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 also require to be upgraded as well. By doing so, we will be able to ensure wholesome and future-proof education for aspirants who wish to build their careers in the world of AI.

For instance, those 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 specialisation 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 ML 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.

Read this on YourStory.com: https://yourstory.com/2020/02/ai-transformation-future-work-business-tech-leaders

Artificial Intelligence is unleashing exciting growth opportunities for the enterprises & GCCs , at the same time , they also present challenges and complexities when sourcing, negotiating and enabling the AI deals . The hype surrounding this rapidly evolving space can make it seem as if AI providers hold the most power at the negotiation table. After all, the market is ripe with narratives from analysts stating that enterprises and GCCs failing to embrace and implement AI swiftly run the risk of losing their competitiveness. With pragmatic approach and acknowledgement of concerns and potential risks, it is possible to negotiate mutually beneficial contracts that are flexible, agile and most importantly, scalable. The following strategic choices will help you lock in winning AI deals :

Understand AI readiness & roadmap and use cases

It can be difficult to predict exactly where and how AI can be used in the future as it is constantly being developed, but creating a readiness roadmap and identifying your reckoner of potential use cases is a must. Enterprise and GCC readiness and roadmap will help guide your sourcing efforts for enterprises and GCCs , so you can find the provider best suited to your needs and able to scale with your business use cases. You must also clearly frame your targeted objectives both in your discussions with vendors as well as in the contract. This includes not only a stated performance objective for the AI , but also a definition of what would constitute failure and the legal consequences thereof.

Understand your service provider’s roadmap and ability to provide AI evolution to steady state

Once you begin discussions with AI vendors & providers, be sure to ask questions about how evolved their capabilities and offerings are and the complexity of data sets that were used to train their system along with the implementation use cases . These discussions can uncover potential business and security risks and help shape the questions the procurement and legal teams should address in the sourcing process. Understanding the service provider’s roadmap will also help you decide whether they will be able to grow and scale with you. Gaining insight into the service provider’s growth plans can uncover how they will benefit from your business and where they stand against their competitors. The cutthroat competition among AI rivals means that early adopter enterprises and GCCs that want to pilot or deploy AI@scale will see more capabilities available at ever-lower prices over time. Always mote that the AI service providers are benefiting significantly from the use cases you bring forward for trial as well as the vast amounts of data being processed in their platforms. These points should be leveraged to negotiate a better deal.

Identify business risk cycles & inherent bias

As with any implementation, it is important to assess the various risks involved. As technologies become increasingly interconnected, entry points for potential data breaches and risk of potential compliance claims from indirect use also increase. What security measures are in place to protect your data and prevent breaches? How will indirect use be measured and enforced from a compliance standpoint? Another risk AI is subject to is unintentional bias from developers and the data being used to train the technology. Unlike traditional systems built on specific logic rules, AI systems deal with statistical truths rather than literal truths. This can make it extremely difficult to prove with complete certainty that the system will work in all cases as expected.

Develop a sourcing and negotiation plan

Using what you gained in the first three steps, develop a sourcing and negotiation plan that focuses on transparency and clearly defined accountability. You should seek to build an agreement that aligns both your enterprise’s and service provider’s roadmaps and addresses data ownership and overall business and security related risks. For the development of AI , the transparency of the algorithm used for AI purposes is essential so that unintended bias can be addressed. Moreover, it is appropriate that these systems are subjected to extensive testing based on appropriate data sets as such systems need to be “trained” to gain equivalence to human decision making. Gaining upfront and ongoing visibility into how the systems will be trained and tested will help you hold the AI provider accountable for potential mishaps resulting from their own erroneous data and help ensure the technology is working as planned.

Develop a deep understanding of your data, IP, commercial aspects

Another major issue with AI is the intellectual property of the data integrated and generated by an AI product. For an artificial intelligence system to become effective, enterprises would likely have to supply an enormous quantity of data and invest considerable human and financial resources to guide its learning. Does the service provider of the artificial intelligence system acquire any rights to such data? Can it use what its artificial intelligence system learned in one company’s use case to benefit its other customers? In extreme cases, this could mean that the experience acquired by a system in one company could benefit its competitors. If AI is powering your business and product, or if you start to sell a product using AI insights, what commercial protections should you have in place?

In the end , do realize the enormous value of your data, participate in AI readiness, maturity workshops and immersion sessions and identification of new and practical AI use cases. All of this is hugely beneficial to the service provider’s success as well and will enable you to strategically source and win the right AI deal.

(AIQRATE advisory & consulting is a bespoke global AI advisory & consulting firm and provides strategic advisory services to boards, CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions & create AI powered enterprises . Visit www.aiqrate.ai , reach out to us at consult@aiqrate.ai )

The management of talent has always been and continues to be a major challenge for most industries. This is particularly true for knowledge based industries like information technology. The dramatically changing dynamics of the Indian Technology industry compound the challenges and opportunities faced by the industry.

Never since the advent of mass production has an industry seen such dramatic volatility in such short period of time. The revolution before primarily added to the productivity of the labor and moved across the globe. The current revolution is not merely transcending national borders – it is redefining jobs, eliminating others and creating new opportunities.

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There’s little doubt that Artificial Intelligence (AI) is driving the decisive strategic elements in multiple industries, and algorithms are sitting at the core of every business model and in the enterprise DNA. Conventional wisdom, based on no small amount of research, holds that the rise of AI will usher radical, disruptive changes in the incumbent industries and sectors in the next five to 10 years.

Additionally, it’s never been a better time to launch an AI venture. Investments in AI-focused ventures have grown 1800% in just six years. The rationale behind these numbers comes, in part, from the fact that enterprises expect AI to enable them to move into new business segments, or to maintain a competitive edge in their industry.

Strategists believe this won’t come as a surprise to CXOs and decision-makers as acceleration of AI adoption and proliferation of smart, intuitive and ML algorithms spawn the creation of new industries and business segments and overall, trigger new opportunities for business monetization. However, a few questions loom large for CXOs: How will these new industries and business segments be created with AI? And, what strategic shifts can leadership make to monetize these new business opportunities?

The creation of new industries and business segments depends on dramatic advances in AI that can take a swift adoption journey to move from discovery to commercial application to a new industry. New industry segments around AI are in the making and are far from tapped. A cursory look at new age businesses: Micro-segmented, hyper-personalized online shopping platforms, GPS driven ride-sharing companies, recommendation-driven streaming channels, adaptive learning based EdTech companies, conversational AI-driven new and work scheduling are just a few of the imminent and visible examples. Yet a lot more can be done in this space.

AI adoption brings intentional efforts to adapt to this onslaught of algorithms and how it’s affecting customer and employee behavior. As algorithms become a permanent fixture in everyday life, organizations are forced to update legacy technology strategies and supporting methodologies to better reflect how the real world is evolving. And the need to do so is becoming increasingly obligatory.

On the other side, traditional and incumbent enterprises are reverse engineering investments, processes, and systems to better align with how markets are changing. Because it’s focusing on customer behavior, AI is actually in its own way, making businesses more human. As such, Artificial Intelligence is not specifically about technology, it’s empowered by it. Without an end in mind, self-learning algorithms continually seek out how to use technology in ways that improve customer experiences and relationships. It also represents an effort that introduces new models for business and, equally, creates a way of staying in business as customers become increasingly aware and selective.

Today, AI expertise is focused more on developing commercial applications that optimize efficiencies in existing industries and is focused less on developing patented algorithms that could lead to new industries. These efficiencies are accelerating the sectoral consolidation and convergence, and are less about new industry creation.

However, AI’s most potent, long-term economic use may just be to augment the discovery and pursuit of solving large, complex and unresolved problems that could be the foundations of new industry segments. Enterprises have started realizing the significance of having a long-term strategic interest and investments in using AI in this way. Yet few of the above mentioned examples are testimony to AI triggering new industry segments and business opportunities. The real winners in the algorithm-driven economy will be business leaders that align their strategies to augment AI expertise from ground zero, keep a continuous tab on blockbuster algorithms, and redefine new business segments that enable monetization of new opportunities.

AI has immense potential to jumpstart the creation of new industries and the disruption of existing ones. The curation of this as a strategic roadmap for business leaders is far from easy, but it carries great rewards for businesses. It takes a village to bring about change, and it also takes the spark and perseverance of an AI strategist to spot important trends and create a sense of urgency around new possibilities.

Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI. The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

“Discounts, schemes, tour packages, and seasons and travellers to target are formulated using this intelligent data combined with behavioural science and social media attribution to know customers behaviour and insights. “

Let’s take a close look at the AI-driven application areas in the travel and hospitality industry and the impact on the ensuing business value chain:

Bespoke and curated experiences

There are always a few trailblazers who are up for a new challenge and adopt new-age exponential technologies. Many hotel chains have started using an AI concierge. One great example of an AI concierge is Hilton World wide’s Connie, the first true AI-powered concierge bot. Connie stands at two feet high and guests can interact with it during their check-in. Connie is powered by IBM’s Watson AI and uses the Way Blazer travel database. It can provide succinct information to guests on local attractions, places to visit, etc. Being AI-driven with self-learning ability, it can learn and adapt and respond to each guest on personalised basis.

In the travel business, Mezi, using AI with Natural Language Processing technique, provides a personalised experience to business travellers, who usually are strapped for time. It talks about bringing on a concept of bleisure (business+leisure) to address the needs of the workforce. The company’s research shows that 84 percent of business travellers return feeling frustrated, burnt out, and unmotivated. The kind of tedious and monotonous planning that goes into the travel booking could be the reason for it. With AI and NLP, Mezi collects individual preferences and generates personalised suggestions so that a bespoke and streamlined experience is given and the issues faced are addressed properly.

Intelligent travel search

Increased productivity now begins with the search for the hotel, and sophisticated AI usage has paved the way for the customer to access more data than ever before. Booking sites like Lola.com provides on-demand travel services and have developed algorithms that can not only instantly connect people to their team of travel agents who find and book flights, hotels, and cars, but have been able to empower their agents with tremendous technology to make research and decisions an easy process.

Intelligent travel assistants

 Chatbot technology is another big strand of AI, and not surprisingly, many travel brands have already launched their own versions in the past year or so. Skyscanner is just one example, creating an intelligent bot to help consumers find flights in Facebook Messenger. Users can also use it to request travel recommendations and random suggestions. Unlike ecommerce or retail brands using chatbots, which can appear gimmicky, there is an argument that examples like Skyscanner are much more relevant and useful for everyday consumers. After all, with the arrival of many more travel search websites, consumers are being overwhelmed by choice – not necessarily helped by it. Consequently, a chatbot like Skyscanner is able to cut through the noise, connecting with consumers in their own time and in the social media spaces they most frequently visit.

Recently, Aero Mexico started using Facebook Messenger chatbot to answer very generic customer questions. The main idea was to cater to 80 percent of questions, which are usually repeat ones and about common topics. Thus, AI is of great application to avoid a repetitive process. Airlines hugely benefit from this. KLM Royal Dutch Airlines uses AI to respond to the queries of customers on Twitter and Facebook. It uses an algorithm from a company called Digital Genius, which is trained on 60,000 questions and answers. Not only this, Deutsche Lufthansa’s bot Mildred can help in searching the cheapest fares.

Intelligent recommendations

International hotel search engine Trivago acquired Hamburg, Germany machine learning startup Tripl as it ramps up its product with recommendation and personalisation technology, giving them a customer-centric approach. The AI algorithm gives tailored travel recommendations by identifying trends in users’ social media activities and comparing it with in-app data of like-minded users. With its launch, users could sign up only through Facebook, potentially sharing oodles of profile information such as friends, relationship status, hometown, and birthdays.

Persona-based travel recommendations, use of customised pictures and text are now gaining ground to entice travel. KePSLA’s travel recommendation platform is one of the first in the world to do this by using deep learning and NLP solutions. With 81 percent of people believing that intelligent machines would be better at handling data than humans, there is also a certain level of confidence in this area from consumers.

Knowing your traveller

Dorchester Collection is another hotel chain to make use of AI. However, instead of using it to provide a front-of-house service, it has adopted it to interpret and analyse customer behaviour deeply in the form of raw data. Partnering with technology company, Richey TX, Dorchester Collection has helped to develop an AI platform called Metis.

Delving into swathes of customer feedback such as surveys and reviews (which would take an inordinate amount of time to manually find and analyse), it is able to measure performance and instantly discover what really matters to guests. Métis helped Dorchester to discover that breakfast it not merely an expectation – but something guests place huge importance on. As a result, the hotels began to think about how they could enhance and personalise the breakfast experience.

Intelligent forecasting: flight fares and hotel tariffs 

Flight fares and hotel tariffs are dynamic and vary on real-time basis, depending on the provider. No one has time to track all those changes manually. Thus, intelligent algorithms that monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.

Trivago and Make my trip are screening through swamp of data points, variables, and demand and supply patterns to recommend optimised travel and hotel prices. The AltexSoft data science team has built such an innovative fare predictor tool for one of their clients, a global online travel agency, Fareboom.com. Working on its core product, a digital travel booking website, they could access and collect historical data about millions of fare searches going back several years. Armed with such information, they created a self-learning algorithm, capable of predicting future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers, and deals.

Optimised disruption management: delays and cancellations 

While the previous case is focused mostly on planning trips and helping users navigate most common issues while traveling, automated disruption management is somewhat different. It aims at resolving actual problems a traveller might face on his/her way to a destination point. Mostly applied to business and corporate travel, disruption management is always a time-sensitive task, requiring instant response.

While the chances of getting impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of cancelled flights every day. With the recent advances in AI, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveller and the carrier. The 4site tool, built by Cornerstone Information Systems, aims to enhance the efficiency of enterprise travel.

The product caters to travellers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management. In an instance, if there is a heavy snowfall at your destination point and all flights are redirected to another airport, a smart assistant can check for available hotels there or book a transfer from your actual place of arrival to your initial destination.

Not only are passengers are affected by travel disruptions; airlines bear significant losses every time a flight is cancelled or delayed. Thus, Amadeus, one of the leading global distribution systems (GDS), has introduced a Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption. The tool helps airlines instantly address and efficiently handle any threats and disruptions in their operations.  

Future potential: So, reflecting on the above-mentioned use cases of the travel and hospitality industry leveraging Ai to a large extent, there will be few latent potential areas in the industry that will embrace AI in the future :

“Undoubtedly, we will witness many travel and hospitality organisations using AI for intelligent recommendations as well as launching their own chatbots. There’s already been a suggestion that Expedia is next in line, but it is reportedly set to focus on business travel rather than holidaymakers.”

Due to the greater need for structure and less of a desire for discovery, it certainly makes sense that AI would be more suited to business travellers. Specifically, it could help to simplify the booking process for companies, and help eliminate discrepancies around employee expenses. With reducing costs and improving efficiency two of the biggest benefits, AI could start to infiltrate business travel even more so than leisure in the next 12 months.

Lastly, we can expect to see greater development in conversational AI in the industry. With voice-activated search, the experience of researching and booking travel has the potential to become quicker and easier than ever before. Similarly, as Amazon Echo and Google Home start to become commonplace, more hotels could start to experiment with speech recognition to ramp up customer service. This means devices and bots could become the norm for brands in the travel and hospitality industry.

The travel and hospitality industry transformation will morph into experience-driven and asset-light business, and wide adoption of AI will usher a new-age customer experience and set a benchmark for other industries to emulate. Fasten your seat belts … AI will redefine the travel and hospitality industry.

The extra ordinary promise of AI : Global & Indian enterprises have a lot to gain from unleashing innovation with AI —but harnessing their potential demands focused investment and a new way of working with external partners.

Here are few salient features of how AI has become game changing trend in spurring innovation; existing challenges and few strategic approaches of unlocking innovation with AI :

• 22% growth : From 2015 through 2019, disclosed private investment in seven deep tech sectors grew an average of 22% per year, equaling nearly $60 billion in total investment. Corporate venture capital is also playing an increasingly active role.
• Total investment : Nearly $60 Billion Invested in Deep Tech’s Fastest-Growing Sectors in 2019; Artificial intelligence corners close to $25 Bn
• About 1800 AI led startups in the US accounted for roughly half of this total investment, but other countries are catching up fast.

Existing Challenges

• Complex ecosystems : Multiple types of players including startups, venture capital firms, governments, universities and research centers, and early-adopter user groups
• Dynamic Interactions : Few central orchestrators; business relationships based on informal networks rather than formal contracts

Strategic approaches of unlocking innovation with AI :

• Cooperate in order to compete : Think beyond the enterprise’s immediate goals; commit to a long-term vision for the development of the ecosystem as whole
• Identify capabilities that add value : Define what the enterprise can offer to nurture the ecosystem and bring AI to market—not only money but also access to customers, data, networks, mentors, and technical experts
• Don’t pick winners in advance : AI startups are evolving rapidly. Continuously monitor the ecosystem to identify successful startups, applications, and business models as they emerge
• Blur the boundaries with partners : Make it easy for AI partners to navigate your corporate system. Define a clear role for them in your innovation strategy, ensure senior-executive sponsorship, and engage the core businesses
• Streamline decision making and governance : Success requires partnering more nimbly with fast-moving AI startups. Embrace agile ways of working.
• Develop breakthrough solutions by combining expertise from previously unconnected fields or industries. Be alert for game hanging opportunities that deliver both economic and social value.

AI will transform business and society in the future. The time to craft a AI strategy for unleashing innovation is now.

AIQRATE works closely with global & Indian enterprises , GCCs , VC/PE firms and has an extensive yet curated database of 1000 + global AI startups , boutique and niche firms benchmarked on our “Glow Curve” assessment.

(AIQRATE advisory & consulting is a bespoke AI advisory and consulting firm and provide strategic advisory services to boards , CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions and create AI powered enterprises . visit : www.aiqrate.ai ; reach out to us at consult@aiqrate.ai )

AI = The Future of “H” in HR : Introducing AIQRATE’s consulting offerings for CHRO/CPO/HR leaders

AI = The future of “H” in HR . In today’s competitive businesses , the role of AI in planning, operations & strategy has transformed from being a competitive differentiation to a competitive necessity . The age of “ trust me , this will work” is over. In the current business mandate , where HR is held accountable for delivering business results , it has become imperative to harness the power of AI . AI can elevate HR from a tactical support function to a strategic transformative function . HR business function disruption thru Talent Sciences : business capability of using AI and algorithmic modeling to drive HCM decision making will form the backbone of HR function.

Introducing AIQRATE’s consulting offering for Chief Human Resource Officer (CHRO) / Chief people officer (CPO) / Chief Talent officer (CTO) /HR Leaders working across Enterprises , GCCs , SMBs , Startups , Public Institutions : 

1. AI master class session : Contextualized for CHRO , CPO : demystify AI , AI strategy canvas , AI landscape & wide applications , HR vale chain interventions
2. AI advisor on-demand : Build AI led decision making strategies and processes across the HR value chain and strategic interventions
3. AI talent mapping strategies : Execute AIQRATE “T-REX” framework for building enterprise wise AI skilling & learning regime
4. AI led interventions for CHRO/CPO : Reimagine HR domain , HR business function problems and scenarios leveraging AIQRATE consulting expertise
5. Analytics to AI maturity assessment : Gauge your enterprise AI adoption maturity with AIQRATE “Elevate” transformation journey framework

AIQRATE’s extensive yet bespoke consulting offerings for CHRO/CPO/HR leaders focuses on building AI led strategies on talent workforce decisions and tracking performance of HR strategic initiatives and also on building data driven discovery algorithms on improving HR process efficiencies and outcomes. 

AIQRATE’s attempts to gear up HR leaders to the future of work and our curated offerings will enable navigate four broad shifts for HR leaders :

1. Accentuate strategic business acumen

2. Augment AI driven expertise for decision making

3. Amplify “transformation driven impact “ within the HR business function.

4. Accelerate “innovation driven culture” within the HR team

Reach out to us at consult@aiqrate.ai for detailed view and approach on our extensive AI consulting offerings for CHRO/CPO/HR leaders .

Business transformation programs have long focused on productivity enhancements —taking a “better, faster, cheaper” approach to how the enterprise works. And for good reason: disciplined efforts can boost productivity as well as accountability, transparency, execution, and the pace of decision making. When it comes to delivering fast results to the bottom line, it’s a proven recipe that works.

The problem is, it’s no longer enough. Artificial Intelligence enabled disruption are upending industry after industry, pressuring incumbent companies not only to scratch out stronger financial returns but also to remake who and what they are as enterprises.

Doing the first is hard enough. Tackling the second—changing what your company is and does—requires understanding where the value is shifting in your industry (and in others), spotting opportunities in the inflection points, and taking purposeful actions to seize them. The prospect of doing both jobs at once is sobering.

How realistic is it to think your company can pull it off? The good news is that AIQRATE can demonstrate that it’s entirely possible for organizations to ramp up their bottom-line performance even as they secure game-changing portfolio wins that redefine what a company is and does. What’s more, AL led transformations that focus on the organization’s performance and portfolio appear to load the dice in favor of transformation results. By developing these two complementary sets of muscles, companies can aspire to flex them in a coordinated way, using performance improvements to carry them to the next set of portfolio moves, which in turn creates momentum propelling the company to the next level.

Strategic Steps towards AI led Transformation:

This aspect covers AI led “portfolio-related” moves. The first is active resource reallocation towards building AI led transformation units, which I define as the company shifting more than 20 percent of its capital spending across its businesses or markets over ten years. Such firms create 50 percent more value than counterparts that shift resources at a slower clip.

Meanwhile, a big move in programmatic M&A driven by AI led spot trending—the type of deal making that produces more reliable performance boosts than any other—requires the company to execute at least one deal per year, cumulatively amounting to more than 30 percent of a company’s market capitalization over ten years, and with no single deal being more than 30 percent of its market capitalization.

Making big moves tends to reduce the risk profile and adds more upside than downside. The way I explain this to senior executives is that when you’re parked on the side of a volcano, staying put is your riskiest move.

AI led Transformations that go ‘all in’ by addressing both a company’s performance and its portfolio yield the highest odds.

The implication of these transformation stories is clear: approaches that go all in by addressing both a company’s performance and its portfolio yield the highest odds of lasting improvement. Over the course of a decade, companies that followed this path nearly tripled their likelihood of reaching the top quin tile of the AI transformation power curve relative to the average company in the middle.

Play to win with AI

Life would be simpler if story ended here. However, you’re not operating in a competitive vacuum. As I described earlier, other forces influence your odds of success in significant ways—in particular, how your industry is performing. Research studies have indicated that companies facing competitive headwinds would face longer odds of success than those with tailwinds.

Companies that combined big performance moves with big portfolio moves (including capital expenditures, when not the only portfolio move employed) saw a big lift in their odds. Life is still challenging for these companies—their net odds are dead even—yet this is superior to the negative odds of the other situations.

Winning thru competitive advantage with AI

In an improving industry, the returns to performance improvement are amplified massively. This runs contrary to the very human tendency of equating performance transformations with turnaround cases

The takeaway from all this is that two big rules stand out as commonly and powerfully true whatever your context: first, get moving with AI , don’t be static; second, go all in if you can with AI led transformation programs —it’s always the best outcome (and also the rarest).

Running the AI led transformation program

In my experience, the companies that are most successful at transforming themselves with AI ,sequence their moves so that the rapid lift of performance improvement provides oxygen and confidence for big moves in M&A, capital investment, and resource reallocation. And when the right portfolio moves aren’t immediately available or aren’t clear, the improved performance helps buy a company time until the strategy can catch up.

To illustrate this point, consider the anecdote about Apple that Professor Richard Rumelt describes in his book, Good Strategy/Bad Strategy. It was the late 1990s; Steve Jobs had returned to Apple and cleaned house through productivity-improving cutbacks and a radically simplified product line. Apple was much stronger, yet it remained a niche player in its industry. When Rumelt asked Jobs how he planned to address this fact, Jobs just smiled and said, ‘I am going to wait for the next big thing.’

While no one can guarantee that your “next big thing” will be an iPod-size breakthrough, there’s nothing stopping you from laying the groundwork for a successful AI led transformation. To see how prepared, you are for such an undertaking, ask yourself—and your team—the following five questions. I sincerely hope they provoke productive and transformative discussion among your team.

1.Where is the new business value chain that’s driven by AI

Achieving success with big, portfolio-related moves requires understanding where the business value flows in your business and why. The structural attractiveness of markets, and your position in them, can and does change over time. Ignore this and you might be shifting deck chairs on the Titanic. Meanwhile, to put this thinking into action, you must also view the company as an ever-changing portfolio. This represents a sea change for managers who are used to plodding, once-a-year strategy sessions that are more focused on “getting to yes” and on protecting turf than on debating real alternatives. Get high-powered decision-making algorithms to navigate you thru this transformation.

2. Put your money in building an AI led strategy

Only 10% of the US fortune 200 companies have AI led strategy; this is an impending strategic aspect that cannot be ignored. The dimensions of reimagining customer experience, building innovative products and services and transforming the businesses need to have an AI led strategy move by the CXOs

3.Are you ready for disruption?

Increasingly, incumbent organizations are getting to the pointy end of disruption, where they must accelerate the transition from legacy business models to new ones and even allow potentially cannibalizing businesses to flourish. Sometimes this requires a very deliberate two-speed approach where legacy assets are managed for cash while new businesses are nurtured for growth.

4.Will our company take this seriously?

Embracing AI led transformative change requires commitment, and gaining commitment requires a compelling change story that everyone in the company can embrace. Philips recognized this in 2011 when it launched its “Accelerate” program. Along with productivity improvements and portfolio changes (including a big pivot from electronics to health tech), the company shaped its change story around improving three billion lives annually by 2030, as part of a broader goal of making the world healthier and more sustainable through innovation. Massive thrust and investment was laid by Phillips leadership team on AI led transformation programs.

5.Is the leadership ready for the transformation?

Leading a successful AI led transformation requires a lot more than just picking the right moves and seeing them through. Among your other priorities: build momentum, engage your workforce, and make the change personal for yourself and your company. All of this means developing new leadership skills and ways of working, while embracing a level of commitment as a leader that may be unprecedented for you.

In the end, AI led strategy for transformation is a process and start of a journey …. embrace it or feel the heat of leaving behind. The new age competition is agile and nimble and AI led transformation strategy is a right move to thwart the competition.

“Enterprise Data Engineering” may sound dated and not so cutting-edge buzzword now. But in this age of several disruptive ideas, concepts and technologies, Enterprise Data Engineering has caught-up with all the necessary advancements and capabilities equally if not better. With organizations becoming more and more keen to be data driven and embrace AI, there is heightened necessity of robust Enterprise Data Engineering Strategy (EDE Strategy). Organizations that do not have efficient EDE Strategy laid out would be ignoring or mismanaging their data asset. Inefficient use of data asset and insights would weaken the competitive edge and their competitors will leap ahead multi-fold in no-time.

EDE Strategy being the master plan of enterprise wide data infrastructure, is a foundational component for any meaningful corporate initiative in recent years. “Explosive growth of data” is being identified as a challenge, an opportunity, a new trend, a significant asset, a source of immense insights, etc. Social media, connected devices and detailed transaction logging are generating huge stream of data which simply can’t be ignored. In simple terms, a panoramic view and wholesome control of a data setup is essential to make data a powerhouse for organizational strategic growth.

Global IP traffic annual run rate is projected to reach 3.3 zettabytes by 2021 (TV & Smartphones together will be accounted for more than 60% of this IP traffic) -CISCO

Along with changing landscape of data, associated disciplines are now aligning to the new demands. EDE Strategy ensures that, these alignments are in line with the roadmap and caters to business users and their requirements.

EDE Strategy encompasses multiple strategy elements that are seeking increased attention now-a-days due to multiple advancements in & around them. Let me introduce some of those strategy elements here:

Automated Data Ingestion & Enrichment

Data Ingestion has now become seriously challenging due to variety & number of sources (social media, new devices, IoT, etc.), new types of data (text stream, video, images, voice, etc), volume of data and the increasing demand for immediately consumable data. Data Ingestion & Enrichment are almost coming together in several scenarios. Due to this, Data Ingestion is almost going off the ETL tools and is fast adopting Pythons and Sparks. Streamed data is not just about absorbing the data as it happens at the source. It is also about enabling AI within the ingestion pipeline to perform data enrichment by bringing together needed data sets (internal & external) and making the Ingestion-to-Insight transformation automatically in seconds/minutes.

ML/AI Models and Algorithms

ML/AI infuses smartness in building data objects, tables, views and models to oversee the data flow across data infrastructure. It uses intelligence in identifying data types/keys/join paths, find & fix data quality issues, identify relationships, identify required data sets to be imported, derive insights, etc. So, the advent of ML/AI in data engineering infuses intelligence into learning, adjusting, alerting and recommending by leaving complex tasks & administration to humans.

Cloud Strategy

The most significant shift seen in the digital world recently is the amount of data being generated and transported. Studies say, 90% of the data existing today were generated in just last 2 years. This is going to increase multi-fold in the coming years. The on-prem based infrastructure and provisioning processes aren’t agile enough to scale rapidly on demand. Even if this is managed, the associated overhead of buying, managing and securing the infrastructure becomes highly expensive and error prone. So, it is essential for organizations to opt for highly efficient and intelligent data platforms on cloud. Cloud offers several advantages across cost, speed, scale, performance, reliability and security. It is also maturing away from initial IaaS into newer services and players. But it doesn’t mean organizations simply initiate the cloud migration and get it done at the press of a button. There should be a carefully drafted Cloud strategy & execution roadmap for adopting cloud in alignment to organization requirements and constraints. Data and information on cloud has the potential to give organizations the flexibility, scalability and ability to discover powerful insights. Cloud also enables applying ML/AI for discovering dark data, monetizing opportunities and disruptive business insights.

Data Lake

Among the data-management technologies most significant space is of data lake. Data Lake is not a specific technology but a concept of housing “one source of truth” data for an organization. When implemented, data lakes can hold and process both structured and unstructured data. Though name indicates huge infrastructure, data lakes are less costly to operate if on cloud. It doesn’t require data to be indexed or prepared to fit specific storage requirements. Instead it holds data in their native formats. Data is then accessed, formatted or reconfigured when needed. Though data lakes are easy to initiate due to easily accessible and affordable cloud offerings, it requires careful planning and incremental adoption model for large scale implementations. In addition, the ever-changing data regulatory & compliance standards add to challenges of implementing and managing data lakes.

Master Data Management (MDM)

Though there is ongoing debate on whether MDM is needed where data lake is the central theme. Schema-on-Write, Schema-on-Read, unstructured data, cloud, etc., are the main contention points in these debates. No matter who wins these debates, it is important for us to know more about MDM while discussing on data engineering. Because, MDM is very essential for organizations to serve their customers/clients near real-time and with better efficiency. As Master Data is the key reference for transactions, typically all independent applications maintain them locally. This leads to redundancy, inconsistency and inefficiency when these data are brought together. It becomes a big challenge while integrating and processing these data due to complexity, chances for errors and increased cost. So, it is important to address MDM element in the EDE strategy carefully by considering organization objectives. Multiple models are practiced for implementing MDM like, registry, hybrid, hub, repository, coexistence, consolidation, etc., which would be discussed in my next articles.

Visualization

Visualization is an exercise that helps in understanding the data in a visual context like patterns, trends, relations, etc. This sounds like an external element or a client to the data engineering. Then, why is Virtualization an important element in EDE Strategy?

Gone are the days of graphs and charts for human analysis. Especially due to data deluge, fitting so much information in a graph or chart is almost impossible for human-beings. They need help in building meaningful representations by consuming huge amount of data. ML/AI is the answer to this wherein its models/algorithms show patterns and correlations by studying huge data-sets in no-time. So, to enable AI to process data, it is important to arrange and label the data suiting it the best. Hence, Visualization is an important aspect to be considered while constructing an Enterprise wide Data Engineering Strategy.

Conclusion

In this new era of data being the new oil, every interest required is being taken to improve the way data is received, cleansed, enriched, assembled and transformed.  EDE Strategy ensures establishing effective deployment/management guidelines and continuously improves them because data environments are living organisms. A solid EDE Strategy is highly essential to cater to the demands of this new age. All organizations must have EDE Strategy for realizing their digital vision. Ignoring to have a well laid Enterprise Data Engineering Strategy is as good as regressing in this competitive world. 

What constitutes ‘fair use’ of data is increasingly coming under scrutiny by regulators across the world. With the digital detonation that has been unleashed in the past few years, leading to a deluge of data – organisations globally have jumped at the prospect of achieving competitive advantage through more refined data mining methods. In the race for mining every bit of data possible and using it to inform and improve algorithmic models, we have lost sight of what data we should be collecting and processing. There also seems to be a deficit of attention to what constitutes a breach and how offending parties should be identified and prosecuted for unfair use.

There’s growing rhetoric that all these questions be astutely addressed through a regulation of some form. With examples of detrimental use of data surfacing regularly, businesses, individuals and society at large are demanding an answer for exactly what data can be collected – and how it should be aggregated, stored, managed and processed.
If data is indeed the new oil, we need to have a strong understanding of what constitutes the fair use of this invaluable resource. This article attempts to highlight India’s stance on triggering regulatory measures to govern the use of data.Importance of Data Governance

Importance of Data Governance

Before we try to get into what data governance should mean in the Indian context, let us first look at the definition of data governance and why it is an important field of study to wrap our head around.

In simple terms, data governance is the framework that lays down the strategy of how data is used and managed within an organisation. Data governance leaders must stay abreast of the legal and regulatory frameworks specific to the geographies that they operate in and ensure that their organisations are compliant with the rules and regulations. A lot of their effort at present is aimed at maintaining the sanctity of organisational data and ensuring that it does not fall in the wrong hands. As such, the amount of time and effort expended on ensuring that these norms are adequately adhered to is contingent upon the risk associated with a potential breach or loss of data.

In effect, a framework of data governance is intended to ensure that a certain set of rules is applied and enforced to ensure that data is used in the right perspective within an organisation.

Data Governance in Indian Context

India is rapidly moving towards digitisation. Internet connectivity has exploded in the last few years, leading to rapid adoption of internet-enabled applications — social media, online shopping, digital wallets etc. The result of this increasing connectivity and adoption is a fast-growing digital footprint of Indian citizens. Add to this the Aadhaar programme proliferation and adoption – and we have almost every citizen that has personal digital footprint somewhere – codified in the form of data.

With a footprint of this magnitude, there is an element of risk attached. What if this data falls in the wrong hands? What if personal data is used to manipulate citizens? What are the protection mechanisms citizens have against potential overreach by stewards of the data themselves? It is time we found answers to these very pertinent questions – and data governance regulation is the way we will find comprehensive answers to these impending conversations

Perspectives for India

The pertinent departments are mulling over on a collective stand that should be taken while formulating data governance norms. For one, Indian citizens are protected by a recent Supreme Court ruling that privacy is a fundamental right. This has led to a heightened sense of urgency around arriving at a legislative framework for addressing genuine concerns around data protection and privacy, as well as cybersecurity.

As a result of these concerns, the Central government recently set up a committee of experts, led by Justice BN Srikrishna, tasked with formulating data governance norms. This committee is expected to maintain the delicate balance between protecting the privacy of citizens and fostering the growth of the digital economy simultaneously. Their initial work – legal deliberations and benchmarking activity against similar legal frameworks such as GDPR (General Data Protection Regulation) – has resulted in the identification of seven key principles around which any data protection framework needs to be built. Three of the most crucial pointers include:

1. Informed Consent: Consent is deemed to be an expression of human autonomy. While collecting personal data, it is critical that the users be informed adequately about the implications around how this data is intended to be used before capturing their express consent to provide this data
2. Data Minimisation: Data should not be collected indiscriminately. Data collected should be minimal and necessary for purposes for which the data is sought and other compatible purposes beneficial for the data subject.
3. Structured Enforcement: Enforcement of the data protection framework must be by a high-powered statutory authority with sufficient capacity. Without statutory authority, any remedial measures sought by citizens over data privacy infringement will be meaningless.
Striking the right balance between fostering an environment in which the digital economy can grow to its full potential, whilst protecting the rights of citizens is extremely difficult.

With a multitude of malafide parties today seeking to leverage personal data of citizens for malicious purposes, it is crucial that the government and the legal system set out a framework that protects the sovereignty and interests of the people. By allaying fears of misuse of data, the digital economy will grow as people become less fearful and more enthusiastically contribute information where a meaningful end outcome can be achieved.