Iot & AI

The future when people will come home after work and ask their TV to turn on and the washing machine to wash clothes in the economy mode doesn’t seem so distant.

We can already talk to virtual assistants like Google Home, Siri or Alexa to search for a movie or order a new scarf with delivery at door. Why not doing the same thing with everything else?

In fact, this is what everyone now calls the Internet of Things which is according to Wikipedia basically the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect, collect and exchange data. For any IoT service to be worth buying, such actions must demonstrate true value and yield benefit to the user. Of course, they vary from adequate physical actions (e.g. deploying a taxi to the site) to simply informing users (e.g. sending a message to inform a user that they have run out of milk).

It is here at the data analysis step that the true value of any IoT application is determined, and this is where Artificial Intelligence provides a crucial role by making sense of data streamed from devices. AI serves to detect patterns in this data from which it can learn to adjust the behavior of IoT service.

Probably the best example of AI and IoT successfully working together is self-driving cars by Tesla Motors. Cars act as “things” and use the power of Artificial Intelligence to predict the behavior of cars and pedestrians in various circumstances. Moreover, all Tesla cars operate as a network. When one car learns something, they all learn it.

Automated vacuum cleaners are a good example of artificial intelligence “embodied” in a robot. For example, iRobot by Roomba controlled through an app can map and “remember” a home layout, adapt to different surfaces or new items, clean a room with the most efficient movement pattern, and dock itself to recharge its batteries.

Another good example of AI and IoT combined together is a smart thermostat solution by Nest Labs. Nest’s smartphone integration allows to check and control temperature from anywhere. The device analyzes temperature preferences and work schedule of its users and adapts temperature accordingly.

Applications, where IoT works together with AI, are only growing, creating new markets and opportunities and they are highly unlikely to lose ground in the nearest future.

The Internet of Things is getting smarter. Companies are incorporating artificial intelligence—in particular, machine learning—into their IoT applications. The key: finding insights in data.

With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI.

Artificial intelligence plays a growing role in IoT applications and deployments. Both investments and acquisitions in startups that merge AI and IoT have climbed over the past two years. Major vendors of IoT platform software now offer integrated AI capabilities such as machine learning-based analytics.

The value of AI in this context is its ability to quickly wring insights from data. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound. Compared to traditional business intelligence tools—which usually monitor for numeric thresholds to be crossed—machine learning approaches can make operational predictions up to 20 times earlier and with greater accuracy.

Other AI technologies such as speech recognition and computer vision can help extract insight from data that used to require human review.

In its essence, the technology of IoT is about devices with built-in sensors, which provide data to one or more central locations through internet connectivity. That data is then analyzed and corresponding actions are initiated. AI applications for IoT enable companies to avoid unplanned downtime, increase operating efficiency, spawn new products and services, and enhance risk management.

AVOIDING COSTLY UNPLANNED DOWNTIME

In a number of sectors—industrial manufacturing or offshore oil and gas, to name two—unplanned downtime resulting from equipment breakdown can cost big money.

Predictive maintenance—using analytics to predict equipment failure ahead of time in order to schedule orderly maintenance procedures—can mitigate the damaging economics of unplanned downtime. Machine learning makes it possible to identify patterns in the constant streams of data from today’s machinery to predict equipment failure. In manufacturing, Deloitte finds predictive maintenance can reduce the time required to plan maintenance by 20–50 percent, increase equipment uptime and availability by 10–20 percent, and reduce overall maintenance costs by 5–10 percent.

INCREASING OPERATIONAL EFFICIENCY

AI-powered IoT can also help improve operational efficiency. Just as machine learning can predict equipment failure, it can predict operating conditions and identify parameters to be adjusted on the fly to maintain ideal outcomes, by crunching constant streams of data to detect patterns invisible to the human eye and not apparent on simple gauges.

Machine learning often finds counterintuitive insights: A shipping fleet operator’s machine learning tools determined that cleaning their ships’ hulls more often—an expensive, downtime-causing process—actually increased the fleet’s overall profitability. The math went against shipping industry instincts: Hulls kept smooth through frequent cleaning improve fuel efficiency enough to vastly outweigh the increased cleaning costs.

ENABLING NEW AND IMPROVED PRODUCTS AND SERVICES

Enhancing IoT with AI can also directly create new products and services. Natural language processing (NLP) is getting better and better at letting people speak with machines, rather than requiring a human operator. AI-controlled drones and robots—which can go where humans can’t—bring all-new opportunities for monitoring and inspection that simply didn’t exist before.

Fleet management for commercial vehicles is being reinvented through AI, which can monitor every measurable data point in a fleet of planes, trains, trucks or automobiles to find more efficient routing and scheduling, and reduce unplanned downtime. Cloudera claims its fleet management AI has cut downtime for fleet vehicles monitored by Navistar devices up to 40 percent.

ENHANCING RISK MANAGEMENT

A number of applications pairing IoT with AI are helping organizations better understand and predict a variety of risks as well as automate for rapid response, enabling them to better manage worker safety, financial loss, and cyber threats.

Applications already in use include detecting fraudulent behavior at bank ATMs, predicting auto driver insurance premiums based on their driving patterns, identifying potentially hazardous stress conditions for factory workers, and monitoring law enforcement surveillance data to identify likely crime scenes ahead of time.

IMPLICATIONS FOR ENTERPRISES

For enterprises across industries, AI is a natural complement to IoT deployments, enabling better offerings and operations to give a competitive edge in business performance.

Machine learning for predictive capabilities is now integrated with most major general-purpose and industrial IoT platforms, such as Microsoft Azure IoT, IBM Watson IoT, Amazon AWS IoT, many more

A growing number of turnkey, bundled, or vertical IoT solutions take advantage of AI technologies, especially machine learning. It is often possible to use AI technology to wring more value from IoT deployments that were not designed with the use of AI in mind. IoT deployments generate huge, constant streams of data, which machine learning excels at examining to identify patterns that lead to greater value.

THE FUTURE OF IoT IS AI

It may soon become rare to find an IoT implementation that does not make some use of AI. The International Data Corp. predicts that by 2019, AI will support “all effective” IoT efforts and without AI, data from the deployments will have “limited value.” If your company has plans for implementing IoT-based solutions, those plans should probably include AI as well.

The Internet of Things (IoT) is a term that has been introduced in recent years to define objects that are able to connect and transfer data via the Internet. ‘Thing’ refers to a device which is connected to the internet and transfers the device information to other devices. The cloud-based IoT is used to connect a wide range of things such as vehicles, mobile devices, sensors, industrial equipment’s and manufacturing machines to develop a various smart systems it includes smart city and smart home, smart grid, smart industry, smart vehicle, smart health and smart environmental monitoring. In the IoT, cloud computing environment has made the task of handling the large volume of data generated by connecting devices easy and provides the IoT devices with resources on-demand.

An increasing number of physical objects are being connected to the Internet at an unprecedented rate realizing the idea of the Internet of Things (IoT). A recent report states that “IoT smart objects are expected to reach 212 billion entities deployed globally by the end of 2020”. Similarly, while the number of connected devices already exceeds the number of humans on the planet by over 2 times, for most enterprises, simply connecting their systems and devices remains the first priority. A recent report state that, “The overall Internet of Things market is projected to be worth more than one billion U.S. dollars annually from 2017 onwards”. As a result, data production at this stage will be 44 times greater than that in 2009, indicating a rapid increase in the volume, velocity and variety of data.

Hence, IoT based smart systems generate a large volume of data often called big data that cannot be processed by traditional data processing algorithms and applications. Here will therefore, by difficulty in storing, processing and visualizing this huge data generated from IoT based system. However, there is highly useful information and so many potential values hidden in the huge volume of IoT based sensor data. IoT based sensor data has gained much attention from researchers in healthcare, bioinformatics, information sciences, policy and decision makers in governments and enterprises. Nowadays, Artificial intelligence methods play a significant role in various environments including business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring.

The IoT and Artificial Intelligence (AI) will play a vital role in numerous ways in the future. There are multiple forces which are driving the growing need for both technologies and more and more industries, governments, engineers, scientists and technologists have started to implement it in manifold circumstances. The potential opportunities and benefits of both AI and IoT can be practiced when they are combined, both at the devices end as well as at server. For example, AI combined with Machine learning can study from the data to analyze and predict the future actions in advance, such as order replacements in marketing and failure of equipment in an industry just in time. Moreover, AI can be used with machine learning in smart-homes to make a truly grand smart home experience. Similarly, AI methods with IoT can be used to analyze the human behavior via Bluetooth signals, motion sensors, or facial-recognition technology and to make the corresponding changes in lighting and room temperatures. This special issue aims to gather recent research works in emerging artificial intelligence methods for processing and storing the data generated from cloud-based Internet of Things.

Threat intelligence in enterprises

A threat intelligence is a fairly new concept still evolving as product / service where the concept is to gather raw data about existing or emerging threats and threat actors from several sources, and then analyzes and filters that data to produce usable information in the form of management reports and data feeds for automated security control systems. Its primary purpose is to help organizations understand the risks of and better protect against major threats specifically zero-day threats. We can tune the service to deal with advanced persistent threats and exploits, especially those most likely to affect their specific environments.

Learning about relevant threats as soon as possible gives organizations the best chance to proactively block security holes and take other actions to prevent data losses, breaches or system failures.

Threat intelligence service models

Threat intelligence service companies like ITS, we are relative newcomers to this section of security industry, so there are still a lot of differences among the types of services each vendor delivers.

Some such services simply provide data feeds that have been cleansed of most false positives. The most common for-a-fee services provide aggregated and correlated data feeds (usually two or more), as well as customized alerts and warnings specific to a customer's risk landscape. Another type of threat intelligence service handles data aggregation and correlation; incorporates information automatically into security devices (firewalls, security information and event management, etc.); and provides industry-specific threat assessments and security consulting.

Many types of threat intelligence platforms are sold on a subscription basis, usually at two or three capability levels, and is delivered via a cloud platform. We at India Training Services offer managed services for delivery across on-premises systems. This comprises of training and a solution installed on cloud platform for the enterprise as one Threat Management solution.  

Threat intelligence platforms can dramatically improve the efficiency of security staff in proactively blocking security incidents, because subscription costs tend to run moderately high to very expensive, and because of the equipment needed for on-premises deployment, threat intelligence platforms are currently geared mainly toward larger midmarket organizations and enterprises. As the cloud continues to move down market, however, threat intelligence tools are bound to do likewise.

The history of threat intelligence

Threat intelligence solutions or platforms came into being mainly because of the plethora of data available, whether generated internally or acquired from external feeds, on current and emerging IT security threats. It takes considerable time, effort and expertise to sift through the data and transform it into information that's pertinent to an organization, however.

Security companies, such as Symantec, that make it their business to track threats and provide frequent updates to their antivirus products, have maintained global threat databases for years,  populated from software agents running on millions of client computers and other devices. Such data, along with feeds from other sources, is the foundation for the information provided by developed threat intelligence tools.

Understanding threat intelligence service data

Data from various threat intelligence sources differs in quality and structure, and must be validated. Validating data involves human and machine analysis for processing, sorting and interpreting.

Apparent threats are also correlated against the entire pool of threat data to identify patterns that indicate suspicious or malicious activity, and are also linked to technical indicators for categorization purposes. Finally, the data is converted into contextual information that provides insights about the tactics and behavior patterns of emerging or advanced threats and threat actors.

In the end, the threat information that's usable and actionable must be accurate, timely, relevant to the customer, align with the customer's security strategies and be easily incorporated into existing security systems.

Characteristic features of threat intelligence solutions

Now that we've understood the purposes and benefits of threat intelligence, let's look at the most common features found in these kinds of services.

• Data feeds: Many types of data feeds are available through threat intelligence platforms. Examples include IP addresses, malicious domains/URLs, phishing URLs, malware hashes and many more. A vendor's threat intelligence feeds should draw data from its own global database, as well as from open source data, information from industry groups and so on, to produce a pool of data that is both broad and deep.
• Alerts and reports: Most services provide real-time alerts, along with daily, weekly, monthly and quarterly threat reports. Intelligence may include information about specific types of malware, emerging threats, and threat actors and their motives.

Security analysts or IT security staff members are needed to manage data feed information. The data is either incorporated into proprietary equipment (typically from the same vendor that provides the feed), or the information may be available in standard file formats, such as XML, CSV, STIX or JSON, for use in a variety of security management tools and platforms.

Depending on the level of information in the data feeds, staff might need specialized or specific training from the vendor.

Some companies offer managed security services that offload most of the administrative burden associated with a proactive security approach. A managed service may include experts that provide threat intelligence reports, monitor an organization's assets 24/7 and provide threat mitigation and incident response.

The cost of threat intelligence platforms varies as much as the services themselves. Data feeds alone can cost thousands of dollars per month, and related expenses include the costs of maintaining a 24/7 security operations center staffed with technicians and analysts. By way of comparison, managed security services are typically tens of thousands of dollars per month, easily running into six or seven figures per year for larger environments.

As with most things in business, the least expensive services require more human time and effort on the customer side.

Because threat intelligence services vary widely, a key challenge in selecting such a service is knowing what the organization needs on what is the most critical information to maintain, how the information will be distributed / used and having the right staff in place to use that service appropriately.

There a large number of threat intelligence services out there, and they all deliver and collect data about emerging threats in different ways. Some are better at providing detailed global threat reports, while others are capable of drilling down and delivering reports to customers that are highly industry- or (even) company-specific. In addition, there are some services that better serve an organization with existing defense equipment, while others provide threat intelligence that's easily integrated into an organization's existing security controls -- no matter the equipment in place.

We deliver a range of training courses carefully designed to help people and organizations protect themselves against crippling data attacks. We can tailor a specific training module to meet your needs, or you can contact me at ravindrapande@gmail.com.

Also we have pre-developed modules on Threat Intelligence planning

·         Developer Security Training

·         QA Security Test Testing Training

·         Mobile Penetration Testing Training

·         Wireless Penetration Testing Training

·         Security Awareness Training

·         Web Application Penetration Testing Training

·         Infrastructure Penetration Testing Training

Visit us at www.indiatrainingservices.com

Smartness Progress IoT

Our smartphone is about to get smarter, thanks to artificial intelligence (AI) and machine learning (ML). And that has huge implications for enterprise support for mobility. We at India Training Services were analyzing the inherent risks & educate the enterprise as well as individuals to address security laps in such smart adoption. This is just a summery of our finding in last few months.

Enterprise mobility has long promised to allow workers to be productive wherever they are, to speed up business processes and to improve accuracy and efficiency by putting the most up-to-date data in the hands of workers in the field, says Kevin Burden, vice president of mobility research and data strategy at 451 Research. The addition of AI will help deliver on those promises.

The ways it will do that are multifaceted, with the effects seen in the areas of device management, user experience, security, applications and the very devices themselves. At the same time, new concerns about privacy are sure to arise as AI and ML become ever more efficient at gathering data points.

AI is going to mean new applications and even possibly new device types, primarily because AI will alter and improve the business logic within apps. Applications will be able to take advantage of advanced user interfaces with speech and visual gesture recognition. One element of enterprise mobility that will clearly benefit from AI is the organizational challenges that were created by having a disparate and mobile workforce. Application providers will apply ML to user activity streams, giving organizations insight into how end users spend their time, he says. As patterns of behavior are identified, organizations will be able to improve processes and the user experience.

Easier authentication is one example. Pattern recognition is an AI strength. Because AI can gather huge amounts of such data and recognize anomalies with ease, it can make authentication much more transparent for users..

Some of the more advanced algorithms detect how a user enters text and analyze their gait. Pair those distinctive patterns with information on the user’s active connections and GPS data. The number of layers of multi-factor authentication or constant requirements to enter passwords could be greatly reduced. Take this mobile device management course from India Training Services and learn how to secure devices in your company/ group/ homes without degrading the user experience.

Another AI/ML important improvement will be in speech-to-text capabilities, allowing that technology to replace smartphone data input in some situations. Verticals such as medical and others will use speech for data input for basic tasks such as records and workflow updates on regular basis.  The applications will become intuitive in whole new ways: ML will also be integrated more into mobile applications to enable quicker & intelligent decisions, responses and inputs to anticipate user actions, as opposed to requiring users to look for options in windows and drop downs.

It's not just IT will benefit from AI’s and ML’s assistance with device management. The technology can be used to scan all of the devices in an organization and proactively notify the administrator of issues, such as the discovery that 25% of the organization’s Android devices are two versions out of date. Even more helpful for IT organizations that are short of personnel is the potential to automate actions based on the information discovered by AI/ML. The technology will really pay off for IT once the systems can use AI to detect and remediate issues on the fly.

IT is also likely to appreciate many of the AI-fueled user-experience enhancements that are coming to email, contact and calendar tools as vendors add personal-assistant technology. It’s fairly common already for calendars to use AI to tell users when they should leave for an appointment. This is already started in many event management programs.

The advantage to IT isn’t direct, but many IT departments want users to stick to their company-provided email, contact and calendar tools when working, as a way to protect and segregate work data from personal and other needs. The new user-facing convenience features could make using those tools more appealing to users.

While it’s still getting clear day by day that how AI will impact the overall mobility market on a long-term basis, it is certain that the enterprise mobility management space is very crowded, without any real significant differentiation, so vendors will look to AI for new ways to innovate build more cost effective & time saving ways to to get results out of this technologies.

AI and security, perhaps the area with the greatest potential to get a boost from AI, and particularly its pattern-recognition chops, is security. Certainly many vendors are already incorporating AI/ML in their security offerings as a way to boost performance.

One area where vendors already have offerings is ML-based mobile threat detection. For example, major strategic game uses ML in its new immature Threat Defense mechanisms, which employs usage and behavioral analysis to detect suspicious behaviors in mobile apps or networks and then learns from the information it gathers to continuously improve its ability to detect malware and rogue networks.

Many new Mobile developers have integrated deep learning into its endpoint security products that provide what it calls “predictive security.” The company aims to extend this deep learning layer to all endpoints, including  mobile ones. It has also introduced an email protection tool that uses the same technology to intercept more threats before they can make it onto the endpoints.

Other vendors see an opportunity to use AI to help IT departments that are stretched thin to make sense of all the data that is gathered by their existing endpoint management tools. Among them is Citrix, whose unified endpoint management offering also manages all devices that enter the workplace, including laptops, mobile phones, tablets and wearable. The Citrix security analytics application monitors those devices and helps IT to apply security policies and ensure that the network remains secure.


Citrix Analytics also performs user-behavior analytics, applying machine learning to categorize users as high, medium or low risks and then adjusting the risk scores as more data comes into the system.
IBM, meanwhile, has developed MaaS360 with Watson, a cloud-based application designed to help IT administrators make sense of the massive amounts of data generated by endpoints and their users, apps and content. It applies cognitive technologies to security, end-user productivity, mobile app management and administration.

Enterprise mobility management users are inundated with more information than they can absorb about apps, configuration/policy best practices, productivity tools, and emerging threats and vulnerabilities, IBM explains. IBM MaaS360 delivers cognitive insights, embedded in the platform, to help organizations wade through the information they’re gathering and distill it into insights and recommendations that are relevant to their business. The core of MaaS360 is IBM Watson technology, which can index and annotate huge volumes of datasets to look for relevant data that applies contextually to each individual client deployment of MaaS360.

A privacy backlash? One dark cloud on the AI/ML front is data privacy.

Users have become more aware of the perils of their personal information ending up in the hands of companies such as Facebook and Google, etc. So the idea of an employer or other company retaining the outputs of their mobile devices, apps and data usage which some calls workplace analytics is sure to meet opposition from some users.

These concerns can’t be ignored, especially given the emergence of strict regulations such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act of 2018. These regulatory concerns could strip the utility from mobile offerings dependent on AI/ML. While user push back on data will not negate the value of AI/ML in mobile offerings, it could impede the collection of data for some or all users and without any data the results will be deteriorated. That, in turn, could make the data less useful for some groups of users or some regions, while still providing value to others.

To improve this, organizations must forthright in discussing what data they collect and how it will be used. May big IT players advises clients to illustrate the outcome and its benefit to users and take pains to note what won’t be collected or done with data. The list of what IT does not do with data should almost always be longer than the list of what it does or can do with data.

Feel free to contact me at ravindrapande@gmail.com in case need any further details.

Startup Failures

I have observed / worked with say few startups now and some are failures as far as planned business goals achievement concerned. Are they in  business yes only one moved away from software development.  I  am just trying to collect my thoughts on these failures. Don’t want to blame anyone in particular but try to review as a third party PMO stand point. PMO as I am a manager & leader tried my best to go and educate the CEO and other founders without much impact yes this is my short coming. This is my personal analysis so please don’t escalate to anyone or any business.

There is a clear difference between leadership and management. Leadership is of the spirit, management is of the mind. Managers are necessary, but leaders are essential. We must find managers who are not only skilled organizers, but inspired and inspiring leaders.

I’ve often said real leaders refuse to take the credit for success, but they will always accept responsibility for failures.  Yes; but it goes with the territory.  In this  blog I’m going to toss out the politically correct story-lines and reveal the top reasons that leaders fail

In the points listed below I’ll examine some of the more common reasons attributed to business failure, and I’ll likewise assess the roles and responsibilities of leadership as they pertain to said reasons being leadership failures:

 Lack of Vision: It is the role of the CEO to clearly define and communicate the corporate vision. If there is no vision, a flawed vision, or a poorly communicated vision, the responsibility falls squarely in the lap of executive leadership. Moreover, if the vision is not in alignment with the corporate values there will also be troubled waters ahead.

 Poor Branding: A poor brand generally means leadership has failed. Brands fall into decline for only one reason – leaders have abdicated their responsibility. They have allowed their brand equity to erode, and failed to deliver on the brand promise. Leaders who don’t steward their brand as one of the greatest corporate assets deserve the fate that awaits them. Branding is an inline activity you can’t wait for I will build then start bending, in my understanding branding start with vision, with idea inception.

Lack of Character: It doesn’t matter what your title is, if you don’t do the right things for the right reasons you will fail. Leaders who don’t display character won’t attract it or retain it in others. Leaders, who fail to demonstrate a constancy of character won’t create trust, won’t engender confidence and won’t create loyalty. Vision understanding builds a responsibility of execution so if the CEO is with visionary in most of the cases but if acts as external observer things start going in ”this was/ is his responsibility “ way & so the blame game and rectifications killing the schedules &  deliveries.

Lack of Execution: Everything boils down to execution, and ensuring a certainty of execution is job number one for executive leadership. Entrepreneurs or CEO s who don’t focus on deploying the necessary talent and resources to ensure that the largest risks are adequately managed, or that the biggest opportunities are exploited have a leadership team destined for failure. Or many CEO builds team stating this is not I will do but just observe for example initial sales left to technocrats which cripple everything. Sales is the art where mostly technocrats fail as they are more in love with the produce as creator than business angle understanding

 Flawed Strategy: A flawed strategy simply reveals weak leadership. While there are exceptions to every rule, companies tend to succeed by design and fail by default. Show me a company with a flawed strategy and I’ll show you an inept leader.  This is major killing point and there is no fixed formula only business augmentation understanding will take you to end.

Capital Shortages : I have witnessed well capitalized ventures fail miserably, and severely under-capitalized ventures eventually grow into category dominant brands. A lack of capital can provide a socially acceptable excuse for business failure, but it is not the reason businesses fail. Raising, deploying, and managing capital is ultimately the responsibility of leadership. The amount of capital required to run a business is based upon how the business is operated. Therefore if leadership operates the business without consideration for capital constraints, or irrespective of capital formation issues, then the blame should fall squarely on the shoulders of leadership. Moreover, if executive leadership squanders capital through irresponsible acts, there will also be severe consequences.  This is major issue with Indian executions but still

Poor Management: It is the job of leadership to recruit, mentor, deploy, and retain management talent. If the management team is not getting the job done, it’s not a management problem, it’s the fault of executive leadership. Show me a leader that blames his management team for failure to execute and I’ll show you a poor leader. 8. Lack of Sales: A lack of sales is ultimately attributable to a lack of leadership. Strategy, pricing, positioning, branding, distribution, compensation, or any number of other metrics tied to sales force productivity all rest with executive leadership. A lack of revenue is not someone else’s problem, it’s a leadership problem.

Toxic Culture: The truth is nothing stifles productivity and creates conflict like a toxic culture. That said, a toxic culture simply cannot exist where good leadership is present and engaged. If the lunatics have gained control over the asylum be sure to fit leadership for a straight-jacket as well.

No Innovation: Leaders create a culture of innovation or they kill it. Leaders who can’t stay in front of the market tend to get run over by it. Great leaders have a strong bias to action. They don’t rest upon past accomplishments, and are always seeking to improve through change and innovation. Those leaders who don’t openly embrace change will be doomed by their antiquated outlook.

Market Target miss: Good leadership pursues sound market opportunities. Pursuing the wrong market, or pursuing the right market improperly is also the fault of executive leadership. Scaling a business too fast, too slow, or worse yet, not designing a scalable business to begin with is a leadership issue. No market equals no leadership…

Poor Professional Association: Nobody has cornered the market on knowledge and wisdom. If leadership doesn’t seek out the best quality advice available to them, then they will likely not make the best decisions. All CEO s and entrepreneurs need top quality professional advisers. There is no excuse for C-level leaders to have blind spots.  When a leader has a “miss” or a blind-spot, he or she is simply showing the arrogance of operating within the limitations of their own thinking.

The Inability to Attract and Retain Talent: Great leaders surround themselves with great talent. They understand that talent be gets more talent. If your company doesn’t possess the talent it needs to achieve its business objectives no one is to blame but leadership.

Competitive Awareness: A business does not need to be the category dominant player to avoid failure. That being said, it is the leadership’s responsibility to understand the competitive landscape and navigate it successfully. If a company isn’t consistently winning, it’s not what the competition is doing, but rather poor leadership that creates the inability to compete.

Obsolescence or Market Changes: If executive leadership is in touch with the market it will be difficult to be caught by surprise. It is the responsibility of executive leadership to make sure that the proper attention is given to innovation, business intelligence and market research to manage the risk of obsolescence and market changes.

A few words on leaders & Team chemistry, Leaders develop guidelines with their team - they constantly enlarge the guidelines as the team becomes willing to accept more responsibility. This can be as a simple as coding standards or security guidelines at work.  Leaders change their role according to the demands of the team - for example they become more of a coach or facilitator. Leaders involve team members as working together or owning together - in finding new ways to achieve agreed-upon goals.  Leaders create the opportunity - for group participation and recognize that only team members can make the choice to participate. This need to happen otherwise we are moving towards doom.

Bottom line…businesses don’t fail – leaders do. The talent that it takes to operate at the C level is matched only by the amount of responsibility that goes with the territory. If it was an easy job everyone would be a CEO or entrepreneur. Thoughts ? wire me at ravindrapande@gmail.com happy to learn and grow @ India Training Services