Geoffrey, can you provide a brief overview of your role as the Vice President of Data Analytics at Alight and your expertise in AI and data analytics within the HR domain?

I look after Alight’s AI, personalization, and analytic capabilities from a product and data science perspective. This includes Alight’s chatbots, search engines, personalized nudges, and recommendations capabilities we provide to our clients and their employees as well as some of the AI-enabled automations we’re putting in-place to deliver high-quality ongoing service.  

We’re continuously enhancing our capabilities. For example, We recently unveiled Alight LumenAI, our next-generation AI engine to power Alight Worklife ®, Alight’s employee experience platform. 

We observed three consistent imperatives for creating leap-frog HR AI capabilities: 

1. Adoption of Generative AI (Gen-AI) 

2. Tying together AI capabilities with one unified view of an employee

3. The use of ever-growing internal and external datasets to improve model performance.  

That’s why we launched Alight LumenAI – to ensure we could continue bringing market-leading AI capabilities to our HR clients.

I’ve been passionate about AI and data-enabled SaaS products for a long time, regardless of sector, and that’s reflected in my prior roles building AI-powered experiences in cybersecurity at SecurityScorecard, finance at Bloomberg, or consumer goods at Arena AI.

I joined Alight because applying AI and data science to the Human Resources (HR) space is a chance to deploy AI “for good” – ensuring people are enrolling in the right benefits, preparing appropriately for retirement, having a seamless employee experience, and generally maximizing the wellbeing opportunities offered by their employers.

Right now is an especially exciting time to be at Alight: our clients are being pushed by their CEOs to demonstrate transformational AI strategies within HR and the AI capabilities Alight offers fit very well into these strategies and can deliver  transformational wins.

AI and employee experience:

How do you envision generative AI and AI-powered platforms shaping the future of the employee experience in the workplace?

There’s a baseline level of transformation happening everywhere, where most of the tools we use to do our work are getting generative AI upgrades. 

Taking a step back, we broadly see AI fitting into 5 categories within the HR domain – and these mirror the capabilities that we bring to market for our clients:

• AI Personalization: capabilities that drive a greater than 10% increase in targeted client HR outcomes through personalized, “next best action” content
• AI Assistance: capabilities with natural language/intent models to maximize digital engagement, supporting a 90% self-service rate
• AI Recommendation: capabilities providing automated decision support and choice optimization for benefits and care, saving employees on average $500 in premium expenses annually
• AI Insights: capabilities with data trend analysis for high-precision employer analytics to identify hotspots in the employee experience and HR processes
• AI Automation: capabilities that streamline repetitive workflows, such as document processing or at-scale call monitoring

Whereas in the past, an HR team might adopt a few of the above capabilities, we’re seeing that teams succeeding with AI transformation are adopting tech across all 5 of those categories.

Can you give examples of how AI-driven innovations have already improved the employee experience in organisations?

 We’ve been working with clients to deploy AI for years, even before the surge in generative AI interest that has taken place over the last year.  A great example we’ve seen is through our Interactive Virtual Assistant (IVA), or chatbot, that has helped employees answer their benefits questions in a personalized and self-service way and helps drive a 90%+ digital interaction rate (so folks aren’t needing to get in touch with a call center).  

When we launched IVA about 5 years ago, its initial performance was “ok” – but in the intervening years we’ve spent millions of dollars on teams tuning the algorithm based on the results of performance across 30M interactions with employees – so that now our IVA offers market-leading performance. 

It’s an important lesson to remember: AI systems often require ongoing maintenance and investment by professionals to achieve differentiated performance.  Having a “human in the loop” is incredibly important.

Our AI-powered IVA continues to see increased engagement from employees and was recently enhanced to also execute transactions – for example, by allowing employees to re-enroll in their health coverage plans during annual enrollment.  

We’re also excited to be piloting a GenAI-enhanced version of our IVA, powered by Alight LumenAI, that provides more holistic and helpful answers to questions where information was locked-up in complicated policy or benefits documents.  The results have been pretty spectacular – one of our clients when they used it for the first time said, “this is amazing, can we just roll this out now!”

Efficiency and productivity:

In what specific ways can AI enhance efficiency and productivity for both employees and employers in today’s evolving work environment?

In the HR vertical, efficiency is often about trying to reduce call, ticket and email volume for HR teams so that work shifts away from repetitive administrative employee needs and towards more consultative high-value activities.  

Anything AI can do to reduce the volume of administrative calls and tickets is immensely helpful.  AI can help HR teams diagnose, at scale, what is driving the high call and ticket volumes to shorten what are often very long process-improvement cycles…and it can also help create more effective interception-points to help individuals self-service their needs.  

For example, imagine an employee with an HR need logging into their internal HR portal, and then using an IVA chatbot to try to answer their question, and then using a voice-based Interactive Virtual Response (IVR) call-routing system when they call the call center. That’s three interception points where AI has an opportunity to help an employee self-service for a better, faster experience before they get to an agent.

Intelligent Document Processing is another great example of how we partner with clients to improve experience and reduce cost.  Many HR processes still depend on employees submitting documents (deposit checks, birth certifications, etc) and so when we deploy intelligent document processing we reduce the time it takes to process documents and provide feedback to users from 10+ minutes to near-instant.  Not only is this fast feedback loop a better experience for employees, it also tends to reduce calls to the call centers from employees asking about document status.

Personalization:

How can AI enable more personalised experiences for employees, and why is personalization important for overall employee satisfaction and engagement?

Personalization is a pretty broad term and can encompass many things. It can start as basic as knowing what benefits someone is eligible for and only showing them information about those and scale all the way to using AI to nudge or prompt employees according to a next-best action framework.  

Without a baseline of personalization in place, employees can quickly become disengaged by an experience that feels irrelevant to them. Once that baseline is there, you can start to play with personalization that drives outcomes. We partner often with clients on personalized communication campaigns that drive outcomes such as increased 401k contribution, HSA contribution, or increased utilization of specific programs like healthcare navigation.

For example, in March 2024, a pharmaceutical client selected Alight to help improve the financial wellbeing of their workforce through personalized messaging that encouraged the adoption of changed saving behaviors.  With only 75% of employees participating in a Health Savings Account (HSA) and a majority saving below the maximum allowed amount, the company aimed to encourage greater participation in retirement and health savings plans and ensure that employees were taking advantage of the company match to both the 401(k) and HSA.

With a focus on employees who had not yet maximized the value of tax-advantaged accounts, the company partnered with Alight to leverage personalized email and web messaging that would influence saving behaviors. This personalized messaging was made possible with adaptive, “Always On” AI technology that dynamically adjusted engagement strategies to drive up retirement and health savings contributions over time. Upon partnering with the client, Alight took strategic steps to ensure seamless integration and successful implementation of the AI-driven program. 

Key initiatives included:

• Assessment: The Alight team conducted a comprehensive needs assessment to understand the specific challenges and goals of the client in-depth.
• Data analysis: Extensive analysis of existing data, including employee participation rates, savings patterns and financial behaviors, were undertaken to inform the AI-driven personalization strategy.
• Integration planning: Alight collaborated closely with the client to develop an integration plan, identifying areas for personalized content implementation within existing communication channels.
• Customization framework: A tailored framework for content personalization was established that considered the unique characteristics of the client’s workforce and desired outcomes.
• Pilot programs: Small-scale pilot programs were initiated to test the effectiveness of the AI-driven approach, allowing for adjustments and refinements before full-scale implementation.
• Continuous monitoring: The Alight team implemented continuous monitoring and feedback mechanisms to track the success of the AI-driven program and ensure ongoing adaptability.

Post-implementation, Alight conducted thorough assessments of the system’s impact on both 401(k) and HSA participation, and success was substantiated by the substantial increase in employee contributions to both. Additionally, tax savings projections were delivered to show the true value of these funds.  Planning, testing and effective execution of the new AI-driven messaging system took less than six months.

As a result, the pharmaceutical company realized a substantial 17% increase in employees starting or increasing their 401(k) savings. Achieved a commendable 6% increase in employees starting or increasing contributions to the HSA. Notably improved the average 401(k) contribution rate, showcasing an impressive increase of 5.4%, and demonstrated tangible financial impact with an average increase of $1,750 in employee HSA contributions.

Measuring value:

What strategies can companies employ to effectively measure the value derived from their investments in employee experience and well-being initiatives, using data-driven insights?

Most importantly, companies need to know the outcome they’re trying to achieve upfront, and they need to be measuring that on an ongoing basis.  Once that’s in place, there are varying levels of sophistication clients can deploy to measure and attribute changes in the planned outcome to the interactions they are executing.  

The gold-standard for these is treatment vs. control groups, but even basic attribution can give a basic measure of success.  In many cases, if there is a specific action an employer is trying to drive, they can track who took that action after experiencing a personalized nudge, and attribute these to the personalized nudge. Examples of impact we’ve seen using this basic measurement methodology include:

• Nudges delivered over 6 months to direct employees to financial coaches resulted in a 7% increase in enrollment in HDHP health plans
• Nudges delivered over 6 months to encourage employees to contribute more to their HSA campaigns resulted in a 33% conversion rate from messaging to action, and the increase in HSA contributions yielded ~$1M in FICA tax savings for the employer 

Data utilisation:

Could you elaborate on how organisations can responsibly utilise employee data to enhance the employee experience while maintaining data privacy and security?

Sure – organizations need to think both about overall data security as well as ensuring appropriate use of data specific to each experience use-case.  In general, the less places you send and store your employee data, the better and the less opportunities there are for data breach or inappropriate use.  When it comes to appropriate use of data, using it to enhance the employee experience should be governed by standard data risk management and security review processes.

Alight’s clients include government entities and defense contractors, so we’ve already been operating in a very robust data and cybersecurity framework.  Last year we formalized our approach to AI risk and now assess all use-cases of AI technology against an 8-part risk framework that looks at things like data risk, bad output risk, bias risk, etc.

Challenges of implementing AI:

What are the common challenges that organisations face when implementing AI-powered solutions for employee experience, and how can they mitigate these challenges?

We like to use an “AI Intrapreneur” framework that lays out five important considerations for any new AI use case and recommend careful consideration –if you’re thoughtful about these five factors you will successfully launch an AI use-case:

• Pick the right areas – Focus on problems AI can solve now, not speculative future capabilities. Validate with small, low-risk pilots.
• Resource wisely – Build in-house for differentiated capabilities, use vendors for commoditized capabilities.
• Avoid high-risk AI uses – AI will make mistakes: don’t use AI where those mistakes have severe consequences.
• Keep humans in the loop – Humans must oversee AI systems. Design AI use cases for human oversight.
• Measure extensively – Rigorously measure performance, error rates, biases and business impact. Establish feedback loops.

We took the above approach in our current Gen-AI IVA pilot – testing with a small number of users at a small set of clients, building some of the technology ourselves so that we could be differentiated in the market, and being very thoughtful about how we keep humans in the loop to ensure accurate answers to employee’s HR-related questions.

Ethical considerations:

Are there ethical considerations organisations should be aware of when integrating AI into employee experience initiatives, and how can they ensure ethical AI practices?

The most important ethical consideration – which we touched on in the above – is understanding what the consequence is of a bad model output and its consequence on a person.

Leadership and management changes:

With the integration of AI, how do you foresee the role of leadership and management evolving in HR and employee experience, and what challenges might this transformation present?

The biggest shift is likely to be that whereas before managers might be managing the quality of output of their team, they will now spend an increasing amount of time managing the quality of an algorithm’s output.  No AI system is perfect, and they all require some amount of human oversight.

Final thoughts:

As AI technologies evolve rapidly, what advice would you offer HR and business leaders to stay informed and leverage the latest AI innovations effectively for employee experiences?

Read and absorb as much as possible and stay curious!  Don’t expect to stay fully up to date – even AI researchers are getting surprised these days by sudden developments in the field.

More generally, be aware of your organization’s overall risk appetite and be comfortable with it – some organizations want to be on the leading edge, others may want to take a more conservative approach – both are OK.

Geoffrey Peterson

Vice President of Data & Analytics at Alight

Geoffrey Peterson is the Vice President of Data and Analytics at Alight Solutions, a role he’s held since 2023. Before joining Alight, he was Global Head of Product Management and Data Governance at Bloomberg and a Senior Product Manager at Security Scorecard. Earlier in his career, he was a Business Analyst and Associate at McKinsey & Company before moving into management roles at South African Breweries Limited. Peterson earned a BA in Computer Science and Economics from Harvard University and an ME in Computer Science from Cornell University.

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Burnout is at an all-time high as employees face ever-increasing productivity expectations while being forced to do more with less. Approximately 82% of employees are at risk of burnout this year, and 95% say their emotional state plays a big part in their productivity. The research underscores the importance of happy, healthy employees–and while it might seem like simple altruism at the corporate level, there are real financial consequences of letting well-being fall by the wayside. Most notably, burnout costs employers as much as $300B annually. 

Today, while most research centers on the burnout of knowledge workers at large, one subset group is faring worse than others: software developers. These teams are the foundation of nearly every modern company, and research shows they’ve been stretched thin to the point of crisis. Solving developer burnout must become a top priority for organizations, or they risk losing the very engine that powers their progress.

Burnout: By The Numbers

According to Harness’ State of the Developer Experience Report, relentless workloads are the leading cause of burnout in the software industry and are the primary reason most developers quit. Over half (52%) of developers cite burnout as the main factor driving their peers to leave their jobs. One of the primary causes of burnout is developer toil: the prevalence of manual, repetitive tasks that consume significant time and effort without providing significant value to the business. The report found that nearly half of developers say they can’t release code to production without risking failures. If that code does need to be rolled back, an astounding 67% of developers do so manually.

The problem is exacerbated by scope creep, which almost two-thirds (62%) of developers experience. To keep up with their workload, nearly a quarter (23%) of engineers work overtime at least 10 days a month, and 97% of developers admit to context switching, meaning they move between unrelated tasks throughout the day, further reducing their productivity.

Additionally, hiring isn’t helping quickly enough, as organizations struggle to onboard new hires quickly enough to alleviate the pressure on current employees. The report found that 71% of respondents said onboarding takes at least two months, leaving existing engineering teams to shoulder the extra workload in the interim.

Solving the Developer Burnout Crisis

While the challenges are steep, there are numerous ways to circumvent these problems and ultimately improve developer mental health:

Automate Toil

Automating toil refers to the process of leveraging technology to eliminate repetitive, mundane, and time-consuming tasks, freeing up human resources for more strategic and creative endeavors. By implementing advanced software that automates monotonous tasks, such as code rollbacks, organizations can enhance efficiency, reduce the risk of errors, and improve overall productivity. This approach not only streamlines workflows but also enables employees to focus on higher-value activities that require critical thinking and innovation. Consequently, automating toil is a key strategy for leaders looking to optimize DevOps while improving the employee experience.

Sabbaticals & “No Meeting” Days

Sabbaticals are generally thought of as a “big company” benefit. But startups, and specifically smaller companies with high percentages of software developers, can reap major benefits from implementing these programs. Harness offers a “Sabbatical for Startups” program, which offers employees time off to focus on self-care through health & wellness benefits. Thus far, the program has decreased turnover while improving employee well-being–and these results aren’t limited to Harness; they’re also backed by research from Adecco Group.

Dual Onboarding

A dual-onboarding process can cut down on onboarding time, enabling new employees to make an impact faster. This two-pronged approach separates the orientation process from functional onboarding. During orientation, employees learn about the company’s culture, values, policies, and procedures, ensuring they understand the organizational environment. In the functional onboarding phase, new hires receive role-specific training and get acquainted with the tools and resources they will use daily. By distinguishing these components, employees quickly adapt to the company culture while gaining the skills needed for their roles, allowing them to contribute to their teams and projects more rapidly.

Listen to Feedback Consistently

Progress cannot be made without opening the doors to transparent feedback company-wide. Employee engagement surveys can help determine levels of motivation and engagement across the organization, while manager surveys, conducted away from the performance review cycle, enable honest feedback about leaders within the company. Departmental surveys can take inventories of their teams and help strategize for the future. Combining the results of these surveys provides a comprehensive view of employee experience, enabling HR managers to advocate for their employees effectively. Addressing mental health and burnout among developers requires a multifaceted approach. By implementing advanced tools, innovative programs, and open feedback channels, companies can create a healthier, happier, and more productive environment for their employees.

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In response to rapidly changing workplace needs, many companies launched into scaling up their tech stacks and providing employees with new tools that promised greater efficiency, improved productivity, and a better digital experience. Research shows, however, that 40% of employees and 44% of executives believe that employees are either somewhat or significantly over-provisioned by tech at work. As a result, workplaces now are grappling with an abundance of tools that were poorly matched both to employees’ needs and to specific workplace challenges. 

The underlying disconnect is this: companies focused too much on providing new equipment to teams in an attempt to make broad, sweeping improvements in productivity or to accelerate business transformations. In turn, they fell short of providing the right equipment to the right employee at the right time.

Without specific and actionable insights into employees’ daily tech-related experiences—such as whether latency is affecting their work or whether a desktop is about to bluescreen—executives and IT leaders are, essentially, winging it with end-user tech investments. And, yes, while these tech investments are well-informed by expert opinions and strategic assessments of both the market and the company’s needs, IT leaders inevitably need more data to make a compelling business case that the C-suite will approve.

Fortunately, IT leaders today have a seat at the strategy table, as IT’s old-school “cost center” designation has evolved. IT leaders are data-driven stakeholders that hold the key for unlocking strategic insights about the health of an organization, its systems, and the overall digital risk the company faces. As such, IT leaders need data that speaks to issues such as system downtime, latency, and other tech issues both at the individual level and across the organization. 

Resource allocation based on IT health

Measuring the health of an organization’s entire tech stack doesn’t come down to a single data set. A company’s IT health includes multiple data points collected from multiple systems. Calculating the financial impact of IT decisions requires collating data related to KPIs, digital employee experiences (DEX), technical debt, and the entire IT estate. 

This data-driven approach isn’t just a tech one; it’s a financial one as well. Without a proactive approach to IT, the monetary impact of IT issues on an organization’s bottom line can be considerable. With an average of 54 minutes per week, per worker lost to IT issues, companies need a clear and measurable solution to minimize productivity costs. 

Translating the state of IT health into monetary values helps executives make the case for new initiatives and investments or even changes to existing processes or systems. With DEX insights, such as health scores and granular-level data, companies can identify which devices are being used effectively, which are not, and whether a particular team needs more tech resources allocated to their department. With the ability to view the financial impact of IT issues across the entire IT estate, companies can determine where gaps actually exist and need to be filled. This data-driven insight ensures that companies don’t perpetuate the problem of digital overload by providing their teams with tech that they don’t need or will not use. 

Determine benchmarks to prepare for scaling.

Organizations looking to scale and grow need clear markers of success long before they level up their investment in IT systems to prevent too large of an investment or too little preparation. Setting benchmarks is essential. Companies can achieve this step by comparing historical trends based on data instead of guesswork. This must happen alongside real-time data for a full picture of the digital employee experience (that is, each employee’s experience with the tech stack allocated to them—whether good or bad). 

Context around certain IT moves and decisions, as well as the impact of those moves on workplace productivity and performance, is crucial for enabling strategic planning. For instance, it’s possible to parcel data into meaningful, informative sets based on the workplace environment (hybrid or remote), the employee experience with the digital tools they need for their roles, and the systems used. 

Benchmarks will be critical for growth planning, including any M&A plans on the docket, enterprise-wide system integrations (such as EMR rollouts for healthcare organizations), or widespread software updates. Armed with easily-digestible benchmark data, IT teams can sort out any issues ahead of an influx of talent, system mergers, and digital transformation projects. 

In these scenarios, IT leaders can emerge as true business heroes, instead of the old days when the “IT hero” was associated with reactively saving a company from extended downtime. The IT executives’ ability to tie downtime, latency, and systems issues (and more) to the business’s bottom line—based on data-informed calculations—will elevate strategic planning. The monetary value of an organization’s IT health is rapidly increasing as companies look to eliminate redundancies, streamline workflows, and create better digital employee experiences. Up until now, measuring the baseline of IT health—and tying that baseline to a financial tally—has been cumbersome and inefficient. Now, IT leaders can determine the issues affecting productivity through a single dashboard of a digital experience platform, enabling companies to quickly measure the impact of software, hardware, and network issues on workplace productivity, in turn immediately remedying any issues.

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Kindly brief us about yourself and your role as the Co-Founder & CEO at Leena AI.

Ever since I was a child, I found immense pleasure in creating new things with my own hands, from Legos with friends to electrical circuits for school projects. I loved how things would come to life and add value to everyone around me. I’ve always had a creative mindset, and that played heavily into my wanting to be an entrepreneur. Throughout my IIT-Delhi days, I had been building and creating, whether in tech fests, competitions, or computer algorithm assignments and trying to figure out ways to make people’s lives easier and better. 

Over time, I realized that I wanted to utilize my creative mindset to find solutions and establish the next best thing that the world needed. That is one specific thing that has kept me going and even led me to build Leena AI. 

I have seen Leena AI scale through from a three-person team working from each other’s apartments to more than 300 people globally today. It has been an extremely enriching journey, replete with learning and growth. The one thing that has stayed constant for all of us at Leena AI is our hunger for growth – both individually and as a team. Our passion continues to be wanting to add value to people’s lives and helping our own team grow personally, as well as professionally. 

Can you provide an overview of Leena AI and its core offerings in the field of artificial intelligence?

Leena AI is an AI work assistant designed for modern enterprises. We recently launched our own large language model, WorkLM, which empowers enterprises worldwide to redefine how employees engage with work, delivering a transformative impact on productivity and efficiency.

WorkLM, built on our breakthrough language model architecture, possesses an unparalleled predictive text generation capability, producing human-like responses in context. This further empowers employees with a versatile toolset to accomplish tasks with exceptional precision and speed, unlocking unprecedented levels of productivity and efficiency.

We also seamlessly integrate with 1,000+ applications, including SAP, Salesforce, ServiceNow, ADP, Oracle, Workday, and Microsoft Office 365. Our solutions have been successfully deployed across 90+ countries, serving over 400+ customers, including leading enterprises such as Nestlé, Puma, Coca-Cola, Sony, and Etihad Airways, to name a few.

What specific applications of AI does Leena AI focus on, and how do they benefit businesses and organizations?

Our solutions are powered by Generative AI, and our proprietary large language model, WorkLM, has been built especially for enterprise employee experience. It harnesses our breakthrough language model architecture to provide an unparalleled predictive text generation capability, resulting in human-like responses. 

The specific applications include:

• Intelligent Virtual Assistant: WorkLM can underpin a smart virtual assistant capable of understanding and executing complex tasks, managing multi-turn conversations, and providing real-time information.
• Handling Complex Commands Across Multiple Systems: WorkLM can interpret and execute complex commands across multiple systems and knowledge sources, intelligently building workflows on the fly to streamline operations.
• Identifying Knowledge Gaps and Creating Knowledge: WorkLM can identify knowledge gaps within an organization and generate knowledge from existing non-standard sources such as ticket resolutions and emails.
• Intelligent Robotic Process Automation (RPA): WorkLM, integrated with RPA, can enhance efficiency and effectiveness by understanding and automating routine tasks.
• Business Intelligence from Enterprise Data: WorkLM can analyze extensive enterprise data to provide actionable business intelligence, facilitating data-driven decisions.
• Helpdesk Intelligence: WorkLM can enhance the capabilities of a helpdesk by providing accurate responses and solutions to a wide array of queries and issues, thereby reducing resolution times and improving customer satisfaction.
• Text Analysis: WorkLM can mine insights from large volumes of text data, performing tasks such as identifying trends, sentiment analysis, or extracting specific information.
• Email Auto-completion: WorkLM can provide contextually relevant suggestions for email drafting, improving communication efficiency.
• Document Generation: WorkLM can generate reports, meeting minutes, and other types of documents based on specific requirements, translating complex instructions into high-quality, human-like text.
• Human-like Autonomous Agents: WorkLM can drive autonomous agents that handle a variety of low-value tasks within an enterprise, freeing human resources for more strategic endeavors.

How does Leena AI ensure data privacy and security in its AI-powered products and services?

Since our solutions are powered by our own large language model, WorkLM, it allows us to protect our clients’ data as we are not required to share it with any third party. We are also SOC2-certified and GDPR-compliant. 

Can you share any examples or case studies where Leena AI has successfully implemented its AI solutions to solve real-world business challenges?

While there are many examples, I would like to point out one with UHA Health Insurance. One of the primary challenges they faced was the lack of a robust platform to drive real-time engagement and collect employee feedback. It was also difficult for them to get employees’ pulse, especially when remote working became the norm during the pandemic.

At the time, employee engagement became more daunting with extended lockdowns, forcing employees to work remotely. UHA saw remote work as the ideal time to take an agile engagement approach to get a holistic view of employee engagement. While there were many online surveys to choose from, Leena AI’s innovative and interactive survey caught UHA’s attention. Our customizable avatar, easy integration prowess for easy deployment, bank of questions to choose from, a team of subject matter experts to consult with, and analytics to understand their employee pulse better made for a great solution for UHA.

After partnering with Leena AI, UHA noticed that more than 75% of UHA’s associates participated in the pulse survey, along with thousands of qualitative comments and feedback, making it a successful engagement partnership.

Additionally, our smart sentiment analysis technology analyzed thousands of associates’ comments to help identify what’s actually working well and what needs improvement. UHA was able to identify the problem areas and add them to its business plan for the coming year. 

In your opinion, what are the key challenges and opportunities in the AI industry today, and how is Leena AI positioned to address them?

The biggest opportunities we see today are, how can we use virtual assistants in enterprises to elevate employees from doing repetitive, mundane work, and free their time so they can add strategic value to the business. 

For example, our client Coca-Cola Beverages Vietnam, with a workforce of over 4,000 employees meant that on any given day, the HR executives would receive numerous employee queries within the organization ranging from leave status, medical insurance, payroll, etc. This took valuable time for the HR team to sign off multiple employee approvals. Moreover, as per company policy, approval from the CEO or the Director was necessary to clear high-value travel requests. Since most of these requests were urgent in nature, the CEO and the Director had to allocate a significant amount of time for approvals. Needless to say, the task was repetitive and monotonous.

Essentially, they needed a platform that could help them automate the resolution of queries and employee processes at the same time. Additionally, the platform had to act as the linchpin for the current systems that were operating in isolation. Leena AI’s conversational platform fit the bill perfectly and worked as an effective single platform for accessing all the information while interacting with the employees seamlessly.

We created a single communication interface for employees by integrating the existing systems of SAP, Salesforce, the local attendance system, and other internal software. We also integrated with Workplace by Facebook, which was already in use by the company, to prevent the set-up and training of a new application. We witnessed over a 40% reduction in time for approving employee requests as well as a 50% reduction in the employees’ time to access relevant company information.

What are the key factors that organizations should consider when implementing AI solutions, and how does Leena AI support its clients throughout the implementation process?

A lot of the time CXOs want to implement AI because it seems like the next cool thing to do. But that actually is not a good enough reason. Instead, Organizations should identify specific problems and accordingly seek an AI-powered solution. Once they reach that stage, they should ideally do a trial basis or a pilot with their vendor of choice, post which they can scale it across the organization.

At Leena AI we follow a similar process. We customize our solutions based on our client’s needs. So we begin by auditing their existing processes and analyzing their tickets and building solutions accordingly. We typically start with one solution and then scale it along the way. After approximately nine months we do another deep dive and conduct another audit to identify whether any changes need to be made.

How does Leena AI ensure continuous improvement and adaptation of its AI models to stay up-to-date with evolving technology and business needs?

Most of our AI development and improvement is based on the specific requirements of our customers. Therefore, we take these factors into consideration, which further forms our product roadmap. 

Are there any ethical considerations that Leena AI takes into account when developing and deploying AI solutions?

There are three crucial factors we consider: The first is ensuring that our Generative-AI powered solutions deliver correct responses, i.e. those that are ethical and moral. In order to ensure this, we run two models simultaneously so we can fact-check every response to every prompt. There is WorkLM, which has a learning capacity of seven billion parameters, and a smaller model of 2.5 billion parameters. We test every response across both parameters and once they deliver the same response, we have the guarantee that they are both correct and in alignment. 

The second factor we take into consideration is drawing a line on the kind of decisions AI should and should not be able to make. 

Finally, the third factor is protecting employees and their jobs. Some of our solutions can potentially take over people’s jobs, and so, before deploying such products, we have a discussion with our clients on the learning and development of their respective employees. This can be through upskilling, reskilling, inter-departmental functions, and so on. We are working on creating a practice around this, to advise all our customers and the industry at large to manage the socio-economic impact. 

What advice would you give the budding entrepreneur aspiring to venture into the AI space?

It is an ever-changing space! Their focus should be on fundamentals such as understanding algorithmic design or learning languages such as Python, which is important in this industry. Essentially one can learn best practices through hands-on experience, so I would advise them to try new things as much as possible since most of the frameworks are open-source today. 

That said, building something is only one aspect, but production is another ball game altogether. For example, having a seven billion parameter model is easy, but running it successfully in production is a challenge. Therefore, I would recommend that organizations focus on building constantly while simultaneously figuring out the production aspect i.e. taking their respective products to the market.

Adit Jain

Co-founder, and CEO at Leena AI

Adit, an IIT-Delhi grad, and Y-Combinator alumni, brings a deep understanding of HR needs for an exceptional employee experience. With experience at EY and IIM Lucknow, Adit embarked on his entrepreneurial journey with Chatteron in 2015. Transitioning to Leena AI, inspired by a study on Chatteron’s profitability, he never looked back.

Adit draws inspiration from books like “Zero to One” by Peter Thiel, “Founders at Work” by Jessica Livingston, and “Good to Great” by James C. Collins. His advice to aspiring entrepreneurs? “Just do it.” Taking action and persevering increases your chances of success, despite failures and necessary pivots along the way.

Adit’s accomplishments include being featured in the Forbes 30 Under 30 US and Canada list for Enterprise Technology.

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