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Ascendancy of artificial intelligence (AI) revolution has been made possible by the machine enabled , ensemble configured 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.
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, behaviors) 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
Whilst 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 centers of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowd source 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 & Behavioral Science
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 every day. 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, analytics and mobile and cloud engineering will be the top technology areas where the need for re-skilling will be the highest.whilst 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 & behavioral science.
In fact, integrating design thinking , behavioral science and artificial intelligence can give you “immense synergies ” that future-proof whatever career you decide to pursue. To meld these three disciplines together, one must:
Understand where and how artificial intelligence and behavioral 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 & Behavioral Science 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 & Behavioral 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 “AI an colossal waste of time & money.” Only by combining quantitative insights gathered using AI, machine/deep learning, and qualitative research through behavioral 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.
(AIQRATE, A bespoke global AI advisory and consulting firm. A first in its genre, AIQRATE provides strategic AI advisory services and consulting offerings across multiple business segments to enable clients navigate their AI powered transformation, innovation & revival journey and accentuate their decision making and business performance.
AIQRATE works closely with Boards, CXOs and Senior leaders advising them on their Analytics to AI journey construct with the art of possible roadmap blended with a jump start to AI driven transformation with AI@scale centric strategy followed by consulting them on embedding AI as core to business strategy within business functions and augmenting the decision-making capabilities with AI. Our bespoke AI advisory services focuses on curating & designing building blocks of AI strategy, embed AI@scale interventions and create AI powered organizations.
AIQRATE’s path breaking 50+ AI consulting frameworks, methodologies, primers, toolkits and playbooks crafted by seasoned and proven AI strategy advisors enable Indian & global enterprises, GCCs, Startups, SMBs, VC/PE firms, and Academic Institutions enhance business performance & ROI and accelerate decision making capability.
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With the global lock down caused by the COVID-19 and the unforeseen loss of business momentum , the luxury of time now seems to have disappeared completely. Businesses that once mapped strategy planning in one- three-year phases must now reset and scale their strategic initiatives in a matter of days or weeks. In one of the survey initiated by Harvard university , about 70 percent of top fortune 1000 companies senior executives said the pandemic is likely to accelerate the pace of their business transformation. The acceleration is evident already across sectors and geographies. Consider how multiple banks have swiftly migrated physical channels online. How healthcare providers have moved rapidly into tele-health, insurers into self-service claims assessment, and retailers into contactless shopping and delivery.
The COVID-19 crisis seemingly provides a sudden glimpse into a future world, one in which artificial intelligence has become central to every interaction, forcing both enterprises and individuals further up the adoption curve almost overnight. A world in which digital channels become the primary customer-engagement model, and automated processes become a primary driver of productivity—and the basis of flexible, transparent, and stable supply chains. A world in which agile ways of working are a prerequisite to meeting seemingly daily changes to customer behavior. This being powered by a robust AI driven algorithmic engines . If a silver lining can be found, it might be in the falling barriers to improvisation and experimentation that have emerged among customers, markets, regulators, and organizations. In this unique moment, enterprises can learn and progress more quickly than ever before. The ways they reset and revive post crisis will deeply influence their performance in tomorrow’s transformative world, providing the opportunity to retain greater agility as well as closer ties with customers, employees, and suppliers. Those that are successfully able to make gains will likely be more successful during recovery and beyond.
Now is the time to reassess business strategy and curate AI strategy core to the business models & processes—to provide near-term readiness to employees, customers, and the broad set of stakeholders to which businesses are increasingly responsible and those that position you for a post crisis world. In this world, some things will snap back to previous form, while others will be forever changed. Playing it safe now, understandable as it might feel to do so, is often the worst option.
A Black Swan event demands new strategic approaches : AI Strategy comes to the rescue
Every enterprise knows the virtues of how AI pilots new business models in “normal” times, but very have implemented AI strategy @scale and velocity suddenly required by the COVID-19 crisis. That’s because in normal times, the customer and market penalties for widespread “test and learn” can seem too high, and the enterprises obstacles too steep. Shareholders of public companies demand immediate returns. Finance departments keep tight hold of the funds needed to move new initiatives forward quickly. Customers are often slow to adjust to new ways of doing things, with traditional adoption curves reflecting this inherent inertia. And organizational culture, with its own siloes, hinders agility and collaboration. As a result, enterprises often experiment at a pace that fails to match the rate of change around them, slowing their ability to learn fast enough to keep up. Additionally, they rarely embrace the acceleration needed to move quickly from piloting initiatives to scaling the successful ones, even though analyst studies have shown that swift moves to curate AI strategy early and at scale, combined with a sizeable allocation of resources against AI implementation , correlate highly with value creation As the COVID-19 crisis forces your customers, employees, and supply chains into digital channels and new ways of working, now is the time to ask : Does my enterprise have an AI strategy to reimagine customer experiences , innovate new products & services and transform my business for competitive advantage ? Strange as it may seem, right now, in a moment of crisis, is precisely the time to boldly advance your move to curate an AI strategy .
AI Strategy Curation : Strategic Focus Areas :
Crafting an AI strategy goes beyond building light weight , beta mode algorithms , pursuing adhoc business problems for driving AI engagements or cobbling up together a bunch of AI geeks ; it requires a strategic approach driven by boards , CXOs’ , business leaders and decision makers to focus on the following key areas :
1.Craft Novel Business capabilities embedded with AI
By now you have built your contingency response model and insights hub; you need to coordinate your crisis response. This insights hub provides a natural gathering point for crucial strategic information, helping you stay close to the quickly evolving needs of core customer segments, and the ways in which competitors and markets are moving to meet them. Mapping these changes helps address immediate risks, to be sure, but it also affords looking forward in time at bigger issues and opportunities—those that could drive significant disruption as the crisis continues. Just as AI has disrupted business models and value chains in the past, the COVID-19 crisis will set similar “ecosystem”-level changes in motion—not just changes in economics but new ways of serving customers and working with suppliers across in a new ecosystem. In the immediate term, for example, most enterprises are looking for virtual capabilities for their previously physical offerings, or at least new ways of making them accessible with minimal physical contact. The new offerings that result can often involve new partnerships or the need to access new platforms and digital marketplaces in which your company has yet to participate. As you engage with new partners and platforms, look for opportunities to move beyond your organization’s comfort zones, while getting visibility into the places you can confidently invest valuable time, people, and funds to their best effect. AI based strategy that involves building recommended intelligence systems, reasoning and intuition to address complex problems and explore ideal future states, will be crucial.
2. Embed AI into your core business model
Going beyond comfort zones requires taking an end-to-end view of your business and operating models. Even though your resources are necessarily limited, the experience of leading enterprises suggests that focusing on embedding AI in to the areas that touch more of the core of your business will give you the best chance of success, in both the near and the longer term, than will making minor improvements to noncore areas. Enterprises that make minor changes to the edges of their business model nearly always falter in their business goals. Tinkering leads to returns on investment below the cost of capital and to changes that are too small to match the external pace of disruption. Enterprises that rapidly adopts embedding AI driven algorithms and using those to redefine their business at scale have been outperforming their peers. This will be increasingly true as companies deal with large amounts of data in a rapidly evolving landscape and look to make rapid, accurate course corrections compared with their peers. On a short term basis , this may mean , opening up business models for introspection , however, embedding AI into the core business areas : marketing , sales , supply chain , finance will radically change your enterprise’s ability to derive insights & intelligence.
3. Reset your business strategies with AI
No enterprise can accelerate the delivery of all its strategic imperatives without looking to M&A to speed them along. This is particularly true with AI strategy, where M&A can help companies gain talent and build capabilities, even as it offers access to new products, services, and solutions, and to new market and customer segments. More broadly, we know from research from previous black swan events that enterprises that invest when valuations are low outperform those that do not. In more normal times, one of the main challenges enterprises face in their AI led transformations and adoption is the need to acquire AI talent and capabilities through acquisitions of startups that are typically valued at multiples that capital markets might view as dilutive to the acquirer. The current downturn could remove this critical roadblock, especially with enterprises temporarily free from the tyranny of quarterly earnings expectations.
In the next part of the series , I will elaborate on the steps and interventions that are required to craft & curate an AI strategy . Stay Tuned…..
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NASSCOM CoE for DSAI conducted AI Parley to explore the opportunities COVID-19 has created in the AI space. Panel discussions were held to learn if COVID-19 has acted as a trigger to drive large scale adoption of AI by Govt and Enterprises. Sameer Dhanrajani, CEO & Co-founder, AIQRATE, moderated the Panel of eclectic guests from industry and academia to lend their views on Setting up AI for Success: Strategic focus areas to deliver in crisis.