5 Q’s for Layla Nasr, CEO of Makila AI
The Center for Data Innovation spoke with Layla Nasr, CEO of Makila AI, a Canadian artificial intelligence (AI) startup that helps businesses generate value from workforce data. Nasr discussed the ways AI can transform the field of human resources.
This interview has been edited.
Gillian Diebold: How does Makila help organizations find value in under-utilized data?
Layla Nasr: The pressure keeps increasing on large organizations. Key stakeholders demand that they meet their financial and diversity, equity, and inclusion (DEI) objectives. So, how can these organizations make better business decisions to achieve these objectives? They need more than just data. Data is abundant—insight is not. Sophisticated Excel sheets and traditional transactionally-focused software offer you data: the “what” in the “here and now.” For example, “We expect to lose 8 percent of our workforce to attrition in the next 12 months.” But, these software tools won’t tell you the “why” behind that percentage in workforce attrition. For example: Is the attrition due to disengagement, poor management, skill gaps, and so on? What can be done to lower or increase the attrition rate? What will be the impact of this attrition on extra-financial KPIs? That’s where Makila comes in.
Our customers, in both the private and public sector, can use our intuitive solution, powered by artificial intelligence, to surface insights from their financial and HR data and predict outcomes based on key factors. As a result of these captured insights, organizations can adjust their objectives and re-evaluate strategies to perform better.
The same holds true in finance. For example, our algorithm empowers you to carry out financial forecasting and simulations. You can visualize the progression of sales and the projection of revenues and compare them to budget across all dimensions. During the year, you can follow in real-time the evolution of the actuals compared to the forecast and also, even more usefully, visualize an updated predictive forecast at any time.
Do you want to know how the variable compensation of your salespeople will impact your commissions paid? You can project the expected payout based on whatever sales volumes and compensation percentages you wish and get both individual and global costs. The same predictive simulations are possible for various aspects of your organization’s operations.
Working with these insights and predictions, management can make better decisions faster and make the necessary strategic adjustments. Best of all, these queries into the data and their results can be expressed in natural language, so any person with a minimum of training can easily understand and interpret the results.
Diebold: How does AI help human resources professionals?
Nasr: Every organization has its own unique HR context and issues they want to solve, such as finding the right talent, creating a diverse and inclusive workforce, enhancing employee engagement, and so on. The key thing to remember, though, is that the right AI-driven algorithm can deliver benefits at every level of operation, from day-to-day efficiencies to insights that help redefine objectives and strategies. Let me give you some examples of these benefits.
First, to use an HRIS, or human resources information system, powered by AI technology, you have to collect all the structured and unstructured HR data of the organization. You centralize it, clean it, standardize it, and test its reliability. Putting in place that automated process alone leads to major time savings due to the inherent acceleration of data and document processing, instant distribution of data and its analysis, and the significant reduction of repetitive tasks, administration, and record-keeping.
Second, the proper use of AI tools enables you to detect and correct biases that could skew HR analysis. Currently, we’re developing machine learning algorithms that would surface insights regarding these biases. Based on the conclusions of this data-driven inquiry, the HR team can then refine their talent lifecycle strategies and also meet their DEI objectives.
Third, an AI-powered HR platform empowers HR professionals to build their talent pool with greater finesse. Let’s say your organization is about to integrate a group of new employees through an acquisition. Using our algorithm, you can analyze the HR data of these new employees and discover answers to questions like the following: Which individuals are most likely to leave or stay? What factors will contribute to their departure or retention? What happens to the group if we alter the compensation plan?
HR can then run simulations on the AI-driven platform to see what will happen to the group of potential new employees if certain factors are magnified or reduced. With these insights in hand, HR can present them to the leadership team and make recommendations.
Diebold: What are the biggest challenges businesses face in scaling AI technologies for workforce decisions?
Nasr: Scaling AI technologies for workforce decisions is not for the faint of heart. AI requires immense capacities for computer processing and storage, much more than most organizations have used in the past.
Data collection is a big challenge. The organization has to collect all its workforce data, both structured and unstructured. This data usually comes from not only different sources but also from different geographic locations and is governed by the workforce regulations of the respective jurisdictions. Vast quantities of data need to be cleaned and made reliable. Usually, the data has issues, such as missing values, incorrect responses, biased perspectives, and ambiguity, which may result in erroneous conclusions. To clean this data properly requires well-designed systems.
For instance, many cases about AI being biased against women and people of color show how important it is to carefully test and validate data before use. Machine learning models need error-free datasets to offer accurate predictions for effective AI solutions. AI agents will be expected to have ethical standards of some sort built into them as they become more empowered to make their own choices. Yet, data and AI ethics are difficult to operationalize. To do so requires the commitment of senior leadership and collaboration across the board.
The needs for cyber and IT security are often underestimated. A large organization has probably never had so much of its data collected in one place. Besides the actual AI computers and databases, other points of vulnerability may exist, such as the communications infrastructure or the cloud facilities. These circumstances create a new form of business risk. Your workforce data, like other valuable assets, need to be protected by rigorous controls and policies.
Diebold: Makila recently created a Gender Equality Index tool for organizations to better understand salary gaps in their workforce. Can you describe the technologies behind the index?
Nasr: Our product aims to measure, analyze, and interpret gender equality at the organizational level. To deliver this solution, we collaborate with Alixio Group, which is a strategic consulting and HR operational services group. This collaboration allows us to go well beyond the Gender Equality Index, to address all subjects around professional equality and pay equity.
This partnership provides businesses with a unified analytical consulting approach supported by a digital solution, powered by AI. The solution integrates the data needed to calculate the index and run automatic calculations, followed by a complete graphical visualization of the results, to facilitate the sharing of results with various stakeholders.
Diebold: What AI use cases should businesses pay attention to in the coming years?
Nasr: AI is changing every industry and business function, which results in increased interest in AI, its subdomains, and related fields such as machine learning and data science. But as the saying goes, “The future is already here. It’s just not evenly distributed yet.” Today, only a minority of large organizations are taking full advantage of the possibilities of AI. In the coming years, I expect AI-powered use cases will become table stakes for any competitive organization. Since our areas of expertise are HR and finance, I’ll focus on use cases in those business functions.
In recruiting, for example, AI and ML technologies are being used to analyze labor markets and competencies, match skill sets, and detect bias in job descriptions and the ranking of candidates. These technologies are particularly valuable for organizations with large volumes of candidates or those looking for highly specialized profiles.
HR leaders are also very interested in what employees think of their organization. AI/ML technologies can review, analyze, and report on vast amounts of comments and sentiments expressed in employee surveys and communication channels and social media feeds. As you know, sophisticated marketers like to personalize their messages. Similarly, HR teams communicating with employees can use AI/ML tools to review an individual’s career path to suggest opportunities that exist in the organization. Along the same lines, in learning and development, HR can suggest learning paths based on the employee’s experience, skill set, and career goals.
In terms of finance, more and more organizations use digital channels for payment. As a result, the odds of fraud and cyberattacks also increase. The real-time analysis of these transactions by AI-powered platforms can help detect and predict the chances of questionable transactions and potential security threats.