Artificial intelligence (AI) is becoming increasingly integrated into enterprise software, and B2B SaaS leaders are facing pressure to adopt AI technologies into their overall software strategy.
In a survey among C-suite executives, 75% ranked AI as one of their top three business priorities, but only 25% of them reported to have achieved significant value from their AI investments.
To successfully navigate these emerging trends, decision-makers will need to critically assess their readiness and strategic approach to AI integration. Below are eight critical questions to guide SaaS leaders in making well-informed AI investment decisions.
1. What specific business problem are we aiming to solve with AI?
What specific use cases do you need to solve in the first place? Without a well-defined business problem, your AI initiatives run the risk of becoming a costly experiment with limited impact.
For instance, Omega Healthcare utilised UiPath’s AI document automation to process over 100 million transactions since 2020, saving 15,000 employee hours per month, reducing documentation time by 40%, and achieving 99.5% accuracy. This earned the company a 30% ROI for clients. The company’s utilisation of AI targeted specific pain points in the organisation’s business processes that were solved or aided by its AI strategy.
2. Are we selecting tools that align with our long-term AI strategy?
The tools businesses adopt today will determine how adaptable and competitive they are tomorrow. According to IDC, by 2027, 40% of large global enterprises are expected to use AI agents and automated workflows to manage knowledge work, fundamentally changing how teams handle tasks and projects. In response, companies are shifting toward agentic platforms that coordinate task execution, data flow, and governance.
A typical agentic stack is made up of three key layers. The orchestration layer includes tools like Microsoft Autogen, which provide the core infrastructure for managing memory, using tools, and making decisions, which helps avoid vendor lock-in. The reasoning layer is supported by LangChain Agents, which package reusable logic for data retrieval, planning, and multi-step task execution. Finally, the execution layer includes domain-specific platforms that automate tasks for specific functions, or even for businesses themselves. For example, AutonomyAI’s Agentic Context Engine integrates directly into company codebases to deliver production-grade front-end code, enabling non-developers, such as UX designers and project managers, to contribute to the front-end pipeline. The engine learns a company’s history by tapping into its codebase, enabling its autonomous agents to intelligently produce output and adapt to company changes over time.
When these layers are built on shared standards, it becomes far easier to introduce new models, compliance rules, or monitoring tools without disrupting your company’s overall system. Investing in flexible, interoperable tools can help sustain long-term agility and minimize the risk of future bottlenecks in your AI strategy.
3. Do we have the data foundations to support AI?
Solid data pipelines can turn AI from theory into practice. Without such a groundwork, even state-of-the-art models can succumb to the “garbage in, garbage out” issue. A 2024 survey of IT leaders found that 42% of large companies that are deploying or planning to deploy GenAI found data quality to be the number one concern in achieving success.
The fix is in disciplined pipelines and versioned feature stores. For instance, DoorDash’s rollout of product knowledge graphs enriched its product information, making it easier for customers to find exactly what they were looking for. The company started small with annotating and leveraging LLMs to increase its database of annotations, enabling the company to quickly scale up its efforts and come up with smarter, more intuitive recommendations.
4. What is the total cost of ownership (TCO) for this AI implementation?
Beyond initial licensing or training costs, AI systems entail expenses related to infrastructure, personnel, monitoring, and retraining.
This is essential in maintaining sustainability while meeting the demands of an increasingly AI-driven economy. This involves optimisation of resource usage, such as energy consumption and datacenter resources.
According to a report by Ansys, businesses need to provide credible ROI estimates to justify AI investments. This includes accurate TCO analysis, including whether this will involve capital expenditure or mostly operations and maintenance, such as with cloud-focused investments. These involve maintenance, performance tuning, and additional integrations. A TCO assessment should incorporate these, taking the entire lifecycle of one’s AI projects into consideration.
5. Can we explain how our AI models make decisions?
Transparency in AI decision-making is crucial for regulatory compliance and customer trust, supporting responsible and accountable use of AI. The EU AI Act mandates explainability in high-risk AI systems. This is particularly applicable in sensitive industries like finance and healthcare.
A good case study would be how a fintech SaaS company can use SHAP analysis to explain loan eligibility predictions. Such transparency can improve customer retention and reduce regulatory friction.
6. Are we prepared to handle AI model drift over time?
AI models can degrade in performance due to changes in data patterns, which is a phenomenon known as model drift. A 2024 study found that 91% of machine learning models suffer from model drift, emphasising the need for continuous monitoring and retraining.
For example, in the case of a HR SaaS tool, automated monitoring and retraining pipelines can help detect classification drift in employee attrition models. This can then prevent the AI model from generating false positives during a labor market shift. Realignments along the way will ensure that models take changing operational realities into consideration.
7. How will AI integration impact our current teams and workflows?
AI adoption can significantly alter workflows and job roles. For example, a UK government trial of Microsoft 365 Copilot found that users saved an average of 26 minutes per day, with some gaining over an hour. Over 70% reported reduced time on mundane tasks, and more than 80% were reluctant to give up the tool post-trial.
A RAND report recommends that organisations need to commit to their AI initiatives for a certain duration to ensure effective implementations. “Before they begin any AI project, leaders should be prepared to commit each product team to solving a specific problem for at least a year.”
RAND emphasises that organisations will need to rethink these processes and focus on the connections and interactions among team members and stakeholders. Instead of forcing teams to adapt to certain processes, teams should be empowered to adopt the processes to fit their workload.
8. What ethical and security measures are in place for AI usage?
AI systems can pose risks related to data privacy, bias, and security. OpenAI’s ChatGPT faced regulatory bans in some jurisdictions due to concerns about data handling and transparency. In response, a cross-industry initiative involving healthcare and legal tech providers introduced sector-specific audit frameworks for AI outputs and bias detection. Organisations with predefined ethical standards, security protocols, and third-party auditing mechanisms are better prepared to mitigate potential regulatory and compliance risks.
Conclusion
Investing in AI does not only involve technology, but also a rigorous framework for readiness, strategy, and sustainability. Organisations will need these to achieve real returns or risk superficial implementations. Those that align early AI adoption with end-to-end workflow improvements will be better equipped to adapt to a technology landscape increasingly driven by AI.
Read more:
8 Questions Every SaaS Leader Should Ask Before Investing in AI
