How organizations can optimize generative AI costs

With AI agents, the technology can dynamically decide what to do, bringing the power of AI into workflows. Previously, processes like this would have had hundreds or thousands of manual steps—with agentic AI, it’s possible to get the desired output more easily and in seconds. For many organizations, this makes work that was previously considered impossible to achieve due to cost now within reach.

• In 1984, the educational psychologist Benjamin Bloom published a seminal paper titled “The 2 Sigma Problem.” His research compared conventional teaching methods to a mastery teaching approach combined with one-on-one tutoring.
• For the past years, business transformation initiatives have been the sole mandate of the technology team.
• “They’re being asked to leverage AI or help accelerate the adoption of AI in organizations to achieve productivity gains.
• Today, businesses are eliminating that hierarchal cycle of business process transformation by embedding AI-powered applications and solutions directly into existing workflows via APIs without having to redesign the entire tech platform first.

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If you have competitors that have embraced AI and are getting positive results, you’ll lose some element of advantage, whether it be product, price or otherwise. Don Murray is Co-founder & CEO of Safe Software and has spent his career helping organizations bring life to data to make better decisions. Anchor AI efforts in genuine business challenges and customer needs rather than following transient trends. Many have since theorized that AI-powered tutors, particularly advanced large language models and multimodal bots, could provide the solution Bloom envisioned.

Document these best practices and encourage wider dissemination through knowledge sharing sessions. IT leaders should consider augmentation and customizations sequentially, only moving to a more advanced approach if a simpler one doesn’t meet the required output quality. They can evaluate the different approaches not only to achieve better output quality, but also to reduce running costs — especially if the model usage will be high volume and predictable. Running costs can be mitigated by careful choice of models that balance price/performance, or even by efficiently fine-tuning a model on a specific dataset through instruction tuning or continuing pretraining.

What was once a costly task estimated to take decades of effort can now be done in just a few days. After pumping all the documents into an AI-powered workflow, information is pulled from these documents and assessed, with some of the documents routed to another AI agent for further processing. From the initial prompt, it finds patterns in the data and locates the desired documents containing the data with an exceptionally high level of knowledge. Before the explosion of generative AI tools, extracting valuable information from scanned documents was largely manual and therefore time-consuming and expensive. As a result, the valuable data in these documents was “locked” away and, in many cases, left in printed form. Generative AI can unlock all this data, and the biggest cost is now likely scanning the physical documents to get them into a digital form where AI can operate on them.

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Now as AI expands, there’s a need to incorporate this new category of risk into GRC frameworks. Information technology leaders should create an AI model garden and offer multiple, diverse models for users to safely experiment. Make the model costs transparent to the users via reporting tools, which enables them to make better economical choices without jeopardizing their accuracy, performance and other selection metrics. The friction of AI adoption isn’t silicon-based (the technology itself) but instead is carbon-based (as in the people adopting it). People are naturally resistant to change and worry about things they don’t understand. Agentic AI pushes back on that one-to-one ratio, using multiple AI-powered workflows to create automations that take action.

Degree of Innovation

This is modification because it transforms the learning activity into something more collaborative and dynamic, altering the original activity in a meaningful way. For instance, Adobe does not include student projects from K–12 and higher education institutions in training data sets, and there are guardrails on generative AI prompts and outputs encouraging appropriate student use. On the visual side, there’s Adobe Firefly, a family of generative AI tools for visual creation. Dynamically explore and compare data on law firms, companies, individual lawyers, and industry trends.

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Organizations should analyze and uncover hidden gen AI costs across various approaches. Avoid expensive model customizations and understand the tradeoffs of self-hosting AI models. Conduct monthly or quarterly reviews of gen AI costs to ensure ongoing optimization and instill a culture of accountability.

This enables organizations to choose a provider that delivers superior price and performance, yet meets the security and support needs of the organization. “A lot of organizations started with pilots a year ago or even prior to that, but now they’re starting to see real efficiency gains in areas like code generation and application testing,” says Michael Harper, Managing Director at KPMG LLP. “Those activities are giving organizations more confidence in using these tools and helping them to move forward.”

The shortage of experienced AI talent and the need for all employees to embrace gen AI is making internal development more vital than ever. An update on the legal tech market’s past week, from product launches to new partnerships. Rohit Kapoor is chairman and chief executive officer of EXL, a leading data analytics and digital operations and solutions company.

The future of AI is not just about breakthroughs, but about the execution, adaptability and leadership that transform potential into lasting impact. AI is evolving at an unprecedented pace, yet most organizations struggle to turn research breakthroughs into scalable, production-ready systems. While AI-driven features can deliver quick wins, foundational AI investments such as generative AI, core AI infrastructure and enterprise-scale machine learning (ML) models demand years of sustained effort, disciplined execution and cross-functional collaboration. At the same time, the rapid proliferation of AI tools has created new fragmentation and oversight issues, with 44 per cent of software executives identifying increasing technical debt and AI sprawl as critical risks. In short, despite its transformative potential, the use of generative AI in teaching and learning comes with unique barriers, risks and costs. These challenges must be carefully addressed to ensure that AI enhances, rather than undermines, education.

Now, imagine the instructor using polling software to display real-time data analytics on students’ understanding of the material, enabling them to adjust their teaching in the moment. This represents adaptation, as the technology enhances the original activity by adding functional improvements. On the instructional side, AI-driven instructional coaches offer considerable promise for increasing teacher engagement and efficiency. Already, bots are helping educators to design more effective courses, develop engaging assignments and streamline feedback and grading processes.

Conventional teaching—the predominant instructional model at all levels of education—follows a one-teacher-to-many-students format with periodic testing to assess learning. In contrast, mastery teaching integrates frequent formative assessments and feedback loops, ensuring students do not progress until they demonstrate mastery of prior material. As an example, AI may be used to skim through resumes and find certain keywords, assess the experience of the candidate, and even foresee how effective they will be in the business culture. When employees have been recruited, AI can then monitor their performance, detect areas where they are lacking, and propose individual development opportunities to make sure employees become the best they can be. An example is that customer service bots that are AI-based can be used to manage basic customer questions at any time of the day or night, so that human agents can tend to more complicated customer requirements.