How a New Hire Impacts Team Dynamics and Overall Productivity

How a New Hire Impacts Team Dynamics and Overall Productivity

Productivity + Team Dynamics New Hire Impact 

Loads of companies and recruiters use some type of screening tests but few look at the all important concept that faster productivity + team dynamics in whether a potential new hire is both a role fit AND a team fit   While some have a one-size-fits-all behavioral analysis testing for the candidate only, what are the recruiter or hiring manager comparing the candidate to?

Some measure job skills, others measure interpersonal and communication skills, planning and organizing, and some measure aptitudes, and still others cognitive ability.

There are even some that test applicants on their ability to make presentations or on their behavior pattern in a simulated meeting, however they still fail to consider profiling hiring teams in order to form a target candidate profile as part of measuring team fit to make a positive impact on team dynamics.

Productivity + Team Dynamics New Hire Impact by Role Fit + Team Fit Analysis

Essentials for Productivity + Team Dynamics New Hire Positive Impact

One way to understand the first part of performance based recruiting is in the discovery step prior to planning a search strategy. Sadly too many internal recruiters and HR managers put too much emphasis on matching potential candidates to a “one-size-fits-all” corporate culture.  They fail to take into account that EACH TEAM is UNIQUE.  Each team has it’s own culture that is not a clone identity to the corporate culture.  If you want to achieve faster productivity + team dynamics that are positive for the new hire, you must take that individual team culture the new hire will work within as part of your search strategy.  What the executive search consultants at NextGen do is to ask the stakeholders (listed below) to take a brief less than 10 minutes online survey that can be taken 24/7.

1. his/her direct report/hiring manager

2. at least 2-3 internal customers

3. for sales, product management, product marketing, and sales engineering roles, we recommend at least  1-2 key external customers on whom this position will have an impact.

This is where many internal hiring managers miss the boat.  Many in Human Resources and even some executives fear asking external customers (who can be direct customers, partners, or vendors) to participate.  Their immediate thought is to perceive this as negative.  Rather it is completely positive as those external stakeholders value and appreciate you have included them on designing a target candidate profile.  It makes for better customer interaction because you are taking into account how not only how they interface with this role, but also the impact the potential new hire will have on productivity + team dynamics.

It is designed to gauge and measure each respondents view of the role and team in terms of values and motivations, relational communications traits, decision making and conflict resolution skills.  These questions in the survey, combined with how each stakeholder views the OBJECTIVES of the role instead of the requirements and responsibilities, is used to create a Composite Team Profile.  With the information gathered the original job spec, the discovery step, and the composite team profile, the recruiter can effectively construct a Search Strategy including a Target Candidate Profile for screening and assessment. The end goal is to identify, recruit, assess, and determine a shortlist of candidates that are both a role fit and team fit, meaning that they have a high likelihood of achieving the objectives of the role.  In other words, faster productivity + team dynamics becomes a positive impact for that hire.

Faster Productivity + Team Dynamics New Hire Assimilation

In Part 4 of Performance Based Recruiting, we will discuss how to customize an Executive Onboarding Program which prepares the candidate you hired to meet or exceed your performance objectives, assimilate quickly into the culture, and contribute positively to faster productivity + team dynamics.  By profiling hiring team to define a target candidate profile, you discover that team fit compatibility is essential to team dynamics.

Productivity + Team Dynamics New Hire Impact by Team Fit Analysis

How to Achieve New Hire Quick Assimilation, Faster Productivity, Longer Retention

NextGen’s award-winning Leadership Vault search process has resulted in 94% of our placements still working for the company we staffed at 3.5 years of employment.  In addition, the most common feedback is that the candidates we presented not only met, but exceeded client expectations.  Combined with an industry leading 24 to 36 months replacement guarantee and performance based recruitment fees, we are often called upon when other search firms have failed to deliver.    If you have a key staffing need in aerospace, artificial intelligence, cyber security, industrial manufacturing and automation, medical devices, mobile telecom, wireless, or Internet of Things, click on the image below to schedule a brief 10 minute chat with none of our practice leads.

 

Cost of Failed Executive Hires – Eliminate Poor Recruiting Techniques

Cost of Failed Executive Hires – Eliminate Poor Recruiting Techniques

Cost of Failed Executive Hires – Eliminate Poor Recruiting 

The cost of failed executive hires is tremendous. This is NOT limited to just CXOs and SVPs – same goes with VP and Director levels. It is surprising how many mid-cap and large conglomerates retained big-named search firms without realizing that with the exception of a few principals that manage the business and no longer recruit, most of the recruiters for these big firms are a revolving door – they flow in and out according to economic times. Boards should look towards smaller well-established retained executive search firms who have experts that have been working there for a dozen years or more and have a solid history of recruiting in your niche industry. The cost of failed executive hires is not only damaging – it can prove to be fatal. First are the costs related to the executive compensation, benefits, and severance package, as well as indirect cost such as travel, poor strategy and poor business plan execution, lost market share, lack of direction for the rest of the staff, and lack of trust in the Board of Directors. I see it all the time where I not only need to find a replacement CEO, CTO, CFO, or SVP, but also one that has turnaround experience and is willing to come into a situation that is not ideal for immediate success.

Several times over my career in Executive Search I find myself working with a candidate who believes they are an ideal fit for a retained search assignment I am conducting. My retained search work is “performance based” NOT contingency based, which means that when a client has paid a deposit for my time and effort, I am expected to get the job done.For an executive search consultant, getting the job done means nothing short of bringing forward high-impact “business changing” candidates to our clients. These are the “A Players“.  Not only must they have the required experience, expertise, and a proven track record of success, but he/she must prove to me that they can meet the challenges of the position, meet or exceed my client’s expectations, and make a “direct positive impact” on my client’s business.

Most fail to understand the impact on the cost of failed executive hires is. The truth is that by and large, about 55% of all employees at any given company are in fact “C” Players. They can do the job they were hired to do; they show up for work on time, do the job they were assigned, and are loyal to their managers.  However, they lack the entrepreneurship risk taking mentality, the “take charge” attitude, and the take no prisoners’ mindset required to make an impact on the marketplace.  In addition, they are easily replaced by outsourcing at a lower cost as well as artificial intelligence, software automation, and robotics.  And the latter three can run 24/7 without sick days, benefits, training, and their performance is most often anything but mediocrity.

Avoiding the Cost of Failed Executive Hires

While for many positions it is acceptable until technology advancements eliminate many “C” players, companies will continue to hire them for many roles.  At functional leadership and key engineering, sales, and operations the cost of failed executive hires can be fatal.  Many VPs and Directors will look to “B” players, which based on my 20 years experience in executive recruiting, are roughly up to 30% of all employees at any given company.  They outperform “C” players any day of the week and possess the intuitiveness and hunger to succeed that makes them valuable to their employer. They have a track record of success, albeit in their department but rarely make a definitive impact on the company’s overall performance in the marketplace or the business strategy.So what really is an “A” player? The misconception is that “A players” only exist at the executive level.  That is purely a myth.  Most “A players” were born that way or evolved into it in childhood, teenage, or young adult years.  These unique individuals comprise the Top 14% of the global workforce.  They are easily recognized early on.  In their youth, they were leaders and entrepreneurs – whether having the most lawns to cut, starting a community newspaper, excelling in Junior Achievement or inventing a product or service company that was acquired by a bigger fish.

These “A players” are not always leaders as their ideas, thought processing, and inventiveness make them excellent engineers. Same goes with RSMs and MAMs who can blow out the quotas as an individual, but if you try to move them into leadership levels they fail.  The strategic thinker and the ability to “sell their ideas” type of “A players with superb interpersonal communications and conflict resolution skills are in fact the type of functional leader or senior corporate executive that is a “game changers” not only within a client’s vertical market, but have had similar success in other vertical markets within that industry or in a different industry altogether. They have a responsibility to the owners (founders, investors, and stockholders).

​​​​Cost of Failed Executive Hires is due to a poor Talent Acquisition Process

A Forbes article by a Silicon Valley CEO reveals that the cost of failed executive hires is estimated to be more than $500,000 or 2.5 times salary. And that does NOT include organizational, opportunity, productivity, and transitional costs for the new executive. As an Officer or Board member, you must ask yourself, why would you risk letting mediocre executives hires to occur?The same can be said of a VP of Engineering who needs principal level systems software engineer for that matter. You may save yourself a few dollars in the short run recruiting someone with your existing recruiting process, but the long term effects in the cost of failed executive hires may cost YOU and YOUR STOCKHOLDERS much more than 2.5 times salary or a recruitment search fee.

How to Alleviate the Cost of Failed Executive Hires

NextGen Global Executive Search not only reduces the cost of failed executive hires, we virtually eliminate them.  The award-winning Leadership Vault search method, developed over 30 years, is a the recruitment platform utilized by our executive search consultants that properly measures the potential candidates we identify by psychometrics to determine a strong match to role fit and team fit, document KPIs and the depth of candidates’ industry relationships, and provide a custom onboarding program that includes new hire self-development planning and a mentoring / coaching program that is easy to realize with little investment of time.  Backed by an industry leading 12 to 36 months replacement guarantee, the common feedback is the hire we placed met or exceeded their objectives.  Looking to fill a key  functional leadership or senior executive role in your company?  View our numerous recruitment case studies and client testimonials. in aerospace, artificial intelligence, cyber security, industrial automation, mobile, wireless and IoT or click the image below to contact us today.

 

Cyber Security with Artificial Intelligence Elements and Ai Platforms

Cyber Security with Artificial Intelligence Elements and Ai Platforms

 

Cyber Security with Artificial Intelligence Elements and Ai Platforms

Cyber-security has always been a major concern for providers, vendors and operators of IT systems and services. Despite increased investments in security technology, this has not changed, as evident in several notorious cyber-attacks and related security incidents that have taken place during the last couple of years.  It’s time to revolutionize cyber security with artificial intelligence.

For instance, earlier this year, the global “WannaCry” ransomware attack has severely affected the operations of numerous organizations worldwide, including major organizations such as Britain’s National Health Service (NHS). “WannaCry” has manifested the potential scale and physical consequences of cyber-crime incidents, while confirming the importance of proper cyber security measures.

Beyond their financial and business implications, cyber-attacks have a significant socio-economic impact as well, as they reduce citizens’ and businesses trust in IT systems and services. This lack of trust is a major issue in an increasingly connected world and in an era where IT systems are a primarily vehicle for increased competitiveness and productivity.  It’s therefore important to understand the factors that increase the number and scale of cyber security attacks, along with options for alleviating security incidents against IT infrastructures, such as phishing, botnets, ransomware and DDoS (Distributed Denial of Service) incidents.

Drivers of Advanced Cyber Security

Effective cyber-protection requires modern, advanced and intelligent cyber-security systems. The scale, complexity and sophistication of these systems are driven by the following factors:Technology Evolution: The evolving technological complexity of cyber infrastructures renders their protection more challenging. For example, the rise and expanded use of Internet-of-Things (IoT) technologies provides cyber-crime opportunities based on the hacking of individual devices. Such hacking was hardly possible before the advent of the IoT paradigm. This is evident in the emergence of large scale IoT attacks, such as last year’s IoT-based massive Distributed Denial of Service (DDoS) attack that brought down the Dyn’s Domain Name System (DNS) and affected major internet sites like Twitter, Amazon and Spotify.Complex Regulatory Environment: Nowadays, IT infrastructures’ operators and IT service providers need to adhere to quite complex regulatory requirements, including sector specific requirements (e.g., regulations for financial institutions) and general-purpose regulations such as EU’s general data protection regulation. The implementation of security policies and controls that address these regulatory requirements contributes to the rising complexity of cyber-security systems.Convergence of Physical and Cyber Security: IT systems are increasingly becoming connected and interdependent to physical systems and processes. This is for example the case with most industrial organizations, which converge their cyber physical infrastructures as part of their digital transformation in the Industry4.0 era. Industry4.0 infrastructures in sectors like energy, manufacturing and oil & gas form large scale cyber-physical systems. This cyber-physical nature leads gradually to a convergence of physical security and cyber-security measures and policies towards greater effectiveness and economies of scale. Converged cyber and physical security measures are more appropriate for identifying and mitigating complex, asymmetric security incidents, which are likely to attack both cyber and physical systems at the same time. Overall, while this convergence is beneficial for industrial organizations, it leads to a widening complexity for the respective security systems.New Business Models and Opportunities: The increased reliance of products and services on cyber infrastructures provides new business opportunities for providers of cyber-security solutions and services. As a prominent example, a new wave of cyber-insurance services is currently designed to support the emerging connected cars and semi-autonomous driving paradigms. These include for example, insurance business models that consider IT-derived information about the driver’s behavior as a means of adapting the car insurance fees. Supporting these opportunities implies additional security measures concerning for example the secure and trustworthy transmission of information that supports them.

Novel Approaches and Paradigm Shifts in Cyber Security​​​​

Confronting the recent wave of sophisticated cyber-attacks requires new approaches to threat identification, assessment and mitigation. Some of the main characteristics of these approaches, include:

  • Integrated and holistic nature: Instead of protecting specific devices and IT systems, there is a need for holistic, cross-cutting mechanisms that span all the different layers of modern cyber-security infrastructures, including individual device, fog/edge computing nodes, as well as cloud infrastructures. The implementation of holistic, cross-cutting mechanisms must be driven by integrated approaches to threat modelling, which identify, assess and rate vulnerabilities/threats across all different layers of a cyber-infrastructure. Assessment and rating is a key to prioritizing the deployment of specific security measures at the most appropriate places of the infrastructure. This is very important given that organizations operate based on quite constrained budgets for IT security, which makes it impossible to provide full protection against all possible vulnerabilities.
  •  Intelligence and dynamism: To cope with the emerging complex, large scale, dynamic and asymmetric attacks, there is a need for intelligent and dynamic mechanisms that can correlate information from multiple sources to timely identify security incidents and vulnerabilities. In practice, this requires the deployment of advanced data-driven techniques to security identification and assessment, based on machine learning and data mining models that implement a data-driven approach to cyber-security.
  •  Adherence to latest security standards: Fortunately, security standards have been evolving in-line with the rising sophistication of cyber-security attacks. This means that adhere to standards can be a safe path to designing and deploying systems that support the above-mentioned holistic approach to cyber-security. Organizations are therefore implementing security standards from the popular ISO/IEC 27001 on Information security management to the Security Framework of the Industrial Internet Consortium for securing cyber infrastructures that support industrial processes.
  •  User Friendly and Human Centric: Novel approaches to cyber-security should consider the human factor, to alleviate the need for end-users to understand security systems and processes. This is particularly important for organizations (such as Small Medium Businesses), which lack the knowledge and equity needed to invest in security training of their personnel.
  •  New delivery models: Organizations are increasingly adopting new delivery models for security services, such as Managed Security Systems and Security-as-a-Service. These models obviate the need for on premise installations and enable enterprises to leverage security services in a flexible pay-as-you go fashion.

The implementation of solutions with the above-listed characteristics signals a paradigm shift in the way security is designed, deployed and provided. This shift is destined to increase the cyber-resilience of organizations, including large enterprises and SMBs.

How to Revolutionize Cyber Security with Artificial Intelligence

In quest for dynamic, intelligence and holistic cyber-security mechanisms, security experts are nowadays considering the employment of AI based mechanisms. This consideration is largely motivated by recent advances in deep neural learning and AI, which facilitate the identification of very complex patterns based on human like reasoning.

Relevant technology advances have empowered Google’s Alpha AI to defeat grandmasters in the Go game, which is considered a milestone in the evolution of AI technologies. Likewise, AI techniques can be used to detect and assess complex attack patterns, as a means of preventing or alleviating large scale security incidents such as “Wannacy”.

The idea to deploy  or revolutionize cyber security with artificial intelligence can provide some compelling advantages, including:

  • Detecting complex attacks: Deep learning techniques based on advanced neural networks enable the detection of non-conventional, non-trivial security incidents that can be hardly detected based on commonly used rules and conventional reasoning.
  •  Predictive Security Analytics: AI is a perfect enabler for predictive security, through employing predictive data analytics based on deep learning. This can enable a paradigm shift from reactive to predictive security. Based on predictive security, organizations can anticipate the occurrence of threats to timely prepare and apply proper mitigation strategies.
  •  Security Automation: AI systems can increase the automation of security measures, through triggering mitigation actions automatically, upon the detection of cyber-security threats. While human involvement is always necessary and desirable, one way to revolutionize cyber security with artificial intelligence is to increase security automation, while delivering advanced protection functionalities at a lower cost.

Current and Future Status to Revolutionize Cyber Security with Artificial Intelligence

Despite these benefits, AI security implementations are still in their early stages. This is because there are several challenges to be addressed towards effective AI deployments. For example, there is a need for collecting and using large amounts of data, which are not always readily available. Therefore, AI systems are usually supported by the deployment of additional security monitoring probes, at the device, fog, edge and cloud layers of the cyber-security infrastructure.

Likewise, the effective deployment to revolutionize cyber security with artificial intelligence requires domain knowledge to avoid failures of the deep learning networks, such as failures due to overfitting on the training data. Such domain knowledge requires the collaboration of security experts, data scientists and experts in field processes, which is not always easy to achieve. Finally, there is also a need for aligning the operation of AI-based security systems with the business objectives and security policies of the organization, which can be extremely challenging.

In order to alleviate these challenges, enterprises need to consistently collect and manage security datasets, while at the same time assembling a security team with proper skills including both data science and security expertise.

Moreover, they need to leverage emerging AI-based tools in order to revolutionize cyber security with artificial intelligence for extracting knowledge from datasets, such as TensorFlow and H2O.ai.

Finally, it’s good to adopt an incremental deployment approach, which boosts the acquisition of knowledge and experience in the AI field, while gradually meeting business objectives.  As enterprises face unprecedented security challenges, new approaches are required. AI will be certainly among the most useful tools in organizations’ cyber-resilience arsenal. Despite early challenges, the best means to revolutionize cyber security with artificial intelligence are still to come.

Cyber Security Recruitment for Enterprise – Network – Mobile – Cloud – IoT – Ai

Many companies that develop machine learning platforms and utilize artificial intelligence are discovering potential issues with cyber security within deep learning networks, especially within FinTech, AdTech, and augmented reality for consumers. If you need help to identify and recruit key cyber security or Ai engineers, sales management, functional leaders, or senior executives, take a look at NextGen Executive Search.  For further information on our cyber security executive search firm or to contact us directly, click the image below.

 

IoT Recruiting – does your search firm deliver results?

IoT Recruiting – does your search firm deliver results?

IoT Recruiting – does your search firm deliver results?

IoT recruiting  where a lot of recruiting firms out there – contingency based, RPOs, and retained search firms claim they can deliver results.   You have the need to find the right candidate for a key senior executive or functional leadership role.  Or you need a key sales, business development, or engineering professional?   

The IoT recruiting team of NextGen Global Executive Search has 30+ years experience working for clients large and small in mobile networks, embedded wireless, IoT data and devices, industrial IoT applications and platforms, blockchains, agriculture, IoT consumer product goods wearables in sports, business, and fitness, as well as artificial intelligence and robotics.

IOT Recruiting that Delivers Results

NextGen has successfully recruited CEOs, CTOs, VPs and Directors in embedded wireless, ecosystem partnership development, firmware  development, and network design for IoT operators, semiconductor and device manufacturers, and wireless sensors.  Reach out to NextGen for your IoT recruiting needs.  NextGen has 30+ years experience recruiting for mobile network operators, cellular infrastructure vendors, and wireless semiconductor device manufacturers.  As 4G gets ready to evolve into 5G, both industrial IoT continues to grow and consumers will realize the benefits of Internet of Things connected to everything from smart homes with smart appliances to security to energy savings.

 

Smart Objects: Blending Artificial Intelligence into the Internet of Things

Smart Objects: Blending Artificial Intelligence into the Internet of Things

 

Smart Objects: Blending AI into the Internet of Things

smart objects artificial intelligence

It’s been more than a decade since the time when the number of internet-connected devices exceeded the number of people on the planet. This milestone signaled the emergence and rise of the Internet of Things (IoT) paradigm, smart objects, which empowered a whole new range of applications that leverage data and services from the billions of connected devices.  Nowadays IoT applications are disrupting entire sectors in both consumer and industrial settings, including manufacturing, energy, healthcare, transport, public infrastructures and smart cities.

Evolution of IoT Deployments

During this past decade IoT applications have evolved in terms of size, scale and sophistication. Early IoT deployments involved the deployment of tens or hundreds of sensors, wireless sensor networks and RFID (Radio Frequency Identification) systems in small to medium scale deployments within an organization. Moreover, they were mostly focused on data collection and processing with quite limited intelligence. Typical examples include early building management systems that used sensors to optimize resource usage, as well as traceability applications in RFID-enabled supply chains.

Over the years, these deployments have given their place to scalable and more dynamic IoT systems involving many thousands of IoT devices of different types known as smart objects.  One of the main characteristic of state-of-the-art systems is their integration with cloud computing infrastructures, which allows IoT applications to take advantage of the capacity and quality of service of the cloud. Furthermore, state of the art systems tends to be more intelligent, as they can automatically identify and learn the status of their surrounding environment to adapt their behavior accordingly. For example, modern smart building applications are able to automatically learn and anticipate resource usage patterns, which makes them more efficient than conventional building management systems.

Overall, we can distinguish the following two phases of IoT development:

  • Phase 1 (2005-2010) – Monolithic IoT systems: This phase entailed the development and deployment of systems with limited scalability, which used some sort of IoT middleware (e.g., TinyOS, MQTT) to coordinate some tens or hundreds of sensors and IoT devices.
  • Phase 2 (2011-2016) – Cloud-based IoT systems: This period is characterized by the integration and convergence between IoT and cloud computing, which enabled the delivery of IoT applications based on utility-based models such as Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). During this phase major IT vendors such as Amazon, Microsoft and IBM have established their own IoT platforms and ecosystems based on their legacy cloud computing infrastructures. The latter have alleviated the scalability limitations of earlier IoT deployments, which provided opportunities for cost-effective deployments. At the same time the wave of BigData technologies have opened new horizons in the ability of IoT applications to implement data-driven intelligence functionalities.

AI: The Dawn of a new era for Smart Objects using IoT applications

Despite their scalability and intelligence, most IoT deployments tend to be passive with only limited interactions with the physical world. This is a serious set-back to realizing the multi-trillion value potential of IoT in the next decade, as a great deal of IoT’s business value is expected to stem from real-time actuation and control functionalities that will intelligently change the status of the physical world.

In order to enable these functionalities we are recently witnessing the rise and proliferation of IoT applications that take advantage of Artificial Intelligence and Smart Objects.  Smart objects are characterized by their ability to execute application logic in a semi-autonomous fashion that is decoupled from the centralized cloud.  In this way, they are able to reason over their surrounding environments and take optimal decisions that are not necessarily subject to central control.

Therefore, smart objects can act without the need of being always connected to the cloud. However, they can conveniently connect to the cloud when needed, in order to exchange information with other passive objects, including information about their state and the status of the surrounding environment. Prominent examples of smart objects follow:

  • Socially assistive robots, which provide coaching or assistance to special user groups such as elderly with motor problems and children with disabilities.
  • Industrial robots, which complete laborious tasks (e.g., picking and packing) in warehouses, manufacturing shop floors and energy plants.
  • Smart machines, which predict and anticipate their own failure modes, while at the same time scheduling autonomously relevant maintenance and repair actions (e.g., ordering of spare parts, scheduling technicians visits).
  • Connected vehicles, which collect and exchange information about their driving context with other vehicles, pedestrians and the road infrastructure, as a means of optimizing routes and increasing safety.
  • Self-driving cars, which will drive autonomously with superior efficiency and safety, without any human intervention.
  • Smart pumps, which operate autonomously in order to identify and prevent leakages in the water management infrastructure.

The integration of smart objects within conventional IoT/cloud systems signals a new era for IoT applications, which will be endowed with a host of functionalities that are hardly possible nowadays. AI is one of the main drivers of this new IoT deployment paradigm, as it provides the means for understanding and reasoning over the context of smart objects. While AI functionalities have been around for decades with various forms (e.g., expert systems and fuzzy logic systems), AI systems have not been suitable for supporting smart objects that could act autonomously in open and dynamic environments such as industrial plants and transportation infrastructures.

This is bound to change because of recent advances in AI based on the use of deep learning that employs advanced neural networks and provides human-like reasoning functionalities. During the last couple of years we have witnessed the first tangible demonstrations of such AI capabilities applied in real-life problems. For example, last year, Google’s Alpha AI engine managed to win a Chinese grand-master in the Go game. This signaled a major milestone in AI, as human-like reasoning was used instead of an exhaustive analysis of all possible moves, as was the norm in earlier AI systems in similar settings (e.g., IBM’s Deep Blue computer that beat chess world champion Garry Kasparov back in 1997).

Implications of AI and IoT Convergence for Smart Objects

This convergence of IoT and AI signals a paradigm shift in the way IoT applications are developed, deployed and operated. The main implications of this convergence are:

  • Changes in IoT architectures: Smart objects operate autonomously and are not subject to the control of a centralized cloud. This requires revisions to the conventional cloud architectures, which should become able to connect to smart objects in an ad hoc fashion towards exchanging state and knowledge about their status and the status of the physical environment.
  • Expanded use of Edge Computing: Edge computing is already deployed as a means of enabling operations very close to the field, such as fast data processing and real-time control. Smart objects are also likely to connect to the very edge of an IoT deployment, which will lead to an expanded use of the edge computing paradigm.
  • Killer Applications: AI will enable a whole range of new IoT applications, including some “killer” applications like autonomous driving and predictive maintenance of machines. It will also revolutionize and disrupt existing IoT applications. As a prominent example, the introduction of smart appliances (e.g., washing machines that maintain themselves and order their detergent) in residential environments holds the promise to disrupt the smart home market.
  • Security and Privacy Challenges: Smart objects increase the volatility, dynamism and complexity of IoT environments, which will lead to new cyber-security challenges. Furthermore, they will enable new ways for compromising citizens’ privacy. Therefore, new ideas for safeguarding security and privacy in this emerging landscape will be needed.
  • New Standards and Regulations: A new regulatory environment will be needed, given that smart objects might be able to change the status of the physical environment leading to potential damage, losses and liabilities that do not exist nowadays. Likewise, new standards in areas such as safety, security and interoperability will be required.
  • Market Opportunities: AI and smart objects will offer unprecedented opportunities for new innovative applications and revenue streams. These will not be limited to giant vendors and service providers, but will extend to innovators and SMBs (Small Medium Businesses).

Future Outlook

AI is the cornerstone of next generation IoT applications, which will exhibit autonomous behavior and will be subject to decentralized control. These applications will be driven by advances in deep learning and neural networks, which will endow IoT systems with capabilities far beyond conventional data mining and IoT analytics. These trends will be propelled by several other technological advances, including Cyber-Physical Systems (CPS) and blockchain technologies. CPS systems represent a major class of smart objects, which will be increasingly used in industrial environments.

They are the foundation of the fourth industrial revolution through bridging physical processes with digital systems that control and manage industrial processes. Currently CPS systems feature limited intelligence, which is to be enhanced based on the advent and evolution of deep learning. On the other hand, blockchain technology (inspired by the popular Bitcoin cryptocurrency) can provide the means for managing interactions between smart objects, IoT platforms and other IT systems at scale. Blockchains can enable the establishment, auditing and execution of smart contracts between objects and IoT platforms, as a means of controlling the semi-autonomous behavior of the smart object.

This will be a preferred approach to managing smart objects, given that the latter belong to different administrative entities and should be able to interact directly in a scalable fashion, without a need to authenticating themselves against a trusted entity such as a centralized cloud platform.

In terms of possible applications the sky is the limit. AI will enable innovative IoT applications that will boost automation and productivity, while eliminating error prone processes.  Are you getting ready for the era of AI in IoT?

Require an “A Player” to Build Blockchains for Smart Objects in an IoT environment?

Whether your company is developing IoT platforms or applications in the cloud, developing blockchain elements, or using Ai to build Industry 4.0 to utilize smart objects, your success is determined by the quality of your existing team and hiring the right professionals in engineering, sales, product management, and strategy,  The experienced recruiters at NextGen Executive Search have a solid history of identifying and recruiting “A players” for functional leadership and executive management roles.  Click on the image below to view more information about our Ai, IoT, and cyber security recruitment experience or to contact us directly.