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Posts Tagged ‘artificial intelligence’

GenAI revolution: transforming KPIs for strategic business success

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Key performance indicators (KPIs) have been the north star guiding business strategy for decades. These criteria measure not only sales and revenue but also customer satisfaction as well as employee engagement. However, as the business landscape continues to evolve at an unprecedented pace, the need for deeper insights and more agile measurement arises. This is where the potential of generative artificial intelligence (GenAI) shines, opening doors to a new era of KPI innovation.

GenAI goes beyond automation to produce entirely novel content. It is a creative catalyst, opening up unprecedented possibilities for KPI innovation. Forget rigid, one-dimensional metrics. Powered by GenAI, KPIs become fluent, adaptive, and poetic, capturing not only the whats but also the whys and what-ifs. 

Reimagining KPIs for exponential growth

  • From static to dynamic: GenAI is capable of integrating dynamic KPIs, meaning they can evolve alongside the company that uses them. KPIs also fit seamlessly into a changing market, with trends and strategies naturally shifting along the way. 
  • Unveiling the unseen: Traditional KPIs often fail to hit the nail on the head by overlooking key, intangible factors that could affect performance. GenAI, however, can delve much deeper. With the help of GenAI, it is possible to determine brand sentiment before a particular campaign is launched, anticipate employee engagement within remote teams, or even predict customer turnover before it happens. 
  • Personalized insights, enhanced action: Data mountains no longer need to be intimidating. GenAI transforms data into personalized narratives, crafting stories tailored to individual stakeholders. Sales teams can access actionable insights, marketing managers can monitor real-time customer sentiment, and CEOs can explore what-if scenarios for strategic foresight. This data-driven storytelling fosters informed decision-making and ignites action across the organization.

A practical guide to unlocking GenAI’s potential for KPI innovation 

To effectively utilize GenAI tools like Gemini and ChatGPT for KPI innovation, follow these guidelines:

  • Define goals and challenges: Clearly articulate objectives, whether uncovering customer sentiment or anticipating market shifts.
  • Frame specific prompts: Use concise prompts such as “generate potential KPIs for measuring brand sentiment on social media.”
  • Provide relevant context: Enhance responses by furnishing background information about your industry, business model, and existing KPIs.
  • Experiment and refine: Iterate prompts, rephrase questions, and provide feedback to improve AI understanding.
  • Collaborate with experts: Involve human expertise in evaluating and implementing AI-generated insights.

While GenAI’s potential for KPI innovation is undeniable, it thrives on synergy, not substitution. The point is this: human guidance is essential. Act now, invest in your future, and become a master of the new KPI era by enrolling in The KPI Institute’s Certified KPI Professional course.

Key safety considerations for generative AI adoption in business

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In May 2023, Samsung Electronics prohibited its employees from using generative artificial intelligence (AI) tools like ChatGPT. The ban was issued in an official memo, after discovering that staff had uploaded sensitive code to the platform, which prompted security and privacy concerns for stakeholders, fearing sensitive data leakage. Apple and several Wall Street Banks have also enforced similar bans.

While generative AI contributes to increased efficiency and productivity in businesses, what makes it susceptible to security risks is also its core function: taking the user’s input (prompt) to generate content (response), such as text, codes, images, videos, and audio in different formats. The multiple sources of data, the involvement of third-party systems, and human factors influencing the adoption of generative AI add to the complexity. Failing to properly prepare for and manage security and privacy issues that come with using generative AI may expose businesses to potential legal repercussions.

Safety depends on where data is stored

So, the question becomes, how can businesses use generative AI safely? The answer resides in where the user’s data (prompts and responses) gets stored. The data storage location in turn depends on how the business is using generative AI, of which there are two main methods. 

Off-shelf tools: The first method is to use ready-made tools, like OpenAI’s ChatGPT, Microsoft’s Bing Copilot, and Google’s Bard. These are, in fact, nothing but applications with user interfaces that allow them to interact with the base technology that is underneath, namely large language models (LLMs). LLMs are pieces of code that tell machines how to respond to the prompt, enabled by their training on huge amounts of data. 

In the case of off-the-shelf tools, data resides in the service provider’s servers—OpenAI’s in the instance of ChatGPT. As a part of the provider’s databases, users have no control over the data they provide to the tool, which can cause great dangers, like sensitive data leakage.

How the service provider treats user data depends on each platform’s end-user license agreement (EULA). Different platforms have different EULAs, and the same platform typically has different ones for its free and premium services. Even the same service may change its terms and conditions as the tool develops. Many platforms have already changed their legal bindings over their short existence.

In-house tools: The second way is to build a private in-house tool, usually by directly deploying one of the LLMs on private servers or less commonly by building an LLM from scratch.

Within this structure, data resides in the organization’s private servers, whether they are on-premises or on the cloud. This means that the business can have far more control over the data processed by its generative AI tool.

Ensuring the security of off-the-shelf tools 

Ready-made tools exempt users from the high cost of technology and talent needed to develop their own or outsource the task to a third party. That is why many organizations have no alternative but to use what is on the market, like ChatGPT. The risks of using off-the-shelf generative AI tools can be mitigated by doing the following:

Review the EULAs. In this case, it is crucial to not engage with these tools haphazardly. First, organizations should survey the available options and consider the EULAs of the ones of interest, in addition to their cost and use cases. This includes keeping an eye on the EULAs even after adoption as they are subject to change.

Establish internal policies. When a tool is picked for adoption, businesses need to formulate their own policies on how and when their employees may use it. This includes what sort of tasks can be entrusted to AI and what information or data can be fed into the service provider’s algorithms.

As a rule of thumb, it is advisable not to throw sensitive data and information into others’ servers. Still, it is up to each organization to settle on what constitutes “sensitive data” and what level of risk it is willing to tolerate that can be weighed out by the benefits of the tool adoption.

Ensuring the security of in-house tools 

The big corporations that banned the use of third-party services ended up developing their internal generative AI tools instead and incorporated them into their operations. In addition to the significant security advantages, developing in-house tools allows for their fine-tuning and orienting to be domain and task-specific, not to mention gaining full control over their interface user experience.

Check the technical specifications. Developing in-house tools, however, does not absolve organizations from security obligations. Typically, internal tools are built on top of an LLM that is developed by a tech corporation, like Meta AI’s LLaMa, Google’s BERT, or Hugging Face’s BLOOM. Such major models, especially open-source ones, are developed with high-level security and privacy measures, but each has its limitations and strengths. 

Therefore, it would still be crucial to first review the adopted model’s technical guide and understand how it works, which would not only lead to better security but also a more accurate estimation of technical requirements.

Initiate a trial period. Even in the case of building the LLM from scratch, and in all cases of AI tool development, it is imperative to test the tool and enhance it both during and after development to ensure safe operation before being rolled out. This includes fortifying the tool against prompt injections, which can be used to manipulate the tool to perform damaging cyber-attacks that include leaking sensitive data even if they reside in internal servers.

Parting words: be wary of hype

While on the surface, the hype surrounding generative AI offers vast possibilities, lurking in the depths of its promise are significant security risks that must not be overlooked. In the case of using ready-made tools, rigorous policies should be formulated to ensure safe usage. And in the case of in-house tool deployment, safety measures must be incorporated into the process to prevent manipulation and misuse. In both cases, the promises of technology must not blind companies to the very real threat to their sensitive and private information.

Artificial intelligence’s potential in reshaping corporate strategic management

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Artificial intelligence (AI) has emerged as a transformative force in corporate strategic management, fundamentally altering the way companies make strategic decisions. AI is crucial in driving innovation even in the face of dynamic business environments and data abundance.

The integration of AI into corporate strategic management offers a myriad of benefits for businesses seeking to navigate the complexities of the modern market, namely:

Data-driven decision-making: AI empowers companies to transform raw data into actionable insights to identify market trends, assess customer preferences, and predict future outcomes more accurately. AI supports data-driven strategy, leading to better resource allocation, risk mitigation, and operational effectiveness. For instance, a company can leverage AI predictive analytics capabilities to forecast future revenue, competitive threats, the likelihood of expansions succeeding, and other core strategic considerations years in advance.

Enhanced strategic planning: AI’s capabilities extend beyond data analysis to encompass strategic planning and scenario modeling. AI-powered tools can simulate tens of thousands of realistic scenarios per minute, allowing companies to evaluate the potential impact of strategic decisions and identify potential risks and opportunities before committing to major investments. Maersk, for example, uses cutting-edge AI algorithms to revolutionize its container shipping operations. These algorithms optimize vessel routes for efficiency, predict equipment maintenance needs to minimize downtime, and provide real-time insights into cargo location and status, ensuring unparalleled transparency and efficiency.

Customer-centric strategies: AI plays a pivotal role in understanding and anticipating customer needs, enabling companies to develop customer-centric strategies that foster long-term customer loyalty and enhance brand reputation. AI-supported tools can analyze customer behavior, preferences, and feedback; thus, AI can provide valuable insights to personalize marketing campaigns, improve product offerings, and optimize customer service experiences. For instance, H&M Group uses AI to create personalized shopping experiences, optimize product offerings, and improve customer satisfaction by analyzing customer data and preferences.

Competitive advantage: AI adoption provides companies with a competitive advantage in a rapidly evolving market. AI-driven strategies enable organizations to adapt quickly to changing market dynamics. By leveraging AI’s capabilities, corporations can outstand competition and establish themselves as leaders in their respective industries. Unilever, for example, leverages a system to analyze sales data, marketing campaigns, economic trends, and weather patterns to predict future demand more accurately. This enables the company to optimize production planning, reduce waste, and improve profitability.

AI challenges within corporate strategic management

While AI presents immense opportunities, it is crucial to address the ethical considerations surrounding its implementation, such as the potential for AI to perpetuate biases and discrimination. AI algorithms can be trained on biased data sets, leading to partial decision-making and deeper inequalities. Transparency and accountability are other ethical concerns in AI decision-making as it is important to foster trust and understanding of how decisions are made. This is to ensure that everyone involved can act on a just and well-informed strategy.

Successful AI integration also requires a cultural shift within organizations. Companies need to develop training and educational programs to equip employees with the needed skills and to work with AI systems and capabilities in harmony. This should improve communication and collaboration, leading to better alignment with corporate strategic objectives.

Companies that embrace and adapt AI strategically will be well-positioned to navigate the complexities of the modern market and achieve sustainable competitive advantage. For effective adoption of AI capabilities, companies need to develop transparent and accountable AI systems and establish clear ethical guidelines for AI use. Additionally, companies need to engage in ongoing dialogues with stakeholders to build trust and ensure that AI is used responsibly and ethically. 

Future-forward: using data analytics in app development

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Jino Noel is a data science and technology leader with extensive experience in building data teams and practices across different organizations. His experience ranges from working in startups to large conglomerates across both Australia and the Philippines. At the time of this interview, he was the Chief Data Officer at Data Analytics Ventures, Inc. (DAVI). Currently, he is the Chief Data Officer at Angkas.

What are the key skills that a Chief Data Officer should possess nowadays?

A Chief Data Officer should have both data-related technical expertise as well as people leadership skills. Leading will always be part of the job, particularly for highly specialized technical people such as data engineers and data scientists. To be able to lead them properly, I believe it is better to be a technical person myself, so I can discuss technical matters fluently, which helps me gain their trust.

What data-related challenges have you faced as the Chief Data Officer of DAVI? How did you overcome these challenges?

Our data-related challenges are the same as any company. Being able to trust our data, cleaning up data from our sources, data latencies, and other related issues. DAVI overcame these by investing in people—hiring high-quality experts in our data engineering, data governance, and analytics teams to help us make sense of the data coming in—and building robust data pipelines that have increased the standard of quality of the data in our data lake.

How does DAVI make use of advancements in artificial intelligence (AI) and machine learning to help its clients understand their customers’ needs and buying patterns?

DAVI has recently started using machine learning to model our users’ propensity to buy certain products. This helps us create more accurate target audiences for our precision marketing campaigns. We are also moving forward with a recommendation engine project, with the goal of improving user engagement with our retail partners and with our promos and campaigns. On top of this, we are improving our machine learning operations expertise to make our model deployments repeatable and robust.

In the digital marketplace, data analytics acts as a guiding compass for app developers, enabling the creation of personalized, high-performing applications that align with user preferences. By leveraging data, developers can understand nuanced user behaviors and preferences, allowing them to tailor apps to meet specific user needs and aspirations.

Dive deeper into these discussions by reading Jino Noel’s full interview with The KPI Institute. Download the free digital copy of PERFORMANCE Magazine Issue No. 26, 2023 – Data Analytics on the TKI Marketplace. You can also purchase a  physical copy via Amazon

Beyond skin-deep: leveraging AI to improve diagnostics accuracy for skin pathologies

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Alfonso Medela is the Chief Artificial Intelligence (AI) Officer at Legit.Health, where he oversees the use of advanced computer vision algorithms. A renowned expert in few-shot learning and medical imaging, his contributions include developing an algorithm capable of diagnosing over 232 skin conditions.

What are the key skills that a Chief AI Officer should possess in the context of your role at Legit.Health?

A Chief AI Officer at a medical organization like Legit.Health needs strong AI expertise, including extensive knowledge of machine and deep learning, and a profound understanding of medical data and healthcare to ensure precise algorithm development. Besides technical skills, strategic thinking and leadership are vital for guiding the AI team and aligning with company goals. Great communication and collaboration skills are also crucial for working effectively with different teams.

Can you describe your experience in developing and implementing AI strategies for computer vision applications, specifically in the context of diagnosing and treating skin pathologies? How have you leveraged AI to improve diagnosis accuracy and enable life-saving therapies?

Heading a team of specialists, we’ve developed advanced algorithms that accurately identify over 232 skin conditions and automate follow-ups for chronic skin conditions. Using deep learning techniques, our platform provides real-time diagnostic support to healthcare professionals, improving their accuracy and enabling early intervention. By collaborating with medical experts and continuously refining our algorithms, we are able to offer a powerful tool that empowers clinicians, transforming healthcare and improving patient outcomes.

What approaches or methodologies do you use to ensure the accuracy and reliability of computer vision algorithms in the context of skin pathology diagnosis? Can you share examples of how you have validated the performance of AI models and ensured their safety and effectiveness in real-world clinical settings?

To guarantee accuracy and reliability, our computer vision algorithms undergo a multi-stage validation process that encompasses retrospective and prospective clinical validations. Rigorous testing is performed on diverse, representative datasets, employing cross-validation to assess model performance. We collaborate closely with medical professionals, reviewing AI model outputs and gathering feedback to iteratively refine our algorithms. Furthermore, we conduct clinical trials and pilot studies to evaluate safety and efficacy. This ensures that our models adhere to real-world requirements and actively contribute to enhancing patient outcomes.

AI stands as one of the most transformative technologies of the modern era, revolutionizing the way people approach complex problems across various fields. From enhancing healthcare diagnostics to driving advancements in autonomous vehicles, AI’s potential is vast and continually expanding.

To explore the full spectrum of Alfonso Medela’s pioneering work in AI and to stay updated with the latest industry insights, read his full interview exclusively featured in the PERFORMANCE Magazine Issue No 26, 2023 – Data Analytics edition. Download your free copy now through the TKI Marketplace or purchase a printed copy from Amazon

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