While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a “symbiotic autonomy.” If you may. It will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Mutual Co-existence – A Symbiotic Autonomy

In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

I don’t think at AI will become an existential threat to humanity. Not that it’s impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don’t think it’s true.

If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity. Also, there is a complete fallacy due to the fact that our only exposure to intelligence is through other humans. There are absolutely no reason that intelligent machines will even want to dominate the world and/or threaten humanity. The will to dominate is a very human one (and only for certain humans).

Even in humans, intelligence is not correlated with a desire for power. In fact, current events tell us that the thirst for power can be excessive (and somewhat successful) in people with limited intelligence.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Intelligence of Accountability

A lot of companies are working hard on making machines to be able to explain themselves — to be accountable for the decisions they make, to be transparent. A lot of the research we do is letting humans or users query the system. When Cobot, my robot, arrives to my office slightly late, a person can ask , “Why are you late?” or “Which route did you take?”

So they are working on the ability for these AI systems to explain themselves, while they learn, while they improve, in order to provide explanations with different levels of detail. People want to interact with these robots in ways that make us humans eventually trust AI systems more. You would like to be able to say, “Why are you saying that?” or “Why are you recommending this?” Providing that explanation is a lot of the research that is being done, and I believe robots being able to do that will lead to better understanding and trust in these AI systems. Eventually, through these interactions, humans are also going to be able to correct the AI systems. So they are trying to incorporate these corrections and have the systems learn from instruction. I think that’s a big part of our ability to coexist with these AI systems.

The Worst Case Contingency

A lot of the bad things humans do to each other are very specific to human nature. Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers, etc were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behavior unless we explicitly build these behaviors into them. Why would we?

Also, if someone deliberately builds a dangerous and generally-intelligent AI, other will be able to build a second, narrower AI whose only purpose will be to destroy the first one. If both AIs have access to the same amount of computing resources, the second one will win, just like a tiger a shark or a virus can kill a human of superior intelligence.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Musk explained that his attempt to sound the alarm on artificial intelligence didn’t have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity

Brain-machine interfaces could overhaul what it means to be human and how we live. Today, technology is implanted in brains in very limited cases, such as to treat Parkinson’s Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend’s trip to Antarctica — hearing the sound of penguins, feeling the cold ice — all while your body sits on your couch.

Final Word – Is AI Uncertainty really about AI ?

I think that the research that is being done on autonomous systems — autonomous cars, autonomous robots — it’s a call to humanity to be responsible. In some sense, it has nothing to do with the AI. The technology will be developed. It was invented by us — by humans. It didn’t come from the sky. It’s our own discovery. It’s the human mind that conceived such technology, and it’s up to the human mind also to make good use of it.

I’m optimistic because I really think that humanity is aware that they need to handle this technology carefully. It’s a question of being responsible, just like being responsible with any other technology every conceived, including the potentially devastating ones like nuclear armaments. But the best thing to do is invest in education. Leave the robots alone. The robots will keep getting better, but focus on education, people knowing each other, caring for each other. Caring for the advancement of society. Caring for the advancement of Earth, of nature, improving science. There are so many things we can get involved in as humankind that could make good use of this technology we’re developing

Global Capability Centers(GCC’s) are at a pivotal turning point as the pace at which digitization is changing every aspect is fast paced and agile. The rapid transformation and innovation of GCC’s today is driven by new age or exponential technologies :AI, internet of things (IoT), blockchain, cloud computing , RPA, Cyber security. Exponential technologies are seen to double their performance every couple of years while reducing their costs in half. In recent times; GCC story is in a changing era of value and transformative arbitrage. Most of the GCCs are aiming towards deploying suite of exponential technologies :RPA, Blockchain, IoT, AI to get into digital play. It is widely predicted that exponential technologies will disrupt and transform capability centers in the coming decades.

This blog aims to demystify emerging exponential technologies and examine the developing role that it could play in both the immediate and long-term future of GCC’s. From applying AI to exploring how blockchain could be used to transform businesses, we will envision ways to apply and adopt exponential technologies to GCC related challenges.

Cloud Based Digital Transformation

Big Data technology and cloud computing are widespread across the globe GCC’s are finding the right way to use it, so they can accomplish their business goals. As automation drives businesses, insights derived from big data analytics are like a data mine for businesses to make data-driven decisions. The onset of big data and cloud has led changing job roles and responsibilities in GCC such as BI/BD engineers, Cloud Architects, BI/BD Solutions Architects, Data Visualization Developer.

Automation, RPA for GCC’s

GCC’s today are rapidly adopting robotic process automation. The aim for the workforce is to focus more on value added tasks. Automation value can be leveraged when Cognitive strikes convergence with RPA and enable autonomous decision making, understanding natural language, self-learning and ability to handle scenarios that entail unstructured data and complex decision making.

Automation is seen as the current and huge opportunity in GCC’s. It has a huge potential in its ability to capture the rule-based market. Robotic Process Automation are delivered as virtual Robots, tools, or a set of scripts, an error free enabled automated process. Some of the emerging roles in this area include RPA developer, Deployment engineer

Blockchain

Increased collaboration between businesses, GCC’s and tech vendors unlock the power of blockchain across multiple use cases. Given its immutable and decentralized nature, blockchain will be invaluable in sectors such as manufacturing, supply chain 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.

Blockchain development is reshaping the GCC environment with  emerging distributed ledger technology. This requires niche skill sets and roles such as Blockchain developers/engineers, Blockchain legal consultant

Artificial Intelligence Predominance

AI’s ability to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives through 2025. The AI foundation consists of numerous technologies and techniques that have grown over many years: recommendation systems, decision trees, linear regression and neural networks impacting the next-gen GCC’s.

Following core trends in AI will dominate across GCC;s:

Adoption of “plug and play”, as-a-service solutions in AI for organizations with less than global-scale resources to think about integrating narrow AI.

Enterprise Conversational AI will see mainstream adoption and will look to add voice enabled interfaces to their existing point-and-click dashboards and systems.

AI and machine learning continue to be the most penetrable technology trends within GCC’s. The capability centers are adopting software tools that are enabled with machine learning and AI capabilities to eliminate manual intervention. the emerging job titles and roles evolve as Data scientists, Statisticians.

Internet of Things (IoT)

As capability centers are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.

The rise of exponential technologies and the need to stay upbeat with it, allows scope for the changing landscape of GCC’s through new opportunities and roles. Technologies :cloud computing, cyber security , AI , blockchain, robotics process automation (RPA) will continue to be in the fore front of this changing landscape. The GCC’s will continue to directly boost the need for skills on the exponential technologies front . Time for GCC heads and talent acquisition leaders to revamp their business and talent strategies .

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.

Artificial intelligence is fast making its way into mainstream education. I do not infer as part of the standard technical curriculum. But several schools, colleges, universities and other academic institutions are adopting AI in the process of delivering impactful education to students and their numbers are rapidly increasing.

Across the world, we are seeing AI augmentation in different facets of the education system – from automating routine tasks that teachers have to perform to crafting personalised education curriculum that is line with a student’s aptitude and areas of interest.

The education sector in India suffers from deep-rooted challenges that need wholesale solutions. The bulk of our students is compelled to go through archaic pedagogical methods that are employed to deliver static and outdated curricula.

For a while now, Bill Gates and other tech stalwarts have been excited by the idea of infusing AI into the education system. Bill Gates calls this bouquet of technology-driven, impactful delivery of coursework as ‘Artificially Intelligent Tutoring Systems’ and hopes that it leads to better internalisation of course content. This column shares some of the areas where AI can leave its mark on the education system and revolutionise the way the next generation of students learn.

Freeing up Teacher’s Time

Teachers are burdened with several menial, low-value tasks that are ripe for an AI augmentation. These tasks neither deliver better learning outcomes nor improve student experience. The time our teachers spend performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignments and performing peer reviews – is enormously wasteful.

The time spent by teachers can be easily unlocked through AI, helping them focus on what they do best – teaching and coaching for success. Bringing in AI into the core way-of-working of schools today will help eliminate these burdensome tasks in the following ways:

• By curating tests for students automatically based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI can help tutors assess the learning level of students and contextually bring up questions. Teachers will be able to administer tests much more easily by using a gradational question bank powered by AI

• Grading the administered tests and assignments. This is another time-consuming and often low-value task that can easily be taken up by AI administered-tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on how they can create a better platform for learning by coaching and solving questions from students. AI-graded tests can also help bring up commonly occurring patterns of errors (ie, are students mainly making the same mistakes?), in effect providing input to teachers on which lesson plans require more impetus in the next class

• Ease out repetitive administrative tasks. Teachers also spend hours over the year submitting periodic reviews to their supervisors and coordinators, taking attendance and peer reviewing the efficacy of other teachers. This workload can also be supported by AI – by maintaining automated attendance logs, summarising the test scores of students and reporting the performance of teachers

Curricula, Content Planning

The present-day curricula delivery process is largely inefficient. The current paradigm requires a teacher to deliver pre-designed, standardised content to a classroom full of students with diverse aptitudes and interest levels. The negative impact of current pedagogical methods can still be manifested through the employability score of the current generation.

By leveraging the variegated applications powered by AI techniques, academia will not only be able to deliver more personalised curricula and lesson plans but also improve students’ understanding and retention of the coursework, leading to an improvement in educational outcomes. Here are a few examples of how we can enable those:

• AI can be instrumental in creating a culture of continuous improvement among teachers. By tracking their performance across different key metrics, the educational system will be able to uncover the areas where teachers need support and coaching more effectively. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and can continuously refine their craft

• By infusing AI into the skills and aptitude assessment process for students, schools will be able to better judge the current level of understanding among students for a particular subject area as well as where their innate inclinations lie. Often, students are unclear or unsure about how they can make the most of their talents and how they can channel them into a trade. AI can help schools map out the data of previous students, their career achievements and tie that back to educational research. This will allow schools to accurately predict the subjects for which a student has a natural inclination and then coach her in that direction

• AI can also use data around student attention, interest combined with their aptitudes and abilities to recommend customised coursework. This will help students build a structured career path. This AI-centric approach will foster personalised training pathways and provide students with the skills needed to succeed in their future professions, rather than burdening them and staggering their confidence as the current system does

Optimising Classroom Experience

To fully unleash the creativity and expertise of teachers, the education system needs to also imbue AI-led applications in the classroom on a day-to-day basis. This will enable teachers to work at full throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:

• AI can help improve the tracking of students’ attention levels and help teachers intervene before a student loses interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. By employing attention tracking devices, teachers can much easily monitor the attentiveness of the class and mind them before they tune out

• By aggregating the attention scores of the classroom, AI can help teachers devise a more potent mix of teaching, testing and activities – to continuously ensure better class performance and engagement

AI can bring a plethora of benefits to the education system at large, providing improved educational outcomes to all stakeholders – students, teachers and parents. Through personalised curricula, improved efficiency in the time management for teachers and effective in-class monitoring and assistance, AI can shift the paradigm of how the education system works and how coursework is consumed and leveraged by the next generation of students.

The Internet of Things(IoT) has profusely pertinent applications. The effectiveness can be realized through operation and integration of the IoT across applications from domestic use to large scale industrial usage.

In this blog write-up, I would like us to deeply examine dynamics of IoT for SME’s to discover a range of applications and advantages to potentially become a driving force by looking at some of the critical unsolved problems. IoT potential is looked through multiple lenses in this sector, particularly its implications and application across SME landscape.

It is imperative to understand that analytics and IoT are two sides of the same coin. Basically, the information gathered from sensors requires analytics applications. The duo combo will influence to make informed decisions based on the data and behaviors collected.

From this perspective, application and its use have been extended for large industries and organizations. One of the potential benefits that IoT offers is in saving of costs related to process automation and rebound in customer satisfaction.

Similar benefits of IoT can be translated and enabled for SMEs in a relevant scale. SME’s have started embracing IoT and its related technologies. Here we would try to demystify IoT for SMEs such as retailers and companies with more moderate capitals by examining the developing role that it could play in both the immediate and long-term future. Smart devices and sensors are vital for this Machine-to-Machine (M2M) link. By applying machine learning to exploring how IoT could be used to transform businesses, we will envision ways to apply and adopt to SME related challenges

IoT in Retail

Retail across business are a strategic fit for IoT characteristics and intelligent sensors that can measure them. Some areas gaining pace in the industry include Automated Checkouts, Personalized Discounts, Beacons, Smart Shelves, In-store Layout Optimization and Optimizing Supply Chain Management

Sensors acts as the gate way for the above-mentioned areas and are placed at strategic points to capture customers interests, popular and moving brands, kinds of customers etc.

This information will enable customer segmentation and create applications designed for each segment such as promotions or discounts especially during launch offers for new products. In conclusion, IoT for SMEs can enable businesses to design enhanced strategies based on captures information through sensors.

Managing warehouses and production lines

Another potential segment that offers a strong application in IoT for SMEs is warehouse management. Sensors enable to track movement of goods in the warehouse or production lines. They also calculate the count of inventory creating a automated systems to create flags when the merchandise/raw material are running short.  Stock replacement/ replenishment requirements can be triggered automatically with alarms.

IoT in supply chain management

Service delivery is another prodigious application of IoT for SMEs. Again, sensors play a critical role in enabling the status of shipment or delivery at every stage. Apart from the above, it is significantly used for calculating improved trajectories for final mile delivery times.

Optimal routes for the delivery to improve the overall customer experience at minimal cost is key application of IoT in this segment.

Predictive and precautionary maintenance

Another application where IoT for SMEs is gaining rapid pace and is highly attractive is predictive and preventive maintenance. Here it enables a system for alerts for early detection and timely replacement of parts or status updates of machines for remote management.

End to end (E2E) operational application of IoT

Intelligent operations begin with integration of data from manufacturing, distribution and sales & marketing divisions. The factual application and advancement of IoT is to integrate all this data in creating new e2e products and services based on preferences of customers in combination to the data collected and validated.

IoT enabled healthcare

Healthcare services and clinics offers personalized service to accompany patients beyond the visits. IoT for SMEs provides solutions for these clinic-based models that result in a competitive advantage.  This enables to redesign the dynamics with patients a simple example could be to prompt a trigger for the ophthalmologic patient to replace glasses or improvement tracking. Use of sensors and Big Data also can give the complete vision of an operation and relevant tracking.

Customer based business models

IoT also offers an opportunity for a personalized service for an SME dedicated to plumbing and its predictive maintenance of spare parts to predict pipe installation failure by reviewing its surrounding conditions via application from your cell phone

The above mentioned are some of the many applications and advantages of enabling IoT for SMEs. The principle remains the same exploiting cutting edge technology allows to improve business model through informed decisions based on the data that IoT provides.

Security is a very important part of the above implementation. All technology is vulnerable to attacks. It is critical for the SMEs to consider security as part of the implementation. Below section illustrates some of the guidelines.

Digital Security for SMEs

Security is an essential aspect for SMEs or large organizations. IoT are also highly vulnerable to such attacks. It is important to factor every aspect of your IT architecture with the right security programs. This is key for deployment and commissioning of your sensors and Big Data programs to secure data.

SMEs need to consider aspects of hardware and software associated with IoT implementation model. Emphasis need to be laid on the types of networks, communications and back-up etc. and take inventory of equipment’s, software’s for the type of security that will protect attacks from identified vulnerabilities.

In summary, SME’s are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. in this sector. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.

The rise of exponential technologies such as IoT and the need to stay upbeat with it, allows scope for the changing landscape of SME’s through new opportunities and roles. IoT will continue to be in the fore front of this changing landscape for SMEs while it is imperative for them directly boost this digital frontier.

The Artificial Intelligence revolution in the enterprise is well under way. According to Gartner’s 2018 CEO and Senior Business Executive Survey, 65% of respondents think that AI will have a ‘material impact on an area of their business’. Due to the combination of three critical factors – improved data availability and machine learning techniques, increased computing power and storage, and a strong enterprise thrust on data-driven decision-making – AI has taken a strong foothold in some of the largest corporations in the world today, commanding executive-level interest, attention and urgency.

Beyond simple automation, AI is powering complex, critical decisions in several areas from Renaissance Technologies’ Medallion Fund, which uses statistical probabilities and quantitative models and has become one of the startling successes in the hedge fund industry, to complex image annotation and deep learning that helps radiologists detect cancer in MRI scans. Here is a look at some of the critical areas where AI is augmenting human decision-making:

Healthy Healthcare

As multiple countries grapple problems from an ageing population, rising healthcare costs and low doctor-to-patient ratios, AI can help improve healthcare outcomes in a variety of ways. For instance, AI is being leveraged for public health studies – from detection of potential physical or psychological pandemics to epidemiology – by mining social media and other data sources.

Further, startups and conglomerates are working on AI for diagnostics – from detection of early warning signals to identifying and quantifying abnormalities/tumours. In the pharma industry, AI is helping improve site studies, drug development and clinical trials through analysis of meaningful data.

Financial Services

A common AI use case for financial services is in the domain of fraud detection and anti-money laundering. AI can help surface bad actors by quickly scanning data for anomalous behaviour. Similarly, AI is also powering customer interaction decisions through intelligent chatbots that can address common concerns, thus reducing the need for human intervention in repetitive, menial tasks. We’re also seeing increased proliferation of robo-advisers – which are advanced AI tools that help make investment decisions by matching investible capital and returns expected.

Managing Media

The media and entertainment industry is going through an AI and digital disruption due to the combination of huge datasets and success of torchbearers like Netflix and Spotify. Content recommendation and personalisation are decisions that are autonomously delivered by AI, which can quickly scan a user’s history and match it with the preferences of similar users.

The industry is also relying on AI to make decisions around content creation, again taking a leaf out of Netflix to make content more engaging and sticky. There is also a strong use case of AI helping identify and attract customers by surfacing tailored content and promotions to increase subscriptions, loyalty and share-of-wallet.

Retail Rejig

Retail was one of the first industries to witness the rise of a data-powered competitor that eventually decimated incumbents. The brick-and-mortar retail industry is now incorporating AI in its decision-making process to replicate the customer experience expectations set by Amazon and the like.

Retailers leverage user purchase to identify next-best product and create tailored loyalty programmes. It is also being increasingly used for rapid experimentation to define store location, layouts and product-shelf decisions. Retailers can better anticipate demand, leading to leaner supply chains and warehouses, optimised inventory and fewer stockouts.

Efficient Manufacturing

Manufacturing companies are bringing in AI interventions to run leaner supply chains to cut the cost of transportation and wastage. AI also enables them to better anticipate demand by looking at historical sales, current uptake and other business environment factors to run on-demand production.

Some AI-led decisions are pervasive across multiple industries. For instance, digital personalisation, ie, serving targeted promotions to customers based on their key purchase drivers, is a multi-industry example of AI in action.

The other is for detecting security threats through anomaly detection and video analytics to identify unauthorised entry. Human Resources is another function that is rapidly changing, with companies using AI to speed up talent acquisition by scanning resumes for relevancy and reducing attrition by identifying key drivers that lead to employees leaving.

Successful AI-led Decisions

The business value of AI is significantly lowered when performed ad hoc, without a strong foundational strategy. It is important that the organisation clearly defines the decisions that should be powered by AI to maintain a high standard of outcomes. The responses will differ from company to company and from industry to industry, but it is important that corporations establish transparent standards for fair use.

We see enough examples of hastily implemented AI, leading to calamitous consequences and companies can no longer hide by saying, ‘The AI made me do it’. To demarcate the clear go and no-go zones for AI, here’s a handy questionnaire to ask yourself:

– Do we have enough superior quality data now and in the future for AI to make the best decision?
– Do we need to bring in insights from multiple sources to contribute to the decision-making process at a speed and scale, which cannot be efficiently handled by human cognition?
– Is human decision-fatigue or bias currently creating a sub-optimal outcome in this area?
– Could there be ethical or moral implications to an AI-led decision that might lead to disastrous consequences?

We also need to address the confidence issues. For instance, a lot of executives look down upon some of the black-box processes performed by AI algorithms. We need to find a way to address these issues by creating a transparent trail of AI decisions and the reasons why AI took a decision. Even in unsupervised learning scenarios, a trail of decisions will not only boost confidence but will also help build better AI and better businesses.

Re-imagined AI-powered decisions will become de rigueur only by the quality of the outcomes they deliver. According to Dr John Kelly, SVP — IBM Research and Solutions portfolio, “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.” This is a crucial way to look at and measure the impact of AI on our businesses, society and lives.

As is the case with investments in any other area of technology, AI needs to deliver demonstrable impact to business top line and bottom line. In today’s competitive landscape of business, enterprises are expected to measure the incremental ROI for every expense and every investment made – technology or otherwise. The case of Artificial Intelligence is no different. It is critical that technology and business leaders demand ROI impact for this technology in order to foster its growth and justify its proliferation in business.

To be sure, there are two key areas where Artificial Intelligence can contribute immense value; Increasing top line figures by unlocking new revenue streams and improving the bottom line through efficiencies in operations. Needless to say, top line gains eventually percolate their way into showcasing bottom line improvement – but for the purpose of this post, we’ll refer to bottom line impact as areas where AI brings in cost efficiencies by helping organizations reduce their overall cost of operations.

Artificial Intelligence driven applications can have a discernible impact on business top lines and bottom lines and help organizations unlock ROI from their implementation.

AI-Powered Topline Growth

Artificial Intelligence-led applications have huge potential to add to top line revenue growth for any organization. Typical AI interventions for this purpose range from improving the effectiveness of marketing and sales functions, improving customer loyalty through laser-guided customer experience initiatives and direct and indirect data monetization.

New Revenue Streams Enabled by Data Monetization:

Business leaders need to realize AI’s potential to unlock new sources of revenue in addition to improving customer targeting and loyalty. One of these ways is data monetization. What is data monetization? Simply put, data monetization refers to the act of generating measurable economic benefits from available data resources. According to Gartner, there are two distinct ways in which business leaders can monetize data. The most commonly seen method from the two is Direct Monetization. The way to realize value from this avenue involves directly adding AI as a feature to existing offerings. Companies like Nielsen, D&B, TransUnion, Equifax, Acxiom, Bloomberg and IMS run their business on licensing their data in a raw format or as part of their application infrastructure. With emerging Data-as-a-Service models and the application for direct insight delivery through intelligent application of AI, direct data monetization is simpler than ever. By wrapping insights alongside the data source, vendors can create a symbiotically powerful exchange of information for both the buyers and sellers of data. On the other hand, Indirect Monetization involves embedding AI into traditional business processes with a focus on driving increased revenue. A popular example of this is corporations who come out with branded, paid-for reports based on the data they own. For instance, professional services companies such as Aon, Deloitte, McKinsey, etc., regularly bring forward insightful industry and function-specific reports based on the data they collect as part of their consulting assignments.

Enabling Intelligent Marketing and Sales

Many of the most prominently cited successes of AI-enabled business transformation comes from the marketing and sales arena. Sales and marketing are constantly on the forefront for exciting inventions in AI since they contribute directly to top line growth. Use cases discovered in this arena span social media sentiment mining, programmatic selection of advertising properties, measuring effectiveness of marketing programs, ensuring customer loyalty and intelligent sales recommendations. AI also has huge potential to drive businesses to explore and exploit eCommerce platforms as a credible channel for sales and to help drive the digital agenda forward. Available tools are helping drive better customer conversions on eCommerce properties – by analysing the digital footprints (clickstream, etc.) of prospective customers, persuading them into making a purchase. In such use cases, AI helps improve personalization at the point-of-purchase, improve conversions and reduce cart abandonment. Marketing and sales use cases today are pretty much at the epicentre of an AI disruption and business leaders need to uncover more use cases that can help drive effective top line growth.

AI Redefining Customer Experience

Customers are the epicentre of every successful organization. Today, we live in times where customers have numerous competitor options to choose from while the switching costs for customers are increasingly lower. Given this scenario, for businesses to win with their customers they need to have a smarter approach to customer experience management.

We have progressed well beyond pre-programmed bots addressing frequently asked questions. AI-enabled systems today go further and provide customers with personalized guidance. The travel and hospitality industries, for instance, are ripe for such disruptive innovations. In many cases, we see chatbots that help customers identify and recommend interesting activities and events that tourists can avail. When applied with human creativity, AI can ensure this redefined understanding of customer experience, while maintaining a lower cost of delivering that experience.

AI for Improving Bottom Line Performance

At an operational level as well, AI can help organizations run a more efficient business. For instance, corporations across industries need to find innovative and fail-safe ways to reduce the cost of manufacturing as well as capping their outlay on the supply chain network. AI-centric solutions can drive down the turnaround time for talent acquisition and transform other facets of the Human Capital function too.

AI Driving Operational Efficiencies

Traditional manufacturing processes are now increasingly augmented by robotics and AI. These technologies are bringing increasing sophistication to the manufacturing process. The successes combine human and machine intelligence making AI-augmented manufacturing a pervasive phenomenon. Today, business leaders in the Industry 4.0 generation need to seriously consider planning a hybrid labour force powered by human and artificial intelligence – and ensure that the two coexist by implementing the right policies and plans in place.

Smarter Supply Chains Powered by AI

Orchestrating a leaner, more predictable supply chain is ripe for an AI-led disruption. We are witnessing not just new products and categories but also new formats of retailers proliferating the industry. This varied portfolio of offerings and channels requires corporations to manage their outlay efficiently on the overall network responsible for the network that manages the entire process from procurement and assembly to stocking and last mile delivery. Multiple use cases exist that leverage multi-source data from internal and external repositories, combining them with information from IOT sensors. AI algorithms are then applied over this combined data infrastructure with the objective of helping business users quickly identify possible weaknesses/flaws in the process such as delays and possible shortages. Business leaders are constantly on the lookout for solutions that can directly lift their bottom line by bringing in more intelligence and automation to their supply chain networks – thus unlocking savings for their businesses.

An Artificial Facelift for the Human Resources Function

The human resources function has historically been considered a cost-center in organizations. In addition to bringing down the costs associated with talent acquisition and management – AI would also help HR teams become leaner, more organized and reduce the turnaround time for talent acquisition. AI interventions are being seen in the areas of employee engagement and attrition management, but some of the most exciting use cases come from the talent acquisition area within the HR function. Multiple organizations are already working on solutions that can eliminate the need for HR staff to scan through each job application individually. By using AI intelligently, talent acquisition teams can determine the framework conditions for a job on offer and leave the creation of assessment tasks to Artificial Intelligence-powered systems. The AI-empowered system can then communicate the evaluation results and recommend the most suitable candidates for further interview rounds.

One of the key reasons why AI is in vogue today is the demonstrable ROI impact that it promises to bring to business processes. With greater computational power and more data, AI has become more practicable than before, but what will sustain its growth is how much incremental value it can eventually unlock for businesses across the globe and power new revenue models for businesses to tap into. It is critical that business and technology leaders earnestly kick off discussions around how to justify the impact of AI and mark down the key metrics that will be used to measure it. Partners and service providers too need to stay on top of finding ways to showcase measurable improvements that their software or services can bring to technology buyers. This will enable the entire AI ecosystem to flourish. 

For the past few years, Artificial Intelligence has initiated unlocking value gains through the automation and augmentation of routinized operational activity. But are we underestimating the potential of machine intelligence? Does it make sense to relegate a powerful technology to perform tactical tasks? Or can AI move further upstream and help corporate boards make more accurate, strategic decisions?

The possibility of AI to enable better decision-making has been heavily discounted thus far. However, with Artificial Intelligence capably enabling more informed decisions in the realm of healthcare and investment banking – two of the most complex arenas where AI has been deployed – the possibility of having machine cognition in the boardroom no longer sounds too far-fetched. At the end of the day, corporate boards make complex decisions, that have huge ramifications for the future of their organizations. It is important that these decisions are based in fact, rather than judgement. AI can help corporate boards make faster, more accurate and unbiased decisions. AI can help inform strategy by giving executives a better understanding of their internal and external environments. Let us look at some key areas where senior executives in organizations can look at making better decisions using Artificial Intelligence.

AI for Executive Decision-Making

Corporate boards and top executives are charged with maintaining the health and competitiveness of an organization. They are responsible for the long-term sustainability and success of their organizations. This, in turn, requires them to stay ahead of the curve and understand their business landscape and intelligently deploy capital across inorganic and organic growth channels. Executives also own the key metrics for their organizations – and ensure that the overall return for the shareholder capital employed continuously beats industry expectations. Let us look at how AI can help transform the activity of executives in these areas.

Executive leaders often rely on numerous reports around key organizational metrics to make decisions that can have massive implications for their businesses. Is a particular segment of the business growing rapidly? Are some cost centers underperforming on their efficiency metrics? Are there laggards in the product portfolio of the enterprise that are dragging performance down? All these numbers have to figuratively be at the tip of an executive’s tongue – so that in key meetings decisions that affect the future of the business can be made more accurately and quickly.

AI-powered smart assistants would be extremely critical to help push the needle on making executive decisions with accuracy and speed. With intelligent bots, executives can be provided updates on the most critical metrics that they care for at the right time when they need them. With AI, it is possible to personalize the insights that are sent to executives – so that they are able to drill down and understand the basis for each metric.

Unbiased Capital Allocation on R&D and M&A Activities

Corporate boards and executives also need to take the long term view of how their companies evolve to thrive in the future. This requires intelligent bets to be taken on budgetary spending – for both organic and inorganic activities. How much money needs to be realistically spent on Research and Development activity and how it can it help corporations maintain larger moats against their competition? Can corporations look at inorganic acquisitions to accelerate the growth of synergistic capabilities that can form much more compelling value propositions?

AI will soon be able to provide comprehensive answers to such questions. By leveraging data from multiple sources combined with intelligent algorithms, AI will be able to weigh these multiple options and identify which one is best suited for each unique situations. In this way again, AI can help executives forecast which decisions can have maximum impact on financial metrics and model the long-term health of the organization.

As corporate boardrooms take serious cognizance of having robotic counterparts augmenting the decision-making process, it is important to consider certain caveats. For AI to work to its full potential, it is important to ensure that it is provided high quality data and continuously refined algorithms. We have seen the fallouts of algorithms going awry before. Biased algorithms working off bad data sets create issues that could potentially disrupt the fabric of the organization. It is therefore important that organizations ensure the implementation of explainable AI that can provide the rationale and take accountability of the decisions that it powers. Finally, it is important that executive leaders also create the right culture within their organizations for AI to thrive. A combination of human intelligence and artificial intelligence is the future and hence it is critical that companies relook at their culture to ensure that both can amicably survive together and put the organization on the right path.

According to research by McKinsey, it is estimated that 16 percent of board of directors did not fully understand how the dynamics of their industries were changing and how new technologies could impact their businesses. This gives AI a huge window of opportunity to permeate through global boardrooms and power better decisions. Decisions that can keep their organizations financially healthy, focused on the long-term and competitively differentiated against their competitors.