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Roadmap 2020


Following some recent high level reports from the likes of the World Economic Forum and McKinsey Global Institute on the future of the workplace in the impending AI and Machine Learning era, Cognisess take a medium-term look at what this might mean for both employers and employees. It is certain that there will be a huge systemic transformation of the workplace and it is clear People Analytics will play a pivotal role in helping people and organisations alike in managing these transitions.

Cognisess predict that whilst these shifts will be profound and wide sweeping, these may not necessary result in the dystopic and inhuman vision of the future that many fear. Instead, we see new opportunities for a dynamic and empowered workforce empowered by access to human analytics and talent management tools which will turn the tables on today’s demand-driven employment industries.

Please read our full Road Map 2020 and let us know what you think:

For more information about Cognisess’ Roadmap and how we are working towards a people analytics enabled future contact us at

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The future of safer work-place driving is here and now

Cognisess’ Chief Scientific Officer Dr Boris Altemeyer unpacks the insights for COGNISESS DRIVE: a new platform application focusing on making work-place driving safer.

Every one of us likes to think that they are a good, or at least an averagely good driver, don’t we? And particularly if you make your livelihood out of driving and have to put yourself forward for a job as a driver within the transport, fleet, haulage or logistics sectors.  However, work- related driving has found itself prominently in the headlines of late – and not always for very good reasons. Some very high profile crashes and incidents have brought corporate responsibility for safety on the roads to the fore. Many have called for more use of in-cab technology to monitor speed, lane changing and other governing telematics.  Some have pinned hopes on the use of driverless trucks as the solution for more road safety. Others have heralded the recent launch of the Tesla Semi electric truck range as an example of a cleaner, safer future. Undoubtedly technology in the future will play a major part in safeguarding and improving driver, public and road safety.  However, the demand for work-related driving in the economy is rapidly increasing and immediate solutions are required to tackle the current situation.

Regardless of whatever technology solutions or regulations may emerge in the future, the fact is that the human factor is always going to play a significant part when it comes to driving, operating or monitoring work-related vehicles. Therefore, as a company dedicated to predictive analytics for people, we wanted to gain a better understanding of the human attributes and performance abilities of anyone we might put near the wheel of a vehicle for work reasons. By combining cognitive science, data, AI and machine learning we set out to assess the range of faculties required for work-placed driving to ensure employers are hiring, monitoring and supporting the safest of drivers.


Through extensive work and studies undertaken with fleet operators and elite performance driving academies over a two-year period, we gained insight into not only what truly ‘ good driving’ looks like but we’ve also been able to calibrate the many different factors that make-up a ‘good driver mind set’. What we evidenced showed that there is more to being a safe or ‘good’ driver than just having fast reaction times. But equally it didn’t suggest that being slow, methodical or over-conscious was the answer either. Instead the mapping of the connections between the driver’s brain where decision-making meets risk-taking was where the most interesting insights were to be found. And particularly when it came to dealing with the unexpected – rather than the routine background mental tasks we normally associate with driving. This gave us a fresh perspective from which we could build a set of driving tests which could give us an objective and repeatable assessment of an individual’s personality type, cognitive faculties and driving knowledge. And this appeared to be consistent and reliable whether the drivers were either very experienced or relatively inexperienced.

Essentially by building out from our existing expertise, technologies and knowledge we combined two sciences: cognition (how the brain performs and functions) and data (how the real time analysis of thousands of data points helps us predict a driver’s approach to risk and decision making). We were then able to map and predict driver performance to provide accurate assessments of risk and safety as well as highlighting reductions in other unseen costs such as driver downtime, replacement vehicles, insurance claims, losses of productivity and of course the effect on public safety, potential damage to reputation and even corporate manslaughter charges as the most severe impact of work-placed driving. Cognisess Drive has since been piloted and tested by a number of major transport companies and driving experts across the past two years including Uber, Fonterra and Arden Motor racing academy.

We are pleased to bring Cognisess Drive to market today as a comprehensive assessment of driver competencies which covers cognition, risk, behaviour, wellbeing, driver attributes and driver knowledge for any environment which requires work-related driver performance. It can be used extensively as a tool for assessing driver potential at the recruitment stage and equally the platform is also designed to use baselines to maintain and sustain optimum driving performance throughout the lifespan of the driver contract or term of employment via performance dashboards which provide operators with predicative analytics and real-time reporting.

This break-through is timely for the fleet, haulage and logistics sectors who are facing immense pressure from the public and regulators to provide greater clarity and confidence that work-related driving practices are of the highest standard. But this is also coming at a time when the driving industry as a whole is equally under pressure with managing-out an aging population of fleet drivers. This means firms have to get better at assessing fitness and competency amongst their current driver pools whilst also getter better at assessing and recruiting younger talent coming from more varied and diverse backgrounds. Cognisess Drive is an example where Human Analytics, can provide data-based evidence to show that the people we are putting behind the wheel are – whatever stage of their working experience and cycles – capable, risk-assessed as well as fit and well enough to be entrusted with public safety. We believe this is only the tip of the iceberg of where Cognitive Neuro-science, AI and Machine Learning technologies can come together to help us make better informed human decisions about humans.

For more information about Cognisess Drive go to:

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Are our strengths any real indication of our potential or performance?

Dr Boris Altemeyer, Chief Scientific Officer, Cognisess explores the pros and cons of basing organisational strategy on strengths versus potential.

As human beings, we all have our strengths and enjoy being reminded of them or praised for them.  It is easy to concentrate on those, as they appear to come naturally to us and often without too much effort. But that doesn’t necessarily make us the rounded individuals or leaders we really aspire to be. Often it is the areas we find hardest to apply ourselves to, which are the ones we need to concentrate more on in order to become better. If not careful, our strengths are something we can hide in rather than open ourselves to the more challenging of rigours and adventures – particularly where we are facing new environments, challenges, threats or opportunities. One of the most misquoted aphorisms used in the modern context is the belief that Darwin’s Theory of Evolution stated ‘only the strongest survive’. He didn’t say that. He in fact said – ‘the most adaptable species were most likely to survive’.

And whilst our strengths are attributes we can and should draw upon to compliment the range of tools we have at our disposal, it must not be forgotten that these are limited to a personal status quo and usually within a single or particular area of application.

At Cognisess, we assess the whole potential and preferences of a person (or team), which can be applied across many different areas and can be developed on further.  Knowing your strengths alone may help you get better at what you are already good at, but it will not in itself necessarily contribute to making a great organisation or culture – particularly if that strength is in over-abundance or possibly not essential to the prevailing challenge. More critically it is about profiling strengths with potential and how these can be applied dynamically in the right circumstances and context. This becomes particularly important when trying to build cognitively diverse teams where innovation, change or transformation are mission critical. After all, if you stick to your status quo – then you are only ever likely to get more status quo. However, if entrenchment is the main strategy for the business – then those status quo strengths may indeed help you achieve that – and probably with some to spare.

A personal development plan based on some strengths is certainly a good starting point – particularly for building confidence. However we believe that personal development plans need to take into account much more than just strengths that already exist. Very often it is the potential and hidden strengths you didn’t know (or think) you had – or were told you didn’t have – that equate to higher performance and greater fulfilment. Often – where there is no clearly defined pathway, or problems are getting increasingly complex and inter-connected within the business – it is having a greater understanding of a person’s or team’s entire skills mix and potential that is crucial. It is this insight within the context dependent situation that makes people (and often the least expected candidates) highly valuable in an organisational context.

From a commercial perspective, there are also very distinct financial differences between the deployments of a multi-dimensional platform like Cognisess versus a simple, one-use, strength-based assessment. Cognisess Pro is not only designed to be a strategic tool that maps whole organisational talent pools and potential, it is also flexible enough to inform quick, tactical decision making in terms of team building, talent profiling, recruitment and development needs assessments.

Because it is very cost effective in comparison to other assessment tools, it is more widely available and affordable to profile entire functions and divisions, rather than be only used by a select group of high-level individuals or high performers.

Furthermore, because the Cognisess system, powered by Deep Learn™ our predictive analytics engine, is based on a recursive learning structure it is powerfully able to ingest people assessment data alongside available workforce and market specific data. This supports the strategic management of human capital and helps identify where minor adjustments in talent deployment and training may lead to significant benefits in terms of performance and ROI. This is particularly useful when used for its performance appraisal features whereby someone’s strengths, talents and potential can be assessed against actual performance outputs.

Our Cognisess Pro assessment platform provides a suite of over 40 assessments, plus the ability to analyse video, audio, and linguistics, for a very competitive price – which is, per candidate / employee, often lower as a per annum subscription than a single strength based assessment.

Increasingly within most organisations, the strength of a person today may not be the answer to competitive edge tomorrow. Adaptability and learning are therefore at the core of everything that Cognisess Deep Learn™ does to predict future performance of a team or individual in a highly dynamic environment.

Identifying potential and making it actionable where it counts – welcome to Cognisess Deep Learn™.

For more information about Cognisess Pro and how it is powered by Deep Learn™ get in touch at

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Deep Learning: Changing HR Forever

Cognisess’ guest blogger Technology4JB takes a look at the opportunities emerging in the people & talent space to use the power of AI and Machine Learning. It provides an easy to understand guide for HR Professionals who are considering what the benefits of this new technology might be.

One of the latest advancements in technology is deep machine learning, which focuses on the capabilities of artificial intelligence (AI) algorithms to adapt and learn from users’ behaviours through brain-like structures called neural networks. Among the industries set to be disrupted by deep learning is human resources.

AI is now being tested out on a number of different Internet of Things connected devices. Gartner claims AI will soon be integrated into every software by 2020, which research VP Jim Hare calls “the biggest gold rush in recent years.”

Tech giant Samsung has been testing deep learning by applying it in various devices, including in ultrasound equipment and in their latest smartphones through Bixby. According to the information shared by O2, Galaxy S8’s Bixby makes things easy for users to ‘do what they want, when they want’. Samsung’s propriety mobile AI assists users in completing tasks, tells them what they’re looking at, learns routines, and remembers what users need to do. This latest development in mobile AI can help further drive the adoption of deep learning in human resources.

With the amount of data that is available in the hiring process, machine learning can unearth more efficient approaches for identifying strong candidates. Through a model that is trained based on data made from example collections, companies are able to create accurate predictions instead of following a static procedure.

There are certain ways that deep learning can assist in making the HR process more efficient such as the following:

Evaluate more candidates quickly

From a nearly endless pool of possibilities, algorithms can help HR in finding and recruiting candidates quickly and effectively. AI can accomplish the time- and labour-intensive task of searching for the right applicant, such as screening CVs or scheduling interviews. Deep learning excels at statistical analysis and pattern recognition that makes evaluating CVs of successful employees easier and helps identifying whether an applicant comes with similar traits and experience that is required for the position. Aside from automating high-volume tasks, the technology integrates seamlessly with the current recruiting process, so it doesn’t disrupt the workflow. Since time for recruitment processes are reduced, it’s less likely for a company to lose the best talent to some of their competitors that aren’t using similar processes.

Improves quality of hires

Based on the statistical figures presented by CareerBuilder about the cost of a bad hire, the study showed that 41% of companies stated a bad hire cost them in the region of £20,000, while 25% said it cost them £40,000. The report even includes the often-cited Harvard Business Review study that revealed 80% of employee turnover is due to bad hiring decisions.

With deep learning and AI present, HR employees will be able to use the data gathered to standardise an applicant’s experience, skills, and knowledge based on the requirements of the job. This enhancement in recruiting is projected to lead to happier and more productive employees, who are less likely to leave their jobs.

Enhance employee satisfaction

Since algorithms work with the idea that ‘A is equal to A,’ the technology will be able to improve employee satisfaction via regular, unbiased performance reviews. Deep learning has the ability to evaluate employee performance based on the data provided to the system and without any personal bias. It can examine past performance trends of individuals, teams, or departments as well as predict future outcomes. Research published on Digitalist Magazine discussed how AI can end bias as the technology can learn how to filter irrelevancies out of the decision-making process. It can quickly select the most suitable applicant from a pile of CVs and guide HR employees based on what it calculated objectively to be the best candidate and not based on personal prejudice. The data collected is assessed by deep learning and can give HR managers and directors insights into the next necessary steps to take to improve the performance of the employees when the software spots a potential problem.

With deep learning and AI performing many of the routine tasks quicker, it makes the whole HR process efficient and employees more productive. While it’s highly unlikely that AI will replace humans in the HR field, its comprehensive capabilities to provide data-driven solutions will undoubtedly be a huge part to the continuing evolution of the industry.

Written by guest blogger Technology4JB

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