AIaaS (AI-as-a-service) for Agile Project Management at SKILup Days 2024

Uh, hey everyone. Uh, I'm super excited to be talking about AI as a service for Agile project management in today's session. Uh, now let's, uh, get to know a little bit more about me and what I do, uh, with AI at work.

Uh, so my name is, uh, Neha, and I am a, uh, TPM at LinkedIn. Uh, if you've seen some of the, uh, series previously, um, I talk a lot about, uh, DevOps and, uh, everything to do with, uh, software, uh, development and, uh, technologies. So, if you ask AI today, what does A TPM do, um, chat GPT will tell you that a, uh, TPM oversees the, uh, planning, execution and, uh, delivery of complex, uh, technical projects.

Uh, while that is true, uh, tpms also help resolve some of the, uh, technical challenges that arise across multiple organizations, uh, in large scale companies. Uh, so in today's, uh, agenda, we'll be going over, uh, AI as a service and some of the, uh, agile project, uh, management, uh, technologies. Um, so here at the, uh, intersection of, um, AI and, uh, agile, uh, what are like the, uh, benefits, uh, who are some of the, uh, AI as a service providers?

Uh, we'll dive a little deep into, uh, use cases, uh, review some of the, uh, challenges and what are the, uh, future, uh, trends in, um, AI as a service. Um, so ai, uh, which stands for artificial intelligence, has been part of our lives, uh, for a while, and has already made a significant impact, uh, but its influence, uh, is about to grow. Uh, even more, uh, recent advancements are, uh, progressing, uh, at such a rapid pace that AI is becoming a transformative force, um, with the potential to reshape entire or, uh, industries, economies and, uh, even, uh, society itself.

Um, it'll, uh, revolutionize the way we work, uh, automating, uh, complex, uh, tasks, uh, generating code and, uh, creating applications and experiences that once seemed, uh, possible impossible. Uh, while, uh, this technology, uh, leap may feel intimidating to some, I encourage everyone to focus on the opportunities it, uh, brings. Um, ai, uh, will evaluate innovation, uh, to unprecedented, uh, heights.

Um, imagine having, uh, the, uh, ultimate, uh, all powerful brainstorming partner at your side all the time. Um, now let's, uh, explore how AI as a service, uh, which also stands for ai, um, a as, uh, which, um, uh, can be leveraged, uh, in agile, uh, project management. Uh, let's begin by understanding, uh, what, uh, ai, um, as a service means.

So it refers to the provision, uh, or provisioning of artificial intelligence, uh, functionalities, um, through cloud-based, uh, platforms. Um, so essentially, uh, it's AI delivered as a service over the internet, but allowing organizations to, um, access sophisticated AI tools and capabilities without, uh, investing heavily in, uh, in-house, uh, infrastructure. Uh, some of the common, uh, services, uh, offered by AI as a service, uh, providers, uh, include, uh, machine learning, uh, frameworks.

So these are the, uh, platforms that allow you to, uh, build, train, and deploy machine learning models. There's also the, uh, natural language processing, which stands for NLP. Um, those are like the tools that enable machines to understand and, uh, interpret human language.

Uh, there's the, uh, computer vision, uh, which is the technology that allows computers to, uh, interpret and, uh, process visual data from the, uh, world. Uh, and there's also data analytics tools. Um, so some of the advanced, um, uh, analytics capabilities, um, to process large data sets and extract actionable, um, insights, uh, are some of the, uh, services that, uh, you know, this service, uh, providers usually pro, uh, usually have.

Um, so by, uh, leveraging these, uh, services, organizations can enhance, uh, their operations and, uh, decision making, uh, processes, uh, without significant upfront cost, uh, typically associated with, uh, any AI development. So, agile is basically an approach that emphasizes, um, uh, iterative, uh, development where requirements and solutions, um, uh, evolve, uh, through, uh, through collaboration between, uh, cross-functional teams. Um, so, uh, integrating AI as a service into, uh, agile methodologies, uh, creates a synergy, uh, that significantly enhances the, uh, project outcomes.

Uh, so AI brings, uh, intelligent automation and, uh, deep data analytics into the, uh, agile process. So by incorporating, uh, ai, uh, teams can automate some of the repetitive tasks, uh, freeing up, um, time for more strategic activities or teams can also, uh, look into our data-driven insights, uh, which enable them to make more informed decisions, uh, quickly. So processes can be, uh, optimized based on, uh, predictive analytics, uh, which, uh, improves the efficiency.

So, uh, some of the teams can, uh, also respond to, uh, changes more, uh, swiftly, uh, that's maintaining, um, agility even, uh, as, uh, the, uh, project, uh, complexity grows. Uh, uh, so let's look into the, uh, role of, uh, ai, uh, in agile, uh, practices. So AI plays a supportive role in various agile practices.

Um, so if you already, uh, follow like your, uh, sprints, uh, in backlog prioritization, AI can analyze factors, uh, such as customer value, um, resource availability and market trends to suggest which user story you can prioritize first. And also during sprint planning, uh, AI can assist, uh, in, um, estimating, uh, tasks, duration and identifying, you know, dependencies or co-dependencies, uh, which help, um, your development team to set, uh, realistic, uh, goals. Um, and, um, the team also would probably be, um, uh, setting aside a lot of time for retros.

Um, so for retrospective analysis, um, AI can, uh, process data from, uh, previous sprints, um, to identify patterns and areas for improvement. So, overall, AI's ability to recognize and, uh, predict risks and recommend actions, um, ensures that, you know, the teams are equipped with the insights, uh, they can make, um, so that they are, uh, empowered to make these informed decisions, uh, throughout the project life cycle. So, the, uh, benefits of, uh, AI as a service in, uh, project, uh, management, um, is that one of the most, uh, uh, one of the most, uh, be, uh, significant benefits, um, in agile project management is the enhancement of decision making capabilities.

So you have a data, uh, processing, uh, where AI algorithms can analyze, uh, vast amounts of data match, uh, faster and more, uh, accurately than humans, um, by also uncovering trends and, uh, patterns. Uh, AI insights, um, provide, you know, strategic decisions. Um, so with also better information, uh, project managers can reduce uncertainties, um, and make more confident decisions.

So there's also, like, you know, high, uh, higher success rates. So every time there is a data-driven decision, uh, it leads to more successful project outcomes. As actions are based on evidence, uh, rather than intuition.

Uh, AI can significantly enhance our team collaboration. So, ai, uh, powered platforms, uh, can streamline communication by organ organizing, uh, information and enabling seamless interactions. Uh, AI can also, uh, filter and highlight our relevant information, ensuring that the team members have access to what they need and whenever they need it.

Um, so in multi, uh, national teams, uh, ai, uh, driven translational tools, um, breaks down, uh, language barriers, uh, facilitating, you know, better understanding within your teams. Uh, so AI can also, uh, analyze communication patterns to gauge, uh, team morale, um, allowing some of, like your senior leadership to address issues like, you know, burnt out or, uh, disengagement proactively. Uh, so some of the, uh, predictive analytics, uh, is, uh, very, uh, powerful.

It's an AI capability and project management. So AI can forecast realistic project timelines and budget requirements by analyzing historical data and current project parameters. Um, AI models can identify, uh, potential roadblocks or risks even before they become critical issues.

Um, having said that, with these insights, um, project managers or program managers can adjust plans proactively by real reallocating, uh, resources or altering, uh, some of the, uh, strategies to ensure that the, uh, project or program, uh, stays on track. So, uh, the, uh, routine, uh, administrative tasks, uh, can, uh, consume, uh, significant time and, uh, resources. So the, uh, AI automates, uh, many of these, uh, tasks, um, uh, for like status updates, AI bot bots can automatically compile and distribute project, uh, status updates.

If you want a program, a report generation, uh, an AI tool can definitely generate detailed, drilled down, uh, data, um, or reports up pulling data from various or multiple sources and presenting it coherently. Um, so there's also like issue tracking which AI would be able to achieve, um, by automated, uh, systems, uh, these can, uh, log and track issues and also assign them to appropriate, uh, team members. Uh, so let's look into, uh, some of the AI as a service providers.

So, out there, there is of course, AWS, which is big in the, uh, market, uh, Amazon Web services, which, uh, offers a comprehensive suite of AI and machine learning services, including, uh, Amazon, uh, SageMaker for building and deploying ML models, uh, Amazon cognition, uh, for image and video analysis, and Amazon L for building conversational interfaces. Um, there's also Google Cloud, which provides a range of AI solutions, including, um, Google Cloud AI for machine learning tools, um, natural, uh, language, uh, API for text analysis, um, vision AI for image processing and dialogue flow for building conversational interfaces. And another big one is the Microsoft Azure, uh, which delivers, uh, AI services through, uh, Azure Cognitive Services, which includes tools for visions, speech, uh, language processing, and decision making.

Um, there's open ai, which is known for its advanced, uh, language processing, uh, model, and, uh, generative, uh, AI capabilities, um, which includes, uh, GPT models. Uh, there's also nvidia, which, uh, specializes in AI, hardware and infrastructure providing, uh, powerful GPU accelerated computing for AI applications. There's also H2O ai, which offers open source machine learning platforms and industry specific AI solutions.

Um, so some of the use cases and applications here that we can, uh, sort of drill down into, um, are that, uh, AI as a, uh, services, uh, enhances a customer, uh, service by, uh, deploying chat bots, uh, virtual as and virtual assistance. Uh, these tools handle customer inquiries almost instantly, uh, and is available 24 by seven, uh, providing, um, you know, a better, um, customer satisfaction and reducing operational costs by automating routine interactions. Uh, in healthcare, you'll see, uh, AI as a, uh, service, um, uh, accelerate, uh, the medical image analysis aiding in oily and accurate, uh, diagnosis.

Um, it also, uh, speeds up, uh, drug discovery by analyzing vast data sets to identify potential compounds, uh, reducing time and costs and research. So some of the financial institutions use AI as a service for real time fraud detection, um, safeguarding assets by identifying suspicious activities promptly. Uh, additionally, AI driven algorithm make, uh, trading optimizes investment strategies, um, that's enhancing profitability.

Um, so AI as a service in retail, uh, provides personalized, uh, product recommendations. Um, it enhances customer experience and also like boost the sales. Um, it helps improve, uh, demand for casting, um, enabling efficient inventory management and reduced, uh, stock issues.

So the manufacturers, uh, leverage, um, AI as a service, uh, for predictive maintenance, uh, reducing equipment downtime by anticipating, uh, failures. AI also enhances quality control through automated inspection, ensuring consistent, uh, product standards. Uh, so in marketing, AI as a service enables a precise, uh, customer segmentation, allowing for targeted strategies.

So it optimizes campaigns by analyzing performance data, uh, leading to higher man, higher engagement, and, uh, better return, better return on investment. So, uh, hopping into some of the, uh, challenges and limitations with AI as a service. Um, one of the main, uh, primary challenges in AI deployment is ensuring the, uh, quality of data used, uh, to train models.

So poor or unrepresentative data can lead to inaccurate, uh, predictions and insights. Uh, furthermore, um, uh, a bias that, uh, can happen in, uh, training, uh, data can result in ai, uh, models that, uh, perpetuate unfairness, or, you know, sometimes it's just not right data. So, uh, the impact of, uh, the data bias, um, can lead to, uh, skewed AI decisions that adversely affect, uh, a lot of groups.

Um, uh, it's crucial to implement, um, data cleaning processes and use diverse representative data sets. So regular audits and, uh, bias detection tools can help identify and correct, uh, biases in AI models. Um, addressing data quality and, um, actually addressing data quality is very essential for building, uh, trustworthy AI systems that yield a reliable and fair outcomes.

Uh, another significant limitation is the, uh, black box nature of many AI models, especially in deep learning algorithms. Um, so complex models often lack, uh, transparency, making it difficult to understand how they arrive at specific decisions. Uh, in some industries, uh, regulations require explainable ai, especially when decisions impact individual's rights, like, uh, loan approvals, uh, medical, uh, diagnosis, um, and such.

Um, so techniques like, uh, explainable, uh, AI are currently being developed to, uh, provide insights into, into model, uh, decision making processes. Uh, enhancing explainability, uh, builds, uh, trust with users and, uh, stakeholders. So this is very crucial, uh, for compliance and ethical, uh, considerations.

Um, AI systems, um, must comply with data protection and, uh, privacy regulations such as general data protection regulation, which is also known as GDPR in, uh, e, uh, in the e eu. And the, uh, California Consumer Privacy Act, uh, which stands for CCPA. So these laws, uh, grant individual, uh, rights over, uh, their personal, uh, data, including, uh, access deletion and consent requirements.

Uh, compliance can be complex due to the way AI models process and store data, uh, potential legal penalties, uh, fines, and, uh, reputational, uh, damage can be some of the consequences of being non-compliant with some of the best practices. It's always crucial to implement privacy by design principles, ensuring there is transparency and data usage, and, um, you're always maintaining a robust data governance policy. Uh, deploying AI responsibly, uh, involves, uh, navigating various ethical considerations.

Um, ensuring that, uh, AI does not perpetuate biases or unfair treatment is at the top of the list. Uh, respecting individual's privacy and handling data sensitivity is important for privacy. Uh, establishing clear responsibility for AI decisions and outcomes solves for accountability.

Uh, maintaining, uh, human judgment and critical decision making processes ensures that there is always human oversight, uh, developing, uh, ethical guidelines and providing, uh, ethics training and involving, uh, diverse teams to oversee AI development and deployment are definitely the actionable steps here. So, AI as a service, um, it relies heavily on cloud infrastructure and internet connectivity. So, in areas with unreliable internet access, uh, dependency on, uh, cloud services can hinder AI application performance.

So, uh, real time applications may suffer from delays due to, uh, data transmission times, which, uh, can, you know, cause some, uh, latency concerns. Uh, transmitting data over the internet can also oppose, uh, security, uh, risk, if not, uh, properly, uh, secured. So some of the, uh, considerations that we can look into is, uh, evaluating the, uh, feasibility of, uh, edge computing, uh, solutions, and ensure that there is a robust, uh, network, uh, infrastructure, and, uh, also employ encryption and security, uh, best practices.

Um, so looking into the, uh, future, uh, trends in AI as a service. Um, so a lot of these, uh, providers are increasingly offering a tailored solution for specific industries. Uh, so, uh, industry specific models, uh, address unique, uh, challenges and regulatory requirements.

Uh, pre-trained models can be integrated more rapidly, reducing, uh, time to value. Uh, so an example is healthcare AI for patient diagnostics, uh, financial AI for compliance and fraud detection. Um, so here, businesses can leverage AI more effectively by adopting solutions designed for their specific context.

Uh, integration of, uh, edge computing with cloud AI services is becoming more, uh, prevalent, um, processes, um, uh, data locally on devices, um, reducing, uh, latency and, uh, dependency on connectivity are some of the edge AI benefits. Um, some of the use cases that we can look into here, uh, real time analytics and internet of things, devices, um, autonomous vehicles and, uh, remote sensors. So, combining edge and cloud computing provides scalable, um, and efficient AI solution.

So the major impact here is that it would enhance performance, privacy, and reliability of AI applications. Um, so federated learning is an emerging approach, uh, addressing, uh, privacy concerns. The concept here is that AI models are, are trained across multiple, um, decentralized services using, uh, local data samples, uh, without exchanging them.

Uh, the benefit is that, uh, it enhances data privacy and security sense. Raw data remains, um, on local devices, um, particularly, um, useful in healthcare and finance. Where sensitive data is, um, uh, prevalent are some of the applications here.

And, um, if you're looking into the, uh, future direction, you can expect greater adoption of this as, uh, privacy regulations tighten and trust in AI becomes, uh, paramount. Uh, the expansion of AI market facilitates easier access to, uh, AI tools and models, um, enabling, uh, en uh, enabling, uh, sharing and selling of AI models, data sets, and, uh, services, um, you know, form a collaborative, uh, platform. Uh, so lowering like the barriers for organizations to implement AI solutions, um, uh, is, uh, an innovation for acceleration.

Um, uh, this helps foster a community of developers, uh, researchers and, uh, businesses. Um, so advanced, uh, hardware like, uh, quantum computing is, uh, beginning to intersect with AI as a service. Um, so quantum ai, uh, utilizes quantum computers to perform complex calculations faster than classical computers.

Um, the potential applications here are optimization, uh, problems, uh, complex, uh, simulations and cryptographic analysis. Um, so looking into the future for this, uh, this could revolutionize, uh, fields requiring immense, uh, computational power expanding, uh, capabilities of AI solution. Uh, so in summary, uh, while AI as a service, uh, uh, presents significant opportunities, it is important to navigate the challenges and stay informed about emerging, uh, trends.

Uh, addressing limitations like, uh, data quality, explainability, and, uh, compliance ensures responsible AI deployment. Uh, keeping an eye out on, uh, future, uh, trends allows organizations to remain, uh, competitive and leverage new advancements in AI technology. Um, as we continue to integrate AI into agile project management, being mindful of these, uh, factors will enable us, uh, to harness AI's full potential effectively and ethically.

Um, thank you. Uh, please feel free to reach out to me if you have any questions.