Better customer experience, lower costs, enhanced accuracy, and new features are a few advantages of applying machine learning models to real-world applications.

According to a survey conducted by Rackspace Technology, 34% of respondents project having up to 10 artificial intelligence and machine learning projects in place within the coming two years. Meanwhile, 31% see data quality as a primary challenge to preparing actionable insights into AI and machine learning projects.

Before applying the power of machine learning to business and operations, companies must overcome various obstacles.

Let’s dive into some of the primary challenges businesses encounter while integrating AI technologies into business operations in data, skills, and strategy.

The Importance of Data Quality

Data still remains a significant barrier in various stages of planning and utilizing an AI strategy. According to the Rackspace survey, 34% of the respondents said low data quality is the foremost cause of machine learning research and development failure, and 31% stated that they lacked production-ready data.

The AI research community has access to several public datasets for practice and testing their latest machine learning technologies, but when it comes to implementing those technologies to real applications, gaining access to quality data is challenging.

To overcome the data challenges of AI strategies, businesses must fully evaluate their data infrastructure, and breaking down silos should be a top priority in all machine learning initiatives. Furthermore, organizations should also have the right methods to filter their data to boost the performance and accuracy of their machine learning models.

Soaring Demand for AI Talent

The next area of struggle for most businesses is access to machine learning and data science talent. However, with the evolution of new machine learning and data science devices, the talent problem has grown less severe.

Before starting an AI initiative, it is advised that all businesses should perform a thorough evaluation of in-house expertise, available devices, and integration opportunities. Additionally, businesses must consider if re-skilling is a logical course of action for long-term business goals. If it’s feasible for businesses to up skill their engineers to take data science and machine learning projects, they will be better off in the long run.

Outsourcing AI Talent

Another area that has seen extensive growth in recent years is the outsourcing of AI talent. According to the Rackspace survey, just 38 % of the respondents depend on in-house talent to improve AI applications. Others either completely outsource their AI projects or use a mixture of in-house and outsourced talent.

A successful strategy requires close communication between AI experts and subject matter specialists from the company executing the plan.

AI projects not only require strategy and technical expertise but also a strong partnership with the company and the leadership. Outsourcing AI talent should be done meticulously. While it can expedite the process of creating and executing an AI strategy, businesses must ensure that their experts are wholly committed to the process. Ideally, organizations should make their in-house team of data scientists and machine learning engineers work with outsourced specialists.

The post Overcoming the Challenges Associated with Machine Learning and AI Strategies appeared first on Artificial Intelligence.

Syndicate Market Research’s New Exclusive Research on Hadoop And Big Data Analytics Market Report Demand grows Rapidly as Our Research Analyst covers the key parameters Required for your Research Need. This Hadoop And Big Data Analytics Market Report covers global, regional, and country-level market size, market shares, market growth rate analysis (include Reseaon of highest and lowest peak Market analysis), product launches, recent trend, the impact of covid19 on worldwide or regional Hadoop And Big Data Analytics Market. The Report also includes Key competitors/players/Manufactures/vendors in recent market trends are Cloudera Inc, Hortonworks, Hadapt, Amazon Web Services LLC, Outerthought, MapR Technologies, Platform Computing, Karmasphere, Greenplum, Hstreaming LLC, Pentaho Corporation, Zettaset. Syndicate Market Research Analyses Research Methodology overview consists of Secondary Research, Primary Research, Company Share Analysis, Model ( including Demographic data, Macroeconomic indicators, and Industry indicators i.e. Expenditure, infrastructure, sector growth, and facilities, etc), Research Limitations and Revenue Based Modeling. Comprehensive analysis of Hadoop And Big Data Analytics Market Based on current analysis & future analysis, which is based on historic data also featured in this Reports. Presenting the Hadoop And Big Data Analytics Market Factor Analysis- Porters Five Forces, Supply/Value Chain, PESTEL analysis, CAGR value, Market Entropy, Patent/Trademark Analysis, and Post COVID Impact Analysis

Our Research Analyst Delivered Free PDF Sample Report copy as per your Research Requirement, also including impact analysis of COVID-19 on Hadoop And Big Data Analytics market Industries

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The above-mentioned Global Hadoop And Big Data Analytics market report presentation has been estimated at length and according to expert analysis, is anticipated to entail an impressive growth of xx million USD in 2020 and is projected to further reach a total growth estimation of xx million USD through the forecast till 2026, growing at a CAGR of xx%, and you get accurate CAGR according to Hadoop And Big Data Analytics market size which actual exist

This global study of the Hadoop And Big Data Analytics Systems market offers an overview of the existing market trends, metrics, drivers, and restrictions and also offers a point of view for important segments. The report also tracks product and services demand growth forecasts for the market. There is also to the study approach a detailed segmental review. A regional study of the global Hadoop And Big Data Analytics Systems industry is also carried out in North America, Latin America, Asia-Pacific, Europe, and the Near East & Africa. The report mentions growth parameters in the regional markets along with major players dominating the regional growth.

Key Research Techniques of Hadoop And Big Data Analytics Market Report include: 

The qualities of this Hadoop And Big Data Analytics study in the industry experts industry, such as CEO::Marketing Director::Technology and Innovation Director::Vice President::Founder and Key Executives of key core companies and institutions in major Hadoop And Big Data Analytics around the world in the extensive primary research conducted for this study we interviewed to acquire and verify both sides and quantitative aspects.

The main sources of Hadoop And Big Data Analytics are industry experts from the Hadoop And Big Data Analytics industry, including management organizations::processing organizations::and analytical services providers that address the value chain of industry organizations. We interviewed all major sources to collect and certify qualitative and quantitative information and to determine prospects.

Hadoop And Big Data Analytics Industrial Analysis: By Applications

Banking, Telecommunication, Healthcare, Transportation, Information Technology, Gaming and Public Organizations, Weather Forecasters

Hadoop And Big Data Analytics Business Market Trends: By Product

Hadoop Packaged Software, Hadoop Application Software, Hadoop Management Software, Hadoop Performance Monitoring Software

Hadoop And Big Data Analytics Market Segment : By Region

Asia Pacific

• China
• Japan
• India
• Southeast Asia
• Rest of Asia Pacific

North America

• U.S.Canada
• Rest of North America

Europe

• UK
• Germany
• France
• Italy
• Spain
• Rest of Europe

Latin America

• Brazil
• Argentina
• Rest of Latin America

The Middle East and Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

TOC of 15 Key Chapters Covered in the Global Hadoop And Big Data Analytics Market: 

• Chapter 1::Industry Overview of Global Hadoop And Big Data Analytics Market;
• Chapter 2::Classification, Specifications and Definition of Hadoop And Big Data Analytics Market Segment by Regions;
• Chapter 3::Industry Suppliers, Manufacturing Process and Cost Structure, Chain Structure, Raw Material;
• Chapter 4::Specialized Information and Manufacturing Plants Analysis of Hadoop And Big Data Analytics, Limit and Business Production Rate, Manufacturing Plants Distribution::R&D Status and Technology Sources Analysis
• Chapter 5::Complete Market Research::Capacity::Sales and Sales Price Analysis with Company Segment
• Chapter 6::Analysis of Regional Market that contains the United States::Europe::India::China::Japan::Korea & Taiwan
• Chapter 7 & 8::Hadoop And Big Data Analytics Market Analysis by Major Manufacturers::The Hadoop And Big Data Analytics Segment Market Analysis (by Type) and (by Application)
• Chapter 9::Regional Market Trend Analysis, Market Trend by Product Type and by Application
• Chapter 10 & 11::Supply Chain Analysis, Regional Marketing Type Analysis::Global Trade Type Analysis
• Chapter 12::The Global Hadoop And Big Data Analytics industry consumers Analysis;
• Chapter 13::Research Findings/Conclusion, Hadoop And Big Data Analytics deals channel, traders, distributors::dealers analysis
• Chapter 14 and 15::Appendix and data source of Hadoop And Big Data Analytics market.

Key Highlights & Touch Points of the Hadoop And Big Data Analytics Market Worldwide for the Forecast Years 2020-2026:

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•     In-depth analysis of the industry’s competitive landscape and detailed information vis-a-vis on various vendors
•     Furnishing of detailed information on the factors that will restrain the growth of the Hadoop And Big Data Analytics manufacturers

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In the digital era, businesses incorporate big data business analytics to enhance decision-making, increase accountability, boost productivity, make better forecasts, determine success, and gain aggressive advantages. However, various companies have difficulties practicing business intelligence analytics on a strategic level.

This year seems to be an excellent year for big data analytics, yet there are some challenges to overcome. According to Gartner, 87% of businesses have low Business Intelligence (BI) and analytics maturity, requiring data guidance and assistance. The obstacles with business data analysis are associated with analytics and can also be caused by extensive system or infrastructure challenges.

Thus, it is time to dive deeper into the most prevalent big data analytics problems, examine possible root causes, and highlight the possible solutions to those problems. Here are the top data analytics difficulties businesses face.

Inaccurate Analytics

Nothing could be more detrimental to a business than incorrect analytics, and this issue needs to be addressed at the earliest.

If the system relies on data with bugs, errors, or is incomplete, it’s highly likely to get poor results. Data quality management and mandatory data validation process, including every stage of the ETL process, can help businesses ensure incoming data quality at various levels. This will help organizations to identify errors and ensure that a modification in one area quickly results in pure and accurate data.

Utilizing Big Data Analytics is Challenging

The level of complexity of the reports is too high and time-consuming. It can be fixed by hiring a UI/UX expert to help businesses develop a robust and flexible user interface for easy navigation and workflow.

It’s advisable to get the team together and define critical metrics to identify what functionality is often used, what needs to be focused, measured, and analyzed. Involving an external expert from the business domain would be an excellent option to help the business with data analysis.

Long System Response Time

The system takes plenty of time to analyze the data. It may not be so important for batch processing, but for real-time systems, such delay can be costly.

The problem with data analytics infrastructure and resource utilization is that it has reached its scalability limit. Also, it could be that the hardware infrastructure is no longer adequate.

The easiest solution is to append more computing resources to the system. It’s useful if it helps improve the system response within an affordable budget and there is proper utilization of the resources.

Costly Maintenance

Every system needs continuous investment for its maintenance and infrastructure. Moreover, business owners are constantly looking for ways to reduce these investments. Therefore, it’s always a good idea to frequently assess the systems to avoid overpaying.

New emerging technologies process more data volumes in a faster and economical way. The best solution is to shift to new technologies to improve reliability, scalability, and availability.

Besides, for not using most of the system capabilities, businesses pay for the infrastructure they utilize. Therefore, improving business metrics and optimizing the method according to business needs will be helpful.

Check Out The New Enterprisetalk Podcast. For more such updates follow us on Google News Enterprisetalk News.

The post Strategies to Overcome Challenges of Big Data Analytics in 2021 appeared first on Artificial Intelligence.

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work.

There’s no questioning the promises of machine learning in nearly every sector. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand. And as many organizations and companies are learning, before you can apply the power of machine learning to your business and operations, you must overcome several barriers.

Three key challenges companies face when integrating AI technologies into their operations are in the areas of skills, data, and strategy, and Rackspace’s survey paints a clear picture of why most machine learning strategies fail.

Machine learning is about data

Machine learning models live on compute resources and data. Thanks to a variety of cloud computing platforms, access to the hardware needed to train and run AI models has become much more accessible and affordable.

But data continues to remain a major hurdle in different stages of planning and adopting an AI strategy. Thirty-four percent of the respondents in the Rackspace survey stated poor data quality as the main reason for the failure of machine learning research and development, and another 31 percent said they lacked production-ready data.

This highlights one of the main hurdles when applying machine learning techniques to real-world problems. While the AI research community has access to many public datasets for training and testing their latest machine learning technologies, when it comes to applying those technologies to real applications, getting access to quality data is not easy. This is especially true in industrial, health, and government sectors, where data is often scarce or subject to strict regulations.

Data problems crop up again when machine learning initiatives move from the research to the production phase. Data quality remains the top barrier when it comes to using machine learning to extract valuable insights. Data engineering problems also pose a significant problem, such as data being siloed, lack of talent to connect disparate data sources, and not being fast enough to process data in a meaningful way.

Both startups and established companies suffer from data problems, though scale seems to be the key differentiator between the two, according to Jeff DeVerter, CTO of Rackspace Technology. “Startups tend to be constrained with not all the right resources to implement a quality data pipeline and consistently managing it over time,” DeVerter said to TechTalks in written comments. “Enterprises usually have scale on their side and with that comes the rigor that’s required.”

The best way companies can prepare for the data challenges of AI strategies is to do a full evaluation of their data infrastructure. Eliminating silos should be a key priority in every machine learning initiative. Companies should also have the right procedures for cleaning their data to improve the accuracy and performance of their machine learning models.

AI talent is still in high demand

The second area of struggle for most companies is access to machine learning and data science talent. According to Rackspace’s survey, lack of in-house expertise was the second biggest driver of failure in machine learning R&D initiatives. Lack of skill and difficulty in hiring was also a key barrier in adopting AI technologies.

With machine learning and deep learning having reached mainstream use in production environments only recently, many smaller companies don’t have data scientists and machine learning engineers who can develop AI models.

And the average salary of data scientists and machine learning engineers matches those of experienced software engineers, which makes it difficult for many companies to put together a talented team that can lead its AI initiative.

While the shortage of machine learning and data science talent is well known, one thing that has gone mostly unnoticed is the need for more data engineers, the people who set up, maintain, and update databases, data warehouses, and data lakes. Per Rackspace’s figures, many initiatives fail because companies don’t have the talent to adapt their data infrastructure for machine learning purposes. Breaking down silos, migrating to cloud, setting up Hadoop clusters, and creating hybrid systems that can leverage the power of different platforms are some areas where companies are sorely lacking. And these shortcomings prevent them from making company-wide deployments of machine learning initiatives.

With the development of new machine learning and data science tools, the talent problem has become less intense. Google, Microsoft, and Amazon have launched platforms that make it easier to develop machine learning models. An example is Microsoft’s Azure Machine Learning service, which provides a visual interface with drag-and-drop components and makes it easier to create ML models without coding. Another example is Google’s AutoML, which automates the tedious process of hyperparameter tuning. While these tools are not a replacement for machine learning talent, they lower the barrier for people who want to enter the field and will enable many companies to reskill their tech talent for these growing fields.

“Lack of in-house data science talent is not the barrier it once was now that more of these services are able to use their own ML to help in this regard as well consulting firms having these talents on-staff,” DeVerter said.

Other developments in the field are the evolution of cloud storage and analysis platforms, which have considerably reduced the complexity of creating the seamless data infrastructures needed to create and run AI systems. An example is Google’s BigQuery, a cloud-based data warehouse that can run queries across vast amounts of data stored in various sources with minimal effort.

We’re also seeing growing compatibility and integration capabilities in machine learning tools, which will make it much easier for organizations to integrate ML tools into their existing software and data ecosystem.

Before entering an AI initiative, every organization must make a full evaluation of in-house talent, available tools, and integration possibilities. Knowing how much you can rely on your own engineers and how much it will cost you to hire talent will be a defining factor in the success or failure of your machine learning initiatives. Also, consider whether re-skilling is a possible course of action. If you can upskill your engineers to take on data science and machine learning projects, you will be better off in the long run.

Outsourcing AI talent

Another trend that has seen growth in recent years is the outsourcing of AI initiatives. Only 38 percent of the Rackspace survey respondents relied on in-house talent to develop AI applications. The rest were either fully outsourcing their AI projects or employing a combination of in-house and outsourced talent.

There are now several companies that specialize in developing and implementing AI strategies. An example is C3.ai, an AI solutions provider that specializes in several industries. C3.ai provides AI tools on top of existing cloud providers such as Amazon, Microsoft, and Google. The company also provides AI consultancy and expertise to take customers step by step through the strategizing and implementation phases.

According to the Rackspace report: “A mature provider can bring everything from strategy to implementation to maintenance and support over time. Strategy can sidestep the areas where AI and machine learning efforts may lose momentum or get lost in complexity. Hands-on experts can also spare organizations from the messy work of cleanup and maintenance. Such expertise, taken together, can make all the difference in finally achieving success.”

It is worth noting, however, that fully turning over an organization’s AI strategy to outside providers can be a double-edged sword. A successful strategy requires close cooperation between AI specialists and subject matter experts from the company that is implementing the strategy.

“This is very similar to companies who move to a DevOps development methodology and attempt to outsource the entirety of the development. DevOps requires a close partnership between the developers, business analysts, and others in the business,” DeVerter said. “In the same way, AI projects require strategy and technical expertise — but also require a tight partnership with the business as well as leadership.”

Outsourcing AI talent must be done meticulously. While it can speed up the process of developing and implementing an AI strategy, you must make sure that your experts are fully involved in the process. Ideally, you should be able to develop your own in-house team of data scientists and machine learning engineers as you work with outside experts.

How do you evaluate your AI strategy?

Finally, another area that is causing much pain for companies embarking on an AI journey is forecasting the outcome and value of AI strategies. Given the application of machine learning being new to many areas, it’s hard to know in advance how long an AI strategy will take to plan and implement and what the return on investment will be. This in turn makes it difficult for innovators in organizations to get others on board when it comes to garnering support for AI initiatives.

Of the respondents of the Rackspace survey, 18 percent believed that a lack of clear business case was the main barrier to adopting AI strategies. Lack of commitment from executives was also among the top barriers. Lack of use cases and commitment from senior management show up again among the top challenges in the machine learning journey.

“AI often wanders around as a solution looking for a problem within organizations. I believe this is one of the greatest impediments to its wide-scale adoption within organizations,” DeVerter said. “As AI practitioners can demonstrate practical examples of how AI can benefit their specific company — leadership will further fund those activities. Like any business venture — leadership needs to know how it will either help them save or make money.”

Evaluating the outcome of AI initiatives is very difficult. According to the survey, the top-two key performance indicators (KPI) for measuring the success of AI initiatives were profit margins and revenue growth. Understandably, this focus on quick profits is partly due to the high costs of AI initiatives. According to the Rackspace survey, organizations spend a yearly average of $1.06 million on AI initiatives.

But while a good AI initiative should result in revenue growth and lower costs, in many cases, the long-term value of machine learning is the development of new use cases and products.

“Short-term financial gains can be myopic if they aren’t paired with a long-term strategy that can be funded by those short-term gains,” DeVerter said.

If you’re in charge of the AI initiative in your organization, make sure to clearly lay out the use cases, the costs, and the benefits of your AI strategy. Decision-makers should have a clear picture of what their company will be embarking on. They should understand the short-term benefits of investing in AI, but they should also know what they will gain in the long run.

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics. This post was originally published here as a series exploring the business of artificial intelligence.

The post Why machine learning strategies fail appeared first on Artificial Intelligence.

This market report plots an intentional review of macroeconomic signs, parent affiliations, and new startup adventures. The report gives the customers data identified with classes, for instance, augmentation, divisions, and locales, expose type, and applications. This market report exhibits the rapidly creating conditions, the top dimension appearing at do genuine execution and settle on worthwhile decisions for advancement and prospering ahead. This market report speaks to a precise methodology of key data that would be given to customers who are searching for it. This report can guide the client to choose the correct strides in basic leadership and key plans that can be useful in the market.

This market research report joins the latest mechanical overhauls and new releases to interface with the clients to design, settle on smart business decisions, and complete their future required executions. The report focuses more on current business and developments, future framework changes, and opportunities and trends that the market is experiencing or going to experience. The report additionally portrays the primary players and how they perform in the market all through. It reveals insight into their financials, SWOT analysis, review, significant and late improvements, developments, and so on

Cloud microservices market is expected to grow at a CAGR of 21.7% in the forecast period of 2020 to 2027. Data Bridge Market Research report on cloud microservices market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecasted period while providing their impacts on the market’s growth.

The global cloud microservices market accounted for USD 631.1 million in 2017 and is projected to grow at a CAGR of 24.1% forecast to 2025.

The renowned players in cloud microservices market are

• Amazon Web Services, Inc.,
• CA Technologies.,
• IBM,
• Microsoft,
• Infosys Limited,
• NGINX Inc.,
• Oracle,
• Pivotal Software, Inc.,
• Syntel, Inc.,
• Gurock,

Marlabs Inc., RapidValue Solutions, Kontena, Inc., Macaw Software Inc.,  UNIFYED.,  Idexcel, Inc. and among others.

The titled segments and sub-section of the market are illuminated below:

• The global cloud microservices market is based on component, organization size, deployment mode, vertical and geographical segments.
• Based on component, the global cloud microservices market is segmented into platform and services. Service is sub segmented into consulting services, integration services, training, support, and maintenance services.
• Based on organization size, the global cloud microservices market is segmented into large enterprises  and small and medium-sized enterprises.
• Based on Deployment Mode, the global cloud microservices market is segmented into public cloud, private cloud and hybrid cloud.
• Based on vertical, the global cloud microservices market is segmented into retail and ecommerce, healthcare, media and entertainment, banking, financial services, and insurance, IT AND ITes, government, transportation and logistics, manufacturing, telecommunication and others).

The regions that have been considered in the study are:

North America

Europe

Asia Pacific

Latin America

Middle East and Africa

The report is inclusive of all the information that is valuable for market entrants. This will enhance the ability of the user to foresee trends and make beneficial and informed decisions. The report is also available for customization according to the requests of the user. These help in detailing the report around the regions or participants that comes under the users’ concern and targets.

Key Coverage of Report:

Total addressable market

Regional analysis [North America, Europe, Asia Pacific, Latin America, Middle East & Africa]

Country-wise market segmentation

Market size breakdown by the product/ service types

Market size breakdown by application/industry verticals/ end-users

Market share and revenue/sales of the key players in the market

Production capacity of prominent players

Market Trends like emerging technologies/products/start-ups, SWOT Analysis, Porter’s Five Forces, and others.

Pricing Trend Analysis

Brand wise ranking of the key market players worldwide

Sales Forecast:

The report contains historical revenue and volume that backing information about the market capacity, and it helps to evaluate conjecture numbers for key areas in the Cloud Microservices market. Additionally, it includes a share of each segment of the Cloud Microservices market, giving methodical information about types and applications of the market.

Reasons for Buying Cloud Microservices Market Report

This report gives a forward-looking prospect of various factors driving or restraining market growth.

It renders an in-depth analysis for changing competitive dynamics.

It presents a detailed analysis of changing competition dynamics and puts you ahead of competitors.

It gives a six-year forecast evaluated on the basis of how the market is predicted to grow.

It assists in making informed business decisions by performing a pin-point analysis of market segments and by having complete insights of the Cloud Microservices market.

This report helps the readers understand key product segments and their future.

Which emerging technologies are believed to impact the Cloud Microservices market performance?

Which regulations that will impact the industry?

Who are the most prominent vendors and how much market share do they occupy?

What are the latest technologies or discoveries influencing the Cloud Microservices market growth worldwide?

(**NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.)

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

The post Global Cloud Microservices Market Recent Study of Business Strategies and Latest Rising Trend appeared first on Artificial Intelligence.

Leaders help organizations maneuver transformational changes. Latest advances in artificial intelligence have triggered large scale changes in every industry. Interestingly, a recent Harvard Business Review article has predicted that “the first wave of corporate AI is doomed to fail.” The authors, Kartik Hosanagar and Apoorv Saxena, have argued that most of the companies are not approaching the AI-focused initiatives in the right way.

Undoubtedly, organizations must persist in their initiatives, experiment continuously, and learn from mistakes. However, the initiatives may still fail due to lack of preparation or simply because of not following a systematic approach. Senior leaders in every organization must rise and face this challenge by creating robust strategies that may help the company thrive in the new age of AI. Let us explore three such critical strategies that leaders must have to harness the powers of artificial intelligence.

Strategy#1: Cut Through the Clutter

Everyone is talking about AI. In this year’s Consumer Electronic Show (CES 2017), intelligent devices took center stage. Companies are desperately trying to ride the wave of AI. Products and services are regularly getting tagged with this breakthrough technology. There is a lot of hype surrounding machine learning, deep learning, neural networks, and related topics.

Consequently, leaders must have a strategy to cut through the clutter and identify how their organizations can use artificial intelligence to create positive outcomes for the business and customers. While it may be too much to expect that an executive would have hands-on experience in technology, but a reasonable level of AI-literacy would be a must. Leaders should:

• have a broad level understanding of AI and how the machine learning approach is superior to the traditional (hard-coded logic driven) computing approach
• realize AI’s potential and limitations (what it can do and what it can’t do) to ensure that there are no exaggerated expectations created within the organization about its capability
• identify how this technology can be leveraged to create value for the customers.

Strategy#2: Creatively Apply the “New Electricity”

When asked about the factors that might be hindering progress of AI in business Eric Brynjolfsson, author and professor at MIT Sloan School, has remarked, “What’s not holding us back is the technology, what is holding us back is the imagination of business executives to use these new tools in their businesses. You know, with every general-purpose technology, whether it’s electricity or the internal combustion engine the real power comes from thinking of new ways of organizing your factory, new ways of connecting to your customers, new business models. That’s where the real value comes.”

Sooner or later, most of the organizations would acquire expertise in AI by upskilling the existing workforce or by hiring new talents. At that time, the primary differentiator would be the way one organization applies this technology to solve customers’ problems vis-à-vis another.

Leaders must spot and identify opportunities to apply these powerful tools by encouraging their teams to tap creative ideas. Such ideas may come from within as well as outside the organization. Moreover, the leaders must foster an ecosystem of innovation that helps the teams to ask the right questions around customer’s pain areas, effectively use the machine learning and AI to solve those issues, and create extraordinary customer experiences.

Strategy#3: Grow and Mine a Reservoir of “New Oil”

This is perhaps the most underrated and yet critical strategy a leader must have in the age of artificial intelligence. Leaders must strategize, invest in, and systematically help in building reservoirs of quality data that can harvested in the future. The businesses that would be able to do this better would differentiate themselves from the rest.

Peter Norvig, director of research at Google and a leading authority on AI once said, “More data beats clever algorithms, but better data beats more data.” One of the most common class of machine learning algorithms are based on supervised learning, which needs millions of data sets for training purposes. The efficacy of these algorithms depends not only on large amounts of data but on data that is labeled or tagged.

Everyone is talking about data, but hardly anyone is emphasizing the urgency to capture data in a systematic way so that it can be used by machine learning algorithms in the future. In any business, there are numerous data sources coming from internal and external stakeholders as well as data generated through human-machine interactions. For example, whenever a company plans to use Robotic Process Automation (RPA) to improve the accuracy and cycle time of operations, it must capture transactional data from various process steps to record how humans are currently performing these tasks. At the time of storing such data, it may complete the necessary tagging and run quality checks to prune human errors or biases. Without such disciplined approach of gathering and labeling data, AI-initiatives may not be effective.

Bottom Line on Strategies Needed to Leverage AI

While AI is destined to transform almost all industries, not every organization will unlock its maximum potential. Executive leaders of every organization must create effective strategies to harness the powers of AI. Specifically, they must have a strategy to cut through the clutter, to creatively apply AI in business, and to create reservoirs of quality data that can be appropriately mined by AI tools in the future.

The post 3 Strategies to Leverage AI in Business appeared first on Artificial Intelligence.