United States — August 24, 2026 — Keshav
Artificial intelligence is moving beyond experimentation and becoming increasingly relevant to day-to-day business operations. Companies across industries are using AI to automate repetitive work, analyse information, support customers, develop software and improve internal processes.
As the number of AI models and automation platforms continues to grow, businesses face a more practical question: where can artificial intelligence deliver meaningful value?
Keshav Sukirya, a technology entrepreneur, AI strategist, founder of Reprise AI, and Sync2.ai, works at the intersection of artificial intelligence, automation and business infrastructure. His work includes AI automation systems, agentic workflows, model distillation and full-stack AI infrastructure, with applications across recruiting, accounting, e-commerce, professional services and other industries.
His approach focuses less on AI hype and more on how businesses can turn increasingly capable technology into reliable and practical systems.
“The important question is not simply which AI model is the most powerful. It is what the technology allows a business to build, how reliably it works, and what it costs to operate.”
AI Is Becoming a Business Tool
The conversation around artificial intelligence has changed significantly.
Rather than focusing solely on what AI might eventually achieve, businesses are increasingly looking at how the technology can be applied to existing operations.
Applications include customer support, research, document processing, software development, marketing, data analysis, internal knowledge management and workflow automation.
For Keshav Sukirya, the shift from experimentation towards implementation is one of the most important developments in the sector.
Businesses no longer need to ask whether AI could eventually become useful. Instead, they need to identify where it can solve a specific business problem.
That might involve reducing repetitive administrative work, improving access to internal information, supporting customer-service teams or connecting AI systems with existing business software.
The strongest implementations, however, are not necessarily those using the most sophisticated technology. They are the ones that solve a clearly defined problem.
Choosing the Right AI for the Job
The rapid growth of AI models has created more choice for businesses, but it has also made technology selection more complicated.
Different models can be suited to different workloads. A system designed for complex reasoning may not be necessary for a straightforward classification task, while a highly capable model may be unnecessarily expensive for a high-volume routine process.
Keshav Sukirya recommends evaluating AI according to the requirements of the task rather than simply selecting a model because it performs strongly on a public benchmark.
“The right model depends on the task. Businesses should evaluate performance against their own workloads instead of choosing a model simply because it is at the top of a public leaderboard.”
This can lead to a multi-model approach.
A business might use a smaller and faster model for routine requests while reserving more capable systems for complex analysis or decision-making. Specialised models may also be appropriate for areas such as coding or document processing.
The objective is not necessarily to find one model that can do everything, but to build a system where the appropriate technology is used for each job.
AI Automation Is Moving Beyond Simple Rules
Traditional business automation generally relied on predefined rules: when one event occurred, software performed a predetermined action.
AI introduces another layer of flexibility.
Modern AI systems can interpret information, work with unstructured data, make decisions within defined boundaries and adapt responses according to context.
This creates opportunities in industries where employees have traditionally needed to read, interpret, organise and respond to information manually.
Recruiting businesses, for example, can use AI to support candidate workflows and information processing. Accounting firms can automate parts of document and data handling, while e-commerce companies can explore AI for customer support, product information and operational processes.
The value of automation does not come simply from removing manual work. Its value comes from improving the underlying process.
For Keshav Sukirya, the starting point is therefore the workflow itself, followed by an assessment of where AI can make that process faster, more consistent or easier to scale.
AI Agents Could Change Business Workflows
AI agents are taking this concept further.
Unlike conventional chatbots, agentic systems can potentially perform multiple steps, use external tools, retrieve information and interact with software to complete broader tasks.
For businesses, this could create opportunities across research, reporting, customer service, lead qualification, software development, data processing and internal operations.
However, the challenge is not simply creating an agent that can complete a task. It is making the system reliable enough for a real business environment.
Real-world workflows involve incomplete information, unexpected requests, technical failures, security considerations and situations where human judgement is still required.
For this reason, reliability, monitoring and human oversight are likely to remain important as businesses introduce more advanced agentic systems.
An AI demonstration can show what is possible. A production system has to demonstrate what is dependable.
Open-Weight AI Creates New Options
Open-weight AI models are also creating additional choices for organisations.
Businesses can potentially gain greater control over how an open-weight model is deployed and where information is processed. For organisations with specific privacy, security or infrastructure requirements, this can be an important consideration.
However, open models are not automatically easier or cheaper to operate.
Running larger models can require significant computing resources, technical expertise, maintenance, monitoring and security.
For some businesses, commercial AI APIs may remain the most practical option. Other organisations may determine that greater control over their AI infrastructure justifies additional investment.
Keshav Sukirya’s view is that businesses should consider the complete operating picture rather than assuming that one deployment model will suit every organisation.
Model Distillation Can Improve AI Efficiency
Model distillation is another area that can be relevant to organisations deploying AI at scale.
The technique can transfer useful capabilities from a larger or more complex model into a smaller model. In suitable circumstances, this can help create systems that are faster and less expensive while retaining capabilities required for a particular workflow.
For businesses, the broader lesson is that the largest available model is not necessarily the best solution.
A smaller or specialised system may be sufficient for a particular task and can sometimes provide better economics at scale.
This reinforces the importance of evaluating AI against the actual requirements of a business rather than relying on model size or popularity.
The Model Is Only Part of the System
One of Keshav Sukirya’s broader observations is that access to advanced AI models is becoming less of a competitive advantage on its own.
Many organisations can access similar foundation models. What they do not necessarily share is the same data, internal processes, integrations, business knowledge and operational infrastructure.
That surrounding system can determine how much value a company receives from the underlying AI.
“The model is only one part of the system. The real value comes from the data, workflows, integrations, and processes built around it.”
For example, an AI assistant can become considerably more useful when it can securely access relevant company information and interact with approved business tools.
This means AI implementation increasingly involves software architecture and integration rather than simply adding an AI interface to an existing product.
What Businesses Should Focus On
For organisations considering AI adoption, Keshav Sukirya advocates a practical approach.
Start with the business problem. Identify a process that is repetitive, expensive, slow or difficult to scale before selecting a technology.
Measure the results. Businesses should track factors such as accuracy, time savings, operating costs and the amount of human intervention required.
Choose technology based on the workflow. Different tasks may require different models, tools or levels of automation.
Build for flexibility. AI technology is changing rapidly, so businesses should avoid unnecessary dependence on a single model or provider where practical.
Protect sensitive information. Organisations should establish clear rules around what information an AI system can access and which actions it is permitted to take.
Keep humans involved where necessary. High-impact or sensitive workflows may still require human review and approval.
These principles can help organisations benefit from current AI capabilities without assuming that today’s technology will remain unchanged.
The Future of Business AI
The AI industry is expected to continue evolving rapidly. New models will emerge, existing models will become more capable or affordable, open-weight systems will develop further and AI agents will become increasingly integrated with business software.
Sukirya believes businesses should therefore avoid building their long-term strategy around a single model or temporary technology trend.
Instead, organisations can focus on building flexible systems that are capable of taking advantage of technological improvements as they emerge.
“Businesses should build for flexibility. The technology will keep changing, so the system should be able to improve without having to start over.”
For companies, the opportunity is not simply to adopt the newest AI model. It is to understand where intelligent systems can improve the way work gets done and then build the infrastructure needed to support that change.
As AI becomes increasingly embedded in business operations, organisations that combine capable technology with strong workflows, reliable integrations and clear business objectives may be better positioned to benefit from the next stage of the AI industry.
About Keshav Sukirya
Keshav Sukirya is a technology entrepreneur, AI strategist, and co-founder of Reprise AI and Sync2.ai. He focuses on AI automation, intelligent agents, agentic workflows, and full-stack AI infrastructure. His work helps businesses automate repetitive processes, improve operations, and deploy practical AI solutions.
Media Details
Media Contact: Keshav Sukirya
Website: keshavsuki.com
Company: Reprise AI / Sync2.ai
Email: [email protected]
Country: United States
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