In the midst of a global shift towards sustainable energy solutions, Electric Vehicles have emerged as the poster child for eco-conscious transportation. With governments, corporations, and individuals increasingly embracing EVs, the spotlight has turned to EV Charging Management, a crucial aspect of this electrifying revolution and sustainability. In this article, we embark on a journey through the intricacies of EV Charging Management, uncovering its significance, challenges, and the promising future it promises to bring.

The EV Revolution: A Charge Towards a Greener Tomorrow

Electric vehicles have taken center stage in the fight against climate change. The transition from conventional ICE vehicles to EVs offers a ray of hope for reducing CO2 and curbing air pollution. As EV adoption surges worldwide, ensuring a robust and efficient charging infrastructure becomes imperative.

The EV Charging Ecosystem: A Closer Look

At the heart of the EV revolution lies the EV charging ecosystem, a complex network of charging stations, energy providers, EV manufacturers, and software solutions. To understand the nuances of EV Charging Management, we must dissect this ecosystem and explore its components.

1. Charging Stations

EVCS are the lifelines of the EV charging ecosystem. These stations come in various forms, from public charging stations dotting urban landscapes to residential chargers nestled in our homes. The availability, accessibility, and convenience of these stations play a pivotal role in shaping the EV experience.

2. Energy Providers

Powering EVs requires electricity, and energy providers are the key players in this game. They ensure that the electrons needed to charge EVs are clean, reliable, and readily available, but this is also not so easy to achieve due to las of resources, manpower, etc.

3. EV Manufacturers

Automakers are racing to develop cutting-edge EVs with longer ranges and faster charging capabilities. These innovations challenge charging management systems to keep pace with evolving technologies.

4. Software Solutions

EV Charging Management relies heavily on software solutions. These systems coordinate charging sessions, optimize energy usage, and provide real-time data to both users and operators. They are also the most important key to sustainability and to ensure no downtime and seamless use of the chargers.

The Challenges of EV Charging Management

While the potential of EVs is undeniable, the path to widespread adoption is fraught with challenges. Let’s explore some of the roadblocks on the journey to seamless EV Charging Management.

Fig.1: EV Management System

1. Range Anxiety

One of the most prominent concerns among potential EV owners is range anxiety. The fear of running out of battery power before reaching a charging station is a major hurdle, demanding enhanced charging infrastructure and faster charging technology.

2. Infrastructure Gaps

Charging station availability varies widely, with rural areas often lacking the necessary infrastructure. Bridging these gaps is crucial to ensure fair EV adoption.

3. Charging Speed

Charging speed is crucial, especially for long trips. Deploying fast chargers while considering grid capacity is a logistical challenge that demands careful planning.

4. Billing and Payment

The complexity of billing and payment systems can frustrate EV users. Simplifying the process and ensuring interoperability between different charging networks is vital for a hassle-free experience.

The Art of EV Charging Management

In the face of these challenges, EV Charging Management emerges as the hero of the story. This intricate dance of technology, infrastructure, and user experience holds the key to unlocking the full potential of EVs.

1. Charging Scheduling and Optimization

Effective Charging Management begins with scheduling. Software solutions allow users to plan charging sessions during off-peak hours, reducing strain on the grid and minimizing energy costs. AI-driven algorithms predict energy demand and adjust charging times accordingly.

2. Grid Integration

Smart grid technology plays a vital role in EV Charging Management. It enables seamless integration between EV charging and the electricity grid, allowing for load balancing and grid stability. Bidirectional charging, where EVs can discharge energy back into the grid during peak demand, is an exciting development.

3. User-Friendly Apps

User-friendly mobile apps make the EV charging experience a breeze. They help locate charging stations, provide real-time availability information, and offer convenient payment options. Moreover, these apps can tailor charging profiles to individual user preferences.

4. Data-Driven Insights

Data is king in the world of EV Charging Management. Collecting and analyzing data from charging sessions helps optimize infrastructure deployment, identify usage patterns, and plan for future expansion.

The Road Ahead: A Sustainable Tomorrow

As we navigate the twists and turns of EV Charging Management, a promising future awaits. Governments, businesses, and innovators are joining forces to build a charging ecosystem that leaves no one behind.

1. Investment in Infrastructure

Governments worldwide are investing heavily in EV charging infrastructure. Initiatives such as the installation of public charging stations, tax incentives, and rebates for EV owners, and grid modernization efforts are paving the way for an electrified future.

 Table 1: Government Initiatives for EV Charging Infrastructure

2. Interoperability

Efforts are underway to create a global standard for EV charging, ensuring that users can access any charging network seamlessly. This interoperability is akin to the universal acceptance of credit cards, making it easier for EV owners to charge wherever they go.

3. Innovation in Battery Technology

Breakthroughs in battery technology promise longer ranges and faster charging times. Solid-state batteries, for instance, could revolutionize the EV landscape, making charging even more convenient.

Figure 1: Evolution of EV Battery Technology

4. The Rise of Electric Fleets

Corporate fleets are electrifying rapidly. The adoption of electric vehicles in delivery services, ridesharing companies, and public transportation not only reduces emissions but also drives the demand for efficient charging solutions.

Conclusion

EV Charging Management is the linchpin of the electric vehicle revolution. With its ability to address challenges, optimize charging, and create a seamless user experience, it is steering us towards a sustainable future. As governments, businesses, and individuals come together to invest, innovate, and embrace change, the road to a greener tomorrow is well-lit and ready to be explored. In the grand narrative of our time, EV Charging Management is the plot twist that promises to electrify the world.

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Workplace EV charging stations can help those employees stay focused on the workday rather than scanning apps to see where they can get a top up before their next task, event, responsibility, obligation. For employers, it can make the difference between attracting and retaining top talent. There are benefits on both sides of this equation.

Let’s say you see those benefits, but the idea of designing and implementing an EV charging program seems daunting.

Why Start a Workplace EV Charging Program

Among workplace challenges, the struggle to find, hire, and retain talent is one of the most talked about and most prevalent. And, as more workers return to the office, often as part of a hybrid work from home program, employers are trying to make the workplace seem more attractive.

One way to attract talent and ease the back to office transition is to add EV charging stations. In fact, nearly 60% of EV drivers have said that workplace EV charging is vital for them and a determining factor in employment. Further, in one study 58% of respondents reported that a company’s sustainability efforts impacted their choice regarding where to work.

There are additional options that allow business owners to leverage EV charging as part of a rewards and recognition program or even a workplace benefit. However, there are also opportunities for a revenue stream, depending on how you want to manage your workplace EV charging program.

For employees, the benefits of workplace EV charging are pretty clear. Most EV drivers charge at home because that’s where they have time to get a full charge and, with home chargers (Level 1) they need that kind of time. For most Americans, we spend nearly as much time at work as we do at home. For workers with a hybrid schedule, that still means a few days a week with a car parked for 4-8 hours at the workplace. With a Level 2 charger, that’s a perfect amount of time to fully charge or just get a top up.

And, if employees have after work errands, or need to get kids to and from activities, or even just a decent commute, charging at work means they can do any or all of those things without range anxiety. In turn, you have happier, less stressed employees, who can effectively manage their work life balance by getting where they need to be after work.

Is Your Workplace Ready for EV Charging?

The simple answer is yes, we’re all ready, but the real answer is a bit more complicated. In addition to some preliminary site analysis, you’ll want to ask some of the same questions we ask prior to implementation of potential public EV charging locations:

• Is there demand?
• Do you have employees who currently drive electric vehicles? Employees who plan to purchase an EV in the next year?
• Are there other charging stations in the area? (this may impact how you implement rather than whether you install or not)
• Do you have your own parking lot or parking garage?
• Is there access from an electrical panel to the potential location of EV charging stations?
• Would your business benefit from the presence of EV charging stations and a focus on sustainability?
• Would EV charging stations help you meet sustainability goals?
• Are there company vehicles which could be transitioned to electric vehicles in the future?

This is a starting point. Obviously, when it comes time for implementation and installation, there’s more work to be done, but if you’ve answered yes to most of those questions, you’re ready to get started!

Challenges to Workplace EV Charging

As the above questions hint, your parking situation could possibly be a challenge. That’s why working with a turnkey solution provider, one who will walk you through the process from site analysis to installation, is important. You’ll want the team architecting your site to have an in-depth understanding of every phase of the project.

For example, a small lot is a challenge, but not one that is insurmountable. It may limit the location and number of EV charging stations, but a small lot suggests a smaller company, and this may be sufficient.

Another challenge, that we often see across sites, is power management. The power available to your site via the grid may be limited. The nature of your business may mean that during certain hours of the day there’s a particularly big draw on that available power. You may be sharing power with other buildings. In this way, power management becomes a primary focus for EV charging station owners.

With the right EV charging platform management system (CPMS), however, you can manage those power loads and adjust charging capacity to ensure you don’t overburden the grid. Further, depending on your site, your business, and a few other factors, there may be government grants of tax incentives available to you to help upgrade your site.

The final challenge workplaces may face are the same struggles others face when there is greater demand than available charging stations. More specifically, cars stay parked at a single charging station even though the vehicle is fully charged, or multiple vehicles and drivers want to charge at the same time. Thankfully, how you implement your EV charging program can help you mitigate this challenge.

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Our societies increasingly depend on capturing, storing and processing data from business processes, customers, Internet of Things, patient monitoring, car servicing, blockchain, virtual reality, smart cities and the metaverse. While many people don’t see data centers, we all rely on them for so much of our daily lives. And now, with the rise of AI technology, ChatGPT, Dall-E, Lensa AI and Git Hub Copilot, there is also growing demand for specialised high-performance computing platforms.

To operate and improve existing data centers, or develop new ones, requires experience and specialist knowledge. Despite the recent growth of ICT specialists in the EU by almost 60% – which is six times more than other sectors – more than half (53%) of data center operators report that finding new talent is still a huge problem. In part, this challenge stems from inadequate skills training in the education system. In Europe, for instance, 30% of young people aged 13–14 years underperform in computing and literacy.

Workforce instability

But even when talent is found, holding on to it is also a challenge for the sector; 43% of employees leave to work in other related industries. European data shows that the average length of employment in the sector is only 1.8 years. Yet reportedly, it takes a minimum of five to six years for a new starter to become fully qualified and confident as a data center engineer.⁵

One reason employees may be leaving the sector almost before they have properly started may be a lack of training. Some 76%⁵ of employees had not completed or renewed certificates in the last year because there was either a lack of time, money or training programmes, and that lack of training could be viewed as career-limiting.

All of that is compounded by an ageing workforce. According to a DataCenter Knowledge survey, 87% of respondents were older than 44 years, the average age is 53, and a retirement tsunami is expected in the coming decade. That will create challenges in educating and mentoring the younger talent entering the sector to become tomorrow’s leaders.

Reasons not to be cheerful

The industry is clearly not attracting enough talent, which may be due to a whole host of reasons. Data centers are often based in remote locations, which makes commuting longer, more expensive, and perhaps for younger recruits, too far from the bright lights and fun times of towns and cities. The industry is also known to have sustainability issues due to its high carbon footprint and high-water usage needed to cool servers, just at a time when many industries are using their sustainability credentials to compete effectively in employment markets.

Data centers are modernising, and there are signs that sustainability issues are being addressed. The switch from water evaporation cooling systems to closed loop systems to re-circulate air-chilled water, for example, is reducing consumption². And companies like Google are now publishing their water and CO2 footprints. Gartner predicts 75% of them will have a sustainability programme by 2027 compared to just 5% in 2022, which will help make the industry more attractive to young and older, more experienced employees. Perhaps, however, they are not moving fast enough.

Probably the most compelling issue for the lack of recruitment into data centers is the invisibility of the industry, along with the general lack of knowledge about the range of career opportunities it can offer beyond being a programmer or data processor.  The sector is working hard to attract people into roles centred around operations, business development, engineering, project management and IT, with some success in recent years. However, there is still a way to go if the current labour crisis is to be overcome.

Sourcing talent

One way to solve the mid and senior-level staff shortages is to encourage more career-changing applicants from other industries – even those with unusual backgrounds. That requires defining a broader set of skills than those of traditional technologists brought in from Science Technology Engineering and Mathematics fields. The industry must recognise that anyone with a technical and creative mindset or aptitude can be trained or retrained to fill vacant job roles and have a fulfilling career.  Critical thinking, strategy, problem-solving, an entrepreneurial mindset and the ability to work both independently and as part of a team are all skills needed in data centers and can be found in sectors as diverse as the military, pharmaceuticals, aerospace and even the arts.

Another way to address the skills gap and widen the pool of potential candidates is to attract more women into the industry. Informa conducted Data Center Knowledge’s 2022 salary survey and found that a depressingly vast majority (85%) of respondents were male. It also reported that 73% of data center technicians were men and only 27% were women – a steady ratio for over a decade. Yet many women will also have the potential to be part of the next generation of leaders; they too, will have the transferable skills the industry so desperately needs.

So, if the data center sector is to source talent from new pools of candidates, how can it assess their transferable skills, the individual’s suitability, the positive impact they could make, and the diversity of their experience and thinking?

Assessing potential

The traditional approach is to use CVs, interviews and personality tests. Still, whilst these are a good indication of past experience and expertise, they are less appropriate for assessing an individual’s natural proclivities, which are often the best indicators of future performance. Equally, personality assessments can point to certain traits, but they do not indicate a person’s potential impact on the future growth of a business.

There are new approaches to assessing job applicants, however, which can supplement or even supersede the conventional CV and reveal more than a personality test. One such tool is the Game Changing (GC) Index® which measures an individual’s proclivity, i.e. their individual approach to making an impact and contributing to a role, a team and the business. It is based on a simple proposition that everyone wants to live a life of purpose and potency. In work, it means people do well when they feel they are making a valued contribution.

By measuring and understanding an individual’s natural tendency, the company can align candidates to its people and its business. The recruits may have, for example, an innate ability to visualise the future and develop clear, actionable strategies, or they may see possibilities and generate ideas that can transform businesses and processes. Some people are gifted at getting the most out of people in the pursuit of common goals to deliver tangible results, whilst others see potential and focus on the pursuit of excellence. Although transferable skills and expertise are important, knowing the likely impact that an individual will have is priceless.

But the GCI is not only invaluable for the data center industry to attract new recruits, it can also help keep staff motivated by ensuring their natural talents are used and help them feel they are making a valued contribution and have a clear career path. Employers can make informed decisions about who is best suited for different roles and how to create the right environment for them to thrive, which in turn leads to unbiased decisions that support the diversity of thought and achieve their personal and their business objectives.

Ultimately the right candidate in the right role, the right team and the environment will help retain and nurture them in a lifelong career within the sector. Over time this will help the industry overcome its current labour crisis and build a more resilient labour force which will drive the industry’s future.

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It is evident that the growing significance of data analytics cannot be ignored. Analytics is key in gleaning actionable insights from the deluge of data that organizations sift through, emerging as the bridge between raw data and informed decision-making.

Statista estimates that the big data market could grow up to 180 trillion gigabytes by 2025 — the storage capacity equivalent to 72 million human brains! Yet, despite the large amounts of data generated and collected each day, research also shows that over 68% of data goes unutilized by corporations. The staggering amount of untapped data represents missed opportunities and potential, but also serves as a resounding call for organizations to seek more efficient ways — such as robust data centers — to harness and master this invaluable resource.

Data centers as the focal point of data analytics

Today, data is more prevalent than ever. With every touch of a button, mouse click, and swipe on a screen, data is generated with each interaction, giving rise to a digital tapestry of information that exists online. As data creation continues to grow with the proliferation of digital services, this has inevitably led to the mounting need for data centers that excel in effectively managing and processing these vast data quantities.

Demand for data centers has surged in recent years, with Cushman & Wakefield’s latest report highlighting how the accelerating digital economy has driven the growth and expansion of the data center market, especially in Asia Pacific. In fact, in Singapore alone, the data center market is expected to exceed one gigawatt in 2024. This has inevitably created a growing need for more efficient and reliable data center infrastructure, as governments and organizations look for sustainable ways to fulfill demand for more computing power.

Yet, expanding and optimizing data center operations isn’t as straightforward as simply adding on new equipment. Piecemeal upgrades over a long span of time can result in an unnecessarily complex environment, where data center teams have to deal with a mix of hardware and software from multiple vendors, and try to gauge the remaining lifespan of aging technologies. Besides technical integration issues, manpower challenges may include a lack of coordination between facilities and IT teams, and difficulty finding talent due to the ongoing skills shortage globally.

While costs and agility are perennial issues for any business, server downtime and power outages have increasingly costly implications as businesses move online. This can range from the loss of potential revenue, bruised reputation to equipment damage. How then, can the expansion and enhancement of data center infrastructure be facilitated smoothly?

Convergence of AI and analytics

There is a huge opportunity for AI-driven analytic and power management systems to step in and plug this gap. These technologies have potential to create positive and lasting change in addressing the traditional challenges that have plagued how data center operations are managed. Already, over half of data center operators have considered utilizing AI to make routine operational decisions. By leveraging them to further enhance tools such as predictive analytics and automation, organizations will be well-equipped to support their evolving power demands, and enhance their resource efficiency and systems reliability for success.

The need for a clear, unified view of the entire data center infrastructure, across all physical and virtual components, is more critical than ever as businesses expand their computing infrastructure. It’s no longer just about asset optimization, but having the ability to automate, monitor and plan ahead through real-time data and analytics. This allows operators to assess the operational capabilities and limitations of their data center facility more accurately, reduce operational costs and ensure continuous uptime.

Beyond remote monitoring and management, AI-driven analytic systems are able to significantly simplify asset tracking and management workflows through functions such as 3D visualization, thermal mapping, and capacity planning. This better equips teams to plan for system upgrades, avoid unnecessary expenditures and support evolving power demands.

Additionally, AI will be able to enhance existing solutions like electrical power management systems (EPMS) that allow organizations to monitor power usage around the clock, and aid them in meeting their sustainability goals. AI-driven algorithms can optimize energy usage by adjusting cooling systems, lighting, and power distribution based on real-time conditions. Teams are also better able to forecast energy demand and consumption through predictive analytics and monitoring, and prioritize the use of renewable energy sources in the long run.

A new era of data analytics

As data proliferation only continues to grow, it is clear that data analytics has an indispensable role in maximizing the efficiency of data centers. Data centers that harness the full potential of data analytics to make strategic decisions are better equipped to meet the evolving needs of businesses, such as identifying real-time trends and patterns or safeguarding the integrity and security of the data they have. This ability will be key for organizations to continue leveraging the wealth of information at their disposal, and remain at the forefront of a smarter and more connected world.

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What is Generative AI?

Generative AI is a class of technology that can create new content, data and information based on ingesting large amounts of information so the algorithm can learn about relationships of terms, language and concepts.   There are generalized models (called Large Language Models or LLMs) which are based on broad ranges of ingested data and content as well as and more specific models based on a particular industry, domain or set of tasks and processes.

Generalized models cannot answer questions about company specific knowledge unless they are fine tuned or trained on that knowledge (which is typically proprietary and behind the organization fire wall)

Generative AI can be used to access company specific information and make answers to questions more natural sounding and conversational which emulates how a human might answer the question.

Use Cases for Generative in the Enterprise

The best known use cases revolve around content generation – developing marketing copy, creating email campaign messaging, writing letters, creating job descriptions and other routine, time consuming tasks.  It is important to have human oversight around these tasks to provide more personality to the content and to ensure that it is on target and aligned with the company brand.

Gen AI can also be a good brainstorming tool – providing outlines for research, generating ideas for initiatives and fleshing out ideas with content, developing solutions and designing products based on parameters and requirements.

There are many data focused applications including anomaly detection (including identifying fraud patterns), data synthesis, data fill and data security.

However the most important application that is overshadowed by the astounding capabilities of content generation is access to corporate information.  Organizations have struggled for years to access, organize and manage data and content effectively.  Generative AI is the key to accessing “dark data”  (the unstructured content that has been accumulating for years but not managed or curated) as well as accessing day to day information and streamlining countless processes.
Why Governance Matters More Than Ever

A governance framework consists of decision-making bodies, decision making rules/procedures and mechanisms to ensure compliance with those rules.

Because Generative AI can make up answers that sound reasonable but are incorrect, the proper guardrails need to be in place. These include a mechanism for “retrieval augmented generation” (RAG) an approach that effectively tunes the generative AI based on knowledge sources that are the gold standard for the enterprise.  The algorithm can be directed to only retrieve answers from a specific source or sources and instructed to answer “I don’t know” if it does not have the answer from those sources.   This effectively eliminates hallucinations and leverages the language model in processing a response from approved data sources that is more conversational.

Using RAG will be the greatest single application for generative AI in the enterprise.  Using Gen AI to access business intelligence, performance measures, support material and organizational knowledge will speed the flow of information throughout the organization in unprecedented ways and become a tremendous source of competitive advantage when correctly deployed.

What Needs to be Governed?

Because the source of training or tuning content (the knowledge base used for retrieval in RAG), is so critical, that information needs to be structured, managed and curated either by the departments or through a centralized managed process that ensures consistency in data and content standards and consults with subject matter experts (SMEs) as needed.  Those SMEs are essential to the content lifecycle process since they understand the domain and can verify that questions are answered correctly.

A core part of governance is monitoring the KPIs that are impacted through the Generative AI initiative.  A process owner needs to gather baselines prior to deployment and establish targets to determine what success looks like after deployment. Those KPIs can be monitored at various levels (from data scorecards and standards through the processes that are enabled and ultimately business outcomes and alignment with enterprise strategy) and linkage from the most granular level up through the business outcome level will show whether there is a meaningful impact from the effort (this metrics driven approach can be applied to any initiative).

A governance body assigns more detailed levels of responsibility down to the data steward who is actually responsible for execution and monitoring of the effort (there are many roles in deployment but someone needs to “own” the data and be responsible for data quality and fitness to purpose.

These decision making structures ensure data integrity and confirm the value of investments through comparison of baselines to measures post deployment.  These baselines are referenced using a library of use cases (the things the system needs to do or the tasks users need to perform in furtherance of their goals) that are clear and unambiguous in order to be testable.  That library is further refined and expanded over time and used in a variety of ways – from user acceptance testing to ongoing fine tuning and validation of changes to architecture, data, algorithm, design or any other effort to improve performance.  Sometimes a change can have a negative impact  and it is important to roll back changes or make course corrections as necessary.

Standard governance processes help the organization select the most appropriate model, test against core use cases and content (where known “gold standard” answers are known for comparison purposes) and refine the Generative AI based on high quality knowledge and content sources.

Ethical Issues

Misinformation: One area of generative AI that has ethical implications is misinformation and deception .  Generative AI can write convincing marketing and communications copy.  The ethical challenge is ensuring that content is correct, not misleading or outright deceptive.  Human review of output is essential and  clear polices and procedures need to be in place to ensure compliance.

Ownership of content: Another issue is that of intellectual property ownership and copyright infringement.  This is another area where restricting the Generative AI to corporate content will reduce this risk.  Generative AI may be using content that may have restrictions on reuse or derivative applications.

Transparency and traceability:  It is difficult to identify where answers come from when using Generative AI and to determine the quality of source information.  This is another area where a retrieval augmented approach has value. Since the content is coming from a specific source, an audit trail can be maintained and people can trust the answer (assuming they trust the information source)

Bias: if data sources are biased (which typically are based on how the data is generated) then the algorithm will be biased.  It is important to understand and mitigate this bias through human review, broad data collection that reflects various demographics and use of bias detection tools.  There are a range of offerings in this area that allow for exploration of model behaviors, detection of discrimination, understanding of data set distributions and other auditing and review approaches[1].  Different models will serve different purposes – it is important to test various models against core use cases.

Privacy and security:  Corporate data needs to remain behind the firewall and not ingested into public LLMs  where proprietary information can become public.  The same issue occurs with personal data.  Strong security protocols need to be in place and tested on an ongoing basis.

Governance Implementation

Training and awareness:  Stakeholders at various levels need to understand core principles, what AI can and cannot do and how it will impact them and their role in the organization.  There is a great deal of fear of losing one’s job to AI and employees need to understand how they can  leverage it to make them more productive rather than fear its impact.  Executives need to understand key success factors and that AI is not a silver bullet – data and content curation is even more critical to success than ever.

Review of compliance: Governance standards that are not implemented are of little value. There needs to be a process for revieing execution and metrics for compliance

Collaboration: a cross functional team allows for a range or perspectives for developing governance standards and guiding AI deployment.

Clear roles and responsibilities:  bring the correct stakeholders to meetings to address issues.  If people feel that a meeting is not appropriate for them or is a waste of time, they will not continue to participate.  Make the punishment fit the crime with RACI charts to determine who is Responsible Accountable, Consulted or Informed for a particular governance task or process.

Conclusion

Generative AI holds tremendous promise for organizations of all types.  Correct deployment involves understanding risks, potential rewards and requirements and preconditions for successful implementation, maintenance and ongoing improvement. Governance is the glue that holds various interests together and enables the organization to realize the value of their investments in AI.  Generative AI for the enterprise is an area with huge potential value.  Setting the enterprise up for success will ensure that the right resources are correctly deployed and the value from this revolutionary tool is realized.

[1] https://towardsdatascience.com/5-tools-to-detect-and-eliminate-bias-in-your-machine-learning-models-fb6c7b28b4f1

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A variety of debris types from rocket bodies and defunct satellites to satellite fragments greater than 10 cm in diameter make up the more than 30,000 pieces of debris currently tracked in a variety of near earth orbits.  However, this represents only 1% of the millions of pieces of debris such as bolts, paint chips and frozen fuel droplets, etc. currently not trackable and smaller than the 10 cm tracking limit. There have been 3 significant collisions that have contributed to the debris problem in the past. The first was a Chinese anti-satellite test in 2007, a second was a collision between an inoperative Russian Cosmos satellite and an active Iridium satellite and a third occurred in 2021 as a result of a Russian anti-satellite test.  Each contributed more than 3000 pieces of debris.  This debris is traveling in the lower earth orbits at speeds of approximately 9 km/s. An active satellite collision with an object greater than 10 cm would be a catastrophic event. Collisions with smaller objects could be fatal depending on the location where the strike occurs or at best it could be a life limiting event.

US Space Policy Directive-3, National Space Traffic Management Policy, dated June 18, 2018, refined the responsible organizational agencies and their respective roles for space debris mitigation and remediation.  The recent National Orbital Debris Research and Development Plan dated January 2021 further refines the requirements into 3 essential elements. One sub-element characterizes the R&D priorities as: Develop remediation technologies and techniques for small-debris objects. PRRISM addresses this sub-element.  Further, it aligns with the R&D topical areas assigned to the following agencies missions: Department of Commerce (DOC), Department of Defense (DOD) and the National Aeronautics and Space Administration (NASA).

Ironstar Engineering, LLC founded in 2020, offers an innovative, and patented remediation solution to the growing problem of small space debris in near earth orbit.  Ironstar is seeking funds to advance its technology. In September 2020, Ironstar Engineering published a peer-reviewed article describing the science and methodology of using solar wind for space debris remediation. The article was published in the quarterly Journal of Space Safety Engineering. In the near-Earth orbital environment, PRRISM will speed up orbital decay of debris objects. PRRISM combines scientific research of the sun-earth electromagnetic dynamic environment and applies proven engineering principles.  The PRRISM EM wave antenna will match to key parameters of the solar plasma in the polar cusp to optimize solar wind forces. This technology will create constructive interference and result in a temporary but stronger dynamic pressure force at the polar cusp exit. The targeting computer on board PRRISM will establish a 3D intercept with the debris cloud using telemetry data from the remote space debris (SDS) and solar plasma (SPS) sensors causing the deorbiting of targeted debris in large quantities.

This is a dual use venture that addresses the growing threat of space debris in near earth orbits. Not only are military space operations compromised, but everyday communications, navigation, and weather satellites are at risk of a collision from known trackable space debris or from the millions of unseen and un-trackable space objects.  Space debris is a threat to all active satellites operating in low Earth orbit particularly in the most highly populated polar orbits referred to as the sun synchronous orbits (SSO).  PRRISM would operate in a highly elliptical orbit at a 10 Earth radius apogee and will remove all small debris from polar orbits.

The Department of Commerce (DOC) is charged with managing space situational awareness (SSA) and space traffic management (STM).  In support of the DOC is the Air Force, Space Force and NASA.  Thousands of satellites are being added to low Earth orbit increasing the congestion on the orbital highways and the risk of collisions. Mitigation of this risk is expected to contribute to making a significant positive impact in the technology-driven future.

According to Space News, satellite development costs would range between $50 million and $400 million depending on instrumentation and materials. Launch costs vary between $50 million and $400 million. CubeSats would cost an estimated $50,000 to build with launch costs of $30,000. A launch on the SpaceX Falcon 9 is $57 million. Additionally, there is launch insurance of $20 million, on-orbit insurance of $20 million, and satellite operations costs of $15 million over a 15 year life.  In summary the consequence for losing one satellite could be close to $1 billion. Continued growth of satellites in near earth orbits risk a Kessler syndrome resulting in a cascade of debris causing collisions that would close certain orbits. PRRISM is a novel use of space-based assets. In the near-Earth orbital environment, it will operate outside the debris orbits and speed up orbital decay of debris objects allowing for safe satellite operation and space commerce growth.    www.ironstarengineering.com

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With each new project breaking ground, thought leaders emerge to shape the construction industry’s future by introducing fresh perspectives, pioneering technologies, and fostering collaboration. The construction industry, a vital pillar of global development, is undergoing a profound transformation driven by technological advancements, changing demographics, environmental concerns, and shifting market demands.  This article looks into the pillars of thought leadership that are driving modern construction forward in profound ways.

1. Establish Innovation as the Cornerstone:

Thought leaders in modern construction recognize that innovation is the bedrock upon which the industry stands. From implementing advanced building materials to leveraging cutting-edge construction techniques, these visionaries push the boundaries of what is possible. Whether it’s the integration of augmented reality for enhanced project visualization or the adoption of 3D printing for rapid prototyping, innovation is the key to unlocking efficiencies and delivering projects that were once deemed impossible.

2. See Sustainable Construction as a Guiding Principle:

As environmental concerns take center stage, thought leaders in modern construction champion sustainability as a non-negotiable pillar. They advocate for eco-friendly practices that reduce the industry’s carbon footprint while ensuring long-term resilience. These leaders emphasize the use of renewable materials, energy-efficient designs, and waste reduction strategies. By prioritizing sustainable practices, they not only mitigate the impact of construction on the environment but also set a precedent for future projects to follow suit.

3. Value Collaboration and Knowledge Sharing:

In an industry as multifaceted as construction, collaboration is a linchpin of success. Thought leaders foster a culture of knowledge sharing, recognizing that by pooling expertise, breakthroughs can be accelerated. They actively engage with professionals across disciplines, forging partnerships that bring together architects, engineers, contractors, and technologists. This collaborative approach paves the way for holistic project solutions and ensures that best practices are disseminated industry-wide.

4. Advocacy for Digital Transformation:

Thought leaders spearhead the digital transformation that is reshaping construction. Embracing Building Information Modeling (BIM), data analytics, and project management software, they streamline processes and enhance decision-making. By advocating for the integration of technology, they not only increase project efficiency but also drive a shift towards data-driven insights, which lead to more informed and impactful choices.

5. Use Thought-Provoking Dialogue:

True thought leadership extends beyond practical implementation—it sparks meaningful conversations. Leaders in modern construction contribute to industry discourse through articles, seminars, webinars, and conferences. By sharing insights and challenging established norms, they stimulate critical thinking and inspire others to question and evolve conventional practices.

6. Value Mentorship and Education:

Recognizing the importance of nurturing the next generation, thought leaders in modern construction will engage in mentorship and educational initiatives. By imparting their experiences and wisdom, they equip aspiring professionals with the tools needed to shape the industry’s trajectory. This mentorship creates a ripple effect, ensuring a continuous cycle of innovation and progress.

Thought leadership stands as the driving force behind modern construction’s evolution. Through innovation, sustainability, collaboration, technology adoption, meaningful dialogue, and mentorship, thought leaders shape the industry’s present and future. The construction industry is navigating a dynamic landscape characterized by rapid technological advancement, heightened environmental awareness, and evolving market demands. As the industry adapts to these changes, it paves the way for innovative practices, sustainable solutions, and enhanced collaboration. By embracing these trends and leveraging emerging technologies, the construction sector is poised to build a more resilient, efficient, and forward-looking future.  Their influence extends far beyond individual projects, setting benchmarks that elevate the construction sector as a whole. As construction continues to reach new heights, these pillars of progress illuminate the path ahead, guiding us towards a future where innovation and thought leadership intertwine to create lasting impact. Will you and your company be the next “thought leader” to step up a make a difference in this industry moving forward?

Christopher Salem, BIO:

Chris Salem is an accomplished Business Acceleration Strategist, Certified Business Workplace Coach, Executive Coach. Professional Keynote Speaker, Award-Winning Author, Business Trainer, and Radio Show Host & Media Personality dedicated to empowering business leaders and their businesses with boosting their brands and business simultaneously from ordinary to extraordinary. His goal is straightforward – to help serious business leaders foster better workplace environments that lead to higher engagement and retention with their team while generating more clients, increasing customer experiences, and scaling their overall revenue and profits quickly and inexpensively.

Over the years, he has developed a keen understanding of the complex issues facing business leaders in the type of volatile economy we have today.  His experience has helped him develop skills to educate business owners quickly and effectively to successfully apply the right strategies in the correct order, allowing them to grow their business to its maximum capacity.

His book Master Your Inner Critic / Resolve the Root Cause – Create Prosperity went international best seller in 2016. He also co-authored the recent edition to Mastering the Art of Success; with Jack Canfield. His weekly radio shows, Sustainable Success is part of the Voice America Influencers Channel and Business Influencers with Tal Radio, part of the Touch-A-Life foundation.  He works with organizations such as such as JP Morgan – Chase, Ralph Lauren, GE Research, Oracle, UTC – Pratt & Whitney, Raytheon, Microchip Technologies, Anthem, US Census Bureau, United Healthcare, Laticrete Corporation, Hubbell Corporation, Conning & Company, Foxwoods Casino, the US Senate, and NYPD Forensics Department including universities such as University of Hartford, Westchester Community College, Bay Path University, Worcester State University, and spoke on overcoming limited beliefs for peak performance at Harvard University’s Faculty Club. Finally, he is part of the Faculty Facilitator team at the American Management Association.

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Recent Data-Driven Infrastructure Projects

Streetlight Priority Zones

In 2019, the City of Dallas’ Department of Transportation (DDOT) evaluated several cross-departmental priorities to drive where to invest in light-emitting diode (LED) streetlighting.  Whereas streetlights can affect crime rates, vehicular and pedestrian accidents, economic development, and quality of life, DDOT used data and geographic information system (GIS) spatial analysis to overlay Racially and Ethnically Concentrated Areas of Poverty (RECAP) Zones, market value analysis, housing reinvestment areas, and target area action grids (crime data) to create target locations for streetlight investments.

Streetlight Community Wireless High-Speed Internet (Wi-Fi) Pilot Projects

Evolving from the Streetlight Priority Zones, in 2020, multiple City of Dallas departments began evaluating how to address the digital divide or those households with low access to the internet.  Through GIS spatial analysis, City of Dallas teams identified ten neighborhoods that were in the streetlight priority zones, had low access to the internet, and were near City of Dallas facilities such as recreation centers or fire stations.  The City of Dallas cross-departmental team installed streetlights within the ten neighborhoods, installed Wi-Fi equipment on those streetlights, and installed telecommunications fiber from the adjacent City facilities to the Wi-Fi equipment on the streetlights.  The Streetlight Community Wi-Fi Pilot Projects not only addressed the needs included in the Streetlight Priority Zones but also provided community Wi-Fi to approximately 320 homes that data showed were lacking access to the internet.

Red Cloud Neighborhood Smart Cities Pilot Project

Building on the success of the Community Wi-Fi Pilot Projects, in late 2021, the City of Dallas took its lessons learned to another level with the Red Cloud Neighborhood Smart Cities Project.  Following the same data analysis used to identify Streetlight Priority Zones and lack of access to the internet, the City of Dallas team also evaluated the conditions of the infrastructure in the Red Cloud Neighborhood to develop a comprehensive project to reconstruct all of the neighborhood’s streets, alleys, and sidewalks, while installing new streetlights for community Wi-Fi, a number of artificial intelligence (AI)-enabled cameras, and environmental monitors.  The Red Cloud Neighborhood Smart Cities Pilot Project, which won a Smart 50, Smart Cities award in 2023, used data to drive the infrastructure, lighting, and smart cities technology investments to bring community Wi-Fi to 190 homes and the quality of life for the residents of the Red Cloud Neighborhood.

Convergence of Data for Developing a 2024 Bond Program

Understanding how the City of Dallas has used data to drive infrastructure investments, City teams are currently working on how to infuse data in the development of an approximate $1B, 2024 Bond Program.  While there are approximately $16B in infrastructure and facility needs, City of Dallas teams have developed a data-driven framework to prioritize the $16B in needs to fit within the approximate $1B of available funding.

Priority GIS Overlays

Historically, for allocating street construction dollars, the City of Dallas has programmed capital construction projects in its bond programs through a combination of equal distribution for each City Council District and then an allotment based upon condition, or technical scoring, of the streets.  In the 2024 Bond Program development, City teams are evaluating not only technical scores of projects, but other project scoring priority variables or data including:

• Equity,
• High Crime Areas,
• Proximity to Transit Oriented Development,
• Market Value Analysis,
• 311 Customer Service Requests,
• Project Intersection with Another Project (to gain economies of scale in construction).

Using the project scoring variables listed above, the City of Dallas team has developed a grid map (shown below) to guide staff and stakeholders to the locations of the most-impactful projects.  Through GIS spatial analysis, projects are not only scored based upon technical scores (up to 80 points) but through additional points (up to 20) based upon the Priority GIS Overlays.

GIS Priority Overlay Map

(Darker Grids Indicate Higher Priority Areas Based Upon Overlapping Priority Variables; Red Lines/Points indicate water infrastructure projects)

Summary

As many municipalities are facing similar infrastructure needs, funding challenges, and competing priorities, leaders must use data to make objective infrastructure investment decisions.  While quantitative and GIS-based analysis may not lead to ultimate decisions for infrastructure investments, they are effective tools that can be used to narrow down vast amounts of data and identify priorities.  However, in a push to be a data-driven organization, recent and current examples highlighted in this article show how data drives infrastructure investment decisions in the City of Dallas.

Biography

Dr. Robert M. Perez serves as Assistant City Manager for the City of Dallas and has gained over 22 years of public sector experience while working for the City of San Antonio and the City of Dallas.  Prior to being appointed as Assistant City Manager, Robert served as the City of Dallas’ Director and Assistant Director of Public Works.  Before joining the City of Dallas, Robert worked for the City of San Antonio for 16 years in leadership positions and other capacities within the departments of Public Works, the Office of Management and Budget, the San Antonio Metropolitan Health District, and Community Initiatives.

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AD is crucial for EVs, and vice versa

The symbiotic relationship between autonomous driving and electric vehicles (EVs) is profound. Due to the need of integrating state-of-the-art sensors and computing hardwares on vehicle, AD systems demand high power consumption and power efficiency. Traditional internal combustion engines, with their myriad of moving parts, don’t quite match the sleek electric powertrains in this domain. EVs, being inherently more efficient, responsive and predictable, cater to the exacting demands of AD systems.

Conversely, the proliferation of AD can accelerate the adoption of EVs. As AD technology becomes a mainstream choice, there will be a shift in the perception of vehicles from being purely utility- driven purchases to experience-focused ones. In such a scenario, the tangible benefits of EVs – like lower operating costs and reduced emissions – will make them the preferred choice for autonomous systems. Moreover, AD will make EVs not just a means of transportation, but more of a living space with enjoyable experiences.

AD is more than software and requires full-stack specialties

Many mistakenly believe that making a car autonomous is simply about layering sophisticated software over existing vehicle systems. In reality, achieving true autonomy requires deep integration across the full vehicle stack, from hardware to software.

The sensors that allow AD to perceive its environment, the algorithms that process this data, and the actuators that execute driving actions all need to work in perfect harmony. This mandates a seamless melding of various disciplines – mechanical engineering, computer science, electrical engineering, and more.

Moreover, ensuring safety in AD is paramount. Redundancies need to be considered into every sub- system, and these redundancies must be rigorously designed, validated and verified. The vehicle should be able to react appropriately even in the event of unforeseen failures, ensuring the safety of its occupants and other road users.

Automotive semiconductors are critical enablers for AD & EVs

The heart of the digital revolution in vehicles beats thanks to automotive semiconductors. As the driving force behind sensors, computation units, and electrical architectures, semiconductors play a pivotal role in making autonomous driving a reality.

Advanced sensors, from lidars to radars to cameras, rely on semiconductor components to function accurately and reliably. The data these sensors generate is immense. Processing this data in real- time requires powerful computation units, again enabled by advanced semiconductor technology.

Furthermore, the entire electrical and electronic (EE) architecture of a modern vehicle, which supports everything from infotainment to advanced driver assistance systems, is built upon semiconductors. As demands grow, with higher data rates and lower latencies, the role of automotive semiconductors will only become more significant.

In Conclusion

Autonomous driving, though still in its nascent stages, represents the pinnacle of what the automotive industry can achieve with digital transformation. It’s a testament to human ingenuity, showcasing how cutting-edge technology can redefine age-old industries. As we stand on the cusp of this revolution, it’s essential to recognize and appreciate the myriad of components and specialties that converge to make it possible. The road ahead is exciting, and the journey promises to be transformative.

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“Innovation is not a single event, but a long journey of exploring and integrating diverse knowledge.”
Rosabeth Moss Kanter, Author and Harvard Professor

Innovation will benefit from, and arguably needs, participation from a cognitively diverse group rather than an individual.  Organisational challenges can be complex, so the odds of finding innovative solutions rise when more than one type of creativity and a range of experiences are applied.

Everyone is unique.  One person’ creativity may possess manifest in solving real and immediate problems with great pragmatism, while another’s may generate left-field ideas at a complete tangent to any immediate issues.  Differences in life and professional experience also bring fresh perspectives.  Without this kind of diversity there’s a risk of getting the same old same old…

“We cannot solve our problems with the same level of thinking we used when we create them”
Einstein

But how do you achieve cognitive diversity if you have a homogenous, stable workforce?

Well one way is to recruit for greater diversity.  Few organisations are enlightened enough to do this strategically, so it’s a smart way to gain advantage over competitors.  As it happens, due to the shrinking pool of talent in the jobs market, many organisations are  being forced to recruit outside of their intended focus anyway.  This may be no bad thing if it is done astutely and seen as an opportunity.

Another way (albeit considered less often), is for innovators to connect with others outside their organizational boundaries.  Technical innovators are rightly reticent about discussing technical challenges externally in order to protect intellectual assets.   But most senior technology executives will readily admit that tech challenges are typically the easy ones.  The tough ones are usually to do with the evolution of systems and processes, and dealing with people dynamics.

You’d think it’s a no-brainer to encourage innovators to meet and talk across organisational boundaries about these “softer” yet trickier issues.  However, there’s a fly or two in the ointment.  In the tech world, innovators are not always conscious of the role non-tech elements play in the challenges they face, even when they impede progress.  Added to this, many tech innovators are introverts – they can maintain a laser-like focus on the detail of the tech aspects of product development, but reaching out to talk to strangers about broader non-tech issues can take them outside of their comfort zone. To “get out there” can for them, be quite hard work and when under pressure to perform and deliver, it can go right against the grain.

But sometimes they get it.  The wiser more experienced innovator may have learned over time that it makes sense.  Younger innovators from the naturally more collaborative Y generation don’t question its value.  Both realise that the courage and effort involved in putting themselves out there to have friendly conversations with other innovators from different environments is more than offset by the rich rewards – mindsets are loosened in a gentle non-threatening way, inevitably boosting their own creative juices, and generating valuable serendipity.  Plus once there, they find it’s enjoyable!

The Cambridge dictionary defines collaboration as “the act of working together”, and Merriam-Webster defines it as “working jointly with others or together especially in an intellectual endeavour”.  The word originates in the mid-19th century and comes from the Latin col and laborare, to ‘work together’.  By taking action to get out to meet with, and then do the respectful work of listening to one another, innovators are in fact collaborating.

Tech innovators who collaborate in this way are doing important work that will benefit their organisation.  Plus they themselves are enriched through exposure to different styles of creativity, different life experiences, and different organisational culture approaches.

“If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions”
Einstein

The critical components here are having conversations with others to think together with the intention to enhance understanding of shared challenges.  Solutions tend to follow naturally back in the workplace.  Meanwhile, trust is built with fellow-collaborators.

Indeed trust matters inside organisations too – more than ever.  With hybrid working, many organisations report a sense of growing fragmentation both between the layers of the hierarchy and across silos.  Harvard psychologist Amy Cuddy says that we form a view of someone’s trustworthiness within seconds and that we assess two factors – warmth and competence.  Interestingly it’s not competence we prioritise – it’s warmth.

In cavemen days, “it was more important to figure out if your fellow man was going to kill you and steal all your possessions than if he was competent enough to build a good fire”.
Amy Cuddy

What happens to the perception of warmth when we are rarely in the same room as our colleagues, and instead see them virtually?  Over time the reduction in the lived experience of warmth must have a cost.  Commitment both to the organisation and to colleagues is likely to suffer.  And yet cross-organisational trust is essential for innovation and for making imperfect systems work.  Could many organisations be looking at an inherent performance degradation over time, exacerbated as older more connected employees retire?
If you’re a leader, it’s important to be considering how to mitigate this and be planning ways to bring people together across hierarchies and silos to better understand and resolve the challenges facing the organisation.

The conclusion?  Collaboration – internally as much as externally – is the secret sauce of innovation.  Not paying attention to it, is a high risk strategy in a hybrid world.  It may need to be carefully engineered and facilitated, but the rewards should be well worth it.

Siobhan Soraghan BSc MBA
Director, Active Insight; Curator, the Innovation Network
wisdom at work

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