step in establishing a solid AI-solution business case is, separating the hype and buzz around AI from its actual capabilities in a specific, real-world context. This includes a realistic view of AI’s capabilities and an honest accounting of its limitations. Get a grasp of what AI can do, prioritize use cases, and don’t lose sight of the economics – without a compelling business case no innovation survives. Besides the technical feasibility and complexity of the required AI engine, the overall impact potential – derived from estimates of the financial baseline and optimization potential – should be key prioritization parameters. A balanced understanding and ownership of user-desirability and business viability of the proposed AI-based solution is essential for the compelling business case.

Build prototype, test & learn

This will require looking at all potential ideas to be evaluated against the internal and/or external customer needs, potential emerging and/or existing technologies, accessible data sources, and breakdown of processes where significant value-creation can emerge. Where possible build a minimum viable product (MVP) to test & learn about the possible hurdles and issues that might come up during deployment, while refining the solution with the end-user. Small and fast steps will assure the right focus, e.g., through simulation-based pilots that allow companies to quickly test the impact estimates in the business case. Best-practice companies set up cross-functional AI taskforces which are able to prototype a solution in one to three weeks given that data is readily available, test it with the business units, and decide how to proceed.

Sources of data

AI largely depends on access to new, unique, or rich data assets. Data is at the heart of the disruptions occurring across economies and it has been recognized as an increasingly critical corporate asset. Without data it is impossible to get the AI engine started. Because of this, business leaders should know what their data and the information therein is worth, and where they can obtain the data relevant for their company’s future success. One important capability will be making data usable that is not available in a relational format or that cannot be analyzed with traditional methodologies. Much data being produced in industry today is “flat data,” without relational structure. Making this data usable requires new approaches that can efficiently handle both data volume and types. Keep in mind that data needs to be present in a format that can easily be tailored to a specific approach to AI and machine learning, e.g., supervised learning techniques require labeled data in the training mode. This situation requires a thoughtful approach on which data to store in its original granularity and which to aggregate or pre-analyze. With increasing data storage capacities in the cloud as well as more powerful computing facilities close to the sensors in the internet of thing (IoT), flexibility increases rapidly.

Subject matter expertise & domain knowledge

Companies need to rely on their domain knowledge with an in-depth understanding of their business, process, and industry, so that it can be codified and provide a significant boost to the performance of an AI algorithm before self-learning starts. It also ensures that the business problem gets clearly articulated and extends a deep understanding of the dependencies between systems, technologies, and players. So, leverage the domain knowledge you have to boost the AI engine – specialized know-how is an enabler to capture AI’s full potential and may become an advantage against your comptetion.

Train algorithms for actionable insights

AI algorithms by themselves are lines of computer code that represent linear / non-linear regressions, decision trees, random forests, and high-speed computational analysis. They are not natively intelligent, and they require sensory input and feedback in order to develop intelligence. This AI training will require company-specific data and human effort. Data scientists must feed machines lots of data to properly weight countless correlations and connections, ultimately creating an algorithm whose intelligence is limited to that specific realm of data. This classic inductive approach to learning explains why AI requires large volumes of data with the right level of veracity. Once it is trained, the algorithm can accept live data and deliver actionable insights. This data-to-actionable insights process does not differ from the workings of standard computer programs. But an AI system continues to learn and transform itself with the data it is fed. Most companies will need to rely on internal data scientists to find, collect, collate, and create data sources and to develop and train company-specific AI systems.

Scale successful AI-based solutions

After an AI-based solution is sufficiently trained it can be iteratively deployed across the organization. Even in the production environment these AI-based solutions will undergo regular reviews and adjustments. The fastest way to scale up AI initiatives is to acquire AI talent on a temporary basis and then bring it in-house over time. As the company embeds talent, it can create AI units that serve as a center of excellence and an internal repository of its current thinking on the subject. This is an iterative and incremental model that allows the company to develop AI capabilities organically. Redesigning business processes in this fashion takes time, but it allows the company to develop its own internal expertise.

Build capabilities

Strong leadership support goes hand in hand with stronger AI adoption. As with all cultural and organizational changes, leadership is critical. To get the most out of AI in the long run, an organizational culture open to the collaboration of humans and machines is required. Humans will need some time to adjust to this paradigm shift. That means the shift to an AI-ready culture should be a priority early on. It may also require investment to build the capabilities of workers, especially mid-level managers, to understand how to use data-driven AI insights. Companies must create cross-functional teams of approximately five to ten employees each—small enough to collaborate closely but large enough to possess the necessary skills to execute ideation of AI-solution areas successfully and deploy them iteratively into the production environment. These agile teams perform a given process from beginning to end, batching tasks to increase productivity and parallel processing to maintain forward momentum. All elements of AI capabilities, including competencies, accountabilities, governance, and culture must be developed. The more critical issue is the need for organizational flexibility and cross-functional teamwork among people with AI and business expertise as the organization’s people and machines work together more and more closely. In many cases, the change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges. As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant re-skilling of the workforce.

Algorithms, platforms, services, and infrastructure

Various developments are encouraging the new wave of AI, including increased computation power and the availability of more sophisticated algorithms and models. Perhaps most important, data volume generation is exploding, with network devices collecting billions of gigabytes every day. Companies do not need to develop their entire AI infrastructure internally. Supporting platforms and services are available in the market. Companies can rent raw computing power in the cloud or deploy it on the premises with specific hardware that can process many tasks in parallel—an essential capability for AI techniques such as deep neural networks. Companies can also access rapidly developing AI data architectures based on open-source code. Most cutting-edge AI algorithms are available in the public domain, and leading scientists have pledged to continue to publish and open-source their work on these algorithms. In addition, businesses have made AI platforms, such as Google’s TensorFlow, available as a service. For companies that want to be on the leading edge, however, the marketplace may not always offer the best options.


While some firms are still reeling from previous digital disruptions, a new one in AI-based solutions is taking shape. But it is in its early days. There’s still time to make AI a competitive advantage. The future of AI—including the extent of its potential to shift value creation in a radical way—remains highly uncertain. The best way to combat this uncertainty is to plot out and test several scenarios and to create a roadmap tying together the individual initiatives. These efforts will enable companies to sensibly modify their original plan and address its implications with regard to data, skills, organization, and the future of work as they grow their digital maturity.

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