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Posts tagged ‘Artificial Intelligence’

Apr 8

Five Ways AI Can Help Create New Smart Manufacturing Startups

smart manufacturing, AI, machine learning

AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.

PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.

 Abundant AI startup opportunities in smart manufacturing and industry 4.0 

According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.

Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.

AI, Industry 4, smart manufacturing

McKinsey and the Massachusetts Institute of Technology (MIT) collaborated on a survey to identify machine intelligence leaders’ KPI gains relative to their peers. They found that leaders achieve efficiency, cost, revenue, service, and time-to-market advantages. Source: Toward smart production: Machine intelligence in business operations, McKinsey & Company. February 1, 2022.

Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:

  1. AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
  2. Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
  3. Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
  4. Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
  5. Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.

Talent remains an area of need 

Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings’ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.

Oct 17

1 Comment

2021 State Of The Machine Learning Market: Enterprise Adoption Is Strong

data science, machine learning, enterprise software, AI, artificial Intelligence
  • 59% of all large enterprises are deploying data science (DS) and machine learning (ML) today.
  • Nearly 50% of all organizations have up to 25 or more ML models in use today.
  • 29% of enterprises are refreshing their data science and machine learning models every day.
  • The higher the data literacy an enterprise can achieve before launching Data Science & Machine Learning initiatives, the higher the probability of success.

These and many other insights defining the state of the data science and machine learning market in 2021 are from Dresner Advisory Services’ 2021 Data Science and Machine Learning Market Study. The 7th annual report is noteworthy for its depth of analysis and insight into how data science and machine learning adoption is growing stronger in enterprises. In addition, the study explains which factors drive adoption and determine the key success factors that matter the most when deploying data science and machine learning techniques. The methodology uses crowdsourcing techniques to recruit respondents from over 6,000 organizations and vendors’ customer communities. As a result, 52% of respondents are from North America and 34% from EMEA, with the balance from Asia-Pacific and Latin America. 

“The perceived importance of data science and machine learning correlates with organizational success with BI, with users that self-report as completely successful with BI almost twice as likely to rate data science as critical,” said Jim Ericson, vice president, and research director at Dresner Advisory. “The perceived level of data literacy also correlates directly and positively with the current or likely future use of data science and machine learning in 2021.” 

Key insights from the study include the following:

  • 59% of large enterprises are deploying data science and machine learning in production today.  Enterprises with 10K employees or more lead all others in adopting and using DS and ML techniques, most often in R&D and Business Intelligence Competency Center (BICC)-related work. Large-scale enterprises often rely on DS and ML to identify how internal processes and workflows can be streamlined and made more cost-efficient. For example, the CEO of a manufacturing company explained on a recent conference call that DS and ML pilots bring much-needed visibility and control across multiple plants and help troubleshoot inventory management and supply chain allocation problems.
machine learning
  • The importance of data science and ML to enterprises has doubled in eight years, jumping from 25% in 2014 to 70% in 2021. The Dresner study notes that a record level of enterprises sees data science and ML as critically important to their business in 2021. Furthermore, 90% of enterprises consider these technologies essential to their operations, rating them critically important or very important. Successful projects in Business Intelligence Competency Centers (BICC) and R&D helped data science and ML gain broad adoption across all organizations. Larger-scale enterprises with over 10K employees are successfully scaling data science and ML to improve visibility, control, and profitability in organizations today.
machine learning
  • Enterprises dominate the recruiting and retention of data science and machine learning talent. Large-scale enterprises with over 10K employees are the most likely to have BI experts and data scientists/statisticians on staff. In addition, large-scale enterprises lead hiring and retention in seven of the nine roles included in the survey. It’s understandable how the Business Intelligence (BI) expertise of professionals in these roles is helping remove the roadblocks to getting more business value from data science and machine learning. Enterprises are learning how to scale data science and ML models to take on problems that were too complex to solve with analytics or BI alone.    
machine learning
  • 80% of DS and ML respondents most want model lifecycle management, model performance monitoring, model version control, and model lineage and history at a minimum. Keeping track of the state of each model, including version control, is a challenge for nearly all organizations adopting ML today. Enterprises reach ML scale when they can manage ML models across their lifecycles using an automated system. The next four most popular features of model rollback, searchable model repository, collaborative, model co-creation tools, and model registration and certification are consistent with the feedback from Data Science teams on what they need most in an ML platform. 
machine learning
  • Financial Services prioritize model lifecycle management and model performance monitoring to achieve greater scale from the tens of thousands of models they’re using today. Consistent with other research that tracks ML adoption by industry, the Dresner study found that Financial Services leads all other industries in their need for the two most valuable features of ML platforms, model lifecycle management and model performance monitoring. Retail and Wholesale are reinventing their business models in real-time to become more virtual while also providing greater real-time visibility across supply chains. ML models in these two industries need automated model version control, model lineage and history, model rollback, collaborative, model co-creation tools, and model registration and certification. In addition, retailers and Wholesalers are doubling down on data science and machine learning to support new digital businesses, improve supply chain performance and increase productivity.
machine learning
  • Enterprises need support for their expanding range of regression models, text analytics functions, and ensemble learning. Over the last seven years, text analytics functions and sentiment analysis’ popularity has continually grown. Martech vendors and the marketing technologists driving the market are increasing sentiment analysis’ practicality and importance. Recommendation engines and geospatial analysis are also experiencing greater adoption due to martech changing the nature of customer- and market-driven analysis and predictive modeling. 
machine learning
  • R, TensorFlow, and PyTorch are considered the three most critical open-source statistical and machine learning frameworks in 2021. Nearly 70% of respondents consider R important to getting work done in data science and ML. The R language has established itself as an industry standard and is well-respected across DevOps, and IT teams in financial services, professional services, consulting, process, and discrete manufacturing. Tensorflow and Pytorch are considered important by the majority of organizations Dresner’s research team interviewed. They’re also among the most in-demand ML frameworks today, with new applicants having experience in all three being recruited actively today.   
machine learning
  • Data literacy predicts DS and ML program success rates. 64% of organizations say they have extremely high literacy rates, implying that DS and ML have reached mainstream adoption thanks partly to BI literacy rates in the past. Enterprises that prioritize data literacy by providing training, certification, and ongoing education increase success odds with ML. A bonus is that employees will have a chance to learn marketable skills they can use in their current and future positions. Investing in training to improve data literacy is a win/win.
machine learning
  • On-database analytics and in-memory analytics (both 91%), and multi-tenant cloud services (88%) are the three most popular technologies enterprises rely on for greater scalability. Dresner’s research team observes that the scalability of data science and machine learning often involves multiple, different requirements to address high data volumes, large numbers of users, data variety while supporting analytic throughput. Apache Spark support continues to grow in enterprises and is the fourth-most relied-on industry support for ML scalability.   
machine learning

Mar 18

How To Digitally Transform Talent Management For The Better

How To Digitally Transform Talent Management For The Better

Bottom Line:  CHROs and the HR teams they lead need to commit to keep learning and adopting digital technologies that help improve how they hire, engage and retain talent if they’re going to stay competitive.

Driven by the urgency to keep connected with employees, customers and suppliers, McKinsey’s recent Covid-19 survey finds global organizations are now seven years ahead of schedule on digital transformation initiatives. HR’s role is proving indispensable in enabling the fast pace of digital adoption today. By providing Business Continuity Planning (BCP), HR’s contributions to digital transformation separate the organizations that thrive despite crises versus those left behind, according to McLean & Company’s 2021 HR Trends Report. The graphic below from the report shows how effective HR has been in supporting the rapid changes needed to keep employees communicating and engaged.

The McLean and Company Trends Report also shows that talent management’s major gaps need attention now before they grow wider. These areas include analyzing the employee skills gap (24%), developing employees on new competencies (24%), and training new employees in specific new skills (21%). Improving talent acquisition, retention, diversity and inclusion, and employee experiences by digitally transforming them with greater personalization at scale and visibility is key.  CHROs and the HR teams they lead need to close these gaps now.

How To Digitally Transform Talent Management For The Better

 

How To Get Started Digitally Transforming Talent Management

Start with the gaps in talent management you see in your organization. The largest gaps are often in the following four areas: recruiting and talent acquisition; retention of top talent and diverse talent; lack of visibility into employee capabilities; and workforce strategies not aligned to business strategies. Key challenges that need to drive digital transformation in these four areas include the following:

  • Legacy recruiting and Applicant Tracking Systems prioritize HR’s needs to capture thousands of resumes instead of delivering an excellent candidate experience. Attracting and recruiting the most qualified candidates in a virtual-first world is a daunting task. Organizations who are leaders in digital transformation quickly realized this and relied on automating the applicant experience so much it began to resemble the Amazon 1-Click Ordering experience. McKinsey’s recent Covid survey found that 75% of organizations digitally transforming their operations, including HR, were able to fill tech talent gaps during the crisis:

How To Digitally Transform Talent Management For The Better

Source: McKinsey & Company, 2020, How Covid-19 has pushed companies over the technology tipping point—and transformed business forever

 

  • Top talent retention is more of a problem than many organizations realize, with top performers receiving between five and ten recruiter calls a month or more. The average tenure of employees at companies has been decreasing for nearly two decades. And a primary driver is not for lack of opportunity, but because employees can’t find a career path internally as easily as they can find a growth opportunity at another company. It’s possible to retain the top talent by guiding employees to what’s next in their careers. Of the many approaches to providing employees a self-service option for personalized coaching guidance at scale, Eightfold’s Talent Intelligence Platform is delivering results at such notable companies as Air Asia, Micron, NetApp, and others. Eightfold found that 47% of top talent leave within two years, but most would happily stay if given the right opportunity. The following video explains how Eightfold helps its customers retain talent:

 

 

  • Employees often lack visibility into new internal opportunities, and both HR and business leaders lack visibility into employees’ unique capabilities. There’s often a 360-degree lack of visibility into new internal career positions from the employee’s side and a lack of awareness on the employer’s side of their employee’s innate capabilities. The lack of visibility from the employer side limits their ability to benchmark talent, create programmatic, scalable, and flexible career development opportunities and ultimately redeploy talent in an agile way that serves business strategies that are evolving rapidly in response to the impacts of the global pandemic.
  • Workforce strategies that don’t align and support business strategies waste opportunities to improve morale, productivity, and employees’ professional growth. While organizations have invested heavily in valuable infrastructure, including Learning Management Systems (LMS) and other employee experience and development tools, they often lack a unified platform to help deliver the right growth opportunities to the right person at the right time.

Achieving Greater Automation, Visibility And Personalization At Scale

Talent management is core to any digital business and the competitive outcomes each can produce today and in the future. To make greater contributions, Talent Management needs to deliver the following by relying on a unified platform:

  • Talent Management platforms need to combine ongoing business insights based on operations data, technology management data, and business transformation apps and tools to create new digitally-driven employee experiences quickly.
  • A key design goal of any Talent Management platform has to be delivering personalized candidate or prospect experiences at scale through every communications channel an organization relies on, both digital and human.
  • The best Talent Management platforms provide the apps, data, and contextual intelligence to drive task and mission ownership deep into an organization and reinforce accountability. What’s noteworthy about Eightfold’s Talent Intelligence Platform is that it has designed-in empathy and the ability to deliver quick, effective decisions that further reinforce team inclusion. Eightfold’s many customer wins in Talent Management illustrate how combining empathy, inclusion, and accountability in a platform’s design pays off.

As McLean & Company’s 2021 HR Trends Report shows, taking a band-aid approach to solving Talent Management’s many challenges is effective in the short-term. Turning Talent Management into a solid contributor to business strategies for the long-term needs to start at the platform level, however. Eightfold’s approach to combining their Talent Management, Talent Insights and Talent Acquisition modules, all supported by their Talent Intelligence Platform, enables their customers to define their digital transformation goals and strategies and get results.

Source: McKinsey & Company, 2020, How Covid-19 has pushed companies over the technology tipping point—and transformed business forever

Conclusion

The Talent Management goal many organizations aspire to today is to digitally transform candidate or prospect experiences so well that people have an immediate affinity for the company they apply to, and the self-service options are so intuitive they rival Amazon’s 1-Click Ordering Experience. Across any industry, digital transformation succeeds when customers’ expectations are exceeded so far that a new category gets created. Uber’s contextual intelligence, rating system, and ability to optimize ride requests is an example. UberEats provides the same real-time visibility into every step of each order, creating greater trust. Domino’s Pizza Tracker app keeps customers informed of every phase of their orders. What’s common across all these examples is personalization at scale, real-time automation across service providers, and real-time visibility. Those same core values need to be at the center of any Talent Management digital transformation effort today.

Mar 14

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

10 Ways Covid-19 Vaccine Supply Chains Need To Be Protected By Cybersecurity

  • The Pharma industry has lost $14 billion through Intellectual Property (IP) cyber theft worldwide, according to the United Kingdom Office of Cyber Security and Information Assurance.
  • 53% of pharmaceutical IP thefts and related breaches are carried out by someone with insider access, also according to the United Kingdom Office of Cyber Security and Information Assurance.
  • The pharma industry’s average total cost of a data breach is $5.06 million, with one of the highest costs of remediating the breach at $10.81 million across all industries, according to a recent ProofPoint study.
  • Over 93% of healthcare organizations experienced a data breach in the past three years, and 57% have had more than five data breaches, according to the Cybersecurity Ventures 2020 Healthcare Cybersecurity Report.
  • Gartner predicts the privileged access management (PAM) market will grow at a compound annual growth rate (CAGR) of 10.7% from 2020 through 2024, reaching $2.9 billion by 2024.

Bottom Line: Having developed COVID-19 vaccines in a fraction of the time it takes to create new treatments, pharmaceutical companies need to protect the priceless IP, supporting data, and supply chains from cyberattacks.

Showing how powerful global collaboration between pharmaceutical industry leaders can be, the world’s leading vaccine producers delivered new vaccines in record time. The IP behind COVID-19 vaccines and their supporting supply chains need state-of-the-art protection comprised of cybersecurity technologies and systems, as the vaccines’ IP is an asset that cyber attackers have already tried to obtain.

Pharmaceutical’s Growing Number of Threat Surfaces Make Cybersecurity a Priority 

In the race to create a COVID-19vaccine by collaborating across the industry, pharmaceutical companies have exposed more threat surfaces than existed before the pandemic. In R&D, Clinical Trials, Manufacturing, and Distribution, there’s a proliferation of new threat surfaces cyber attackers are targeting today, as evidenced by threat analysis reports from the U.S. Homeland Security Department’s Cybersecurity & Infrastructure Security Agency (CISA).

The report provides specifics about how cyber attackers could impersonate an executive from a Chinese biomedical company known for having end-to-end cold chain expertise, which is essential for delivering vaccines reliably. The cyber attackers conducted spear-phishing attacks against global companies who support the global cold chain needed for distributing vaccines. There were credential harvesting attempts against global organizations in at least six countries known today to access vaccine transport and distribution sensitive information.

Launching a phishing campaign with the goal of harvesting details on key executives and access credentials across the cold chain is just the beginning. According to Lookout’s Pharmaceutical Industry Threat Report, some of the most significant threat surfaces are the most problematic today, including the following:

Research & Development & Clinical Trials

  • Collaborative research teams across pharmaceutical manufacturers globally
  • Scientists creating initial compounds and completing primary research to define a vaccine.
  • Integration of study sites at the test device and reporting system level

Manufacturing and Distribution

  • Plant workers’ systems, including tablets with build instructions on them
  • Physician & Pharmacist Networks
  • Distribution Channels and their supporting IT systems

Cyber attackers are taking a more synchronized, multifaceted approach to attacking Covid-19 supply chains, reiterated in CISA’s report. There’s evidence that state-sponsored cyber attackers attempt to move laterally through networks and remain there in stealth, allowing them to conduct cyber espionage and collect additional confidential information from victim environments for future operations. Cyber attackers are initially focused on phishing, followed by malware distribution, registration of new Covid-specific domain names, and always looking for unprotected threat surfaces.

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

By combining multiple cybersecurity best practices and strategies, pharmaceutical companies stand a better chance of protecting their valuable IP and vaccines. Presented below are ten ways the pharmaceutical industry needs to protect the COVID-19 vaccine supply chain today:

  1. Prioritize Privileged Access Management (PAM) across the vaccine supply chain, ensuring least privilege access to sensitive data starting with IP. CISA’s note finds that there have been multiple attempts at capturing privileged credentials, which often have broad access privileges and are frequently left standing open. PAM is needed immediately to institute greater controls around these privileged accounts across the supply chain and only grant just enough just-in-time access to sensitive IP, shipping and logistics data, vaccination schedules, and more. Leaders include Centrify, which is noteworthy for cloud-based PAM implementations at the enterprise and supply chain levels. Additional vendors in this area include BeyondTrust, CyberArk, Ivanti, Thycotic, Ping Identity, and Senhasegura.
  2. Assess every supplier’s security readiness in vaccine supply chains, defining minimum levels of compliance to security standards that include a single, unified security model across all companies. In creating a secured vaccine supply chain, it’s imperative to have every supplier network member on the same security model. Taking this step ensures accountability, greater clarity of roles and responsibilities, and a common definition of privileged roles and access privileges.  Leaders in this area include BeyondTrust, Centrify, CyberArk, Ivanti, and Thycotic.
  3. Taking a Zero Trust-based approach to secure every endpoint across the vaccine manufacturer’s R&D, Clinical Trials, Manufacturing, and Distribution networks is necessary to shut down cyber attackers taking advantage of legacy security weaknesses approaches. The pharmaceutical companies and myriad logistics providers see a much faster than the expected proliferation of endpoints today. Trusted and untrusted domains from legacy server operating systems are a time sink when it comes to securing endpoints – and proving unreliable despite the best efforts that Security Operations teams are putting into them. Worst of all, they leave vaccine supply chains vulnerable because they often take an outdated “trust but verify” cybersecurity approach. Leaders include Illumio, Ivanti (MobileIron), Cisco, Appgate, Palo Alto Networks, and Akamai Technologies.
  4. Extend the Zero Trust framework across the entire supply chain by implementing microsegmentation and endpoint security requirements across all phases of the vaccine’s development cycles. This will ensure cyber attackers don’t have the opportunity to embed code to activate later. The goal is to push Zero Trust principles to all related processes integrating with the vaccines’ pipeline, including all dependencies across the entire development lifecycle.
  5. Incorporating Multi-Factor Authentication (MFA) across every system in the vaccine supply chain is a given. Usernames and passwords alone are not enough, and MFA is low-hanging fruit to authenticate authorized users. MFA is based on two or more factors that can authenticate who you are based on something you know (passwords, PINs, code works), something you have (a smartphone, tokens devices that produce pins or pre-defined pins), or something you are (biometrics, facial recognition, fingerprints, iris, and face scans). For example, Google provides MFA as part of their account management to every account holder and has a thorough security check-up, which is useful for seeing how many times a given password has been reused.
  6. Alleviate the conflicts of who will pay for increasing cybersecurity measures by making supplier-level security a separate line item in any CISOs and CIO’s budget. Today certain pharma supply chain CISOs are expected to ramp up cybersecurity programs with the same budget before Covid-19. While there are slight increases in cybersecurity budget levels, it’s often not enough to cover the higher costs of securing a broader scope of supply chain operations. CISOs need to have greater control over cybersecurity budgets to protect vaccine IP and distribution. Relying on traditional IT budgets controlled by CIOs isn’t working. There needs to be a new level of financial commitment to securing vaccine supply chains.
  7. Consider using an AIOps platform adept at unifying diverse IT environments into a single, cohesive AI-based intelligence system that can identify anomalous network behavior in real-time and take action to avert breaches. Based on conversations with CIOs across the financial services industry, it is clear they’re leaning in the direction of AIOps platforms that provide real-time integration to cloud platforms combined with greater control over IT infrastructure. LogicMonitor’s prioritizing IT integration as a core strength of their platform shows, as they have over 2,000 integrations available out of the box. Relying on Collectors’ agentless system, LogicMonitor retrieves metrics such as cloud provider health and billing information. This collector then pulls metrics from different devices using various methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.
  8. Unified Endpoint Security (UES) needs to become a standard across all vaccine supply chains now. Vendors who can rapidly process large amounts of data to detect previously unknown threats are needed today to stop cyberattacks from capturing IP, shipment data, and valuable logistics information. Absolute Software’s approach to leveraging its unique persistence, resilience, and intelligence capabilities is worth watching. Their approach delivers unified endpoint security by relying on their Endpoint Resilience platform, which includes a permanent digital tether to every enterprise’s endpoint. Absolute is enabling self-healing, greater visibility, and control by having an undeletable digital thread to every device. Based on conversations with their customers in Education and Healthcare, Absolute’s unique approach gives IT complete visibility into where every device is at all times and what each device configuration looks like in real-time.
  9. Pharma supply chains need to have a strategy for achieving more consistent Unified Endpoint Management (UEM) across every device and threat surface of the vaccine supply chain. UEM’s many benefits, including streamlining continuous OS updates across multiple mobile platforms, enabling device management regardless of the connection, and having an architecture capable of supporting a wide range of devices and operating systems. Another major benefit enterprises mention is automating Internet-based patching, policy, configuration management. Ivanti is the global market leader in UEM, and their recent acquisition of Cherwell expands the reach of their Neurons platform, providing service and asset management from IT to lines of business and from every endpoint to the IoT edge. Neurons are Ivanti’s AI-based hyper-automation platform that connects Unified Endpoint Management, Security, and Enterprise Service Management. Ivanti is prioritizing its customers’ needs to autonomously self-heal and self-secure devices and self-service end-users.
  10. Track-and-traceability is essential in any vaccine supply chain, making the idea of cyber-physical passports that include serialization for vaccine batches more realistic given how complex supply chains are today. Passports are an advanced labeling technology that provides the benefits of virtual tracking, verification of specific compounds, and yield rates of key materials. Serialization is a must-have for ensuring greater traceability across vaccine supply chains proving effective in stopping counterfeiting. Having digital passports traceable electronically can further help thwart cyber attackers.

Conclusion

By closing the cybersecurity gaps in vaccine supply chains, the world’s nations can find new, leaner, more efficient processes to distribute vaccines and protect their citizens. It’s evident from the results achieved so far in the U.S. alone that relying on traditional supply chains and means of distribution isn’t getting the job done fast enough, and cyber attackers are already looking to take advantage. By combining multiple cybersecurity tactics, techniques, and procedures, the vaccine supply chain stands to improve and be more secure from threats.

Mar 8

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.

Professional Services’ Revenue Challenges Are Complex

Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:

  • Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
  • When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
  • Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
  • Revenue leakage gains momentum and drains margins when the following happens:
    • Un-forecasted delays and timescale creep
    • Reduced utilization rates across each key resource required for the project to be completed
    • Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
    • Incorrect pricing versus the costs of sales & service often leads to customer churn.
    • Revenue leakage gains momentum as each of these factors further drains margin

Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:

How FinancialForce Is Using AI To Fight Revenue Leakage

Why Professional Services Are Especially Vulnerable To Revenue Leakage 

Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.

The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.

Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.

By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.

How FinancialForce Is Using AI To Fight Revenue Leakage

Conclusion

FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay.  Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.

Mar 7

How A Startup Uses AI To Help You Find The Market Research You Need

How A Startup Uses AI To Help You Find The Market Research You Need

  • 95% of the content essential for decision making in an organization is unstructured, residing in PDFs and various file formats that defy easy indexing and quick access, according to MIT Media Labs.
  • 80% of typical organizations’ data is unstructured, slowing down work, often leading to less-than-optimal decision-making, according to an Accenture study published earlier this year.
  • Organizations use 35% of their structured data for insights and decision-making, but only 25% of their unstructured enterprise data, according to an Accenture study on how data is used for decision-making.
  • 60% to 80% of employees can’t find the information they are looking for even when there’s content management or knowledge management system in place, according to IBM’s knowledge management study.

Bottom Line: Stravito is an AI startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable.

When It Comes To Finding Market Research Data, Intranets Aren’t Getting It Done

Facing tight deadlines to get a marketing plan together for a new product, channel, or selling strategy, market research and product marketing teams will give up looking for a report they know they’ve bought and re-purchase it. The tighter the deadline and the more important the plan, the more this happens.

When a quick call to the Market Research Analyst who has access privileges to all the market research subscriptions doesn’t have the reports a team needs, they either move on without the data or repurchase the report. Having spent the first years of my career as a Market Research Analyst, I can attest to the accuracy of IBM’s finding that 30% of a typical knowledge workers’ day is spent searching for information and understanding its context and original methodology. All reports our organization had distribution rights to internally went on the Intranet site. There were hundreds of reports available online on an Intranet platform with mediocre search capabilities.

The company was founded by Thor Olof Philogène and Sarah Lee in 2017, who together identified an opportunity to help companies be more productive getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (HQ), Malmö and Amsterdam.

Instead of settling for less-than-optimal market and industry data that partially deliver the insights needed for an exceptional product launch or sales campaign, marketing & senior management teams need to set their sights higher. It’s time to replace legacy Intranet sites and their limited search functions with AI-based search engines that auto-tag content and build taxonomies based on content attributes in real-time. Stravito combines AI, machine learning, NLP and Search on a single platform that can index every major file type an organization uses, creating a taxonomy that streamlines search queries.

Having AI as the foundation of the Stravito platform delivers the following benefits:

  • AI-powered fast search gives individuals the ability to find and share insights and information quicker than any legacy Intranet technology could. With everyone working from home and self-service being a goal every marketing, business planning and IT department is trying to achieve today, Stravito’s architecture is designed for simple queries and requests anyone can quickly learn to create.
  • Relying on AI and machine learning to alleviate the need to manually upload and tag hundreds of market research reports and analysis. Stravito’s approach to data categorization using AI also identifies and removes duplicate report copies and can be configured to filter out any reports past a specific date. Search perimeters, auto-tagging and in-PDF search options are all configurable. Stravito will rank PDFs by the percentage of relevant content they have for a specific search term, providing a bar graph designating which pages have the most relevant content.
  • Stravito’s design team has successfully combined AI, machine learning and advanced user interface design to produce an application comparable to Spotify, Google and Netflix. Developing and launching an enterprise-level search engine designed for usability first is noteworthy. Many enterprise applications still aren’t achieving this design goal despite being mentioned as a first priority by enterprise software vendors. As can be seen from their search results screen, Stravito’s approach is to combine information discovery and collaboration:

 

  • Stravito deserves credit for finding new ways to use AI and machine learning to accomplish drag-and-drop integration of any commonly used file format in an organization – and then have it assigned to a taxonomy in seconds. Stravito’s innovative use of AI, machine learning and auto-tagging provides its customers with a simple drag-and-drop interface that supports bulk uploads. The platform has API integration designed with any market research or advisory service with an API library compatible with their platform. Their customer base actively relies on Euromonitor and Mintel today, for example.

Conclusion

Stravito fills the gap legacy Intranet technologies and current generation collaboration platforms are not addressing. That’s the need to provide a more powerful search engine, one capable of continually adapting to new information and documents. Supervised machine learning has proven effective for taking on challenges related to creating and keeping taxonomies current. Stavito’s product strategy of providing personalized recommendations for the content of interest is a natural progression of their platform. For organizations overwhelmed with research data yet can’t seem to get the reports to decision-makers fast enough, the Stravito platform is worth checking out.

Jan 23

Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021

Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
  • According to Burning Glass Technologies, the two tech job skills paying the highest salary premiums today and in 2021 are IT Automation ($24,969) and AI & Machine Learning ($14,175).
  • The average salary premiums for the most in-demand tech skills range from $4,204 to nearly $25,000.
  • Startups valued at $1 billion or more are 33% more likely to prioritize one or several top ten tech job skills in their new hire plans versus their legacy Fortune 100-based competitors or colleagues.

These and many other fascinating insights are from Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech, Burning Glass Technologies’ latest research study published earlier this month. Their latest study provides pragmatic, useful insights for tech professionals interested in furthering their careers and earning potential. Burning Glass Technologies is a leading job market analytics provider that delivers job market analytics that empowers employers, workers and educators to make data-driven decisions. 

Using AI To Find The Most Valuable Job Skills

Using artificial intelligence-based technologies they’ve developed, Burning Glass Technologies analyzed over 17,000 unique skills demanded across their database of over one billion historical job listings. The study aggregates then define disruptive skill clusters as those skill groups projected to grow the fastest, are most undersupplied and provide the highest value. For additional details regarding their methodology, please see page 8 of the report.

The research study is noteworthy because it explains how essential acquiring skills is to translating new technologies’ benefits into business value. They’ve also taken their analysis a step further, providing technical professionals with additional insights they need to plan their personal development and careers.

Key takeaways from their analysis include the following:

  • IT Automation expertise can earn technical professionals a $24,969 salary premium, the most lucrative of all tech job skills to have in 2021. Burning Glass Technologies defines IT Automation as the skills related to automating and orchestrating digital processes and workflows. Six of the ten job skills are marketable enough to drive technical professionals’ salaries above $10,000 a year. At an average salary uplift of $8,851, proactive security (cybersecurity) job skills’ market value seems low. Future surveys in 2021 will most likely reflect the impact of the SolarWinds breach on demand for this skill set. The following graphic compares the average salary premium by tech job skill area.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Software Dev. Methodologies (DevOps) expertise is the most marketable going into 2021, with 634,600 open positions available in North America based on Burning Glass Technologies’ analysis. Employers initiated 1,714,483 job postings requesting at least one disruptive skill area between December 2019 and November 2020. With each skill predicted to grow at least 17%, technical professionals have several lucrative options for their personal and professional development plans. The following graphic compares job openings by skill areas for the time frame of the study:
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Quantum Computing, Connected Technologies, Fintech and AI & Machine Learning expertise are predicted to be the fastest-growing tech job skills in 2021 and beyond. Demand for technical professionals skilled in building and optimizing quantum computers and their applications will be in high demand for the next five years based on the study’s findings. Connected Technologies refers to skills related to the Internet of Things and connected physical tools and the telecommunications infrastructure needed to enable them. Fintech skills are related to technologies, including blockchain and others, that make financial transactions more efficient and secure. The following graphic compares the top ten tech job skills predicted to grow the fastest in 2021.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • AI & Machine Learning, Cloud Technologies, Parallel Computing and Proactive Security (Cybersecurity) are the most distributed across industries, translating into more diverse job opportunities for technical professionals with these skills. Professional Services leads all industries in demand for nine of the ten tech job skills, except Parallel Computing, the most in-demand skill in Manufacturing. Factors contributing to Professional Services leading all industries in demand for technical job skills include the following factors. First, their business models need to continue pivoting fast to stabilize during the pandemic. Second, better risk and compliance controls of remote operations are urgently needed. Third, better visibility into services costs across all systems to ensure financial reporting accuracy is a must-have, according to the CFOs I spoke with regarding the survey results. The following graphic compares demand for tech skills by industry sector.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  • Demand for AI and Machine Learning skills is growing at a 71% compound annual growth rate through 2025, with 197,810 open positions today. Technical professionals with job skills in this area see salary premiums of $14,175. Top positions include Data Scientist, Software Developer, Network Engineer, Network Architect, Data Engineer and Senior Data Scientist.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
  •  Positions requiring IT Automation job skills are predicted to grow 59% over the next five years and have 282,380 positions open today. Besides being the most lucrative job skillset to have, IT Automation job skills lead to positions including Software Developer, DevOps Engineer, Senior Software Developer, Systems Engineer and Java Developer or Engineer.
Top 10 Tech Job Skills Predicted To Grow The Fastest In 2021
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies

Oct 25

4 Comments

What’s New In Gartner’s Hype Cycle For AI, 2020

What's New In Gartner's Hype Cycle For AI, 2020
AI is starting to deliver on its potential and its benefits for businesses are becoming a reality.

  • 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations plan to increase their AI investments, according to a recent Gartner poll.
  • 30% of CEOs own AI initiatives in their organizations and regularly redefine resources, reporting structures and systems to ensure success.
  • AI projects continue to accelerate this year in healthcare, bioscience, manufacturing, financial services and supply chain sectors despite greater economic & social uncertainty.
  • Five new technology categories are included in this year’s Hype Cycle for AI, including small data, generative AI, composite AI, responsible AI and things as customers.

These and many other new insights are from the Gartner Hype Cycle for Artificial Intelligence, 2020, published on July 27th of this year and provided in the recent article, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.  Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety and responsible use of AI and AI governance are the catalysts contributing to the second theme.  The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below:

What's New In Gartner's Hype Cycle For AI, 2020
Smarter with Gartner, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.

Details Of What’s New In Gartner’s Hype Cycle for Artificial Intelligence, 2020

  • Chatbots are projected to see over a 100% increase in their adoption rates in the next two to five years and are the leading AI use cases in enterprises today.  Gartner revised the bots’ penetration rate from a range of 5% to 20% last year to 20% to 50% this year. Gartner points to chatbot’s successful adoption as the face of AI today and the technology’s contributions to streamlining automated, touchless customer interactions aimed at keeping customers and employees safe. Bot vendors to watch include Amazon Web Services (AWS), Cognigy, Google, IBM, Microsoft, NTT DOCOMO, Oracle, Rasa and Rulai.
  • GPU Accelerators are the nearest-term technology to mainstream adoption and are predicted to deliver a high level of benefit according to Gartner’s’ Priority Matrix for AI, 2020. Gartner predicts GPU Accelerators will see a 100% improvement in adoption in two to five years, increasing from 5% to 20% adoption last year to 20% to 50% this year. Gartner advises its clients that GPU-accelerated Computing can deliver extreme performance for highly parallel compute-intensive workloads in HPC, DNN training and inferencing. GPU computing is also available as a cloud service. According to the Hype Cycle, it may be economical for applications where utilization is low, but the urgency of completion is high.
  • AI-based minimum viable products and accelerated AI development cycles are replacing pilot projects due to the pandemic across Gartner’s client base. Before the pandemic, pilot projects’ success or failure was, for the most part, dependent on if a project had an executive sponsor and how much influence they had. Gartner clients are wisely moving to minimum viable product and accelerating AI development to get results quickly in the pandemic. Gartner recommends projects involving Natural Language Processing (NLP), machine learning, chatbots and computer vision to be prioritized above other AI initiatives. They’re also recommending organizations look at insight engines’ potential to deliver value across a business.
  • Artificial General Intelligence (AGI) lacks commercial viability today and organizations need to focus instead on more narrowly focused AI use cases to get results for their business. Gartner warns there’s a lot of hype surrounding AGI and organizations would be best to ignore vendors’ claims of having commercial-grade products or platforms ready today with this technology. A better AI deployment strategy is to consider the full scope of technologies on the Hype Cycle and choose those delivering proven financial value to the organizations adopting them.
  • Small Data is now a category in the Hype Cycle for AI for the first time. Gartner defines this technology as a series of techniques that enable organizations to manage production models that are more resilient and adapt to major world events like the pandemic or future disruptions. These techniques are ideal for AI problems where there are no big datasets available.
  • Generative AI is the second new technology category added to this year’s Hype Cycle for the first time. It’s defined as various machine learning (ML) methods that learn a representation of artifacts from the data and generate brand-new, completely original, realistic artifacts that preserve a likeness to the training data, not repeat it.
  • Gartner sees potential for Composite AI helping its enterprise clients and has included it as the third new category in this year’s Hype Cycle. Composite AI refers to the combined application of different AI techniques to improve learning efficiency, increase the level of “common sense,” and ultimately to much more efficiently solve a wider range of business problems.
  • Concentrating on the ethical and social aspects of AI, Gartner recently defined the category Responsible AI as an umbrella term that’s included as the fourth category in the Hype Cycle for AI. Responsible AI is defined as a strategic term that encompasses the many aspects of making the right business and ethical choices when adopting AI that organizations often address independently. These include business and societal value, risk, trust, transparency, fairness, bias mitigation, explainability, accountability, safety, privacy and regulatory compliance.
  • The exponential gains in accuracy, price/performance, low power consumption and Internet of Things sensors that collect AI model data have to lead to a new category called Things as Customers, as the fifth new category this year.  Gartner defines things as Customers as a smart device or machine or that obtains goods or services in exchange for payment. Examples include virtual personal assistants, smart appliances, connected cars and IoT-enabled factory equipment.
  • Thirteen technologies have either been removed, re-classified, or moved to other Hype Cycles compared to last year.  Gartner has chosen to remove VPA-enabled wireless speakers from all Hype Cycles this year. AI developer toolkits are now part of the AI developer and teaching kits category. AI PaaS is now part of AI cloud services. Gartner chose to move AI-related C&SI services, AutoML, Explainable AI (also now part of the Responsible AI category in 2020), graph analytics and Reinforcement Learning to the Hype Cycle for Data Science and Machine Learning, 2020. Conversational User Interfaces, Speech Recognition and Virtual Assistants are now part of the Hype Cycle for Natural Language Technologies, 2020. Gartner has also chosen to move Quantum computing to the Hype Cycle for Compute Infrastructure, 2020. Robotic process automation software is now removed from the Hype Cycle for AI, as Gartner mentions the technology in several other Hype Cycles.

Sep 13

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.

Bottom Line: Eightfold.ai’s innovative approach of combining their own AI and virtual hackathons to create and launch new additions to their Project Marketplace rapidly is a model enterprises need to consider emulating.

Eightfold.ai was founded with the mission that there is a right career for everyone in the world. Since its founding in 2016, Eightfold.ai’s Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically.

What’s noteworthy about Eightfold.ai’s approach is how they have successfully created a platform that aggregates all available data on people across an enterprise – from applicants to alumni – to create a comprehensive Talent Network. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies. Eightfold’s AI and machine learning algorithms are continuously learning from enterprise and individual performance to better predict role, performance and career options for employees based on capabilities.

How Eightfold Sets A Quick Pace Innovating Their Marketplace

Recently Eightfold.ai announced Project Marketplace, an AI-based solution for enterprises that align employees seeking new opportunities and companies’ need to reskill and upskill their employees with capabilities that line up well with new business imperatives. Eightfold wanted to provide employees with opportunities to gain new skills through experiential learning, network with their colleagues, join project teams and also attain the satisfaction of helping flatten the unemployment curve outside. Project Marketplace helps employers find hidden talent, improve retention strategies and gain new knowledge of who has specific capabilities and skills. The following is a screen from the Marketplace that provides employees the flexibility of browsing all projects their unique capabilities qualify them for:

How An AI Platform Is Matching Employees And Opportunities

Employees select a project of interest and are immediately shown how strong of a match they are with the open position. Eightfold provides insights into relevant skills that an employee already has, why they are a strong match and the rest of the project team members – often a carrot in itself. Keeping focused on expanding employee’s capabilities, Eightfold also provides guidance of which skills an employee will learn. The following is an example of what an open project positions looks like:

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Employee applicants can also view all the projects they currently have open from the My Projects view shown below:

How An AI Platform Is Matching Employees And Opportunities

Project Marketplace is the win/win every employee has yearned for as they start to feel less challenged in their current position and start looking for a new one, often outside their companies. I recently spoke with Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, Eightfold’s President, to see how they successfully ran a virtual hackathon across three continents to keep the Marketplace platform fresh with new features and responsive to the market.

How to Run A Virtual Hackathon

Starting with the hackathon, Eightfold relied on its own Talent Intelligence Platform to define the teams across all three continents, based on their employees’ combined mix of capabilities. Ashutosh, Kamal and the senior management team defined three goals of the hackathon:

  1. Solve problems customers are asking about with solutions that are not on the roadmap yet.
  2. Accelerate time to value for customers with new approaches no one has thought of before.
  3. Find new features and unique strengths that further strengthen the company’s mission of finding the right career for everyone in the world.

It’s fascinating to see how AI, cybersecurity and revenue management software companies continue to innovate at a fast pace delivering complex apps with everyone being remote. I asked Ashutosh how he and his management team approached the challenge of having a hackathon spanning three continents deliver results. Here’s what I learned from our discussion and these lessons are directly applicable to any virtual hackathon today:

  1. Define the hackathon’s purpose clearly and link it to the company mission, explaining what’s at stake for customers, employees and the millions of people looking for work today – all served by the Talent Intelligence Platform broadening its base of features.
  2. Realize that what you are building during the hackathon will help set some employees free from stagnating skills allowing them to be more employable with their new capabilities.
  3. The hackathon is a chance to master new skills through experiential learning, further strengthening their capabilities as well. And often learning from some of the experts in the company by joining their teams.
  4. Reward risk-taking and new innovative ideas that initially appear to be edge cases, but can potentially be game changers for customers.

I’ve been interviewing CEOs from startups to established enterprise software companies about how they kept innovation alive during the lockdown. CEOs have mentioned agile development, extensive use of Slack channels and daily virtual stand-ups. Ashutosh Garg is the only one to mention how putting intrinsic motivation into practice, along with these core techniques, binds hackathon teams together fast. Dan Pink’s classic TED Talk, The Puzzle of Motivation, explains intrinsic motivators briefly and it’s clear they have implications on a hackathon succeeding or not.

Measuring Results Of the Hackathon

Within a weekend, Project Marketplace revealed several new rock stars amongst the Eightfold hackathon teams. Instead of doing side projects for people who had time on their hands, this Hackathon was about making Eightfold’s everyday projects better and faster. Their best Engineers and Services team members took a step back, re-looked at the current approaches and competed with each other to find better and innovative ways. And they all voted for the most popular projects and solutions – ultimate reward in gaining the respect of your peers. As well as the most “prolific coder” for those who couldn’t resist working on multiple teams.

Conclusion

Remote work is creating daunting challenges for individuals at home as well as for companies. Business models need to change and innovation cannot take a back seat while most companies have employees working from home for the foreseeable future. Running a hackathon during a global lockdown and making it deliver valuable new insights and features that benefit customers now is achievable as Eightfold’s track record shows. Project marketplace may prove to be a useful ally for employees and companies looking to stay true to their mission and help each other grow – even in a pandemic. This will create better job security, a culture of continuous learning, loyalty and more jobs. AI will change how we look at our work – and this is a great example of inspiring innovation.

 

Aug 31

1 Comment

Where AIOps Is Delivering Results Today

Where AIOps Is Delivering Results Today

Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.

LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.

Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:

Where AIOps Is Delivering Results Today

How LogicMonitor’s Architecture Supports AIOps

One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system.  The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.

Where AIOps Is Delivering Results Today

Using AIOps To Monitor, Analyze, Automate

LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:

  • Kubernetes Monitoring
  • Service Insight
  • Topology
  • Remote Sessions
  • Netflow
  • Configuration Monitoring
  • Public Cloud Monitoring
  • Applications Monitoring

LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.

The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.

Conclusion

LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.

 

 

 

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