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.

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.

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.

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. 

The adoption and benefit realisation from cognitive technologies is gaining increasing momentum. According to a PwC report, 72% of business executives surveyed believe that artificial intelligence (AI) will be a strong business advantage and 67% believe that a combination of human and machine intelligence is a more powerful entity than each one on its own.

Another survey conducted by Deloitte reports that on an average, 83% of respondents who have actively deployed AI in the enterprise see moderate to substantial benefits through AI – a number that goes further up with the number of AI deployments.

These studies make it abundantly clear that AI is occupying a high and increasing mindshare among business executives – who have a strong appreciation of the bottom line impact delivered by cognitive systems, through improved efficiencies.

AI-first Mindset

Having said that, with AI becoming more and more mainstream in an organisational setup, piecemeal implementations will deliver a lower marginal impact to organisations’ competitive advantage. While once early adopters were able to realise transformational benefits through siloed AI deployments, now that it is fast maturing as a must-have in the enterprise and we will need a different approach.

To realise true competitive advantage, organisations need to have an AI-first mindset. It is the new normal in accelerating business decisions. It was once said that every company is a technology company – meaning that all companies were expected to have mature technology backbones to deliver business impact and customer satisfaction. That dictum is now being amended to say – every company is a cognitive company.

To deliver on this promise, companies need to weave AI into the very fabric of their strategy. To realise competitive advantage tomorrow, we need to embed AI across the organisation today, with a strong, stable and scalable foundation. Here are three building blocks that are needed to create that robust foundation.

1. Enrich Data & Algorithm Repositories

If data is indeed the new oil (which it is), organisations that hold the deepest reserves and the most advanced refinery will be the ones that win in this new landscape. Companies having the most meaningful repository of data, along with fit-for-purpose proprietary algorithms will most likely enjoy a sizeable competitive advantage.

So, companies need to improve and re-invent their data generation and collection mechanisms. Data generation will help reduce their reliance on external data providers and help them own the data for conducting meaningful, real-time analysis by continuously enriching the data set.
Alongside, corporations also need to build an ‘algorithm factory’ – to speed up the development of accurate, fit-for-purpose and meaningful algorithms. The algorithm factory would need to push out data models in an iterative process in a way that improves the speed and accuracy.

This would enable the data and analysis capabilities of companies to grow in a scalable manner. While this task would largely fall under the aegis of data science teams, business teams would be required to provide timely interventions and feedback – to validate impact delivered by these models, and suggest course-corrections where necessary.

Another key aspect of this process is to enable a transparent cross-organisation view into these repositories. This will allow employees to collaborate and innovate rapidly by learning what is already been done and will reduce needless time and effort spent in developing something that’s already there.

2. AI Education for Workforce

Operationalising AI requires a convergence of different skill sets. According to the above-cited Deloitte survey, 37% of respondents felt that their managers didn’t understand cognitive technology – which was a hindrance to their AI deployments.

We need to mix different streams of people to build a scalable AI-centric organisation. For instance, business teams need to be continuously trained on the operational aspects of AI, its various types, use cases and benefits – to appreciate how AI can impact their area of business.
Technology teams need to be re-skilled around the development and deployment of AI applications. Data processing and analyst teams need to better understand how to build scalable computational models, which can run more autonomously and improve fast.
Unlike a typical technology transformation, AI transformation is a business reengineering exercise and requires cross-functional teams to collaborate and enrich their understanding of AI and how it impacts their functions, while building a scalable AI programme.

The implicit advantage of developing topical training programmes and involving a larger set of the workforce is to mitigate the FUD that is typically associated with automation initiatives. By giving employees the opportunity to learn and contribute in a meaningful way, we can eliminate bottlenecks, change-aversion and enable a successful AI transformation.

3. Ethical and Security Measures

The 4th Industrial Revolution will require a re-assessment of ethical and security practices around data, algorithms and applications that use the former two.

By introducing renewed standards and ethical codes, enterprises can address two important concerns people typically raise – how much power can/should AI exercise and how can we stay protected in cases of overreach.

We are already witnessing teething trouble – with accidents involving self-driving cars resulting in pedestrian deaths, and the continuing Facebook-Cambridge Analytica saga.

Building a strong grounding for AI systems will go a long way in improving customer and social confidence – that personal data is in safe hands and is protected from abuse – enabling them to provide an informed consent to their data. To that end, we need to continue refining our understanding around the ethical standards of AI implementations

AI and other cyber-physical systems are key components of the next generation of business. According to a report by semiconductor manufacturer, ARM, 61% of respondents believe that AI can make the world a better place. To increase that sentiment even further, and to make AI business-as-usual, and power the cognitive enterprise, it is critical that we subject machine intelligence to the same level of governance, scrutiny and ethical standards that we would apply to any core business process.

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.