The age of Artificial Intelligence is upon us. Businesses and society are now looking towards AI for transformative outcomes. Businesses specifically are investing huge amounts of money on AI technology that will not only bring in efficiencies across multiple processes, but also unlock new revenue streams that will deliver quantum bottom-line impact. With the AI transformation playing out rapidly in our personal and professional lives, we need to deeply understand what the future of work will look like in the age of AI.

Within the business organization, there is a huge need to ramp up skill development interventions. The traditional roles of employees in an organization are rapidly changing as they are expected to stay in step with the developments in the world of AI. Business executives are now needed to deeply understand the potential of Artificial Intelligence and translate it into a viable roadmap for their business. Technology leaders need to take centre-stage in how their organizations adopt and harness the power of AI. The CIO is now fast becoming the key custodian of the most valuable resource in business today i.e. data. We are seeing a fast proliferation of digital evangelists and transformation officers who are charged with developing a framework within which the future of the organization will operate.

Ushering the Future of Work

On a tactical level, the burning question now is how subjects such as Data Science, Artificial Intelligence and Machine Learning can be infused in the career pathways of existing employees. How can organizations can build a steady pipeline of future talents with expertise in AI? Mastery of exponential technologies (AI, cloud computing, blockchain, IOT, cybersecurity etc.) will be remarkably important for both business and technical professionals. It is critical that transformation leaders and digital evangelists are well-versed in building internal capabilities that converge around the nexus of technology competencies, managing a hybrid workforce and ensuring the adoption and dispersion of AI.

For us to usher in the future of work powered by Artificial Intelligence, we need to ensure that a few key enablers come together. We need to expand the scope of executive education and the courseware that goes with it. Next, we need to seriously consider the potential impact of shorter, tactical courses. Corporations need to augment their training programs with shorter, time-boxed courseware that can deliver instant impact for the organization. Finally, we need to reimagine multiple, personalized career pathways. We need to move away from the traditional one-size-fits-all training and deliver more tailored, fit-for-purpose and relevant education to employees. Here are the three critical interventions for the business and technology leaders to execute in order to usher in the future of work that is enabled by AI.

1.Develop New Age Skills and Competencies in AI Technology

Upgrading the technology competencies and skills of business and technology leaders and their teams seems like the most critical first step. With the landscape of technology is rapidly evolving, we need to urgently upskill the present and future workforce to ensure a quality supply of talent. We need new age coursework in computer science that can hugely develop the ability of students in subjects such as Artificial Intelligence Machine Learning, Deep Learning, Natural Language Processing and other AI related concepts. On a broader scale, we also need Universities and colleges to improve the existing knowledge-base of AI enabling technologies such as Cloud, DevOps, Blockchain etc as well for the workforce.

At present we see a decent level of advancement in the field of computer science training and education. However, other trades within the technical area which also require to be upgraded as well. By doing so, we will be able to ensure a wholesome and future-proof education for the aspirants who wish to build their careers in the world of AI. For instance, students studying for a major in the field of electronics could shape their focus on mastering AI-enabling technologies such as GPUs and Quantum Computing. The students presently pursuing a specialization in mechanical engineering could achieve some level of sophistication in allied subjects of robotics and 3D Printing. Subject matter experts in the fields of industrial engineering, operations and supply chain would also do well to extend their skill sets to machine learning and blockchain as well thus creating a convergence of their interest areas and realities of the market – which will empower them with the required tools to succeed in the workplace of the future.

2. Reimagining the Process of Developing of New Age Technology

This interventions pertains to the embedding the design in the process of development and user adoption of AI technology. A commonly held misconception around design of a product or software is that it is restricted to simply the look or feel of the product or software. This is simply not true. As a Steve Jobs once proclaimed – Design is not just what is looks like and feels like. Design is how it works.

For the growth of AI to live up to the hype, we need to reimagine the process by which we develop new age technology. We need to build design into the fabric of the development and engagement process to ensure that the conceived idea is brought to fruition. Transformation evangelists aiming to spearhead the future of work should treat design as the creative process that aids the development of breakthrough products.

We are already seeing several inroads that design frameworks such as Human Centered Design and Empathy-led Design are making in the technology realm. These frameworks not only guide the development process, but also the user experience of the final software / hardware being developed. These frameworks do so by putting the user at the center of the journey.

3.Managing the ‘People’ of the Future Workforce

 As I mentioned before the understanding of traditional roles in the future of work is rapidly changing. New roles are also emerging where data custodians and algorithm at scale engineers are put to work to develop the technology that powers the business of the future. On the macro level, we are seeing rapid changes in the paradigm of staffing as well. With the gig economy in full force, we are seeing more dynamic team compositions – where individuals with varied skill sets are required to continuously augment teams on a need basis. Advances in the fields of technology and management typically ordain large-scale transformation in the manner in which organizations manage their workforce.

On the micro level we are seeing that increased instances of automation are requiring managers to build and scale blended teams comprising humans and AI. This disruption requires a paradigm shift how the future workforce is managed. Teams in the future will showcase increased diversity and will be more interdisciplinary than ever before. Managing teams, careers and coaching for improved performance in the future will require a new set of metrics. Change evangelists need to devise these metrics – which will be imperative to how the workforce of the future is managed.

New technologies will require new approaches to project management and staffing. To ensure the supply of these critical skills, we also need courses that provide an education of subjects such as people management.

Our very understanding of our workplace is being rapidly disrupted. Increasingly a convergence of the right people, process and technology is required to unearth insights from a seemingly exponentially increasing size of data. To turn this data into actionable intelligence that powers business processes must be the focus of business and technology leaders – as well as educationists that build the talent pipeline for the future. Academia is required to urgently intervene and provide theoretical and practical training in AI subjects to both the existing workforce and the future pipeline of talent. We also need a dispersion of soft skills that will enable and evangelize this change. With growing interest and appreciation of technologies and platforms around Artificial Intelligence and the Digital Workplace, organizations need to ask tough questions of themselves. The time is now to consider the various forces at play. With increased AI augmentation and the transformation of processes and people that enable it, the topic of the Future of Work requires immediate and urgent attention.

The failure to manage cyber risks will disrupt digital business in the current era and expose organization to possible impacts beyond opportunity loss. The degree to which CIOs involve in digital risk management will be a critical factor to circumvent such perils.

Digital advancements and change in the technological paradigm such as cloud, IoT and mobility have made cyber security an absolute necessity to safeguard enterprises from ransom ware.

The problem in front of CIOs is not only unregulated IoT devices in the enterprise , but also the nature of the devices themselves. Security needs to be improved in the design process and is the top strategic pillar of priority.

In the face of increasing cyber-attacks and more multifaceted, stringent data privacy laws, security has become a priority discussion in the boardrooms of organisations across different industries.

In this blog, I would like to explore the key drivers to implement a cyber security strategy and some of the preventive measures in case of threat to business. It also illustrates some latest information on cyber security solutions and the organizations response to dealing with the cyber security skills gap. It also analyses on how CIO’s are handling and prioritizing the changing cyber-security landscape.

As CIOs decide on risk levels they’re equipped to accept and pursue their security objectives, as information/data becomes critical for businesses.

Executive engagement towards cyber security

Cyber security accountability must lie with the CIO, but the culture of security needs to be adopted by the whole enterprise. Principal causes of cyber security occurrences result from employee negligence. CIO’s efforts endure to flounder against the number and variations of different cyber-attacks which keeps increasing continuously.

To combat and recognize these threats effectually, CIOs and IT executives need to cement an effective IT security strategy that enables the right tools and technologies at the same time foster a culture of security.

Several mechanisms together with a charter, policy, strategy and governance mechanisms form a digital cybersecurity program that delivers the suppleness required to enable business plans, notify risk trade-offs and respond to ever-changing threat environments.

There are no prescriptive approach  organizations that give comprehensive assurance that all rational steps have been implemented. CIO’s plays the imperative role  for setting direction for the organization to evaluate their own situations and assess a number of factors to make an informed judgment according to different scenarios.

The CIO becomes the key anchor emphasizing the linkage between business and cyber risk. This needs to be accomplished across, technical, non-technical staff, with the influence from the board. This is a critical time for CIOs to be thoughtful in their implementation and communication framework of cyber risk management issues across the stakeholders in the business. Prioritizing organization’s restrained business design and environmental factors, the CIO will be in a position to cover external threats and regulatory requirements

CIOs can’t shield the organizations on all type of risk and is practically not viable. It is imperative to  create a sense of balance between sustainable set of controls to protect their businesses with their need to run them. Taking a risk-based method will be a critical point to establish target levels of cybersecurity readiness. Budgeting alone does not create an environment for improved risk posture, CIOs must prioritize security investments to ensure that there is a true value for budget assigned on the right things this needs to be based on business outcomes.

Attacks and compromise are inevitable, and, by 2020, 60% of security budgets will be in support of detection and response capabilities.” — Paul Proctor, Gartner vice president and distinguished analyst

Cyber Security Sequence CIO’s could consider:

Consider a robust Risk-Based Method to Improve Business Outcomes: Cybersecurity issue requires judicious risk management that can be done effectively. This approach should be measurable and most importantly enable decision making and executive engagement.

Establish Cybersecurity and Risk Governance to enhance Information Security:

Effective governance is a cornerstone of security programs, CIO should ensure there is right leadership for risk management to support and implement governance and mitigate the risks for assurance.

CIOs Should Mitigate Cybersecurity risk have aligned to the Lens of Business Value:

Postulates that CIOs should address cybersecurity challenges like a business function. This will enable them to bring levels of protection that support business outcomes in accordance with the business value.

Cybersecurity is complex, it requires a specifically designed program that enables resilience, agility and accountability

 Organizations that rely on obsolete, basic approaches towards security program management will continue to experience incompetence and internal disconnects. This will reflect in failure to deliver optimum business results. Organizations that roadmap more complex, but agile approach will position themselves for digital business success and resilience.

 The cyber threat landscape continues to evolve with significant attacks happening, especially over the last decade. The changing paradigm of businesses in adopting IoT has a surge in these attacks. Greater amounts of threats coming into that space has a direct relation to consumer related devices, in the form of machine to machine traffic for businesses.

A CIO has an imperative role to instate security across  the organization and business lines. The responsibility extends for effectively handling risk mitigation that span the spectrum across the entire organization. This needs a laser focused approach that is ingrained into the daily operations of the IT setup but as well for the enterprise, products they deliver in the form of digital services.

The CIO’s role in security makes them suitable by the fact that they understand the consequences of technology. As enterprises endure digital transformation, CIO’s recognize that a lot of value comes in the information and delivery of those digital assets. The CIO is equipped with top notch expertise within the organization to comprehend different risk scenarios and successfully implement it across multiple cross-functional areas.

As AI continues to dominate discussions amongst the CXO’s of Fortune 500 companies; Startups might in fact be at the pole position to derive strategic gains accruing from leveraging AI.  Armed with accessible funding, young and upbeat talent professionals and overall buoyancy in the demand consumption, Startups are increasingly challenging and upending incumbent businesses. This is attributed to a substantial extent due to their unwavering focus on adopting exponential – including artificial intelligence(AI) – to acquire, retain customers, embed AI across the business value chain and cement their market share. Several startups have initiated to leverage to disrupt their existing and adjacent industries. The transformative power of AI has been the cornerstone of their exponential growth.

AI continues to be a secret sauce and competitive advantage for startups. Data detonation, lower cost of storage and processing and continuously enriched self-learning machine curated algorithms, AI will continue to be a huge multiplier for startups – by bolstering customer acquisition and retention to improving efficiencies, augmenting the top line and getting embedded across the business value chain of their businesses.

Entrenching Competitive Advantage Through AI

Industry and functional use cases of AI range far and wide. It is imperative that startups first consider their business model to identify the drivers to their business, estimate potential uplift and time-to-value to prioritize the order in which AI use cases are deployed. Here are few areas that can deliver immediate impact.

Understand Your Current Customers

AI can both accelerate the speed and quality with which you understand your current customer base – alongside informing startups of the most opportune ways to serve them. For instance:

• Recommender systems – which are extremely mainstream today. Ecommerce websites are increasingly tapping into the purchase and browsing history of customers, not only to surface their next purchase, but also nudge customers through promotional pricing.
• By using natural language processing (NLP) powered chatbots, startups can very quickly build and scale their customer service function – while ensuring continuous availability at a nominal long-term cost. When combined with sentiment extraction and mining, these ‘intelligent’ agents can pre-process customers’ emotions and provide preferential pricing / promotional offers to customers who have had a negative experience with the startup.
• With AI, startups can capture and re-create customer journey maps – how customers navigate pages, information contained on web-pages and ultimately make the purchase decisions. This can enable startups to build more personalized customer experience on their digital platforms. 

Acquire Your Next Customer

In additional to galvanizing CLTV and other retention metrics, AI can also be a crucial part of the customer acquisition process by:

• Improving the accuracy of prospect targeting, by continuously analyzing the drivers of current buyers and mapping them against the cues provided by current prospects – all the while maintaining a lower cost of customer acquisition
• Measuring and benchmarking the success attribution of marketing initiatives and spends – enabling marketing teams to focus their efforts on high-impact marketing activities to continuously drive improved performance.
• In a B2B setting, AI can help judge a browsing prospect’s propensity-to-purchase / act on a call-to-action (based on past users’ actions). This can inform sales teams’ efforts and act as a strong pre-qualification stage in the B2B sales process.

Accelerate Time-to-Market for Products

Beyond commercial functions, AI can also have a transformative impact on the manufacturing and distribution process and help startups realize significant advantages by:

• Pushing closer to 100% on-demand production – through continuous improvement in demand forecasting. This will help create leaner production units, improve predictability in production schedules and reduce wastages due to over-production.
• Using autonomous physical systems for packaging, shipping and warehouse management
• Running smarter and leaner distribution chain – through better demand forecasting at a micro-level, optimizing the size of the delivery vehicle and delivery routes of vehicles (based on inventory shipped) to contain transportation costs.
• Ensuring optimal stock availability at storefront – while balancing wastage due to oversupply and stockouts due to insufficient supply. This would again be incumbent on improving demand forecasting.  

Running a tight ship

Finally, given that startups typically operate on very tight budgets and at high speed of execution, AI is a crucial intervention to help them run a tighter ship. While all these tasks are crucial – whether you are a startup or a large enterprise, AI can help achieve outstanding outcomes at a fraction of the cost. This can happen by:

• Speeding up the recruitment process through bots and NLP-powered automated resume scanning. This can reduce the TAT for new hires, by sifting through a large pile of resumes to identify and shortlist the most viable candidates for interview.
• Augmenting the budgeting and financial planning process using AI. Here AI can help going through multiple reports and compiling the findings that eventually inform budgeting decisions
• Automating administrative tasks such as travel planning and front-desk management.

Why AI Is a Game-changer for Startups

Startups cannot afford to ignore the disruptive power that artificial intelligence can bring. AI is particularly suited to be a game-changer for startups because:

• Given the size and scale at which startups operate, it is easier to conceptualize and implement AI-centric solutions – without having the navigate the bureaucracy of multiple stakeholders in the decision process.
• Scalability and continuous improvement are built into the very fabric of AI – investments in AI by startups will see exponential value realization with enriched data sets and refined algorithms.
• The need for speed and cost efficiencies is paramount for startups. For startups to truly disrupt their industry incumbents, speed is of essence. A slow pace of growth usually kills startups before their story even takes wings.
• Having seen examples of corporations who ignored their startup rivals burning their fingers (from Blockbuster and Netflix to Yahoo and PageRank) traditional incumbents are increasingly taking note of technology savvy startups and partnering with them to entrench their market position, through VC’s and startup accelerators. If focusing on channels is crucial to growth in your industry, AI-centric processes will provide a clear competitive differentiation over your rivals.

AI is both a necessity and an important lever for Startups to grow exponentially in their markets. Through AI, Startups will be better positioned to successfully disrupt their incumbents, win market share and customer delight. Startups not actively harnessing the power of AI to achieve speed and manage scale will be doing so at their own peril.

AI and blockchain are two of the prime drivers in the technology space that catalyze the pace of innovation and demonstrating radical shifts across every industry. Each of this technical venture comes with a degree of technical complexity and business implications. Fusion of the two will be able to redesign the entire technical landscape along with a human effect from scratch.

Blockchain has its own limitations, it is a mix of  technology-related and culture influence from the financial services sector, but most of them can be conceited by AI in a way or another.

The illustrated points below will be able to give a gist of the potentials that can be realized at the intersection of AI and Blockchain:

Energy consumption in mining: Mining has already proven that it requires tons of energy and is heavy in the economic perspective. AI has mastered in optimizing energy consumption across multiple sectors, similar results can be expected for the blockchain as well. AI can dramatically reduce the costs of maintaining servers and validate potential savings to lower investments in mining hardware.

Federated Learning: Blockchain is growing at a steady pace of 1MB every 10 minutes. Blockchain pruning  is a possible solution through AI. A new decentralized learning system such as federated learning, for example, or new data sharing techniques to make the system more efficient.

Security: Concerns still exist on the security system of built-in layers and applications  for Blockchain  (e.g., the DAO, Bitfinex, etc.). The mileage created by machine learning in the last two years makes AI a solid candidate for the blockchain to guarantee secure applications deployment, especially given the fixed structure of the system.

Blockchain-AI Data gates: Blockchain has proven its ability for record keeping, authentication, and execution while AI drives decisions by assessing/understanding patterns and datasets, ultimately engendering autonomous interaction. The combo (AI and blockchain) will be become a data gate with these several characteristics that will ensure a seamless interaction in the nearest future.

Auditing of AI through blockchain: AI is seen as a black box ( complex set of calculations and algorithms) to distinguish patterns or trends. This makes it a difficult task for the humans to govern the choices taken by the artificial intelligence in yielding results. Accountability of the AI black box is seen as biggest challenge, considering  concerns across the community for tampering or the altering happening to the calculations for the given input which eventually reflects in the output generated. This challenge can be easily comprehended by the blockchain innovation. Implementing robust auditing of these calculations utilizing the blockchain is seen as the biggest driver for enhancing the credibility of the business organizations and reinstating trust in the reliability of the information.

Leverage on Artificial Trust: Future roadmap of this fusion can successfully lead into creation of virtual agents that will create new ledger by themselves. Machine to machine interaction will be the new norm reinstating trust in a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum.

Machine performance monitoring and changes: Blockchain miners (companies and individuals)  pour an incredible amount of money into specialized hardware components. AI can complement such as machine/equipment monitoring to deploy more efficient systems and do away with the unproductive heavy ones.

Blockchain for better information management: AI has a proven mechanism that runs of an incorporated or centralized database. In such a case, there are always chances for information occurrence of a mishap, i.e. gets lost, altered, or undermined. 

Blockchain and artificial intelligence fusion can eliminate the above concern. Under the umbrella of blockchain the data is decentralized and stored within different nodes or systems. This reinstates trust on  that your information is safe and unaltered. Most importantly the information is time-stamped and is in the sequence making recuperation less demanding and exact. 

Some key challenges on the block: The fusion throws open technical and ethical implications arising from the interaction between these two technologies, such as the need to edit data on a blockchain and most importantly the duo pushing to become data hoarder. Experimentations alone will be able to provide a detailed answer on these lines.

In conclusion blockchain and AI are the two sides of the technology spectrum. One efficiently fosters centralized intelligence while the other promotes decentralized applications in an open-data environment. The fusion of the two will be an intelligent way to amplify positive externalities and advance mankind, most importantly reap the maximum potential for business needs.

Across the world of technology, we are seeing the proliferation of new age developments across software and hardware – titled “Exponential Technologies”. The term refers to a wide range of recent technology breakthroughs – Artificial Intelligence, Internet of Things, Cloud Computing, Augmented and Virtual Reality, Blockchain and the allied. They are collectively referred to as ‘exponential’ considering the humungous potential value that they could possibly add to business. As these technologies continue to mature in their development and adoption, the world is gaining a more concrete insight into the worth of these technologies and their use cases. 2019 will most certainly be the year where these technologies will go mainstream – and deliver exponential value to their proponents. With high investor interest (and money) riding on these new age technologies, I am confident that in 2019, there will be a high uptake in their commercialization. Here are the top10 trends that I foresee in 2019 in exponential technologies :

1. Blockchain Beyond the Hype

In 2018, there was no doubt a lot of excitement and buzz as technology vendors and investors started investigating blockchain and cryptocurrency. In 2019, expect blockchain to move beyond the hype and enter the mainstream. Gartner estimates that blockchain applications will create $3.1 trillion in business value by 2030. Over 2018, several tech-savvy businesses started their own experiments with blockchain in areas such as supply chain, which is ripe for a blockchain-powered disruption. Within blockchain, I foresee:

Increased collaboration between businesses and tech vendors to unlock the power of blockchain across multiple use cases. Given its immutable and decentralized nature, blockchain will be invaluable in sectors such as manufacturing, defense and financial services – and we will see innovative use cases coming out of these domains

Within blockchain, smart contracts specifically will gain immense traction. The business value of smart contracts is remarkably clear – they drastically reduce the time and effort for routine but lengthy paperwork processes, while maintaining the sanctity through a blockchain network

Due to the numerous crypto frauds seen uncovered in the last year, more and more sovereign governments will push legislation to regulate and establish clear rules around blockchain and cryptocurrency. I have no doubts that this will have a net positive impact – as it will demonstrably improve the consumer confidence and enterprise adoption for these technologies by laying down a clear legal framework for their use

2. 3rd Platform Technology to Accelerate Digital Transformation

A combination of social, mobile, data-driven decision-making and cloud infrastructure and processing is commonly referred to today as 3rd platform technology. In 2019, there will be no stopping the juggernaut of internal IT departments moving ever faster towards digital technology.

According to a research by IDC, it is expected that by 2023, 75% of all IT spending will be on such 3rd platform technology, with over 90% of all enterprises building “digital native” IT environments

Further advanced technologies such as distributed cloud, hyperagile app technologies and architectures, AI at the edge and AI-powered voice UIs will be central to how enterprises enable digital transformation using 3rd platform technologies.

This expansion in demand for 3rd platform technologies will be the outcome on increasing pressures on internal IT to become profit centers and unlocking new sources of revenue for the parent enterprise. Using easily scalable and replicable digital frameworks, early adopter IT departments would be able to commercialize this technologies to their competitors while giving their businesses critical competitive advantage

3. Quantum Computing to Come of Age

Quantum computing is a non-traditional form of computing operating on the quantum state of subatomic particles and representing information as elements denoted through quantum bits. The unmitigated rise in the development and permeation of quantum computing is the third key trend that I see for 2019. It is estimated that by 2023, 20% of organizations will carve out budgets for quantum computing projects, as opposed to less than 1% today.

 With heavier software paradigms such as Internet of Things, Artificial Intelligence and blockchain achieving mainstream status, there will be large scale demand for quantum computing to come out of the shadows of academia and into business. Quantum computing will move well beyond a buzzword and will be part of multiple projects at an experimental scale at corporations.

Quantum Computing will succeed where traditional computing has failed, providing parallel execution and exponential scalability. Such systems will take on problems too complex for a traditional approach or where the latency for traditional algorithms would be untenable

Business leaders across multiple industries – automotive, financial, insurance, pharmaceuticals, military and research organizations – will see massive gains through the advancements in Quantum Computing .

 4.Acceleration in the Pervasiveness of the Internet of Things

While Internet of Things has demonstrably hit mainstream status across industries such as consumer goods and retail, and use cases such as supply chain and logistics, we will see further acceleration in its adoption in 2019

 IOT-enabled hardware devices will proliferate nearly all walks of human life. Devices from sensors, wearables, smart assistants and wearables will be a feature in everyday life for most individuals in the developed world and will be a key focus for powering digital transformation

With increasing demand for IOT-powered devices across use cases will definitively bring endpoint security into focus for enterprises. As IOT devices become the first frontier for communication with consumers through highly sensorized environments, we will see a rapid escalation in the adoption of endpoint security practices and software

To support this deep network of the Internet of Things will require an immediate focus on rapidly enabling 5G connectivity in 2019. Not having a robust underlying infrastructure to support IOT will be disastrous for businesses and individuals who will be highly reliant on it for their day-to-day activity.

 5. Convergence of AI, Blockchain, Cloud and IO

Could a future software stack comprise AI, Blockchain and IOT running on the cloud? It is not too hard to imagine how these exponential technologies can come together to create great value. In 2019, I expect that we will see a strong spread of use cases that effectively combine these technologies.

Internet of Things devices will largely be the interface with which consumers and other societal stakeholder will interact. Voice-enabled and always connected devices – such as Google Home and Amazon’s Alexa will augment the customer experience and eventually become the primary point of contact with businesses

Artificial Intelligence frameworks such as Speech Recognition and Natural Language Processing are making huge advances. These will be the translation layer between the sensor on one end and the deciphering technology on the other end

Blockchain-like decentralized databases will act as the immutable core for managing contracts, consumer requests and transactions between various parties in the supply chain

Cloud will be the mainstay for running these applications requiring huge computational resources and very high availability. I expect more cloud vendors to come forward (Amazon and Google for instance already have) with specialized cloud frameworks to handle the torrent of requests that these type of applications would require.

6.New UI/UX Interfaces to Emerge on the Scene

To unlock and harness the true value of exponential technology it is incumbent that we do not rely only on existing paradigms of end-user interfaces such as web and mobile. We need to reinvent new paradigms and explore game changing new interfaces that will help usher better customer and user experiences.

 Conversational platforms – ones which are primarily activated through voice and voice-recognition AI will conduct numerous exchanges on behalf of customers. Already we are seeing rapid adoption of conversational interfaces such as Google Home, Amazon Alexa and Apple’s Siri. These will only grow and prominence and entire CX use cases will be centered around these platforms

Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) will be increasingly leveraged across a vast selection of topical use cases. Incorporating these alongside traditional interfaces will be crucial to delivering the future of an immersive user experience. According to Gartner, we will shift from thinking about individual devices and fragmented user interface (UI) technologies to a multichannel and multimodal experience.

These immersive experience-led interfaces such as VR and AR will become increasingly popular, with 70% of enterprises experimenting with such technology for consumer and enterprise use and 25% of organizations deploying it into production.

7.Edge Computing to become an Enterprise Mandate

Simply put, edge computing is a computing topology in which information processing, and content collection and delivery, are placed closer to these endpoints. For reducing the latency running AI algorithms and eventual response times, edge computing will become an enterprise mandate for use cases involving a convergence of IOT and AI.

In 2019, adoption of edge computing will be driven by the need to keep the processing power close to endpoints as opposed to a centralized cloud server. Having said that, edge computing will not necessitate the creation of a new architecture. Cloud and edge computing will complement each other. Cloud services will be charged with centralized service execution, not only on centralized servers, but also across distributed servers on-premises and on-the-edge devices themselves.

Five years down the line expect to see specialized AI chips, supporting greater processing power, storage and other advanced capabilities. They will be incorporated into a wider array of edge devices. Not too far into the future, we will see 40% of organizations’ cloud deployments include an element of edge computing and 25% of endpoint devices and systems will execute AI algorithms.

We will see more intelligent and empowered edge computing devices as well. According to Gartner, storage, computing and advanced AI and analytics capabilities will expand the capabilities of edge devices through 2028.

8. DevOps Augmented by AI

Despite almost universal acceptance of the DevOps framework across global enterprises, adoption has been patchy and slow. This is due to numerous reasons, ranging from a distributed toolset and a paucity of expert practitioners. However with the emergence of AI, we will see an increased process automation between software development and deployment, accelerating the enablement of DevOps

AI-powered QA suites will increase the automation quotient in the DevOps process. Given the advancements seen in automation, AI will rapidly intervene in the QA process across unit testing, regression testing, functional testing and user acceptance testing.

DevSecOps will combine the power of DevOps and AI in the field of information security. A centralized logging architecture recording suspicious activity and threats combined with ML-based anomaly detection techniques will empower developers to accurately pinpoint potential threats to their system and secure it for the future.

AI will also break the cultural barriers that typically exist between developer and operations teams. . AI-powered systems will enable DevOps teams to have a single, unified view into system issues across a complex toolchain while improving the collective knowledge of anomalies detected and the pathways for redressal.

 9.Autonomous Things on the Rise:

At present, we are seeing experiments at an advanced level in the field of autonomous things. Autonomous things comprise whole gamut of unmanned objects – from drones, cars and robots. In 2019, I expect there to be a steady rise in the adoption and appreciation of this area of technology

 Autonomous things of today are largely centered around the current paradigm of basic automation and rigid if-else programming rules. The next revolution in the field of autonomous things will be by exploiting the power of AI to exhibit more advanced, proactive and multi-threaded behaviors

Demand for autonomous things will continue to grow, specifically for autonomous vehicles. According to a Gartner survey, by 2021, 10% of new vehicles will have autonomous driving capability, compared to less than 1% in 2017.

Robotics and drones powered by AI will be able to address more complex use cases bringing in further efficiencies to incumbent businesses in the field of logistics delivery, warehouse management and manufacturing

10. AI to Disrupt Cybersecurity

Finally, the last key trend in the exponential technologies space for 2019 pertains to cybersecurity. While this is a remarkably advanced field, we will see continued growth and evolution of cybersecurity in combination with artificial intelligence

 Using anomaly detection and machine learning, AI will hugely disrupt the field of cyber security. Security practitioners will be empowered to identify intrusions and malafide behavior faster using automated, always-on algorithms to constantly survey the secured network for wrongful activity and address concerns before they break-ins occur

AI can be quickly training over a massive data set of cyber security, network, and even physical information. Cyber security vendors will soon roll out AI-enabled solutions that will learn at an abstract level to detect and block abnormal behavior, even when this behavior does not fit within a known pattern. I expect that in 2019 companies will incorporate ML into every category of cybersecurity products.

By extension, we will see a fight between good AI and bad AI in the domain of cybersecurity. There are genuine fears that the next generation of attacks will not be carried out by human hackers but pieces of code designed to rapidly infiltrate a secure environment. Countering that with so-called ‘good AI’ will be crucial in undermining the impact these fast-paced attacks can have

The AI Race is fast heating up. While private enterprises tend to view this through a lens of achieving competitive advantage through breakthrough business and process innovation, there is a much larger play between nations competing to achieve supremacy in the domain of Artificial Intelligence. Across the globe – from Japan in the east to United States in the west – every major industrialized nation is ramping up their efforts (and rhetoric) to build indigenous AI capability. These economies have shown great interest, from the federal to the local levels, to achieving the much-vaunted status as the world leader in Artificial Intelligence. While the approaches by each country may differ – the end goal is some variation of achieving a preeminent position as the single distinguished player in the field of AI.

At this point, it is natural to ask – why? Why are entire economies and governments frantically organizing themselves to win in this race? The answer lies mainly in the size of what is at stake. According to a recent report by global consulting company PwC, AI’s contribution to the global economy is expected to be $15.7 trillion by 2030. The nation that serves the largest portion of this need will not only have the highest revenue, but also the highest number of in-demand professionals, the lowest dependency on other nations in this massive field of work, alongside being the singular force to reckon with in the future of the world.

This might explain why, today, the US and China are at the forefront of this technology. According to the same report, China and North America will see the largest part of the global value-pie ($7trillion and $3.7 trillion respectively). When the stakes are this high, you probably do not want to depend on the benevolence of others. You ought to ensure that every capability you require is available within your own shores. In China, the government stands strongly behind AI adoption, announcing their intention to become “a principal world center of artificial intelligence innovation” by 2030. On the other hand, the US has the highest number of AI startups and one of the deepest wells of venture capital to fund the startups’ endeavors. Not to mention, they are also home to larger tech corporations – Google, Amazon, Facebook, Microsoft, IBM etc. – which are also pioneering AI research in their own way.

While the US and China have taken a quantum leap ahead over their other competitors, the field of AI is not exactly a duopoly. While these two are clearly the leaders across any measurement criteria that you would employ, there are several others in the fray – Japan, South Korea, Germany, France, the UK, Canada, Israel, Russia and India – who are all in various stages of launching their visionary plans and developing on-ground leadership through either private enterprise, public support – or both.

With the size of the prize outlined, the next logical question would be – how is India doing in this space? What steps is India taking to ensure that we do not fall by the wayside as the world runs to win this monumentally important race?

There’s some good news and some not so good news on that front. For one, India is not yet considered among the absolute top rung of AI superpowers today. While we do have significant numbers of STEM graduates passing through academia each year, most of them are currently involved in the so-called lower end of the IT value chain – infrastructure services and maintenance etc. On the bright side, India is uniquely positioned to deliver strong AI leadership, assuming we take steps in the right direction on the policy side, as well as in industry-academic collaboration.

Why do I feel India is uniquely positioned? Consider the following:

• India continues to have a strong continuing focus on STEM education. As AI enters the mainstream curricula of our universities, we will realize the benefits of having a robust intellectual capital in this arena.
• Typically, it is data that powers an AI application. India, with the second largest population in the world (and increasingly connected to smart devices) has the potential to not only generate massive data sets, but also one of the most diverse set of data due to the inherent diversity across class, language and other cultural aspects – which can power the most enriched applications of AI
• There is a strong impetus on the policy front in India for AI – with Digital India, Skill India programs started by this government, in addition to constituting NITI Aayog – a national-level think-tank to execute on a vision rich with emerging technology

So how can we combine India’s inherent advantages, with some strong moves already made in the AI space, to possibly achieve AI supremacy in the near future? Here are three clear areas that require a high degree of attention and action to fulfil that vision.

1. Lead with Policy

With a strong, forward-looking government, India is already making the right noises on the development of AI. NITI Aayog – the think-tank I had mentioned earlier – has constituted a committee to study and deliver a National AI Strategy for India. In their June 2018 discussion paper, they identified 5 areas where India is uniquely poised to deliver AI leadership due to our intrinsic advantages – healthcare, agriculture, education, smart cities and smart mobility and transportation. While the Aadhar program has had its critics, it is likely to be instrumental in building a massive training set of citizen data, enabling India to build some thought-leading application in AI. The government has also pledged to put their money where their mouth is – with $480mn projected to be spent on the Digital India program in 2018. While this spending pales in comparison to the spending of other countries (China has committed $150bn up to 2030), it will be instrumental for founding a strong test-bed for incubating our AI vision. The government is also planning a national data and analytics platform in collaboration with private players to utilize the huge amount of data with the help of AI.

2. Facilitate through Academia

Close to 2.6mn students graduated out of STEM fields from India in 2016. While I mentioned that these graduates have anywhere between no to a rudimentary understanding of AI today – it does represent the huge footfall seen in these fields, who would be well-served through a healthy training in AI-centric technologies.

The more pressing problem can be seen in core AI research. While India is ranked 5th in the world today terms of number of papers published (14,864 between 2010-16), we are still a fair way behind the US (63,344) and China (39,820) on this metric. Worse still, India ranks a distant 19th on the metric of H-Index (measured between 1996 and 2016), which leads to a concern on whether our current research is citation-worthy or rooted in business applicability. So, while the appetite for research exists, the contribution to the overall body of knowledge still needs some upgrading.

To address this, the aforementioned NITI Aayog discussion paper, recommends the set-up of a 2-tier integrated approach for boosting research in both core AI and applied AI. The first – COREs (Centers of Research Excellence in Artificial Intelligence) will be focused on developing a better understanding of existing core research and pushing technology frontiers through creation of new knowledge. The second – ICTAI (International Centre for Transformational Artificial Intelligence) will have a mandate of developing and deploying application-based research through Private sector collaboration. This framework would also consist an umbrella organization addressing issues relating to access to finance, social sustainability and the global competitiveness of the technologies developed. This body would be similar to the Campus for Research Excellence and Technological Enterprise (CREATE), Singapore program or Innovate UK.

3. Implement through Private Industry

While the first two points deal with strengthening the backbone of AI research and education, this final aspect deals with building high-class industry-grade IP with wide applicability. Due to a huge democratization in information, both large tech corporations and startups are aware of the challenges that can be solved through AI and are building solutions to address these challenges. Behemoths IT and consulting players are already investing in academic partnerships to set up a base for IP development and workforce training. Startups too, while not similarly endowed, are looking to build visionary products that will transform the industry through collaboration with academia. Through such an industry-academia collaboration, Indian technology companies would be able to foster synergy by developing bleeding edge research in India which can be gainfully employed to solve global challenges. Extending the Make in India initiative would be crucial to ensure that the intellectual property of the work done by Indians stays in the home country, boosting our credibility in this space.

In conclusion, while India is already among some of the top nations in the world today in the field of Artificial Intelligence, there still is a long way to go to hit the absolute pinnacle in this space. However, given that AI is still is in a nascent stage, there is significant scope for India to still emerge as the leading light in this space. With this sustained and rapid pace of progress, I am certain that India will soon emerge as the preeminent leader in the field of AI.

Global Capability Centers (GCC’s) in India are at an important inflection point. As multinational corporations continue to move to a digital and AI-first paradigm, they are looking at their GCC’s to provide emerging technologies leadership to drive this transformation.

It’s been an exciting evolution for the GCC’s over the last few years. In the not too distant past, multinational corporations look at their offshore captives to contain costs for repetitive, low-value business processes. From there, we saw shared services centers capture a larger slice of the pie in day-to-day business operations of their MNC counterparts, alongside developing centers for research, development, innovation and business transformation. Captives morphed into capability centers, wherein new skills and competencies could be swiftly incubated and scaled.

The numbers pan out well for GCC’s – with nearly a million professionals employed, across 1,500 GCC’s in India, netting an export revenue of over $23mn, the sun is shining brightly for GCC’s. Indian GCCs account for over a fifth of IT-BPM exports and a fourth of India’s export employees. According to a report by analyst firm Nomura, GCCs are growing faster today in terms of revenue attribution than their large outsourcing counterparts (12.4% CAGR for GCCs vs 10.7% for service providers, over the last 5 years). 27% of US-based Fortune 2000 companies already have GCCs in India. GCCs are becoming the centralized technology procurement arm for MNCs as 50% of the Fortune 2000 are planning to shift vendor management to their offshore entities, for the synergistic benefits, as well as to drive outsourcing costs down.

Here’s the inflection point though – as MNCs grapple in an uncertain business environment and business models, changing consumer preferences and consumption modes and digitalization in most areas of the business, they are looking at their GCC leaders to provide the technology disruption that their traditional business desperately needs. For the past few years, analytics and AI has taken a robust foothold in the GCCs, with their India-based talent powering evidence-backed, data-driven decisions for their parent organizations. The next generation of the GCC’s will be expected to provide autonomous decision support and an AI-augmented human intelligence. GCC leaders will need to harness the burgeoning power of AI technologies to power corporate decisions, automate repetitive, low-value tasks through robotization and reinvent business models for the continued success of their business in the new world of business. Digital will be the core element of business model re-design.

Of the multiple reasons driving insourcing decisions, perhaps the most important one is the strong business process integration that GCCs provide. Rather than relying on the volume provided by outsourced companies, MNCs realize that they need to meld quality output with high productivity, delivered by professionals that can reimagine current business functions. Enterprises are increasingly seeing the long-term benefits of investing in a world-class offshore capability center and prioritizing driving investments to these entities. With great investments come great expectations – they need their offshore GCC leaders to have a multidimensional business orientation and act as the key intermediary between the strategic boardroom and the operational engine room.

The future of the GCC is digital and AI-first and to that end, we need to re-imagine the future of the GCC in that direction. Here’s a primer on how AI transformation can be shaped within GCC’s :

Assess Maturity and Develop Roadmap

The first step is doubtless to assess the current state, the desired future state and the gap that exists between the two. Assessments and roadmap development need to be performed in two vital areas – technology and people.

Technology Assessment and Roadmap:

The first step is foundational to the AI and digital reengineering for the GCC. GCC leaders need to take stock of all the processes performed at the center, along with the tools and software driving them. The first step is to classify these processes into traditional vs digital IT. Once this is done, leaders need to further split the traditional IT processes into 3 sub-segments – reimagine, leave as-is or scrap. Whether a software-enabled process has strong business justification for the present and the future will define whether it is scrapped or not.

For the processes that do not get junked, leaders need to check if there are powerful, maturing digital options available – that can improve speed, accuracy and outcomes from the process through digital reengineering. If there is – then that process is ripe for reimagination. If not, and there is a strong business case to keep it as-is, leaders need to put it on a ‘Watch list’ and keep track of technology evolution and commercial-grade solutions emerging in this space. Further, for the reimagined processes, GCC leaders need to also assess the range of technology options available – from RPA to Deep Learning – and develop a roadmap for the automatization of these processes. For instance, deep learning could be progressively applied for high-value tasks which execute complex decision-making, while RPA could be quickly implemented to automate routine tasks, such as report generation etc.

People Assessment and Roadmap:

A similar exercise should also be done for the GCC employees. Leaders need to take stock of the talent pool available within the GCC and map it with the future skills required. Is there enough talent within the current GCC that can be updated with digital skills to develop and run future applications? Or would there be a need to augment internal talent with external consultants – is a key question to ask on the journey to GCCs’ digital transformation. This skill assessment needs to be combined with internal trainings to move existing employees into new roles. For instance, could a portion of the analytics team be moved into automated insight generation, using machine learning? Or can some of the better developers be trained into full-stack developers to build the technology backbone for the organization?

This kind of skill assessment and continuous training will provide the GCC leaders with a continuously updated understanding of the human assets available that can drive enterprise digital transformation. Where certain niche skills may not be available, leaders can look to outsource from topical service providers to help set up their processes and transfer the day-to-day system updates back to the GCC.

Re-engineering the Entity

Once the skills and technology are suitably assessed, the next step is to gear the GCC towards a new set of processes and practices that will help it sustain this digital drive. The new digital and AI-first GCC needs an entirely new set of standards to measure business value delivered and technology performance. This requires a reengineering exercise to change processes, evaluation metrics, and mindsets. Three key factors are at play here:

Process Augmentation:

First, the GCC needs to identify a whole new set of program management practices to build and sustain a digital mindset.

The first of these is the Automation Scorecard. Once the technology assessment and roadmap are completed and the automatable processes are identified, they should be listed onto this scorecard to track and monitor the extend of automation performed on each process.

The second intervention is progressively prioritizing scalable, cloud-based, digital-first software. There is often a strong proclivity to trust and use traditional IT software and this mindset needs to be evolved towards more SaaS-based, API-driven software – which can help organizations dynamically scale the costs and utilization up or down, based on business needs. By moving to a more service-oriented architecture model, GCCs can improve system availability and uptime.

The final intervention is people augmentation. While GCCs have progressively started and scaled their accelerator programs to identify breakthrough technologies solutions, they need to take the people and software integration to the next level. The mandate for these accelerators should be closely tied to the business expectations (as per the technology assessment and roadmap and automation scorecard mentioned above) and their success should be measured through the exponentiality of the results they deliver, not just basic productivity improvements. Additionally, GCC leaders should also seek process and technology guidance from outside consultants so that the accelerator remains true to its purpose and channels the needs of the business

New Metrics Development

The world of digital and AI will require an entirely new set of metrics. While cost optimization and quality of outcomes will remain paramount for any GCC, leaders need to reinvent the intermediate metrics that contribute to productivity and quality metrics. For instance, GCC leaders need to actively capture the extent of automatization delivered in the enterprise, by measuring the man-hours saved (total and monthly). Additionally, they could also leverage the automation scorecard to show progress on the automatization of processes. Thirdly, they need to measure and showcase the quantum of speed and accuracy that is delivered by the new digital process as opposed to traditional IT to their HQs, to highlight outcomes and achievements. Fourth, GCC employees need to be measured for their adeptness at emerging technologies, how much training has been delivered and internalized by employees. 

Evangelize Reverse Innovation

While several GCCs do deliver reverse innovation, the research and development of industry-specific commercial-grade AI and digital solutions should be one of the top evaluation criteria for GCC leaders. Indian executives have a strong frugal mindset, which can naturally deliver innovation under cost constraints – which can then be progressively leveraged by others in similar markets and situations. Identifying processes where reverse innovation can be applied and then commercialized upstream needs to be a top priority for GCC leaders to improve the revenue attributed to their entities. To do so, it is critical to first assess which technology and operational assets they own, that could be useful across new markets.

As Cisco VP – Dan Scheinman once famously said, “We came to India for the costs, we stayed for the quality, and we are now investing for the innovation”. GCCs have quickly moved from invisible, low-value business processing units to invisible high-value technology centers to now visible, high-value AI and Digital innovation hubs. The expectation is to now deliver the digital and AI-centric future for their parent enterprises .

For the larger mass of professionals, the words “artificial intelligence,” or AI, often conjure up images of robots, the sorts of robots that might someday take their jobs. But at the enterprise level, AI means something different. It has enormous power and potential: it can disrupt, innovate, enhance, and in many cases totally transform a business. Forrester Research predicts a 300% increase in AI investment in 2017 from last year, and IDC estimates that the AI market will surge from about $8 billion in 2016 to more than $47 billion in 2020. There’s solid proof that the investment can pay off—if CEO’s can adopt the right strategy. Organizations that deploy AI strategically enjoy advantages ranging from cost reductions and higher productivity to top-line benefits such as increasing revenue and profits, richer customer experiences, and working-capital optimization. The survey shows that the companies winning at AI are also more likely to enjoy broader business success.

So How to make your Enterprise AI Ready?

just one quarter of organizations say they are getting significant impact from it. But these leading businesses have taken clear, practical steps to get the results they want. Here are five of their key strategies:

• Core AI Resource Assimilation using Funding or Acquisition
• Gain senior management support
• Focus on process, not function
• Reskill your teams and foster a learning culture
• Shift from system-of-record to system-of-intelligence apps, platforms
• Encourage innovation

Core AI Resource Assimilation using Funding or Acquisition

As per insights from Forbes and Cowen & Company, 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.

Through 2020, organization using cognitive ergonomics and system design in new AI projects will achieve long term success four times more often than others    

 – Gartner

With $1.7 billion invested in AI startups in Q1 2017 alone, and the exponential efficiencies created by this sort of technology, this evolution will happen quicker than many business leaders are prepared for. If you aren’t sure where to start, don’t worry – you’re not alone. The good news is that you still have options:

• You can acquire, or invest in, an innovative technology company applying AI/ML in your market, and gain access to new product and AI/ML talent.
• You can seek to invest as a limited partner in a few early stage AI focused VC firms, gaining immediate access and exposure to vetted early stage innovation, a community of experts and market trends.
• You can set out to build an AI-focused division to optimize your internal processes using AI, and map out how AI can be integrated into your future products. But recruiting in the space is painful and you will need a strong vision and sense of purpose to attract and retain the best.
• You can use outside development-for-hire shops like new entrant Element.ai, who raised over $100M last June, or more traditional consulting firms, to fill the gaps or get the ball rolling.

Process Based Focus Rather than Function Based

One critical element differentiates AI success from AI failure: strategy. AI cannot be implemented piecemeal. It must be part of the organization’s overall business plan, along with aligned resources, structures, and processes. How a company prepares its corporate culture for this transformation is vital to its long-term success. That includes preparing people by having senior management that understands the benefits of AI; fostering the right skills, talent, and training; managing change; and creating an environment with processes that welcome innovation before, during, and after the transition.

The challenge of AI isn’t just the automation of processes—it’s about the up-front process design and governance you put in to manage the automated enterprise. The ability to trace the reasoning path AI technologies use to make decisions is important. This visibility is crucial in financial services, where auditors and regulators require firms to understand the source of a machine’s decision.

Taking down Resistance to change of Upper Management

One of the biggest challenges to digital transformation is resistance to change. The survey found that upper management is the group most strongly opposed to AI implementation. C-suite executives may not have warmed up to it either. There is such a lack of understanding about the benefits which the technology can bring that the C-suite or board members simply don’t want to invest in it, nor do they understand that failing to do so will adversely affect their bottom line and even cause them to go out of business. Regulatory uncertainty about AI, rough experiences with previous technological innovation, and a defensive posture to better protect shareholders, not stakeholders, may be contributing factors.

Pursuing AI without senior management support is difficult. Here the numbers again speak for themselves. The majority of leading AI companies (68%) strongly agree that their senior management understands the benefits AI offers. By contrast, only 7% of laggard firms agree with this view. Curiously, though, the leading group still cites the lack of senior management vision as one of the top two barriers to the adoption of AI.

Reskilling Teams and HR Redeployment

HR and corporate management will need to figure out new jobs for people to do. Redeployment is going to be a huge factor that the better companies will learn how to handle. The question of job losses is a sensitive one, most often played up in news headlines. But AI also creates numerous job opportunities in new and different areas, often enabling employees to learn higher-level skills. In healthcare for example, physicians are learning to work with AI-powered diagnostic tools to avoid mistakes and make better decisions. The question is who owns the data. If HR retains ownership of people data, it continues to have a role. If it loses that, all bets are off.

HR’s other role in an AI future will be to help make decisions about if and when to automate, whether to reskill or redeploy the human workforce, and the moral and ethical aspects of such decisions. Companies which are experimenting with bots and AI with no thought for the implications need to realize that HR should be central to the governance of AI automation.

Given the potential of AI to complement human intelligence, it is vital for top-level executives to be educated about reskilling possibilities. It is in the best interest of companies to train workers who are being moved from jobs that are automated by AI to jobs in which their work is augmented by AI.

The Dawn of System-of-Intelligence Apps & Platforms

Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future.

By 2019, AI platform services will cannibalize revenues for 30% of market leading companies    -Gartner

Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behaviour, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

The Process of Supporting Innovation

Besides developing capabilities among employees, an organization’s culture and processes must also support new approaches and technologies. Innovation waves take a lot longer because of the human element. You can’t just put posters on the walls and say, ‘Hey, we have become an AI-enabled company, so let’s change the culture.’ The way it works is to identify and drive visible examples of adoption.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years.

In the End, it’s about Transforming Enterprise

AI is part of a much bigger process of re-engineering enterprises. That is the major difference between the sci-fi robots of yesteryear and today’s AI: the technologies of the latter are completely integrated into the fabric of business, allowing private and public-sector organizations to transform themselves and society in profound ways. You don’t have to turn to sci-fi. The story of human/machine collaboration is already playing at an enterprise near

AI today can be described in terms of three application domains: cognitive automation, cognitive engagement and cognitive insight.

• Cognitive automation: In the first AI domain are machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools to develop deep domain-specific expertise and then automate related tasks.
• Cognitive engagement: At the next level of the AI value tree lies cognitive ‘agents’: systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
• Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow to detect real time key patterns and relationships from large amount of data across multiple sources to derive deep and actionable insights.

  Here are five key applications of artificial intelligence in the Banking industry that will revolutionize the industry in the next 5 years.

AML Pattern Detection

Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately.

HSBC has partnered with Silicon Valley-based artificial intelligence startup Ayasdi to automate some of its compliance processes in a bid to become more efficient. The banking group is implementing the company’s AI technology to automate anti money-laundering investigations that have traditionally been conducted by thousands of humans, the bank’s Chief Operating Officer Andy Maguire said in an interview last week.

Chatbots

Chat bots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots.

The State Bank of India (SBI) on Monday announced SBI Intelligent Assistant (SIA) — a chat assistant aimed to address customer enquiries like a “bank representative” does. Developed by Payjo, an artificial intelligence (AI) banking platform, “SIA” is equipped to handle nearly 10,000 enquiries per second or 864 million in a day — which is nearly 25 per cent of the queries processed by Google each day.

Algorithmic Trading

Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is carried out by automated artificial intelligence systems. Most of these hedge funds follow different strategies for making high frequency trades (HFTs) as soon as they identify a trading opportunity based on the inputs.

A few hedge funds active in AI space are: Two Sigma, PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel, Voleon, Vatic Labs, Cubist, Point72, Man AHL.

Fraud Detection

Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.

Mastercard announced the acquisition of Brighterion. Brighterion’s portfolio of AI and machine learning technologies provide real-time intelligence from all data sources regardless of type, complexity and volume. Its smart agent technology will be added to Mastercard’s suite of security products already using AI.

Customer Recommendations

Recommendation engines are a key contribution of artificial intelligence in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines have been very successful and a key component in revenue growth accomplished by major banks in recent times.

With Big Data and faster computations, machines coupled with accurate artificial intelligence algorithms are set to play a major role in how recommendations are made in banking sector. For further reading on recommendation engines, you can refer to the complete guide of how recommendation engines work.

JPMorgan, which is spending big on technology as it looks to cut costs and increase efficiency, last year launched a predictive recommendation engine to identify those clients which should issue or sell equity. And now, given the initial success of the engine, it’s being rolled out to other areas.

Strategic Challenges of AI

As with any new endeavor, there are several challenges associated with the development and application of AI solutions.

• Most banks and credit unions are in the early stages of adopting AI technologies. According to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.
• Also, one of the biggest challenges is finding the right talent. With only slightly more than half of survey respondents (55%) stating they have identified an AI leader within their company, more than half of those have appointed the head of innovation as the leader.
• In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.
• 12% of the overall group weren’t using AI yet because they felt it was too new, untested or weren’t sure about the security.
• There is no clear internal ownership of testing emerging technologies— only 6% of those surveyed having an innovation leader or an executive dedicated to testing new ideas and processes.  

How to make AI Part of Banking Ecosystem

The potential of open banking and artificial intelligence are intertwined, making up the foundation for a new banking ecosystem that will most likely include both financial and non-financial components. By partnering with fintech providers and data analytic professionals, the power of organizational data and insights can be realized. The partnerships and structure decided upon today will determine an organization’s competitive differentiation in the future.

Multiple providers are offering AI-based solutions and, as a result, banks need to navigate between specialist players and AI powerhouses. The goal will not to become more automated and less personalized, but to use technology and customer insights to become a lot more personalized and contextual.

The banking industry is still in the early stages of developing strong AI solutions. While these solutions can impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience. Singaporean bank DBS had the vision to launch Digi bank, India’s first mobile-only bank. Being paperless and branchless, Digi bank had to rely on emerging technologies like conversational AI to succeed. Digi bank was built with one-fifth of the cost of a regular retail bank and can contain 82% of customer inquiries with bots. Some banks just want to hand off responsibility to the vendor but Digi bank’s approach is to empower the customer with self-service tools. They don’t want to be professional services

There are four key recommendations that experts make to financial services firms who are looking to effectively exploit the value of AI. These are:

• Look to invest, learn and pair up with experts from outside of the industry
• Make use of cognitive computing to make better use of data
• Implement the right mix of platform technologies
• Strive to maintain a human touch.

   In conclusion, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend. This trend is likely to grow exponentially in the future. Companies that embrace this trend are likely to be winners

Previous week , I had an opportunity to moderate a fireside chat at NASSCOM Martech conference that carried a theme around changing role for CMO with the advent of AI and I could notice a substantial set of queries during the conference on how AI will redefine marketing. As understandable, each new technology can create fear, uncertainty, and doubt until we understand it better. And AI, with all its hype, fits that bill. But to remain current and relevant, CMOs must quickly understand and apply AI. Here’s a short AI CMO Primer.

Can I put off AI until later?

The answer is no! AI is here. Waiting to deal with it could put you well behind the curve. Leading businesses are already either using AI to profound effect, or actively planning for it.

• Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases.
• In a June 2016 report, Weber Shandwick found that 68% of CMOs report their company is “planning for business in the AI era” with 55% of CMOs expecting AI to have a “greater impact on marketing and communications than social media ever had.”

To wait is to get left behind. And as you’ll see later, getting started doesn’t have to be painful or costly.

 What is AI, machine learning, and cognitive intelligence?

Academic experts might hate my explanation, but differentiating between AI, machine learning, and cognitive intelligence from a practical CMO perspective isn’t necessary. I use AI as an umbrella term to refers to software that carries out a task which normally requires human intuition—including learning and problem solving.

AI can be thought of as a set of repeatable steps and, while AI doesn’t technically replicate free-will and decision making, it does map out these steps and use computer processing speed to make its way through them to come to an outcome—like how a person would. It can do this much faster, and taking into account far more relevant data than a human would.

Is AI ready for marketing now?

AI has come at the right time, along with the explosion of Big Data. In essence, with access to an incredible amount of data, it’s never been more important for organizations to make sense of it and leverage important pieces out of the noise.

With the exponential growth of cheap, fast, scalable, and interconnected computing and storage in the cloud, the horsepower and data to efficiently run AI algorithms is now within everyone’s reach.

But, that being said, it is also sadly true that there’s one very simple reason why progress towards full automation and AI marketing is relatively sluggish – because most machines aren’t actually learning anything. All of these platforms that exist today, there’s no machine learning. And if it is, their machine learning is, ‘Did someone open an email? Yes, give them a point. That’s not real machine learning. Which is a problem, because effective automation is fast becoming a prerequisite of effective marketing. From chatbots to real-time contextual geographic marketing, modern marketing solutions demand insight-driven automation to deploy the right message quickly, at scale.

marketing automation (especially AI marketing) will have to eventually free marketers from manual work which comprises ‘98% of their eight hours a day’, empowering them to spend their time more productively tackling the creative jobs that machines aren’t well suited to. This requires three key problems AI marketing providers need to solve:

1. The creation of effective, scalable machine learning which can optimize a campaign without human input.

2. Ensuring that decision-making system’s logic is transparent and easily comprehensible by marketers seeking to analyze and augment those automated insights.

3. Designing a prescriptive system which can not only predict future actions – but understand why the user would make those actions. 

How can AI be applied to marketing?

AI has the potential to revolutionize customer engagement, customer service, and marketing automation. It can enhance the way we communicate with new, current, and inactive customers, and automate admin functions at the backend. In other words, it can help make marketing operations more efficient and effective.

AI can far more accurately predict next best action, by churning through (in real-time) all relevant data about the customers – purchases, interactions, social media posts, email exchanges – and then learn from the results and do it on a scale not previously possible.

For example, let’s say you have a few million customers and want to communicate with them as if you know them very well, providing everyone the right offer at the right time. AI can enable this level of personalization at a scale of millions of individuals, and in near real time.

In essence, AI can save marketers time and bring companies far closer to their customers, without worrying about IT, data lakes, data quality, or hiring armies of data scientists. 

Do I need to become an AI expert?

The short answer is no. AI systems shouldn’t require you to become a mathematician. With AI system, you’ll be able to focus on the results not the process of churning through of thousands, millions, or trillions of data points to arrive at the insights you need about your customers.

How much will it cost?

Surprisingly, AI systems can reduce costs and eliminate waste. AI systems can significantly reduce the requirement for data engineers and data scientists, or the need to depend on IT teams.

  And AI can take wasted effort out of the system by providing a deeper understanding of what your customers want and how to interact with them effectively.

How do I get started?

First, start exploring today. Read, talk to people, and evaluate first hand. Select a contained, but impactful area business problem. A subset of your customer loyalty system could make a great initial project. Loyal customers should be the life blood of most companies, but often are underserved as it’s difficult to pull together and analyze all relevant data in a timely manner. This is a perfect fit for AI because there’s typically a lot more known data for AI to analyze about current customers, as compared to prospects. And it’s a project where you can start seeing high-impact results in weeks—perhaps even new revenue from customers who were previously inactive.