Experience the Algorithm Economy : Accentuating strategic value for the enterprises
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Algorithms will not only drive scores of business processes, but also build other self-intuitive algorithms, much as robots can build other robots. And rather than using apps, future users’ lives will revolve personalized algorithms to drive individual choices and behaviors .
Enterprises will license, trade, sell and even give away non-lynch pin algorithms and single-function software snippets that provide new opportunities for innovation by other enterprises. Enterprises will also partner with cloud-based, automated suppliers with the industry expertise to advice on ways to avoid future risk and adapt to technology trends.
Imaginative thinking ! but it’s no surprise that future value will come from increased density of interactions, relationships and sharing between people, businesses and things ̶ or what I call “ Algorithm Economy “ .The greater the maturity of algorithms , the greater potential value you can reap. We’ve seen interconnection coming of age for a while now and have invested heavily in a platform to empower enterprises with fast, direct and secure interconnections with business partners and network and cloud service providers.
Redefining Business Architecture with Algorithms
The term “algorithm economy” is relatively new, but the practical use of algorithms is already well established in many industries. In my opinion , CXOs must begin designing their algorithmic business models, both to capitalize on their potential for business differentiation, and to mitigate the possible risks involved.
Established businesses need to adopt a “bi modal strategy” and build what I called an algorithmic platform, completely separate from legacy systems, that harnesses repository of algorithms, interconnections, the cloud and the Internet of Things (IoT) to innovate, share value, increase revenues and manage risk.
New platforms based on this bimodal model should be far simpler, more cloud-based and more flexible than in the past, with the ability to add and remove capabilities “like Velcro” to support new short- and long-term projects. At the same time, IT should start divesting itself of older systems and functions that are outliving their usefulness or could be better done by other methods. The significant development and growth of smart machines is a major factor in the way algorithms have emerged from the shadows, and become more easily accessible to every organization. We can already see their impact in today’s world, but there is much work ahead to harness the opportunities and manage the challenges of algorithmic business.
CXOs should examine how algorithms and intelligent machines are already used by competitors and even other enterprises to determine if there is relevance to their own needs. The retail sector has long been at the leading edge of using smart algorithms to improve business outcomes. Today, many retail analysts believe that the algorithms that automate pricing and merchandising may soon become the most valuable asset that a retailer can possess. In HR function, algorithms are already transforming talent acquisition as they are able to rapidly evaluate the suitability of candidates for specific roles, but the same technology could easily be applied within an enterprise to allocate workloads to the right talent. In healthcare, the open availability of advanced clinical algorithms is transforming the efficiency of healthcare delivery organizations and their ability to deliver care. The practice of sharing and co-developing algorithms between enterprises with mutual interests could be relevant to most enterprises.
The Challenges of Algorithm Economy
The advances and benefits of algorithm economy will come hand in hand with obstacles to navigate. Whether the problems are anticipated or unexpected, as quantum computing becomes more pervasive, the implications have the potential to make or break organizations. For example, an extreme point of view is that any beneficial effects of algorithms on humanity may be nullified by algorithmically driven systems that are antithetical to human interests. Or, while an algorithmic business model may be deployed with good intentions, it could be manipulated by malicious humans to achieve undesirable outcomes. Undesirable, at least, from the point of the view of the person or organization that owns or controls the algorithm. Algorithms rely on the data they are fed, and their decisions are only as good as the data they are based on. Moreover, tricky ethical problems that do not necessarily have a “correct” answer will be inevitable, as a greater complexity of decision making is left in the hands of automated systems.
The scale of change that is made possible by smart machines and algorithm economy warrants considerable planning and testing. Enterprises that fail to prepare risk being left behind or facing unexpected outcomes with negative implications.
The Transformation required in Algorithm Economy
Making sense of all the data about how customers behave, and what connected things tell an organization, will require algorithms to define business processes and create a differentiated customer experience. Algorithms will evaluate suppliers, define how our cars operate, and even determine the right mix of drugs for a patient. In the purely digital world, agents will act independently based on our algorithms, in the cloud. In the 2020s, we’ll move away from using apps to rely on virtual assistants – basically, algorithms in the cloud – to guide us through our daily tasks. People will trust personal algorithms that thinks and acts for them. Take this to another level and the algorithms themselves will eventually become smart by learning from experience and producing results their creators never expected.
The Final Frontier
Therefore, we have to get the architecture of algorithms robust and steady to derive meaningful objectives. In essence, algorithms spot the business moments, meaningful connections, and predict ill behaviors and threats. CXOs need to be the strategic voice on the use of information, to build the right set of intelligent insights. Experience the Algorithm Economy and the ensuing strategic value for your enterprise . Are you geared up ?
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How AI is shaping the new life in life sciences and pharmaceutical industry
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The pharma and life sciences industry is faced with increasing regulatory oversight, decreasing R&D productivity, challenges to growth and profitability, and the impact of artificial intelligence (AI) in the value chain. The regulatory changes led by the far-reaching Patient Protection and Affordable Care Act (PPACA) in the US are forcing the pharma and life sciences industry to change its status quo.
Besides the increasing cost of regulatory compliance, the industry is facing rising R&D costs, even though the health outcomes are deteriorating and new epidemics are emerging. Led by the regulatory changes, the customer demographics are also changing. The growth is being driven by emerging geographies of APAC and Latin American region.
Disruption in life sciences
Pharmaceutical organisations can leverage AI in a big way to drive insightful decisions on all aspects of their business, from product planning, design to manufacturing and clinical trials to enhance collaboration in the ecosystem, information sharing, process efficiency, cost optimisation, and to drive competitive advantage.
AI enables data mining, engineering, and real time- and algorithmic-driven decision-making solutions, which help in responding to the following key business value chain disruptions in the pharmaceutical industry:
- AI-driven drug discovery – Enables scientists to source scientific findings and insights from external labs or internal knowledge to jump start discovery which will in turn help reduce cycle time for product development aiding faster go-to-market
- Reduce cycle times for clinical trials– Through better insights driven by improved accuracy of machine-based ensemble algorithms
- Supply chain transformation – Building predictive algorithms using a combination of internal and external data would help reduce unforeseen shortages in availability of drugs impacting customer service levels and lost sales revenues
- Product failure prediction – Via root cause analysis and predictive algorithms of product failures (vendor data)
- Risk management – For evaluation of potential risks posed by elemental impurities in a formulated drug product
- Real-time medical device analysis and visualisation– Leveraging interconnecting data from implanted devices and personal care devices
- Behavioural sciences – To more fully understand customer perceptions about their products which helps in proactively fixing product issues or managing communication better
- Enhance reporting systems– To meet the changing regulatory compliance needs more effectively
- Intelligent insights – Renew focus on understanding the underlying business data and generating insights using latest insights and intelligence frameworks
The human microbiome
Though genomics currently hogs the spotlight, there are plenty of other biotechnology fields wrestling with AI. In fact, when it comes to human microbes – the bacteria, fungi, and viruses that live on or inside us – we are talking about astronomical amounts of data. Scientists with the NIH’s Human Microbiome Project have counted more than 100 trillion microbes in the human body.
- Cambridge Semantics has a developed semantic web technologies that help pharmaceutical companies sort and select which businesses to acquire and which drug compounds to license.
- Data scientists at the Broad Institute of MIT and Harvard have developed the Integrative Genomics Viewer (IGV), open source software that allows for the interactive exploration of large, integrated genomic datasets.
- GNS Healthcare is using proprietary causal Bayesian network modeling and simulation software to analyse diverse sets of data and create predictive models and biomarker signatures.
Genomics
- Sequencing millions of human genomes would add up to hundreds of petabytes of data.
- Analysis of gene interactions multiplies this data even further.
In addition to sequencing, massive amounts of information on structure/function annotations, disease correlations, population variations – the list goes on – are being entered into databanks. Software companies are furiously developing tools and products to analyse this treasure trove.
Robust algorithms with massive data engineering capabilities
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How the insurance industry can leverage AI to enhance efficiencies
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This second machine age has seen the rise of artificial intelligence (AI), or intelligence that is not the result of human cogitation. AI is now ubiquitous in many commercial products, from search engines to virtual assistants.
The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made speedy processing and generation of meaningful, actionable insights imperative.
The insurance industry segment has been conservative in adopting AI across the value chain, but P&C /Life/Reinsurance companies have accelerated the pace of AI adoption and initiated deployment of AI use cases across the value chain.
Here are few of the use cases leveraging AI for the insurance industry:
Personalised customer experience: redefining the value proposition
Many insurers are already in the early stages of enhancing and personalising the customer experience. Exploiting social data to understand customer needs and sentiments about products and processes (e.g., claims) are some early applications of AI.
The next stage in robo-advisor evolution is to offer better intelligence on customer needs and goal-based planning for both protection and financial products. Recommender systems and “someone like you” statistical matching will become increasingly available to customers and advisors.
Up next will be understanding of individual and household balance sheets and income statements, as well as economic, market, and individual scenarios to recommend, monitor and alter financial goals and portfolios for customers and advisors.
Automated and augmented underwriting: enhancing efficiencies
This involves automating large classes of standardised underwriting in auto, home, commercial (small and medium business), life, and group using sensor (IoT) data, unstructured text data (e.g., agent/advisor or physician notes), call centre voice data, and image data using Bayesian learning or deep learning techniques.
The industry will also model new business and underwriting process using soft robotics and simulation modeling to understand risk drivers and expand the classes of automated and augmented (i.e., human-performed) underwriting.
We will also see augmenting of large commercial underwriting and life/disability underwriting by having AI systems (based on NLP and DeepQA) highlight key considerations for human decision-makers. Personalised underwriting by a company or individual takes into account unique behaviours and circumstances.
Robo-claims adjuster
This will help build predictive models for expense management, high value losses, reserving, settlement, litigation, and fraudulent claims using existing historical data. It will also help analyse claims process flows to identify bottlenecks and streamline flow, leading to higher company and customer satisfaction.
Building a robo-claims adjuster by leveraging predictive models and building deep learning models that can analyze images to estimate repair costs can change status quo. In addition, use of sensors and IoT to proactively monitor and prevent events can reduce losses.
A claims insights platform that can accurately model and update frequency and severity of losses over different economic and insurance cycles (i.e., soft vs. hard markets) can help the industry. Carriers can apply claims insights to product design, distribution, and marketing to improve overall lifetime profitability of customers.
Emerging risks and new product innovation
Identifying emerging risks (e.g., cyber, climate, nanotechnology), analyse observable trends, determining if there is an appropriate insurance market for these risks, and developing new coverage products in response historically have been creative human endeavors.
Man and machine learning
Artificial general intelligence (AGI) that can perform any task that a human can is still a long way off. In the meantime, combining human creativity with mechanical analysis and synthesis of large volumes of data – in other words, man-machine learning (MML) – can yield immediate results.
For example, in MML, the machine learning component sifts through daily news from a variety of sources to identify trends and potentially significant signals. The human learning component provides reinforcement and feedback to the ML component, which then refines its sources and weights to offer broader and deeper content.
Using this type of MML, risk experts (also using ML) can identify emerging risks and monitor their significance and growth. MML can further help insurers to identify potential customers, understand key features, tailor offers, and incorporate feedback to refine new product introduction.
AI implications for insurers
Improving Efficiencies: AI is already improving efficiencies in customer interaction and conversion ratios, reducing quote-to-bind and FNOL-to-claim resolution times, and increasing new product speed-to market. These efficiencies are the result of AI techniques speeding up decision-making (e.g., automating underwriting, auto-adjudicating claims, automating financial advice, etc.).
Improving effectiveness: Because of the increasing sophistication of its decision-making capabilities, AI soon will improve target prospects to convert them to customers, refine risk assessment and risk-based pricing, enhance claims adjustment, and more. Over time, as AI systems learn from their interactions with the environment and with their human masters, they are likely to become more effective than humans and replace them. Advisors, underwriters, call centre representatives, and claims adjusters likely will be most at risk.
Improving risk selection and assessment: AI’s most profound impact could well result from its ability to identify trends and emerging risks, and assess risks for individuals, corporations, and lines of business. Its ability to help carriers develop new sources of revenue from risk and non-risk based information will also be significant.
Read more at: https://yourstory.com/2020/02/insurance-industry-leverage-ai-enhance-efficiencies
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AI for Startups: 4 adoption recommendations entrepreneurs should keep in mind
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Despite nationwide venture funding hitting a multiyear low, venture capital deployed to artificial intelligence (AI) startups has reached a record high.
Last year, VCs struck 859 deals with AI companies, nearly five times the number that signed on the dotted line four years earlier. To date, the market has 2,045 AI startups and more than 17,000 market followers, with more joining by the day.
AI’s rapid rise has swept up startups and enterprises alike, including US automaker Ford, which recently bought AI startup Argo for $1 billion. The acquisition cements experts’ suspicions of Ford’s coming foray into self-driving technology. Other startups — so many, in fact, that entrepreneurs need a “best of” guide — are betting heavily on bot platforms.
Sixty-eight percent of marketing executives report using AI in their function. For a practice that only went mainstream in 2016 and barely existed four years ago, that’s a remarkable adoption rate.
How, regardless of the platform you choose, can you join forward-thinking entrepreneurs and build your business with AI? Over the last few years, I have worked closely with multiple startups across genres and ,So far, four strategic considerations stand out to leverage AI in your startups:
1. Get to know your next customer
A politician wouldn’t dream of delivering a small-town stump speech to urban constituents. Why? Because you’ve got to know your audience. The same is true for entrepreneurs. Before you broadcast your message, you need to know who you’re trying to reach.
Node, an account-based intelligence startup, uses natural language processing — a fancy term for teaching a computer to understand how we humans speak and write — to develop customer profiles. Node is crunching vast swaths of data to connect the dots between marketers and companies they’re trying to reach.
Once you have ample customer data — Node uses data crawlers to scrape information from social media, news sites, and more — pair machine learning and natural language processing models to extract sentiments from unstructured data. Then, just as senators segment constituents into demographic groups, Node uses cluster analysis to sort clients’ customers into like cohorts.
2. See how people truly use your product
If, heaven forbid, you forgot to tag your neighbour at last week’s house party, Facebook was no doubt there to remind you of your error. How does Facebook know which of your friends you left untagged? It has gone all-in on an AI technique called convolutional neural networks.
Convolutional neural networks, which loosely model how the brain’s visual cortex interacts with the eyes, work by separating an image into tiny portions before running each of those specks through a multilayered filter. It then “sees” where each speck overlaps with other parts of the image, and through automated iterations, puts together a full image.
Many different ways exist to apply this technology, but retail businesses can start with image classification. Try using a convolutional neural network to break down photos of your products posted online. The model can identify customer segments that frequently use your product, where they’re using it and whether they commonly pair other products with yours. Essentially, this automated image analysis can show you how your products fit into customers’ lives, allowing you to tailor your marketing materials to fit.
3. Get inside the user’s experience
Fortunately, AI can take the emotional temperature of thousands of customers at once. Dumbstruck, a video-testing and analytics startup that I advise, has added natural language processing to its emotional analytics stack. This allows it to provide moment-by-moment insights into viewers’ reactions to media. Dumbstruck’s model grows stronger with each reaction analysed, producing a program that perceives human emotions even better than some people can.
4. Provide affordable, always-on support
Customer service is — or should be, according to consumers — the department that never sleeps. More than half of people, 50.6 percent to be precise, believe a business should be available 24/7 to answer their every question and concern. When asked whether businesses should be available via a messaging app, the “yes” votes jump to nearly two in three. Fortunately, bots don’t sleep, eat or go off-script.
A well-built bot can offer cost-effective, constant customer service. Of course, grooming your bot to serve customers requires front-end data — ideally hundreds of thousands of example conversations — but you can get started with a human-chatbot hybrid. With this approach, the bot answers run-of-the-mill questions, while a human takes over for the more complex ones. Then, as data builds and the model matures, you can phase in full automation.
AI’s impact on startups
Startups will gain a competitive edge in capturing the AI market. Larger enterprises will provide the infrastructure to startups for building innovative services. It is somewhat similar to the business model followed when cable technology was introduced.
Startups leveraging AI technology for industry verticals, including agriculture, manufacturing or insurance, are bound to be successful.
Startups can empower established insurance companies like State Farm, Allstate, and Farmers with technology, enabling them to become more proactive in policy planning. For instance, a new AI insurance underwriter will help to forecast natural disasters and accidents, and adjust premiums.
The predictive, decision-making capabilities are more than just a novel technology. You can manage food supply chains with the help of AI. Startups could develop end-to-end farming solutions with AI analytics for reducing food waste. It will have a huge impact in tackling global issues of hunger and famine.
AI will be a fundamental predictive enabler helping us solve large-scale problems, and startups are poised to gain a competitive edge.
Ground-level AI sentiment of startups Regardless of which industry you operate, AI will affect your world in some way. Look into what is present now and how you can use it to gain a competitive edge. The possibilities with AI are endless; enterprises will become efficient, intelligent, and cost-effective. Undoubtedly, the AI revolution will advance to a point where it will offer real-world benefits to every business, large and small.
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How AI is powering the Future of Work: key considerations for business and tech leaders
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The age of Artificial Intelligence (AI) is upon us. Businesses and society are now looking towards AI for transformative outcomes. Businesses, specifically, are investing huge amounts of money on AI that will not only bring in efficiencies across multiple processes, but also unlock new revenue streams that will deliver significant top line and bottom-line impact. With the AI transformation playing out rapidly in our personal and professional lives, we need to deeply understand what the future of work will look like in the age of AI.
Business executives are now needed to deeply understand the potential of AI and translate it into a viable roadmap for their business. Technology leaders need to take centre-stage in how their organisations adopt and harness the power of AI. We are seeing a fast proliferation of digital evangelists and transformation officers who are charged with developing a framework within which the future of the organisation will operate.
Future of Work, redefined
On a tactical level, the burning question now is: how can organisations build a steady pipeline of future talents with expertise in AI? Mastery of exponential technologies (AI, cloud computing, blockchain, IoT, cybersecurity, etc) will be remarkably important for both business and technical professionals.
For us to redefine the future of work powered by AI, we need to ensure that a few key enablers come together. We need to expand the scope of executive education and the courseware that goes with it. Next, we need to seriously consider the potential impact of crisp yet impactful courses. Corporations need to augment their training programmes with shorter, time-boxed courseware that can deliver instant impact for the organisation.
Finally, we need to reimagine multiple, personalised career pathways. We need to move away from the traditional one-size-fits-all training and deliver more tailored, fit-for-purpose and relevant education to employees.
Here are the three critical interventions for the business and technology leaders to execute in order to usher in the future of work that is enabled by AI.
Develop new-age skills and competencies in AI
Upgrading the technology competencies and skills of business and technology leaders and their teams seems like the most critical first step. With the landscape of technology is rapidly evolving, we need to urgently upskill the present and future workforce to ensure a quality supply of talent.
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
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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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AI-Driven Disruption And Transformation: New Business Segments To Novel Market 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.
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Embark on AI@scale journey : Strategic Interventions for CXOs
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AI is invoking shifts in the business value chains of enterprises. And it is redefining what it takes for enterprises to achieve competitive advantage. Yet, even as several enterprises have begun applying AI engagements with impressive results, few have developed full-scale AI capabilities that are systemic and enterprise wide.
The power of AI is changing business as we know it. AIQRATE AI@scale advisory services allow you to transform your operating model, so you can move beyond isolated AI use cases toward an enterprise wide program and realize the full value potential.
We have realized that that unleashing the true power of AI requires scaling it across the entire business functions and value chain and its calls for “transforming the business “.
An AI@scale transformation should occur through a series of top-down and bottom-up actions to create alignment, buy-in, and follow-through. This ensures the successful industrialization of AI across enterprises and their value chains.
The following strategic interventions are to be initiated to build AI@scale transformation program:
- AI Maturity Assessment: This strategic top-down establishes the overall context of the transformation and helps prevent the enterprises from pursuing isolated AI pilots. The maturity assessment is typically based on a blend of AI masterclass, surveys and assessments
- Strategic AI Initiatives and business value chains: This bottom-up step provides a baseline of current AI initiatives. It should include goals, business cases, accountabilities, work streams, and milestones in addition to an analysis of data management, algorithms, performance metrics. A review of the current business value chain and proposed transformational structure should also be conducted at this stage.
- Strategic mapping & gap Analysis: The next top-down step prioritizes AI initiatives, focusing on easy wins and low hanging fruits. This step also identifies the required changes to the operating business model.
- AI@scale transformation program: This critical strategic step consists of both the transformation roadmap, including the order of initiatives to be rolled out, and the creation of a planned program management approach to oversee the transformation.
- AI@scale implementation: This covers implementation, detailing the work streams, responsibilities, targets, milestones, talent and partner mapping.
By systematically moving through these steps, the implementation of AI@scale will proceed with much greater speed and certainty. Enterprises must be aware that AI@scale requires deep transformative changes and need strategic and operational buy ins from management for long term business gains and impact .
AIQRATE works closely with global & Indian enterprises , GCC’s , VC/PE firms to provide end-to-end AI@scale advisory services
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Reimagining the future of travel and hospitality with artificial intelligence
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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.
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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 )