Build, Buy, or Automate? Choosing the Right AI Strategy for Sustainable ESG Success

AI can help you achieve your sustainability goals more efficiently. But how do you use it correctly? Should you develop your own solutions (Build), purchase ready-made systems (Buy), or automate existing processes (Automate)?

Your choice depends on your budget, timeline, and objectives:

• Build: Full control, high costs, ideal for customized solutions.
• Buy: Quick to deploy, less flexible, suitable for standard requirements.
• Automate: Efficiency gains through automation, requires clear processes.

Falling AI costs and rising ESG requirements are making the use of AI more attractive. Make smart decisions by evaluating your resources, compliance needs, and the strategic importance of your sustainability goals. As highlighted by the World Economic Forum, the integration of AI into sustainability efforts is accelerating, driven by both regulatory pressure and the increasing accessibility of advanced technologies.

Build: In-house Development of AI Solutions

What “Build” Means for ESG AI Solutions

Developing your own AI solutions for sustainability strategies requires extensive technical and organizational resources. German data protection authorities have defined clear guidelines for the four phases of the development process: design, development, implementation, and operation/monitoring.

These requirements are based on the standard data protection model and translate legal obligations into seven core principles: data minimization, availability, confidentiality, integrity, intervenability, transparency, and non-linkability. Companies should assess whether the goals of AI systems can be achieved using synthetic or anonymized data to avoid processing personal data.

Documentation requirements include detailed recording of the datasets used, their sources, and the context of collection. Additionally, the system’s objectives, architecture, algorithms used, and supporting experiments must be clearly documented. This level of transparency is increasingly demanded by regulators and investors alike, as it helps ensure both compliance and ethical use of AI in ESG contexts.

Pros and Cons of In-House AI Development

Developing AI solutions in-house offers the advantage of full control over data and algorithms and allows for highly tailored systems. However, this requires significant investment. According to a recent study, 44% of executives see the shortage of AI specialists as the biggest hurdle to adopting generative AI. This talent gap is echoed globally, with organizations competing for a limited pool of skilled professionals (McKinsey, 2023).

Cost overview for in-house development:

• NVIDIA H100: approx. €25,000
• Eight-GPU HGX node: from €200,000
• Electricity costs for a 20,000-GPU cluster: €20 million annually

The global median salary for Machine Learning Engineers in 2025 was around €189,873. While simple AI projects cost between €30,000 and €80,000, complex large-scale projects range from €500,000 to €5,000,000 or more.

The benefits include tailored adaptation to specific ESG requirements and long-term strategic differentiation. Downsides are high upfront investments, longer development times, and the risk of losing valuable talent. These factors are crucial to ensuring the success of internal developments—as the following case study demonstrates.

Case Study: Custom AI for CSRD Reporting

A practical example of these considerations is the development of the CSRD.AI Manager by PwC Germany in collaboration with SAP in 2025. This AI solution was designed to automate data collection, KPI calculations, and report generation to ensure compliance with EU sustainability reporting requirements.

The CSRD.AI Manager is based on SAP AI Core components and the Vector Engine from SAP HANA Cloud, which are used for text and embedding generation. This is complemented by technologies such as SAP Datasphere, SAP Business Technology Platform (BTP), SAP Build Apps, and SAP Analytics Cloud, which support data collection, modeling, and visualization.

The solution integrates PwC Germany’s specific content with customizable data models to flexibly respond to changing compliance requirements. It enables comprehensive ESG reporting, automates manual processes, and ensures data security and integrity throughout the organization.

These approaches not only strengthen CSRD reporting but also connect it with broader ESG goals. The German ESG investment market is expected to reach $5,377.2 million by 2030, with an annual growth rate of 20.7% from 2025 to 2030 (Research and Markets).

For companies considering in-house development, regular risk assessments—such as red teaming for publicly accessible AI systems—are essential. Federated learning is also recommended to enable global model training across multiple data sources without exchanging local data between institutions.

Buy: Purchasing Ready-Made AI Solutions

What “Buy” Means for AI Implementation

Purchasing ready-made AI solutions allows German companies to quickly leverage proven technologies for ESG reporting without launching extensive internal development projects. Regular updates and professional support from the provider make adoption much easier and speed up usage. However, this model comes with both advantages and limitations.

Ready-made ESG AI solutions can analyze content, consolidate data, and make ESG ratings more efficient—which is particularly relevant as more than 75% of institutional investors include ESG criteria in their decisions (OECD). These systems can automatically read complex documents and extract ESG-relevant information. Features like multi-rating optimization allow simultaneous analysis of the criteria from various ESG ratings. Additionally, suggested responses can provide well-founded drafts for ESG questionnaires, greatly simplifying the process.

Pros and Cons of Buying AI Solutions

Compared to in-house development, ready-made solutions have the advantage of being immediately deployable. However, they offer less room for individual customization. Still, they can be a valuable addition to a company’s strategic portfolio by delivering quick results. While in-house development often takes months or even years, purchased solutions can usually be implemented within a few weeks.

Advantages at a glance:

• Direct access to proven technologies
• Regular updates and maintenance by the provider
• Comprehensive support and training opportunities
• Lower upfront investment compared to in-house development

A downside is dependency on external providers, which can also limit flexibility for adjustments. Companies should ensure the chosen solution can handle incomplete or inconsistent ESG data and minimize biases in ESG ratings. Scalability is equally important to efficiently process large data volumes and adapt to the dynamic requirements of ESG factors. A case study demonstrates how purchasing such a solution can facilitate CSRD compliance.

Case Study: Fast CSRD Compliance with Standard Solutions

A concrete example illustrates how ready-made solutions tackle the challenges of CSRD reporting. The rapid market access and standardized features of these systems significantly reduce the effort required to meet regulatory guidelines. Implementation occurs without lengthy development phases and relies on proven technologies to automate complex compliance requirements.

The benefits are especially clear in key challenges: 42% of respondents cited large data volumes as one of the biggest hurdles for CSRD compliance. Interestingly, 37% of companies that report Scope 1, 2, and 3 emissions with external audits already use AI in their reporting processes. Additionally, 76% of respondents said cost is the most important factor when choosing a provider, while nearly 90% believe AI will have a decisive impact on sustainability reporting (Deloitte).

Automate: Automation Platforms for Sustainability

What “Automate” Means for ESG Strategies

In addition to in-house development and ready-made solutions, automation offers a frequently overlooked third way to make ESG processes more efficient. Automation platforms combine AI technologies and standardized workflows to automate tasks such as data collection and reporting. Using Robotic Process Automation (RPA), machine learning, and intelligent data processing, these systems handle repetitive tasks in sustainability reporting. It’s not just about data collection—these platforms also analyze and report data.

A major advantage is the ability to consolidate data from various sources, improve quality, and reduce manual processes. AI can analyze large datasets, including ESG-relevant information, and significantly accelerate administrative tasks.

Automation platforms detect patterns, assess risks, and prioritize ESG actions. They create transparency and traceability, helping identify risks early. By integrating different data sources, a central ESG data base is created, enabling well-informed strategic decisions. These efficiency gains are especially valuable in practice, as application-oriented case studies show.

Pros and Cons of Automation Platforms

Automation platforms offer significant efficiency gains but also come with challenges. A key advantage is scalability and speed: Forecasts predict that by 2026, about 60% of companies will use AI-powered warehouse solutions to improve transparency and response times. This trend highlights the potential of automated systems for the entire value chain.

The benefits can be quantified: By 2030, AI-powered applications could help companies achieve up to 45% of their emission reduction targets under the Paris Climate Agreement. Optimizing energy systems and integrating renewable energy could save up to $110 billion annually in power plant operations and maintenance (BCG).

Key benefits of automation:

• Efficiency boost: Recycling plants could increase efficiency by up to 60% by 2030 through AI-powered waste sorting.
• Emission reduction: AI could save 3.2 to 5.4 billion tons of greenhouse gases annually by 2025.
• Supply chain optimization: AI reduces unnecessary routes, increases efficiency, and lowers emissions.
• Resource conservation: The shift to circular business models is accelerated, improving resource efficiency.

On the other hand, there are limitations. Automation platforms are less flexible than custom solutions, as they rely on pre-built workflows that may not cover all of a company’s specific needs. They also require a well-structured data landscape and clear processes to function optimally.

A practical example of the value of automation can be seen in CSRD reporting. Saša Redžepović, Senior Data Scientist at Planted, explains:

“Companies must use AI strategically for their sustainability goals. This means: immediate use of the technology for ESG progress while also taking responsibility for the energy-efficient use of AI systems. Only then can the full potential be realized.”

Case Study: Automating CSRD Data Collection

The requirements of CSRD reporting highlight how valuable automation can be. The European Sustainability Reporting Standards (ESRS) include more than 1,000 data points that companies must collect and integrate for their reports. Some sources even mention up to 1,200 data points.

Automation platforms tackle this challenge through comprehensive data collection and processing. Companies can conduct double materiality analyses, track sustainability progress, generate reports, and identify data gaps. Automation increases transparency, minimizes errors, and enables the creation of audit trails. Optimized collaboration within teams and with external stakeholders is also greatly enhanced by automated processes.

A real-world example is the use of Manifest Climate in February 2025. This AI-powered tool was used to analyze ESG reports and public disclosures. It helped ESG teams identify gaps in their reports and create compliant disclosures. Integrated software solutions with central ESG dashboards further improve data quality and save resources.

The practical value is also evident in the strategic approach at Planted:

“At Planted, we have a clear focus: use AI only where it delivers real impact for sustainability and reduces complexity. We focus on concrete applications: automated KPI calculation, ESG data analysis, sustainability reporting, CO₂ management. Every AI solution must make a measurable contribution to our clients’ sustainability goals.”

Comparison of Build, Buy, and Automate Approaches

Comparison Table: Key Decision Criteria

Falling AI token costs play an important role in evaluating the different approaches, especially in terms of achieving individual sustainability goals. According to Sequoia Capital, the cost of AI tokens has dropped by 70% in the last two years, making advanced AI applications more accessible for organizations of all sizes.

Falling AI token costs are also opening up previously underutilized applications and making them more economically attractive. However, companies should not base their decision solely on current prices. A long-term view of cost development is crucial to choosing the approach that makes sense both strategically and financially.

How to Choose the Right Approach

The optimal approach depends on an honest assessment of your organizational capabilities and strategic goals. This decision directly impacts the achievement of your ESG objectives. Notably, 67% of all software projects fail due to poor decisions between in-house development and ready-made solutions (Standish Group).

Assess organizational readiness: Companies with weak leadership support or limited cross-departmental collaboration often achieve better results with ready-made solutions. Studies show such companies are three times more successful with purchased solutions than with in-house development. This is especially relevant for ESG initiatives, which require cross-departmental coordination.

Strategic importance for core business: If AI-powered sustainability solutions are meant to provide a competitive advantage, in-house development is the way to go. Saša Redžepović from Planted explains:

“At Planted, we have a clear focus: use AI only where it delivers real impact for sustainability and reduces complexity.”

Time sensitivity: Companies that must report CSRD-compliant by 2025 often don’t have time for lengthy in-house development. Automation platforms offer an efficient solution to process complex datasets and create audit trails at the same time.

Hybrid approaches as an option: Combining different approaches can shorten development times and reduce the risk of vendor lock-in.

Consider long-term costs: When calculating total costs over three years, be sure to include hidden expenses. Maintenance costs typically account for 10–20% of the AI budget, and engineers spend about a third of their time addressing technical debt.

Ensure scalability: By 2026, 60% of companies are expected to use AI-powered warehouse solutions. The chosen solution must remain scalable as needs grow, without costs spiraling out of control.

The final decision should be based on measurable KPIs closely linked to your sustainability goals. As Redžepović emphasizes:

“Every AI solution must make a measurable contribution to our clients’ sustainability goals.”

These considerations lay the foundation for practical implementation, which is explored further in the next decision checklist.

Decision Checklist: AI Implementation in Sustainability Strategies

Integrating AI into your ESG strategy can be a game-changer. This checklist helps you make the right choice between Build, Buy, or Automate.

Assessing Your Internal Capabilities and Resources

It’s important to first analyze your internal capabilities and requirements. In fact, 67% of all software projects fail due to poor decisions between in-house development and purchasing (Standish Group).

• Check data infrastructure and quality: AI and machine learning applications use an average of 24% of storage infrastructure. Storage needs have increased by 27% in recent years. A thorough review of your existing infrastructure is therefore essential for effective AI-powered ESG analytics (Gartner).
• Assess availability of AI expertise: Around 34% of executives report a significant shortage of AI talent. Salaries for such specialists range from €100,000 to €300,000 annually. Additionally, about 40% of employees in digital roles are actively seeking new challenges, and nearly 75% plan to leave their current positions soon (World Economic Forum).
• Promote cross-departmental collaboration: ESG initiatives require close cooperation between departments such as sustainability, IT, and finance.
• Define measurable sustainability goals: Clear objectives help you select suitable AI solutions. The AI ESG Protocol offers a structured approach to assessing risks and opportunities related to AI and ESG.

This analysis forms the basis for reviewing further key factors.

Key Factors to Consider

• Total cost of ownership over the lifecycle: 65% of software costs occur after deployment. Engineers spend about a third of their time addressing technical debt, while R&D teams devote 30% to 50% of their resources to maintaining legacy code. Technical debt grows by about 7% annually (McKinsey).
• Regulatory requirements and compliance: GDPR compliance costs range from €20,000 to €100,000. For companies that must be CSRD-compliant by 2025, annual costs are €10,000 to €100,000.
• Security and data protection: Data breaches cost companies an average of $4.88 million in 2024. While self-developed solutions offer more control over security protocols, ready-made solutions carry some risk due to shared responsibilities (IBM).
• Avoid vendor lock-in: Over 80% of companies that have migrated to the cloud report vendor lock-in issues. Switching costs can be twice as high as the original investment. By 2028, companies are expected to increase their AI spending by 29% annually (Gartner).
• Scalability and future requirements: AI infrastructure is not a one-time investment. Systems must grow, adapt, and be updated regularly. Delays in upgrades can increase costs by up to 600%.

Making the Final Decision: Build, Buy, or Automate

After assessing your capabilities and the factors above, you can set your strategy clearly.

• Assess strategic importance: Align with the strategic relevance of the problem, technical complexity, and availability of internal expertise. Clear goals and realistic timelines are crucial to support your business strategy.
• Define expectations: Set realistic goals for how AI/ML can advance your company. Every solution should measurably contribute to achieving your sustainability targets.
• Consider hybrid approaches: A combination of in-house development and purchasing can be a sensible way to leverage the benefits of both approaches.
• Establish risk management: Use metrics such as the Technical Debt Ratio (TDR) to monitor technical debt. Also, develop your own AI principles and governance mechanisms that include environmental and human rights standards.
• Start pilot projects: Begin with a Minimum Viable Product (MVP) or Proof of Concept (PoC) to test feasibility. Gradual scaling helps allocate resources effectively.

With measurable KPIs and clear sustainability goals, you can make an informed decision and successfully implement your AI strategy.

FAQs

How do I decide whether to develop, buy, or automate an AI solution for sustainability strategies?

The decision to develop, buy, or automate an AI solution for your sustainability strategies depends on several factors. Most important are your available resources, internal expertise, and the specific requirements of your company.

Ask yourself whether you have the technical capabilities and know-how to develop your own solution, or whether it makes more sense to use an existing solution. Automation is a good option if your processes are clearly defined and can be made more efficient with AI.

The main goal should always be to find a solution that best supports your sustainability objectives while aligning with your competencies and business strategy. For further insights, see this Q&A from Penn State.

What potential challenges exist when integrating AI into a sustainability strategy, and how can they be addressed?

Integrating AI into your sustainability strategy can be quite challenging. A key issue is ensuring high-quality, unbiased data, as the accuracy and reliability of AI models depend heavily on data quality. There are also technical hurdles, such as integrating with existing systems, and dealing with ethical questions.

To overcome these obstacles, you need a well-thought-out strategy. Regular data quality checks are just as important as consistently adhering to ethical standards. Open and transparent communication, as well as involving all relevant stakeholders, can also help build acceptance and drive successful implementation. For a deeper dive into ethical challenges, refer to the AI ESG Protocol.

How important are data protection and compliance when selecting or developing AI solutions for ESG goals?

Data Protection and Compliance: Cornerstones for AI Solutions in the ESG Sector

Data protection and compliance are central aspects when it comes to selecting or developing AI solutions for ESG goals (environmental, social, and governance). They help companies comply with legal requirements such as the GDPR and minimize legal and financial risks. The importance of robust compliance frameworks is underscored by the increasing scrutiny from regulators and investors, especially as the volume and sensitivity of ESG data grows.

Another key benefit: They strengthen stakeholder trust. Protecting sensitive data and adhering to ethical standards demonstrate responsibility and foster credibility. Companies that incorporate these requirements into their strategies create a solid foundation for AI applications that are not only sustainable but also legally compliant. For more on the intersection of AI, compliance, and ESG, see this World Economic Forum analysis.