REPORT: Data Engineering 4.0: Evolution, Emergence and Possibilities in the next decade
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Today, most technology aficionados think of data engineering as the capabilities associated with traditional data preparation and data integration including data cleansing, data normalization and standardization, data quality, data enrichment, metadata management and data governance. But that definition of data engineering is insufficient to derive and drive new sources of society, business and operational value. The Field of Data Engineering brings together data management (data cleansing, quality, integration, enrichment, governance) and data science (machine learning, deep learning, data lakes, cloud) functions and includes standards, systems design and architectures.
There are two critical economic-based principles that will underpin the field of Data Engineering:
Principle #1: Curated data never depletes, never wears out and can be used an unlimited number of use cases at a near zero marginal cost.
Principle #2: Data assets appreciate, not depreciate, in value the more that they are used; that is, the more these data assets are used, the more accurate, more reliable, more efficient and safer they become.
There have been significant exponential technology advancements in the past few years ; data engineering is the most topical of them. Burgeoning data velocity , data trajectory , data insertion , data mediation & wrangling , data lakes & cloud security & infrastructure have revolutionized the data engineering stream. Data engineering has reinvented itself from being passive data aggregation tools from BI/DW arena to critical to business function. As unprecedented advancements are slated to occur in the next few years, there is a need for additional focus on data engineering. The foundations of AI acceleration is underpinned by robust data engineering capabilities.
YourStory & AIQRATE curated and unveiled a seminal report on “Data Engineering 4.0: Evolution , Emergence & Possibilities in the next decade.” A first in the area , the report covers a broad spectrum on key drivers of growth for Data Engineering 4.0 and highlights the incremental impact of data engineering in the time to come due to emergence of 5G , Quantum Computing & Cloud Infrastructure. The report also covers a comprehensive section on applications across industry segments of smart cities , autonomous vehicles , smart factories and the ensuing adoption of data engineering capabilities in these segments. Further , it dwells on the significance of incubating data engineering capabilities for deep tech startups for gaining competitive edge and enumerates salient examples of data driven companies in India that are leveraging data engineering prowess . The report also touches upon the data legislation and privacy aspects by proposing certain regulations and suggesting revised ones to ensure end to end protection of individual rights , security & safety of the ecosystem. Data Engineering 4.0 will be an overall trojan horse in the exponential technology landscape and much of the adoption acceleration that AI needs to drive ; will be dependent on the advancements in data engineering area.
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Lock in winning AI deals : Strategic recommendations for enterprises & GCCs
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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 )
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REPORT: Reimagine The Future of Work with New Age Opportunities
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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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AI for Strategic Innovation
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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 )
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AI led strategy for business transformation : A guided approach for CXOs
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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.
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AI led Strategy for Boards : The “new” strategy counselor
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It’s time for boards to craft an AI led strategy . Three strategic aspects can help them and senior leaders to augment decision making process in the board meetings
In the boardroom, and the head of a major global conglomerate is in the hot seat. A director with a background in the manufacturing industry is questioning the economics, an assumption underlying the executive’s industry forecast: that the industry’s ratio of forecast will remain relatively constant. The business leader appears confident about the assumption of stability, which has implications for both the competitive environment and for financial results. But the director isn’t convinced: “In my experience, the forecast changes continuously with the economic cycle and needs to bake in assumptions,” he says, “and I’d feel a whole lot better about these estimates if you had some facts to prove that this has changed.” and the rest of the board doesn’t have it. Finally, the chairman intervenes: “The question being raised is critical and not just for our manufacturing business but for our entire strategy. We’re not going to resolve this today, but let’s make sure it’s covered thoroughly during our strategy off-site and he added , “let’s have some good staff work in place to inform the discussion.”
If the preceding exchange sounds familiar, it should: in the wake of the financial crisis, we find that uncomfortable conversations such as this one are increasingly common in boardrooms around the world as corporate directors and executives come to grips with a changed environment. Ensuring that a company has a great strategy is among a board’s most important functions and the ultimate measure of its stewardship. Yet even as new governance responsibilities and faster competitive shifts require much more—and much better—board engagement on strategy, a great number of boards remain hamstrung by familiar challenges.
Enter AI led strategy for boards
For starters, there’s the problem of time: most boards have about six to eight meetings a year and are often hard pressed to get beyond compliance-related topics to secure the breathing space needed for developing strategy. A recent survey of board members to learn where they’d most like to spend additional time, two out of three picked strategy. A related finding was that 44 percent of directors said their boards simply reviewed and approved management’s proposed strategies. Why such limited engagement? One likely reason is an expertise gap: only 10 percent of the directors felt that they fully understood the industry dynamics in which their companies operated. As a result, only 21 percent of them claimed to have a complete understanding of the current strategy .
What’s more, there’s often a mismatch between the time horizons of board members and of top executives , and that lack of alignment can diminish a board’s ability to engage in well-informed give-and-take about strategic trade-offs. “The chairman of my company has effectively been given a decade,” says the CEO of a company “and I have three years—tops—to make my mark. If I come up with a strategy that looks beyond the current cycle, I can never deliver the results expected from me. Yet I am supposed to work with him to create long-term shareholder value. How am I supposed to make this work?” It’s a fair question, particularly since recent shows that major strategic moves involving active capital reallocation deliver higher shareholder returns than more passive approaches over the long haul, but lower returns over time frames of less than three years.
Compounding these challenges is the increased economic volatility prompting many companies to rethink their strategic rhythm, so that it becomes less calendar driven and formulaic and more a journey involving frequent and regular dialogue among a broader group of executives. To remain relevant, boards must join management on this journey, and management in turn must bring the board along—all while ensuring that strategic co-creation doesn’t become confusion or, worse, shadow management. This is where curating AI strategy for competitive advantage and informed decision making comes to the picture.
Three strategic aspects to ponder on AI led strategy for Boards :
While no one-size-fits-all solution can guide companies as they set out, board members and senior managers ask themselves three simple questions as they approach the development of AI strategy. Using it should raise the quality of decision making , overall engagement and help determine the practical steps each group must take to get there. The usual annual strategic refresh is unlikely to provide the board with an appreciation of the context it would need to address the questions fully, let alone to generate fresh insights in response.
1.Can AI make the boards understand the industry dynamics
Most boards spend most of their strategic time reviewing plans, yet relatively few directors feel they have a complete understanding of the dynamics of the industries their companies operate in or even of how those companies create value. To remedy this problem and to avoid the superficiality it can engender, boards need time—some without management present—so they can more fully understand the structure and economics of the business, as well as how it creates value. They should use this time to get ahead of issues rather than always feeling a step behind during conversations on strategy or accepting management biases or ingrained habits of thought.AI can lay out comprehensive picture of industry and competitive industry dynamics with historical and future forward looking scenarios to make the job of the boards simpler.
2. Can AI trigger enough board–management debate before a specific strategy is discussed?
Aided thru AI and armed with a foundational view based on a clearer understanding of industry and company economics, boards are in a better position to have the kinds of informed dialogue with senior managers that ultimately help them prepare smarter and more refined strategic options for consideration. Board members should approach these discussions with data driven mind-set and with the goal of helping management to broaden its thinking by considering new, even unexpected, perspectives.
During such debates, management’s role is to introduce key pieces of content: a detailed review of competitors, key external trends likely to affect the business, and a view of the specific capabilities the company can use to differentiate itself. The goal of the dialogue is to develop a stronger, shared understanding of the skills and resources the company can use to produce strong returns, as opposed to merely moving with the tide. This is where boards can evangelize and seep in AI in the senior executives group for broader knowledge augmentation .
3.Can AI bring in all strategic options and approaches to the table for board and management ?
Very often, the energizing discussions between the board and management about the business, its economics, and the competition represent the end of the debate. Afterward, the CEO and top team go off to develop a plan that is then presented to the board for approval. Instead, what’s needed at this point is for management to take some time—go thru the self-learning enabled algorithm —to formulate a robust set of strategic options, each followed through to its logical end state, including the implications for the allocation of people, capital, and other resources. These strategic options through the revised algorithmic exercise can then be brought back to the board for discussion and decision making.
Developing AI led strategy is a new phenomenon and will take time to mature —yet will become more powerful algorithmic based decision making process and with board’s increased involvement, which introduces new voices and expertise to the debate and puts pressure on management teams and board members alike to find the best answers. Yet this form of AI led strategy development, when done well, is invaluable. It not only leads to clearer strategies but also creates the alignment necessary to make bolder moves with more confidence and to follow through by committing resources to key decisions. AI led decision making for the boards is here….
(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 )
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The ‘Dark’ side of AI: Algorithm Bias, influenced decision making.. Defining AI Ethics Strategy
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According to a 2019 report from the Centre for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deep fakes when they include doctored video images and audio generated with help from AI) to cyber attacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that don’t require a human controller.
What happens when injustices are propagated not by individuals or organizations but by a collection of machines? Lately, there’s been increased attention on the downsides of artificial intelligence and the harms it may produce in our society, from unequitable access to opportunities to the escalation of polarization in our communities. Not surprisingly, there’s been a corresponding rise in discussion around how to regulate AI.
AI has already shown itself very publicly to be capable of bad biases — which can lead to unfair decisions based on attributes that are protected by law. There can be bias in the data inputs, which can be poorly selected, outdated, or skewed in ways that embody our own historical societal prejudices. Most deployed AI systems do not yet embed methods to put data sets to a fairness test or otherwise compensate for problems in the raw material.
There also can be bias in the algorithms themselves and in what features they deem important (or not). For example, companies may vary their product prices based on information about shopping behaviors. If this information ends up being directly correlated to gender or race, then AI is making decisions that could result in a PR nightmare, not to mention legal trouble. As these AI systems scale in use, they amplify any unfairness in them. The decisions these systems output, and which people then comply with, can eventually propagate to the point that biases become global truth.
The unrest on bringing AI Ethics
Of course, individual companies are also weighing in on what kinds of ethical frameworks they will operate under. Microsoft president Brad Smith has written about the need for public regulation and corporate responsibility around facial recognition technology. Google established an AI ethics advisory council board. Earlier this year, Amazon started a collaboration with the National Science While we have yet to reach certain conclusions around tech regulations, the last three years have seen a sharp increase in forums and channels to discuss governance. In the U.S., the Obama administration issued a report in 2016 on preparing for the future of artificial intelligence after holding public workshops examining AI, law, and governance; AI technology, safety, and control; and even the social and economic impacts of AI. The Institute of Electrical and Electronics Engineers (IEEE), an engineering, computing, and technology professional organization that establishes standards for maximizing the reliability of products, put together a crowdsourced global treatise on ethics of autonomous and intelligent systems. In the academic world, the MIT Media Lab and Harvard University established a $27 million initiative on ethics and governance of AI, Stanford is amid a 100-year study of AI, and Carnegie Mellon University established a centre to explore AI ethics.
Corporations are forming their own consortiums to join the conversation. The Partnership on AI to Benefit People and Society was founded by a group of AI researchers representing six of the world’s largest technology companies: Apple, Amazon, DeepMind/Google, Facebook, IBM, and Microsoft. It was established to frame best practices for AI, including constructs for fair, transparent, and accountable AI. It now has more than 80 partner companies. Foundation to fund research to accelerate fairness in AI — although some immediately questioned the potential conflict of interest of having research funded by such a giant player in the field.
Are data regulations around the corner?
There is a need to develop a global perspective on AI ethics, Different societies around the world have very different perspectives on privacy and ethics. Within Europe, for example, U.K. citizens are willing to tolerate video camera monitoring on London’s central High Street, perhaps because of IRA bombings of the past, while Germans are much more privacy oriented, influenced by the former intrusions of East German Stasi spies , in China, the public is tolerant of AI-driven applications like facial recognition and social credit scores at least in part because social order is a key tenet of Confucian moral philosophy. Microsoft’s AI ethics research project involves ethnographic analysis of different cultures, gathered through close observation of behaviours, and advice from external academics such as Erin Meyer of INSEAD. Eventually, we could foresee that there will be a collection of policies about how to use AI and related technologies. Some have already emerged, from avoiding algorithmic bias to model transparency to specific applications like predictive policing.
The longer take is that although AI standards are not top of the line sought after work, they are critical for making AI not only more useful but also safe for consumer use. Given that AI is still young but quickly being embedded into every application that impacts our lives, we could envisage an array of AI ethics guidelines by several countries for AI that are expected to lead to mid- and long-term policy recommendations on AI-related challenges and opportunities.
Chief AI ethical officer on the cards?
As businesses pour resources into designing the next generation of tools and products powered by AI, people are not inclined to assume that these companies will automatically step up to the ethical and legal responsibilities if these systems go awry.
The time when enterprises could simply ask the world to trust artificial intelligence and AI-powered products is long gone. Trust around AI requires fairness, transparency, and accountability. But even AI researchers can’t agree on a single definition of fairness: There’s always a question of who is in the affected groups and what metrics should be used to evaluate, for instance, the impact of bias within the algorithms.
Since organizations have not figured out how to stem the tide of “bad” AI, their next best step is to be a contributor to the conversation. Denying that bad AI exists or fleeing from the discussion isn’t going to make the problem go away. Identifying CXOs who are willing to join in on the dialogue and finding individuals willing to help establish standards are the actions that organizations should be thinking about today. There comes the aspect of Chief AI ethical officer to evangelize, educate, ensure that enterprises are made aware of AI ethics and are bought into it.
When done correctly, AI can offer immeasurable good. It can provide educational interventions to maximize learning in underserved communities, improve health care based on its access to our personal data, and help people do their jobs better and more efficiently. Now is not the time to hinder progress. Instead, it’s the time for enterprises to make a concerted effort to ensure that the design and deployment of AI are fair, transparent, and accountable for all stakeholders — and to be a part of shaping the coming standards and regulations that will make AI work for all
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Building AI-enabled organisations
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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.
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Design Thinking | Behavioural Sciences: Strategic Elements to Building a Successful AI Enterprise
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Today’s artificial intelligence (AI) revolution has been made possible by the algorithm revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.
But current AI technologies are a collection of big data-driven point solutions, and algorithms are reliable only to the extent that the data used to train them is complete and appropriate. One-off or unforeseen events that humans can navigate using common sense can lead algorithms to yield nonsensical outputs.
Design thinking is defined as human-centric design that builds upon the deep understanding of our users (e.g., their tendencies, propensities, inclinations, behaviours) to generate ideas, build prototypes, share what you’ve made, embrace the art of failure (i.e., fail fast but learn faster) and eventually put your innovative solution out into the world. And fortunately for us humans (who really excel at human-centric things), there is a tight correlation between the design thinking and artificial intelligence.
Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.
Divergence from More Powerful Intelligence To More Creative Intelligence
While algorithms can automate many routine tasks, the narrow nature of data-driven AI implies that many other tasks will require human involvement. In such cases, algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. We don’t want to ascribe to AI algorithms more intelligence than is really there. They may be smarter than humans at certain tasks, but more generally we need to make sure algorithms are designed to help us, not do an end run around our common sense.
Design Thinking at Enterprise Premise
Although cognitive design thinking is in its early stages in many enterprises, the implications are evident. Eschewing versus embracing design thinking can mean the difference between failure and success. For example, a legacy company that believes photography hinges on printing photographs could falter compared to an internet startup that realizes many customers would prefer to share images online without making prints, and embraces technology that learns faces and automatically generates albums to enhance their experience.
To make design thinking meaningful for consumers, companies can benefit from carefully selecting use cases and the information they feed into AI technologies. In determining which available data is likely to generate desired results, enterprises can start by focusing on their individual problems and business cases, create cognitive centres of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowdsource ideas from employees and customers alike.
In assessing what constitutes proper algorithmic design, organizations may confront ethical quandaries that expose them to potential risk. Unintended algorithmic bias can lead to exclusionary and even discriminatory practices. For example, facial recognition software trained on insufficiently diverse data sets may be largely incapable of recognizing individuals with different skin tones. This could cause problems in predictive policing, and even lead to misidentification of crime suspects. If the training data sets aren’t really that diverse, any face that deviates too much from the established norm will be harder to detect. Accordingly, across many fields, we can start thinking about how we create more inclusive code and employ inclusive coding practices.
CXO Strategy for Cognitive Design Thinking
CIOs can introduce cognitive design thinking to their organizations by first determining how it can address problems that conventional technologies alone cannot solve. The technology works with the right use cases, data, and people, but demonstrating value is not always simple. However, once CIOs have proof points that show the value of cognitive design thinking, they can scale them up over time.
CIOs benefit from working with business stakeholders to identify sources of value. It is also important to involve end users in the design and conception of algorithms used to automate or augment cognitive tasks. Make sure people understand the premise of the model so they can pragmatically balance algorithm results with other information.
Enterprise Behavioral Science – From Insights to Influencing Business Decisions
Every January, how many people do you know say that they want to resolve to save more, spend less, eat better, or exercise more? These admirable goals are often proclaimed with the best of intentions, but are rarely achieved. If people were purely logical, we would all be the healthiest versions of ourselves.
However, the truth is that humans are not 100% rational; we are emotional creatures that are not always predictable. Behavioral economics evolved from this recognition of human irrationality. Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making.
Decision making is one of the central activities of business – hundreds of billions of decisions are made everyday. Decision making sits at the heart of innovation, growth, and profitability, and is foundational to competitiveness. Despite this degree of importance, decision making is poorly understood, and badly supported by tools. A study by Bain & Company found that decision effectiveness is 95% correlated with companies’ financial performance.
Enterprise Behavioral Science is not only about understanding potential outcomes, but to completely change outcomes, and more specifically, change the way in which people behave. Behavioral Science tells us that to make a fundamental change in behavior that will affect the long-term outcome of a process, we must insert an inflection point.
As an example, you are a sales rep and two years ago your revenue was $1 million. Last year it was $1.1 million, and this year you expect $1.2 million in sales. The trend is clear, and your growth has been linear and predictable. However, there is a change in company leadership and your management has increased your quota to $2 million for next year. What is going to motivate you to almost double your revenues? The difference between expectations ($2 million) and reality ($1.2 million) is often referred to as the “behavioral gap” . When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive can initiate an inflection point and influence a change in behavior. Perhaps that incentive is an added bonus, President’s Club eligibility, a promotion, etc.
Cognitive Design Thinking – The New Indispensable Reskilling Avenue
Artificial intelligence, machine learning, big data analytics and mobile and software development will be the top technology areas where the need for re-skilling will be the highest. India will need 700 million skilled workforce by 2022 to meet the demands of a growing economy. Hence, while there is a high probability that machine learning and artificial intelligence will play an important role in whatever job you hold in the future, there is one way to “future-proof” your career…embrace the power of design thinking.
In fact, integrating design thinking and artificial intelligence can give you “super powers” that future-proof whatever career you decide to pursue. To meld these two disciplines together, one must:
- Understand where and how artificial intelligence and behavioural science can impact your business initiatives. While you won’t need to write machine learning algorithms, business leaders do need to learn how to “Think like a data scientist” in order understand how AI can optimize key operational processes, reduce security and regulatory risks, uncover new monetization opportunities.
- Understand how design thinking techniques, concepts and tools can create a more compelling and emphatic user experience with a “delightful” user engagement through superior insights into your customers’ usage objectives, operating environment and impediments to success.
Design thinking is a mindset. IT firms are trying to move up the curve. Higher-end services that companies can charge more is to provide value and for that you need to know that end-customers needs. For example, to provide value services to banking customers is to find out what the bank’s customer needs are in that country the banking client is based. Latent needs come from a design thinking philosophy where you observe customer data, patterns and provide a solution that the customer does not know. Therefore, Companies will hire design thinkers as they can predict what the consumer does not know and hence charge for the product/service from their clients. Idea in design thinking is to provide agile product creation or solutions.
Without Design Thinking & Behavioural Science, AI Will be Only an Incremental Value
Though organizations understand the opportunity that big data presents, many struggles to find a way to unlock its value and use it in tandem with design thinking – making “big data a colossal waste of time & money.” Only by combining quantitative insights gathered using AI, machine/deep learning, and qualitative research through behavioural science, and finally design thinking to uncover hidden patterns and leveraging it to understand what the customer would want, will we be able to paint a complete picture of the problem at hand, and help drive towards a solution that would create value for all stakeholders.
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ACCELERATED DECISION MAKING AMPLIFIED BY REAL TIME ANALYTICS – A PERSPECTIVE
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Companies are using more real-time analytics, because of the pressure to increase the speed and accuracy of business processes — particularly for digital business and the Internet of Things (IoT). Although data and analytics leaders intuitively understand the value of fast analytical insights, many are unsure how to achieve them.
Every large company makes thousands of real-time decisions each minute. For example, when a potential customer calls the contact center or visits the company’s website to gather product information, the company has a few seconds to figure out the best-next-action offer to propose to maximize the chance of making a sale. Or, when a customer presents a credit card to buy something or submits a withdrawal transaction request to an automated teller machine, a bank has one second or less to determine if the customer is who they say they are and whether they are likely to pay the bill when it is due. Of course, not all real-time decisions are tied to customers. Companies also make real-time decisions about internal operations, such as dynamically rerouting delivery trucks when a traffic jam forms; calling in a mechanic to replace parts in a machine when it starts to fail; or adjusting their manufacturing schedules when incoming materials fail to arrive on time.
Many decisions will be made in real time, regardless of whether real-time analytics are available, because the world is event-driven and the company has to respond immediately as events unfold. Improved real-time responses that are informed by fact-based, real-time analytics are optional, but clearly desirable.
Real-time analytics can be confusing, because different people may be thinking of different concepts when they use the term “real time.” Moreover, it isn’t always simple to determine where real-time analytics are appropriate, because the “right time” for analytics in a given business situation depends on many considerations; real-time is not always appropriate, or even possible. Finally, data and analytics leaders and their staff typically know less about real-time analytics than about traditional business intelligence and analytics.
Find the certain concept of “Real Time” Relevant to Your Business Problem
Real-time analytics is defined as “the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly.” Real time means different things to different people.
When engineers say “real time” they mean that a system will always complete the task within a specified time frame.
Each component and subtask within the system is carefully designed to provide predictable performance, avoiding anything that could take longer to occur than is usually the case. Real-time systems prevent random delays, such as Java garbage collection, and may run on real-time operating systems that avoid nondeterministic behavior in internal functions such as scheduling and dispatching. There is an implied service-level agreement or guarantee. Strictly speaking, a real-time system could take hours or more to do its work, but in practice, most real-time systems act in seconds, milliseconds or even microseconds.
The concept of engineering real time is most relevant when dealing with machines and fully automated applications that require a precise sequence and timing of interactions among multiple components. Control systems for airplanes, power plants, self-driving cars and other machines often use real-time design. Time-critical software applications, such as high-frequency trading (HFT), also leverage engineering real-time concepts although they may not be entirely real time.
Use Different Technology and Design Patterns for Real-Time Computation Versus Real-Time Solutions
Some people use the term real-time analytics to describe fast computation on historical data from yesterday or last year. It’s obviously better to get the answer to a query, or run a model, in a few seconds or minutes (business real time) rather than waiting for a multihour batch run. Real-time computation on small datasets is executed in-memory by Excel and other conventional tools. Real-time computation on large datasets is enabled by a variety of design patterns and technologies, such as:
- Preloading the data into an in-memory database or in-memory analytics tool with large amounts of memory
- Processing in parallel on multiple cores and chips
- Using faster chips or graphics processing units (GPUs)
- Applying efficient algorithms (for example, minimizing context switches)
- Leveraging innovative data architectures (for example, hashing and other kinds of encoding)
Most of these can be hidden within modern analytics products so that the user does not have to be aware of exactly how they are being used.
Real-time computation on historical data is not sufficient for end-to-end real-time solutions that enable immediate action on emerging situations. Analytics for real-time solutions requires two additional things:
- Data must be real time (current)
- Analytic logic must be predefined
If conditions are changing from minute to minute, a business needs to have situation awareness of what is happening right now. The decision must reflect the latest sensor readings, business transactions, web logs, external market data, social computing postings and other current information from people and things.
Real-time solutions use design patterns that enable them to access the input data quickly so that data movement does not become the weak link in the chain. There is no time to read large amounts of data one row or message at a time across a wide-area network. Analytics are run as close as possible to where the data is generated. For example, IoT applications run most real-time solutions on or near the edge, close to the devices. Also, HFT systems are co-located with the stock exchanges to minimize the distance that data has to travel. In some real-time solutions, including HFT systems, special high-speed networks are used to convey streaming data into the system.
Match the Speed of Analytics to the Speed of the Business Decision
Decisions have a range of natural timing, so “right time” is not always real time. Business analysts and solution architects should work with managers and other decision makers to determine how fast to make each decision. The two primary considerations are:
- How quickly will the value of the decision degrade?
- Decisions should be executed in real time if a customer is waiting on the phone for an answer; resources would be wasted if they sit idle; fraud would succeed; or physical processes would fail if the decision takes more than a few milliseconds or minutes. On the other hand, a decision on corporate strategy may be nearly as valuable in a month as it would be today, because its implementation will take place over months and years so starting a bit earlier may not matter much.
- How much better will a decision be if more time is spent?
- Simple, well-understood decisions on known topics, and for which data is readily available, can be made quickly without sacrificing much quality.
Lastly, Automate Decisions if Algorithms Can Represent the Entire Decision Logic
Algorithms offers the “last mile” of the decision. However, automating algorithms requires a well described process to code against. According to Gartner, “Decision automation is possible only when the algorithms associated with the applicable business policies can be fully defined.”
Final Word
Performing some analytics in real time is a goal in many analytics and business intelligence modernization programs. To operate in real time, data and analytics leaders must leverage predefined analytical models, rather than ad hoc models, and use current input data rather than just historical data.