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Organizational Readiness Summary

AI promises tremendous opportunity for modern businesses, but it also necessitates significant organizational changes. You can begin by building awareness through executive buy-in, employee education and establishing (or expanding) a culture of experimentation.

In addition, Always Curious can help you to assess your current state for AI-ready processes, governance and organizational design, as well as a skills gap analysis. We also regularly help our clients develop strategic roadmaps to define your AI vision and create a phased approach to organizationally address the changes and education required for scalable success.

Organizational Readiness Summary

Organizational transformation is often overlooked when developing your AI strategy. It is imperative to ensure you manage the change process effectively through transparent communication, reskilling and upskilling and fostering a culture of collaboration and experimentation.

Always Curious can help you to assess your current state for AI-ready processes, governance and organizational design, as well as a skills gap analysis. We also regularly help our clients develop strategic roadmaps to define your AI vision and create a phased approach to organizationally address the changes and education required for scalable success.

Organizational Readiness Summary

AI promises tremendous opportunity for modern businesses. As your company has realized, it also necessitates organizational change. It is imperative to ensure you manage the change process effectively through transparent communication, reskilling and upskilling and fostering a culture of collaboration and experimentation.

Always Curious loves to partner with innovation-forward companies such as yours to ensure your organizational transformation scales to support rapid adoption and embedding of best practices in AI.

Our recommendations based upon your response:

Establishing a budget for your AI experiments and projects is an important first step. By ‘ringfencing’ an AI budget, you can establish metrics to guide decisions on what projects to invest in with a clear ROI on returns. Be sure your budget includes some room for ‘failed trials.’ Not every AI initiative will be a success, and your early efforts are likely to cost significantly more as your team learns about the data, technology and feedback loops.

Our recommendations based upon your response:

You have a budget – awesome! But if you don’t have a specific owner of the budget, it can be difficult to assign metrics or ROI to determine the success of your AI initiatives. We often see these initiatives embedded within projects – there’s no issue there, but it may be difficult to determine the actual costs and benefits of the technology you are implementing if the accounting does not feed back into total budget.

Our recommendations based upon your response:

We often see finance as the primary stakeholder in determining the funding of AI initiatives. When approaching the finance team for support of your AI initiatives, be sure to prioritize establishing ROI-related metrics. Some common use cases we see pitched early in the journey with great yields on ROI include fraud detection, risk management, total customer profitability, regulatory compliance and reporting and customer service interactions.

Our recommendations based upon your response:

It is not uncommon for companies such as yours to choose an IT organization to own AI budgets. After all, it’s technology, right?

There are challenges with this approach, however, that you should consider when determining the way in which you spend your budget. IT often lacks necessary domain experience to fully understand the business needs, goals and objectives that can best benefit the overall company, which can lead to failure to realize the true potential of the technology for the company. It’s important to establish a cross-functional AI team in this case, so that the technology team is gaining insights from business units and data science organizations.

Our recommendations based upon your response:

Awesome – your budget is allocated to the data team. But what are the data team’s priorities? Is it focused on modernization of the data stack, new pipelines and sources, or data science initiatives?

Our recommendations based upon your response:

We recommend learning who owns classification of AI project spend by working with your accounting department. Remember that there are opportunities to categorize this spend as R&D and capital spend, which can be a tax savings opportunity.

Determine the key stakeholders that manage the assigned spend category for your AI initiative; it is critical to ensure they understand and are bought into the benefits of your program so that your initiative gets the attention it needs during budget planning cycles.

Our recommendations based upon your response:

IT is a common starting point for budgeting AI initiatives. Remember is that Artificial Intelilgence is actually a product of a human need to solve problems with automate, predict and learn. As such, it is imperative to find a strong business case(s) that not only provides value, operational efficiencies or competitive advantage for your company, but can also be quickly broken into small, iterative chunks that deliver immediate value (or failure so you can adjust focus).

Our recommendations based upon your response:

The culture and nature of R&D is to research, form a hypothesis, test, learn and iterate. Given this mindset, having your AI budget in this function is a huge advantage to your success. Alignment is important – not only on the research to be done, but also the contextual business case(s). Be sure to collaborate with your technology partners, as well as any data stakeholders to ensure that the business case is well defined across the teams for your initial R&D efforts.

Our recommendations based upon your response:

Fantastic! Data ownership is aligned with (likely) your company’s best advocates for ensuring AI success. Because the data governance team is concerned with quality, controls, standards and compliance, you have a head start in planning your solution. From a budgeting standpoint, you are also set up for success as many businesses (especially regulated ones) rely on this team for ensuring data quality and security standards are met.

As you work to budget your initiative, coordinate with the governance team to be sure you understand top priorities within the team – they may not be AI-related and/or could interfere with your AI/ML initiatives. Competing prioritization of any migration, system changes, governance systems (such as MDM) and upcoming regulations can impact your success.

Our recommendations based upon your response:

Fantastic! Data ownership is aligned with (likely) your company’s best advocates for ensuring AI success – the business team that knows the data best. As you work to budget your initiative, work to ensure that AI spend is tightly aligned to business outcomes and key metrics of success for your organization.

Our recommendations based upon your response:

AI projects can be tested safely in controlled environments like simulations, sandboxes, digital twins, and data-augmented settings. This allows for faster development and improved performance while reducing risks to the real world. However, creating and maintaining these environments can be expensive, and ensuring real-world relevance and sufficient data can be challenging without experience in their development.

Always Curious works in these types of environments daily and would be happy to help you ideate opportunities to develop controlled frameworks for AI development.

Our recommendations based upon your response:

AI projects can be tested safely in controlled environments like simulations, sandboxes, digital twins, and data-augmented settings. This allows for faster development and improved performance while reducing risks to the real world. However, creation and maintaining these environments can be expensive, and ensuring real-world relevance and sufficient data can be challenging without experience in their development.

Always Curious works in these types of environments daily, and would happy to help you to ideate some opportunities to develop controlled frameworks for AI development.

Our recommendations based upon your response:

Building AI for an open and creative environment is all about embracing collaboration. Think open-source libraries, online platforms, and hackathons! Use XAI (Explainable AI) to communicate how your AI works, so people can trust it and use it creatively. Don’t be afraid to share data (within reason) and partner with others to get diverse perspectives.

As AI becomes increasingly integrated into our lives, it is more important than ever that we are able to understand and explain how these models work.

Our recommendations based upon your response:

Building AI for an open and creative environment is all about embracing collaboration. Think open-source libraries, online platforms, and hackathons! Use XAI (Explainable AI) to communicate how your AI works, so people can trust it and use it creatively. Don’t be afraid to share data (within reason) and partner with others to get diverse perspectives.

As AI becomes increasingly integrated into our lives, it is more important than ever that we are able to understand and explain how these models work.

Our recommendations based upon your response:

It’s ok that you haven’t started to automate yet – especially if your data is a bit chaotic. Preparing your data is a vital first step to ensure accurate outcomes.

Begin by identifying a few tasks for automation. We recommend starting with analyzing workflows in finance, IT, supply chain, risk, customer service, sales, product… anywhere you find the company activity driven on spreadsheets or emails is a great place to show rapid value for your AI initiatives. Next, analyze repetitive, time-consuming and rules-based manual tasks such as classification, data entry or simple decision making.

Proving a return on investment (ROI) is very important in this initial work. As such, it is critical to understand if your data is clean, formatted, validated, and trusted. If it isn’t… well, then it’s not ready for an AI application. Specific data preparation and organization needs will be dependent on the AI model and task at hand.

Our recommendations based upon your response:

The good news – many SaaS solutions already contain AI components and features that can expedite your ability to implement AI.

The bad news – you need to be sure these tools align with your organization’s overall strategy and creates value. This isn’t always easy to do. Be careful not to fall into the sales trap of just ‘turning on’ features as they are released.

We suggest starting with reviewing the features available across your SaaS landscape (not just one vendor). This will allow you to assess the processes and models that will yield the best value, allowing you to prioritize your efforts at deploying the solutions.

Also consider what data, process gaps, or insights you may be missing by combining data from your SaaS landscape. These hidden insights may yield large cost savings opportunities, operational efficiencies and predictive models that can bring your company a competitive advantage.

Our recommendations based upon your response:

You are already well on the way in your RPA, ML and/or AI journey! Remember in this process that the most impoortant part of embarking on your automation journey is to ensure the data that you plan to feed into AI or ML models is handled ethically and securely, complies with relevant regulations, and appropriately protects user privacy. Ensure data governance policies and procedures are applied (consider the use of data contracts here!) to manage access, usage and quality. And of course, version control is vital to ensure that results can be reproduced as you experiment and learn.

Our recommendations based upon your response:

Have you thought about how you can expedite integration of your newly acquired company or business line leveraging AI? Or perhaps how to breakout business units for future sale?

Organizing your data via data contracts in logical, domain-oriented groupings will allow you to have a better view of insights without disruption as you add and remove data sources over time.

If you have not yet considered moving to the ‘Data as a Product’ thinking, now is a great time to begin to organize your data so that it can easily be held to quality control standards regardless of the state of your business’s M&A (or demerger) activities. This may also impact how you organize your team structure or data science organization to maximize knowledge of the domain (data product).

Our recommendations based upon your response:

Have you thought about how you can organize customer data to scale?

Organizing your data via data contracts in logical, domain-oriented groupings will allow you to have a better view of insights without disruption as you add and remove data sources over time.

If you have not yet considered moving to ‘Data as a Product’ thinking, now is a great time to begin to organize your data so that it can easily be held to quality control standards. This is also an excellent time to think about the unique data domains (or data products) your company can build as a differentiator/competitive advantage.

Our recommendations based upon your response:

It’s a great time to begin to think about how you organize your analytic data for AI consumption.

Organizing your data via data contracts in logical, domain-oriented groupings will allow you to have a better view of insights without disruption as you add and remove data sources over time.

If you have not yet considered moving to ‘Data as a Product’ thinking, now is a great time to begin to organize your data so that it can easily be held to quality control standards regardless of the state of your business’s M&A (or demerger) activities.

Our recommendations based upon your response:

You may not be seeking rapid growth or expansion in the next year – if this is the case, it’s a great time to begin to think about how you organize your analytic data for AI consumption.

Organizing your data via data contracts in logical, domain-oriented groupings will allow you to have a better view of insights without disruption as you add and remove data sources over time.

If you have not yet considered moving to ‘Data as a Product’ thinking, now is a great time to begin to organize your data so that it can easily be held to quality control standards regardless of the state of your business’s M&A (or demerger) activities.

Our recommendations based upon your response:

As you do not currently have a prioritization process for projects across your company, it is important to find out what projects are underway that can benefit from AI or may be competing with resources (time, money, talent) that will create challenges for your initiatives. Gathering a working group of those interested in leveraging AI in their projects will help you to not only bring together a more holistic view of the work underway, but also to create a team of like-minded individuals that can support each other and collaborate on parallel or complimentary business cases.

Our recommendations based upon your response:

It is important to find out what projects are underway that can benefit from AI or may be competing with resources (time, money, talent) that will create challenges for your initiatives. Gathering a cross-functional working group of those interested in leveraging AI in their projects will help you to not only bring together a more holistic view of the work underway, but also to create a team of like-minded individuals that can support each other and collaborate on parallel or complimentary business cases.

Our recommendations based upon your response:

If your company has not already done so, take a look at the AI-enablement projects and activities (data quality, data governance, machine learning, cloud transformations, etc.) that create opportunities for collaboration and cross-organizational learning. Consider an AI working group to share learning (successes and failures) and opportunities to leverage advanced automation opportunities.

Our recommendation based upon your response:

Work on identifying a key advocate(s) in your executive team. AI requires an agile mindset – someone who will be an advocate to continue to experiment even when some projects fail (and they will). Look for change champions, roadblock removers and those with an agile mindset who are willing to support your experimentation, learning and innovation.

Our recommendation based upon your response:

Amongst your advocates, take time to think about who you will leverage as change champions, who can remove roadblocks and who may be a champion for research and development. It’s important that your R&D champion have openness to innovation knowing that not every initiative will be successful.

Our recommendations based upon your response:

Consider where your data for operations is being sourced. If you are primarily operating from spreadsheets and a variety of systems, you likely have a tremendous opportunity to bring finance, human resources, supply chain and/or sales data together in new and unique ways be establishing a space for data analytics, as well as leveraging AI to begin to forecast and conduct deeper analysis on your operations.

Our recommendations based upon your response:

Your SaaS-based ERP likely has a wealth of data, but are you integrating it with other data sets across the organization? Consider use cases for AI by combining this rich data with other sources such as public and your CRM system (if you have one) data to analyze customer and purchasing trends. Many SaaS-baed companies have yet to integrate core data sets across their key systems.

Our recommendations based upon your response:

Data from ERP systems such as yours is often stored in operational constructs with very little business context. These large systems can mask data and/or require data specialists with specific system knowledge to extract data sets and transform them for business uses. Consider how your company uses the system data and where the combination of data elements from your existing system can be brought together by domain, creating business context on an ongoing basis without reliance on heavy engineering lifts to extract and transform the data.

Our recommendations based upon your response:

Work with your ERP engineering team and operations (finance, HR, supply chain, etc.) to analyze how AI can be leveraged to optimize manual workflows, analyze sentiment of your customers or customize experiences for your customers. Always Curious is here to help you explore the possibilities!

Our recommendations based upon your response:

AI is transforming the landscape of customer management, creating exciting opportunities to boost sales performance, automate tedious processes such as lead-nurturing, enhance customer engagement and personalize experiences. If you are not yet using a CRM with innate AI capabilities, consider how AI can be leveraged to optimize manual workflows, analyze customer sentiment, or customize customer experiences. Always Curious is here to help you explore the possibilities!

Our recommendations based upon your response:

Many SaaS tools offer AI-powered features such as sentiment analysis, real-time insights, hyper-personalization, workflow automation, chatbots, lead nurturing and deal prediction and forecasting. If you are not yet using these features, take a moment to explore what is offered from your SaaS partner – it can be an easy way to dip your toe into the AI waters in a secure and trusted manner.

Our recommendations based upon your response:

Many modern CRM systems offer AI-powered features such as sentiment analysis, real-time insights, hyper-personalization, workflow automation, chatbots, lead nurturing and deal prediction and forecasting. If you are not yet using these features, take a moment to explore what is offered from your CRM – it can be an easy way to dip your toe into the AI waters in a secure and trusted manner.

If you are not yet using a CRM with innate AI capabilities, consider how AI can be leveraged to optimize manual workflows, analyze customer sentiment, or customize customer experiences. Always Curious is here to help you explore the possibilities!

Our recommendations based upon your response:

Work with your CRM engineering team to analyze how AI can be leveraged to optimize manual workflows, analyze customer sentiment, or customize customer experiences. Always Curious is here to help you explore the possibilities!

Our recommendations based upon your response:

Consider collaborating or meeting with IT leadership to share the business opportunities and initiatives you hope to achieve with AI. Ask for support with data security, access and any automation of data governance tasks that may assist in ensuring your initiative’s success.

Our recommendations based upon your response:

Partnering with your IT organization to ensure OKRs and goals align across both the business initiatives and IT priorities is imperative to ensuring successful AI integration. If your infrastructure and systems are not secure and appropriately managing quality data outputs, your project will be at risk.

Our recommendations based upon your response:

You may need to implement role-based access control (RBAC) to restrict sensitive data on user roles and permissions to ensure your AI models are ethical and fit for use. Talk to your data governance team (if you have one) or security team to ensure your AI solutions meet data requirements and regulations.

Our recommendations based upon your response:

You likely have implemented role-based access control (RBAC) to restrict sensitive data on user roles and permissions. When developing AI models, this should be taken into consideration, as the total model may include different data elements that may or may not be accessible by the user(s). Continuous monitoring, as well as automated compliance with data privacy regulations can help to ensure that your data models remain ethical and fit for use.

Our recommendations based upon your response:

You may need to implement role-based access control (RBAC) to restrict sensitive data on user roles and permissions to ensure your AI models are ethical and fit for use. Talk to your data governance team (if you have one) or security team to ensure your AI solutions meet data requirements and regulations.

Our recommendations based upon your response:

Before beginning your journey with AI, it is vital that your organization set base policies, procedures and security practices throughout the development and testing cycle for AI. To get started, work with your cybersecurity department (if you have one) to define overarching principles such as data privacy, responsible use, explainability and accountability. The quality and accessibility of your underlying data will be a vital part of the policies. Set clear guidelines for how the AI system(s) or projects can or cannot be used. You will need to ensure that you have clear standards about how the AI tools and systems protect proprietary data, ideas and acceptable data usage. All of this can feel overwhelming at first, but it doesn’t have to be – work with a consulting firm such as Always Curious to help establish base-level policies so that you can quickly ramp up your AI program.

Our recommendations based upon your response:

AI requires advanced automation, controls and clear security procedures throughout the AI development lifecycle. You will need to implement strong data security measures throughout the data pipeline if you have not already done so to ensure secure storage, encryption and access controls to address potential biases. Model development also requires automation to ensure secure coding and testing practices, vulnerability assessments and adversarial training techniques to harden your AI model(s) against attacks. Finally, automation of monitoring and observability leveraging tools such as a data contract will ensure you have anomaly detection and incident response for potential security issues. A number of tools, products and automation approaches are available to help your team address these potential issues, and consulting companies such as Always Curious can help expedite your path to security automation.

Our recommendations based upon your response:

Your automated security policies and procedures give you distinct advantage over your peers against bias, security attacks and other potential security issuews. Make sure you have a robust and ongoing plan to continually feed and improve the system. A best practice is to ensure that every AI-related project begins with analysis of potential risks such as data breaches, adversarial attacks, bias amplification, policy or privacy violations or other unintended consequences. As you work through the assessment, check your automated policies and procedures to ensure that they meet or address as much of the risk as possible. Update policies frequently and set clear guidelines for AI usage within the company, including acceptable data types (and where they are used – such as public or private tools), outputs, user interactions and operational boundaries. Be sure that the policies and procedures are automated not just at onset, but throughout the AI development lifecycle to include model development, deployment and ongoing monitoring. We recommend taking a look at the OWASSP AI security and Privacy Guide (https://www.nist.gov/itl/ai-risk-management-framework) for a robust framework on AI risk management.

Our recommendations based upon your response:

It is crucial to set up continuous monitoring for your AI systems to ensure their safety, security and responsible use. As you begin to build out AI solutions, be sure to talk to your technical teams about the tooling in place for monitoring the data, models and systems so that you can proactively address any errors before AI outputs are used in your business. Alerting should be automated to quickly identify any suspicious or errant problems within the system.

Our recommendations based upon your response:

Ensuring your organization has the latest capabilities in data, model and system reporting is vital to the accuracy of your AI models. If your company has not yet explored data contracts, consider their use to ensure that data quality is monitored and quality alerts are automated. Data contracts will also enforce current data context, use, and ownership as your platform evolves.

Our recommendations based upon your response:

Take the time to work with your technical security team (or consulting group) to build out a security awareness training program specific to the use of AI. This is vital to ensure you mitigate human error, promote responsible interactions (ethical considerations and potential biases that can occur) and enhance your overall security posture. The training should include general cybersecurity principles, specific AI threats (e.g. data breaches, adversarial attacks and algorithmic bias), safety practices to avoid compromised company secrets or proprietary information and reporting procedures if or when a breach or suspicious activity occurs.

Our recommendations based upon your response:

Review your AI security training to ensure it includes general cybersecurity principles, specific AI threats (e.g. data breaches, adversarial attacks and algorithmic bias), safety practices to avoid compromised company secrets or proprietary information and reporting procedures if or when a breach or suspicious activity occurs.

Our recommendations based upon your response:

Traditional security frameworks, such as SOC 2 (which was originally designed for cloud services and data security) are being adapted to address AI. If you are not familiar with these frameworks, it is vital to work with your security team to review how these standards help to build trust and transparency, mitigate risks and enhance compliance and regulatory readiness. A review of your internal processes and controls should be conducted if you don’t have a standard security framework in place – you cannot rely on written policies and procedures that do not have automated checks and balances.

Our recommendations based upon your response:

Ensure your security controls are extended to cover the entire AI lifecycle, including data collection, storage, training and development. Make sure that the model development and deployment standards are automated wherever possible to reduce bias and increase explainability. You may also wish to increase frequency of vulnerability assessments and penetration testing to address potential security weaknesses in the new AI infrastructure as well.

Our recommendations based upon your response:

Consider leveraging some online AI-powered tools for visualization IF they meet your security and privacy policies and practices. There are great data storytelling platforms (e.g. Narrativa, Plotly and Flourish) that can help you to rapidly adopt better visualization and understanding of your AI results. If you have internal software or data engineering teams, consider using python libraries, such as TensorFlow and PyTorch (which are specifically built for machine learning). If you do not choose to advance your visualization, consider learning some conditional formatting and pivot table skills to maximize your use of spreadsheets. Most modern spreadsheet tools have ‘smart graphs’ that can do much of the work for you.

Our recommendations based upon your response:

Data visualization tools help tremendously in conveying your results and insights from AI. However, if you have not thought about how you store your data joins, metrics and logic for the reports and dashboards you build, you may find it difficult to recreate those same results in other tools. Having a centralized data dictionary of business context, metric documentation and lineage of data is vital for scalable AI.

Our recommendations based upon your response:

Data visualization tools help tremendously in conveying your results and insights from AI. However, if you have not thought about how you store your data joins, metrics and logic for the reports and dashboards you build, you may find it difficult to recreate those same results in other tools. Having a centralized data dictionary of business context, metric documentation and lineage of data is vital for scalable AI.

Our recommendations based upon your response:

Modern SaaS solutions offer tremendous insights within tooling; however it is often difficult to combine SaaS outputs across systems or operational processes. Consider what insights may be lost ‘between’ the solutions that would otherwise give your company a competitive advantage with AI or ML models augmentation.

Our recommendations based upon your response:

As you do not have software engineers in house, it will be important to partner with a company that brings software engineering best practices into play as you are building your AI solutions.

Our recommendations based upon your response:

Software engineers tend to focus on domain-based solutions, APIs and microservices in a modern technology environment. Work with your engineering team to ensure these best practices are carried over into data engineering and/or development of data solutions.

Our recommendations based upon your response:

Software engineers tend to focus on domain-based solutions, APIs and microservices in a modern technology environment. Work with your engineering team to ensure these best practices are carried over into data engineering and/or development of data solutions.

Our recommendations based upon your response:

Many companies do not yet have ‘data engineers’; these are team members that focus on the capture, ingestion and organization of data via pipelines, APIs and data stores such as data lakes, data warehouses and data lakehouses. These teams, when they do exist, are often found within business lines outside of technology, so be sure to ask around to see if you have any of this talent in-house. They will be able to assist you in bringing together data sets, identifying a ‘source of truth’ for your data and supporting data quality, governance and transformation. This is a vital foundation for success in AI development and scaling.

Our recommendations based upon your response:

Leverage your outsourced partners to discuss AI readiness within your company. They will be able to assist you in bringing together data sets, identifying a ‘source of truth’ for your data and supporting data quality, governance and transformation. This is a vital foundation for success in AI development and scaling.

Our recommendations based upon your response:

Data engineering is often placed within a technology organization. When this happens, the engineering outputs tend to focus on data lakes and constructs that capture and store operational data. This often creates silos across departments and a diversity of data formats and systems, making integration and analysis difficult.

Data governance, on the other hand, tends to be outside of this organization and often manual in nature, further contributing to issues with data quality.

Finally, because data engineers are working directly with the technology, rather than business team members who know the data best, therre are often issues with communication and collaboration as well as speed to deliver results. When working on your AI roadmap, consider where your data engineering team is placed, how communication flows to and from the team, and where there are opportunities for closer collaboration and integration into AI delivery team models.

Our recommendations based upon your response:

Your data organization is set up with data engineers and analysts working together – but how close are you to the business problems you are trying to solve? Effective AI teams work together versus in individual departments. When working on your AI roadmap, consider where your data engineering team is placed, how communication flows to and from the team, and where there are opportunities for closer collaboration and integration into AI delivery team models.

Our recommendations based upon your response:

On-premise solutions do not limit your ability to develop and deploy AI solutions. In fact, there are several advantages to this approach! Data ownership and control can be managed within your proprietary systems without being beholden to the terms and conditions of a cloud provider, for example. Security and privacy of sensitive data is often seen to be at an advantage as opposed to a cloud environment. Lower latency and reduced costs for the scale of servers are also often a huge advantage.

Despite all of these advantages, you will want to consider several potential key challenges of your on-premise systems. Scaling your data infrastructure may be more complex or time consuming, and compute demands may be an issue.

Speak to your data engineering and/or platform teams early in the development of your AI strategy to ensure the existing solutions will be able to scale and support the volume of data, compute power and outputs your solutions will need.

Our recommendations based upon your response:

Cloud solutions best poise companies to dip their toe into AI and ML solutions more quickly given the native tooling and elasticity of the platform(s).

Partner with your cloud engineering team early in the development of your AI strategy to ensure the existing solutions will be able to scale and support the volume of data, compute power and outputs your solutions will need.

Our recommendations based upon your response:

On-premise solutions do not limit your ability to develop and deploy AI solutions. In fact, there are several advantages to this approach! Data ownership and control can be managed within your proprietary systems without being beholden to the terms and conditions of a cloud provider, for example. Security and privacy of sensitive data is often seen to be at an advantage as opposed to a cloud environment. Lower latency and reduced costs for the scale of servers is also often a huge advantage.

Despite all of these advantages, you will want to consider several potential key challenges of your on-premise systems. Scaling your data infrastructure may be more complex or time consuming, and compute demands may be an issue.

Overall, whether or not to use on-premise data for AI solutions depends on your specific needs and resources. If security, privacy, control, and low latency are your top priorities, then on-premise data may be the best option. However, if you’re on a tight budget or don’t have the technical expertise, then cloud-based data solutions may be a better fit. Work with both your on-premise platforms team and cloud-based engineering to determine the best solution for your AI initiative.

Our recommendations based upon your response:

Since you do not have a data lake, you will need to be strategic in your approach to implementing AI capabilities. We recommend starting with a small, clearly defined business problem leveraging existing trusted data sources such as CRM systems, website analytics or internal data bases. This will avoid some of the upfront cost and time to clean and transform data for AI model use. Though counter-intuitive to the idea of using AI, be sure to prioritize data quality over quantity. This will minimize the chances of hallucinations and false outputs. Many cloud services offer AI as a Service for specific tasks such as customer chatbots or predictive analytics that can be leveraged to start your journey as well.

Our recommendations based upon your response:

Many companies falsely believe that by having a centralized data lake, there is less risk to applying AI. Of course, this false assumption does not take into account data quality and bias. Simply put – if your lake is more of a swamp, you will be fishing out garbage and sludge versus meaningful insight.

Data lakes are also often organized by engineers for engineers versus being organized by data domain. This means that much of your data will need to be transformed to enable joins, aggregation, and comparative analysis before AI can even be applied.

Always Curious can help you to organize your data via data contracts, a modern means of ensuring data quality, context and lineage are addressed.

Our recommendations based upon your response:

If you have to rewrite schemas or it takes weeks to onboard new data sets, you will be at a disadvantage to feed your AI models with timely results and new data sources you find. Partner with your technology and engineering teams to explore ways you can organize analytic data into ‘products’ via data contracts. This will allow you to add, evolve and eliminate data sets over time, providing flexibility and scalability to your current and future solutions.

If you are not familiar with Data as a Product or Data Contract concepts, partner internally with your data engineering teams to learn more or reach out to us here at Always Curious – we are happy to share resources and insights to help your team thrive.

Our recommendations based upon your response:

Your data is ready to scale and grow but make sure you also understand the costs and infrastructure implications of your models. You may wish to consider a ‘data as a product’ model for your analytic data sets – organizing by domain in a microservices-like architecture that allows you to federate governance and bring together unique data sets that may otherwise not have been interoperable.

If you are not familiar with Data as a Product or Data Contract concepts, partner internally with your data engineering teams to learn more, or reach out to us here at Always Curious – we are happy to share resources and insights to help your team thrive.

Our recommendations based upon your response:

Diving into AI without data governance is very risky and not recommended. Though data governance and controls may seem like a hurdle, especially if you are NOT in a regulated industry, it is necessary to ensure the quality of your data meets the needs of your AI and ML models. After all, garbage in equates to garbage out. You don’t have to boil the ocean to get started on data governance. Work with your technology and data teams, or a trusted partner such as Always Curious, to explore how data contracts can help to expedite your data initiatives. This automated approach can be applied to any environment (on-premise or cloud) and can help you to expedite your journey to quality, governed data. Start with very small pilot projects with controlled data sets for best results.

Our recommendations based upon your response:

Be sure to partner with your data governance team to understand the privacy, security and ethical use of the data you plan to integrate into your AI and ML models. Where possible, encourage automation of governance procedures for ownership leveraging concepts and tools such as a business context-driven data dictionary, observability and monitoring tools and data quality enablers such as data contracts.

Our recommendations based upon your response:

If you do not trust the data you hope to use in your AI efforts, the data needs to be the very first place you start. Identify ‘sources of truth’ or downstream systems where the data is at its most pure state. Be sure that documentation exists for how the data is loaded, transformed and translated before you consider bringing it into a data model. If your data is inaccurate, incomplete or biased you will be building on a shaky and unreliable foundation.

Our recommendations based upon your response:

As you know, trusted data is absolutely essential for AI models. Continue partnering with your data governance, engineering and data science teams to continuously monitor and observe data sets, focusing on ways to eliminate bias as your results and data sets grow.

Our recommendations based upon your response:

Outages, delays and errors in your ingestion or data pipelines will corrupt your AI result. Work with your ingestion team (likely data engineering) to understand the root causes of the data issues and address them before trying to apply AI or ML models. Also make sure you have monitoring in place for your infrastructure, APIs and pipelines so that you are alerted early if a delay or outage occurs.

Our recommendations based upon your response:

Ensure you have quality monitoring and observability solutions in place to mitigate any delays or outages in the future. Proactively work with your data engineering department on performance optimization to reduce bottlenecks and inefficiencies in data pipelines that may cause or contribute to future delays or outages. Data quality is an ongoing process that requires continuous monitoring, learning and improvement throughout the data management lifecycle.

Our recommendations based upon your response:

Data discovery and retrieval is extremely challenging if you are working only from operational data stores (data stores that come from processes) versus analytic data sets that are organized by domain. The lack of centralized domain-specific stores also leads to duplication of data across different teams and departments and a lack of consistency in how metrics are applied to key data sets. It creates difficulties in governance and compliance as well. Consider working with your team or a trusted partner to learn more about domain-driven design of key data products (also known as Data as a Product) to gain control over quality of your AI results, reduce churn and increase the efficiency of your analytics teams.

Our recommendations based upon your response:

Organizing by domain (or topic) is a great step toward democratizing your data assets for use by your analytics and data science teams. Continue to build out silo-free cross-functional data sets that can be used in business units independently or in an interoperable manner. We highly recommend exploring concepts such as data contracts, Data as a Product and data mesh to expand the number of data sets that can be leveraged, the variety of content across business units and to continue to remove barriers to accessing data that is rich with meaningful context.

Our recommendations based upon your response:

Locating relevant data can be time-consuming, causing analysts to spend >80% of their time simply searching for data that they can model, apply logic or joins to and then apply business context to in order to create insights.

Our recommendations based upon your response:

You likely have a business context-driven data solution that allows your data science or engineering team to easily search and discover new data sets. If so, ensure proper alignment to data governance and security tools to ensure access to the data sets is limited appropriately. If you have not already done so, explore the opportunity to use a data dictionary that leverages feedback loops from the users for continuous improvement.

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