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5 Practical AI Applications in Commercial Real Estate with Real Business Value

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5 Practical AI Applications in Commercial Real Estate with Real Business Value

No longer a futuristic notion – AI can be used right here and now to maximize profits

The potential benefits of implementing Artificial Intelligence (AI) in almost any industry are well talked about. The idea of having machines to process information like humans do and produce much better outcomes has been considered a futuristic, though tangible prospect up until not so long ago. However, today that notion is closer to reality than ever before, and in some industries, AI is already presenting practical capabilities that deliver real business value – here and now.

One of these industries is Commercial Real Estate, where AI applications are already saving costs, driving better decision-making, boosting asset longevity and improving operational excellence. All of these measurable business benefits can be achieved by applying AI along the commercial real estate ecosystem, in 5 key areas:

#1 Tenant Satisfaction – Boosting Retention, Avoiding Turnover

As in every other sector, here too it is far easier and less costly to keep an existing customer than attract a new one. That’s why tenant satisfaction is so paramount to a property’s prosperity, and it’s also why so many resources and teams are operating to provide the tenants with timely quality service.

However, this becomes an increasingly challenging task when managing multi-campus enterprises with hundreds and sometimes thousands of tenants. This is exactly where AI comes in: by monitoring aggregated data from the building’s existing performance system controls, AI can identify recurring errors, anomalies in response times, or trends in work orders to allow property managers to fix problems faster and gain better insight on the root causes (poor maintenance practices, equipment wear-down, et cetera).

And as opposed to common perception, AI doesn’t only work on smart buildings with sensor-based automated systems, but rather integrates with existing legacy systems, making these capabilities available to practically any property.

#2 Lower Maintenance Costs and Improved Equipment Longevity

Maintenance costs in commercial real estate can skyrocket; from staff to equipment, expenses are high. But the real problem in building maintenance goes deeper than that – the whole value chain is broken: while one department is responsible for preventive upkeep of the equipment, another is responsible for actual repairs in case of equipment break-down, and a different one is responsible for dealing with ongoing fixes – with no one management workflow to link all these together.

This disruptive linkage between the various stages of maintenance is bad for business for the simple reason that the quality of the equipment undeniably affects preventive measures, which in turn have a substantial effect on ongoing troubleshooting. AI aligns all of these into one linked value-chain of predictive analytics: an ongoing equipment monitoring that solicits proactive maintenance activities before a breakdown even occurs, thus substantially reducing costs.

#3 Better Resource Management and Allocation

Traditionally, resource allocation was done based on experience; from the number of electricians needed for one building’s upkeep to how many operation managers are needed to monitor the tenants’ work orders – without analyzing whether it’s too little or too much. Using AI in resource management to analyze the relationship between the workload and the team capacity, provides accurate conclusions on how much staff and resources are needed to handle each project.

#4 Closing the Gap between Service Procurement and Tenant Retention

Providing superior vendor service for the property’s tenants is a top priority, as already established above. However, more often than not, service levels are dropping without proper reporting, since the data on slow response times and pending work orders is lost in the overwhelming data clutter of multiple suppliers across multiple properties. Leveraging AI in these processes enables vendor-specific segmentation in order to identify trends in service levels across different vendors and alert on any anomalies in service.

#5 Predict Cash Flow Deficiencies based on Suspicious Trends in Account Payables

Finding cash flow deficiencies in real-time is already too late. The careful tracking of the company’s cash flow is especially essential in commercial real estate, since any deficiencies can dramatically affect the next acquisition. And while dubbing managers can monitor payment on their ERP systems, they cannot be expected to predict changes in customers’ payment behaviors, track recurring payment delays, or constantly worry about the implications on the company’s cash flow. However, AI algorithms can be used here to identify trends and anomalies in payment patterns, and alert in advance on any behaviors that might affect the company’s cash flow.

Maximizing Property Profits Starts at Leveraging AI Across the Asset’s Ecosystem

Overall, the quality and success of managing the operational and financial aspects of commercial real estate depend on the articulate juggling between tenant satisfaction and retention, timely maintenance, cost control, and quality of service providers. Taking these aspects to the next level requires optimizing the relationship between these links in the value chain, and AI is doing precisely that, thus enabling executives to make better decisions and balance between maximizing profits and keeping maintenance rates low.

How did one giant Canadian commercial real estate enterprise manage to boost its operational performance through the roof by implementing AI-based personal notifications? Read here >

Iris Tsidon5 Practical AI Applications in Commercial Real Estate with Real Business Value
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Turning IoT into Measurable Business Value

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AI Paving the Way to Operational Excellence

There’s no argument that the Internet of Things (IoT) has introduced a real gospel to practically every industry. Promising a future of autonomous devices and sensors seamlessly connected to the enterprise’s IT systems to improve business results and operational excellence, it seems IoT is the new gold rush, and everybody is looking to get a piece.

However, as new waves of technology often do, this IoT revolution carries challenges and hurdles that must be handled wisely in order to achieve the real potential of IoT. At the very front of these challenges stand the biggest of them all- the massive flood of data generated from IoT devices aims to overwhelm existing and traditional data storage and analysis platforms, to the point where all that data is no more than a clutter of 1s and 0s.

The IoT problem

Everybody talks about Big Data. But the sheer magnitude of IoT generated data is beyond anything anyone could have imagined, processing massive amounts of information on a daily basis. This overwhelming overload of data has to be stored, segmented, managed, and analyzed to produce any kind of value to the enterprise.

But that’s not where the problem ends; this data flood is not just coming from one source but is continually streaming from multiple sources at once, all with different protocols, types, and frequencies, just flowing-in in masses.

In this reality, it’s clear that traditional data management and Business Intelligence (BI) systems cannot be expected to process, analyze and produce valuable insights from that endless stream of data, and here’s why –

Why can’t BI tools and traditional data management systems handle the IoT data flood?

The reason is simple – BI systems are inherently designed to produce descriptive analysis and reporting on events that have already taken place. But in the IoT reality, these capabilities are deemed irrelevant because of the volume and speed in which new information constantly keeps coming, immediately making any old data outdated.

In fact, using BI tools to handle IoT generated data will never realize IoT’s true potential because it could never work in real-time. Take manufacturers for example: they want their IoT data to help them improve waste removal, refine their supply chain, cut procurement and execution costs, create more efficient inventory, etc. In order to achieve those goals, they can’t rely solely on monitoring rule-based numeric thresholds- they need real-time actionable insights and accurate operational predictions.

Just like Gartner warn in their report, the flood of unstructured IoT data is expected to overwhelm existing data management solutions, and the lack of new information capabilities adopted especially to handle IoT data is estimated to result in a 25% failure to deploy IoT applications.

In other words, for IoT to realize its full potential, all that data from smart sensors and devices needs to be analyzed in real-time to automatically identify patterns and detect anomalies, to result in actionable business insights and operational excellence. Advanced, intelligent information processing and analytics in the age of industrial IoT has to become a core competency – without it, IoT applications will fail.

From descriptive reporting to predictive analytics

Recent advances in Artificial Intelligence (AI), specifically Machine Learning (ML), present new capabilities to overcome the information silos and overload caused by Big Data and IoT. By collecting disparate data from the multiple sources (including smart sensors as well as IT systems such as CRM and ERP) and processing it intelligently regardless of its type or frequency, machine learning algorithms can automatically sift through it and detect anomalies in real-time, thus predicting outcomes.

Using AI and IoT to boost operational excellence

Optimizing asset maintenance, reliability and efficiency is just the tip of what AI capabilities can do with IoT data; using these AI technologies, enterprises can finally leverage IoT data to realize real business value quickly with actionable insights. Predictive analytics by self-learning systems provides companies with the ability to make the right decisions in order to run the equipment, design their processes to avoid damage, maximize profitability, and ultimately drive business growth with:

  • High profitability
  • Faster time-to-market
  • Improved production and throughput
  • Optimized business processes
  • Improved operational efficiencies
  • High-level services

This predictive approach to IoT data analysis predicts not only what is going to happen, but when it is going to happen, and what we can do about it. It anticipates a problem enough time ahead to create an opportunity to fix it before the damage is even done.

By intelligently combining three vital elements: existing operating needs, equipment capabilities, and business goals – an AI solution with real-time notifications gives enterprises the competitive edge they need to drive growth by boosting operational excellence.

And so, using AI to handle the IoT data flood is not merely another operational metric – but a strategic priority.

For more information on how advanced AI can boost your operational excellence contact us.

Iris TsidonTurning IoT into Measurable Business Value
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New AI-advisor guides your entire team to achieve operational excellence

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Today, organizations invest a lot of resources in providing their managers with tools that will help them make informed decisions. However, merely providing managers with such tools is not enough to achieve operational excellence. Using these tools, managers can know that their department did not reach its goals, however without alerts on changes in real time and a clear knowledge of what exactly caused the failure to meet the goal, they will find it difficult to provide their employees with practical guidance to improve it. For the employee, questions like “what is the root cause of the lack of performance?”, “should I change my priorities?”, “should I change the work method that I am using?”, “should use other tools/materials?”, and many other questions remain unanswered.  

AI-Powered Personal Advisor for Operational Excellence

Imagine if you could have an ultra-smart advisor with superpowers that constantly analyzes all your organization’s data, understand your organization’s goals and come up with real-time insights and recommendations to meet these goals. Now, imagine that you could have this super advisor sitting next to every user in your organization, constantly providing recommendations on what to do next. Sounds like science fiction, right? Well, for Okapi’s clients this has become a reality.

Okapi puts the power of Artificial intelligence (AI) technology in your employees’ hands. Okapi’s platform collects and analyzes data from your entire organization, all your IT (Information Technology) and OT (Operations Technology) systems, etc. All this data is then analyzed using over 4,000 best practice metrics and processed by Okapi’s four AI engines:

  • Sensitivity analysis engine – based on the organization’s goals, this engine focuses on the main operational metrics driving the required outcomes.
  • Anomaly detection engine – real-time alerts on changes from existing patterns.
  • Cause & effect engine – root cause analysis for automatic detection of underlying problems.
  • Predictive engine – forecasting, preventive actions, and effective planning.

The result of this analysis is presented on Okapi’s mobile or web-based interface as personalized real-time notifications. These alerts are essentially the advisor’s recommendations to the specific user on what to do next. For example, the notification below alerts the plant’s shift manager that Okapi’s Anomaly Detection Engine found an increase in the setup time in two specific machines. This insight is based on analysis of data coming from the machines’ sensors and the ERP system.


Providing employees and managers with the tools to achieve the organization’s goals

A property management organization, which manages hundreds of properties around the world, can decide to increase customer satisfaction. But what does it mean for the specific manager of one of its properties?

The system understands the connection between the organizational goal to the departmental goals. For example, the customer satisfaction goal is translated to the maintenance manager’s specific goal is to decrease the number of tenants’ repeated requests. Okapi will find insights into the organization’s data and will constantly produce recommendations that will help the user reach this goal. It may, for instance, find that repeat request for air conditioning on a specific floor was 20% higher than usual. The manager can then use this information to proactively take preventive actions.

Finding insights across multiple systems and throughout the organization

Okapi analyzes the data across the entire organization to find valuable insights. For example, in a manufacturing facility, the organization’s goal was to improve quality, at the department level, this goal was translated into “Increasing the number of QC approved products after the first inspection”. There are many factors that can influence the number of QC approved products, but Okapi found that in another department, lowering the furnace temperature by 2 degrees for specific raw material, increased the number of QC approved products after the first inspection. Okapi used this insight to recommend the performance of the same action in the second department.

Notifications in real-time keep everyone up-to-date

Artificial Intelligence is used to automatically locate, visualize and narrate important findings. AI is creating a world where organizations don’t need to struggle with charts or complex reports anymore.
Everyone, no matter their level of analytical skill, can easily get the answers they need from their data.

This is a new revolution in the way organizations are managed and employees perform. By using this technology, you are essentially putting the world’s most powerful tool in the hands of your employees. It’s like a “magic pill” that allows your employees to be smarter and be able to constantly leverage all the organization’s data to reach operational excellence and achieve your organization’s goals.

Iris TsidonNew AI-advisor guides your entire team to achieve operational excellence
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Okapi’s Spotlight on Data: Interview with Jennifer Sertl

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This month, we’re featuring author, thought leader, and Okapi partner Jennifer Sertl in our latest installment of the Okapi Spotlight on Data Q&A series.

Jennifer Sertl is an author, thought leader, and the founder/president of Agility3R, focusing on strengthening the strategic and critical thinking skills of companies to make their leaders more resilient, responsive, and reflective. She is the author of the book, Strategy, Leadership and the Soul: Resilience, Responsiveness and Reflection for a Global Economy which she co-wrote with Okapi Advisory Board member Koby Huberman.

We talked to Jennifer about the many challenges surrounding organizational data & strategy and her approach to this.

Okapi: As someone dedicated to strategic critical thinking, how well do you think companies are handling the challenges they face around the data they collect and how they distribute & deliver it?

JS: I think Clay Shirky said it best: The issue today is not information overload, it’s filter failure.
I believe that companies have yet to sufficiently codify their strategies to enable them to truly leverage information that they’re collecting. The result? Many businesses attempt to accommodate everyone without a clear strategic intent. But if companies create a strategic filter to streamline the data, it can be more easily leveraged to reinforce certain themes. This allows the disseminated information to be used to increase productivity, employee cohesion, and ultimately optimize brand strategy. This strategic filter reveals the company’s level of innovation, know-how, and collaboration.
Many companies are preoccupied with just “getting the data” and using machine learning to do so. But are they actually executing a strategy and going to market using this data? Many are not there yet.
Okapi: What’s your approach to this challenge?
JS: There’s a business simulation game I run called Interplay. It allows teams to choose out of six strategies: synergy, know-how, velocity, collaboration, cost, and delivery. What we see, (e.g., in a company of 3000 employees with a 12-person executive team), is that a small subset of people each select a different subset of strategies. That means that even at the executive level, there an absence of alignment of priorities. And that impacts execution, for example: If cost is at the epicenter, there’s one way to execute. If it’s delivery, there’s another. So marketing and data can be acquired, but the core issue is getting a consensus around what we care about the most as an organization—strategically.
Okapi: So how do we change the mindset?

JS: If we think of every employee as a strategic agent, we know that every choice they make can bring us closer or further away from brand alignment. So it’s not just what they do, but how they execute that impacts the cumulative outcomes of an organization. By changing how we think, we can hierarchize our choices, rank them in terms of our organization’s priorities, and communicate that at the customer interface level to impact perception and outcomes. It gives us a behavioral blueprint. And it has allowed companies a way to ground themselves, sharpen their skills, and be more productive.

Okapi: That’s what our customers are saying.

JS: That’s what I love about Okapi. With the dashboard in your solution, you’re creating grounded criteria to generate visibility and feedback. Our behavior models are aligned. It just blows my mind that we actually have to strategically convince people that they need to be doing this.

Okapi: Why do you think that is?

JS: I think that understandably, people are reluctant to give all that information away. It can terrify people that everything is so transparent. Company leaders must understand that it’s their job to create clarity and a framework via which information can be leveraged as quickly as possible to win. That’s when the magic happens.

Want to make some operational and organizational magic for your business? Contact us for a demo today. Okapi is a custom data filter, a personal virtual assistant and an integrated, web-and-mobile-enabled dashboard—designed to help you reach your business goals.

Iris TsidonOkapi’s Spotlight on Data: Interview with Jennifer Sertl
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Identifying Your Main Challenges

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Over many years of working with organizations, we have encountered numerous challenges and misses that have repeated themselves. These are the main mistakes which you should look out for in your organization. By heading them off at the pass, you can prevent them all together and move forward with your strategy:
People are not connected enough to the larger business needs; rather, they are motivated by professional considerations, without seeing the prices we pay in the commercial aspects.

Lack of progress:
The tasks truly important for the growth of the business are not progressing. People here work very hard and are very devoted to their work, however, the assignments we need to perform in order to grow the business are not given priority

Unable to change to stay competitive:
In a competitive market, you need the ability to adapt your management infrastructure to change. However, this process as to happen quickly and efficiently. Many organizations do not succeed in changing courses in time to keep up with their competition.

Data is to complicated to understand easily:
To receive a picture of the state of the company, you should not need to dig through intricate Excel reports. Complicated reports and their preparation of consumes lots of valuable time. Creating a system to enabling the receipt of a timely, readily available picture on a current basis will add a great deal of value.

No coherent management plan:
There are many people who think that systematic management is not important, or they don’t use one because setting it up and following a plan is not one of their strong points and they pay too heavy a price for it. They get too involved in facilitating transactions, leading business development, and creating solutions to immediate problems in the company. Managers need to learn how to delegate responsibility for their own current management so that others can work towards the shared objectives we have defined.

To download the full “Six Steps to Operational Excellence” FREE E-Book click here:

Iris TsidonIdentifying Your Main Challenges
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Okapi’s Spotlight on Data Series: Q&A with Dr. Cindy Gordon

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In the latest installment of Okapi’s Spotlight on Data series, we talked to Dr. Cindy Gordon, CEO and Founder of Saleschoice, a predictive and prescriptive sales analytics cloud company. Cindy is a leading expert and recognized thought leader on the subjects of Big Data, SaaS, business innovation, early-stage software commercialization & sales business practices. She is the author of 15 books.

Okapi: What’s your take on the relationship between the data that companies collect & disseminate internally and sales performance?

CG: Data is a huge asset to any organization; a sacred resource, if you will. But it’s also a liability—and we have to treat it that way. Firstly, organizations need to lead by example from the top. A CRO should be using tools that they expect their sales reps to use to promote harmonization amongst the processes.
The second point is that we must reward good behavior when it comes to data. That means laying out very clearly—in every job description—responsibilities around data quality and cleanliness. Too often, information and knowledge are hoarded in other repositories for self-serving reasons, namely sales commissions. But if we can tie awards and recognition to KPIs and data responsibilities, we can actually improve sales performance across the organization.

Okapi: How is this best achieved?

CG: The key is mandatory fields: If you make fields mandatory and tie the completion of these fields to rewards, you get results. The goal is to not allow the salesperson to advance to the next sales gate unless all of the fields have been populated. That’s the only way to manage the gate. Right now, the mistake that companies are making is having too many open fields that are not mandatory. If it’s not mandatory, chances are it won’t be filled in. And when you tie these inputting tasks to commission, (i.e., withholding commission until the data is accurate and complete), we start to see the data coming in.
It comes down to this: if you measure it, you will succeed and if you don’t you won’t. And in order to measure it, you need consequences.

Okapi: Who is doing this well?

CG: In terms of pipeline management and sales forecasting and supply; advancing those opportunities and those target accounts and demonstrating closure to drive top-line profitable growth? There are many companies that do this well.
Take Intel, for example. They have gone through a long journey where they had 30-50 different workflows or instances of Salesforce running in North America. They consolidated everything into one unified workflow across all the different product and solution sets, giving them a 360-degree view of every product cycle, every segment in the lifecycle, etc. They know how many proposals are at the introductory phase, how many of them are in negotiation, etc.—across the whole product solution services portfolio. That’s smart thinking. They care about data quality and controls because of who Intel is and what they stand for.

Okapi: What about Artificial Intelligence? How does it fit in?

CG: When we leverage AI, we are predicting the future. And to do that, we need to rely on good quality data in order to achieve accurate sales forecasts. But science aside, as long as we’ve got 40 to 50 percent, we can at least get a baseline if there’s enough volume of data. And at the end of the day, it’s not about the workarounds. It’s about a clear vision and understanding.
The companies that have really anchored their operating processes with good control systems will be able to take advantage AI more skillfully than others. Take the manufacturing sector, for example. They have worked so hard for so long and may be in a better position because of their dedication to ISO standards and the quality improvement cycle. The hi-tech industries—despite their success—are always a little bit more cowboy-ish in this area and their processes aren’t always as tight. They are driven more by innovation than operations.

Data is one of the most significant resources in our economy and our future. And if we ever hope to get to a true AI layer, we need the best quality, reliable data. It’s like running on a dirt road vs. a bullet train.

Want to ride the bullet train? Contact Okapi for a demo today. Okapi is a custom data filter, a personal virtual assistant and an integrated, web-and-mobile-enabled dashboard-designed to help you reach your sales and operational goals.

Iris TsidonOkapi’s Spotlight on Data Series: Q&A with Dr. Cindy Gordon
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Business Performance KPI Word Puzzle

How knowledgeable are you in the world of business performance improvement based on objectives and KPIs?

In the word puzzle in front of you are some of the core words associated with business performance.

Experts will be able to spot 20 different words 🙂



If you got to this page then you are probably wondering what they are, so here is the full list:

Smart, Objective, Perspective, Scorecard, Okapi, Value, Performance, Vision, Trend, Transparent, Align, Goal, Business, Management, Success, Result, Plan, KPI, Insights, Data

Iris TsidonBusiness Performance KPI Word Puzzle
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Embedding Your Strategy

Last week I visited a big conference at San Francisco which focused organizational objectives.

One of the lecturers was Dr. Donald Sol concluded his research on 300 companies and shared some of his fascinating findings. In his research, he approached the CEOs and COOs of the companies and asked them to interview the people in charge in their organization regarding transforming goals to results.

These interviews discovered that an amazing 60% of the participants could not name the top three objectives of the company for the upcoming year.
Makes sense??

There is a well-known philosophical question: Does a tree falling in the woods with nobody around make a sound?

Does a manager, who has an excellent strategy, think he can steer his company into the right direction if no one is aware of his strategy?

Iris Tsidon,

Iris TsidonEmbedding Your Strategy
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