Strategic collaborations with tech giants such as Microsoft, AWS, and Salesforce has amplified Visionnet’s offerings, blending its deep technical expertise with profound industry insights, helping to enhance its service delivery, ensuring cost-effectiveness and operational excellence for its clients
As digital transformation is increasing, the focus on AI has now shifted to how quickly businesses can implement and scale it to remain competitive. While many are experimenting with AI tools, few are making the use of its full potential. The key difference lies in their strategy. AI-first approach is not about only adding AI as an add-on feature, it is about redesigning operations, decision-making, customer engagement with AI as core. However, this shift is not an option anymore. According to an Accenture report, companies with this mindset experience 2.5x growth and 3.3x more likely to scale generative AI successfully. These businesses are not just innovating. They are building AI into the foundation of their operation, personalized experiences and intelligent automation at every level.
The early adopters like Google, Netflix, Amazon are some living examples. For instance, Netflix’s personalized recommendations, Amazon’s AI-driven inventory management and Google’s AI in every ecosystem points out that an AI-first approach is essential for enterprises now. Moreover, this approach frees employees from routine tasks and helps them channelize their energy and time on more strategic work— thereby increasing their work efficiency, which is paramount across all sectors.
In fact, currently, we are at the tipping point of this shift. Early adopters of AI-first and GenAI are already setting benchmarks. Those who do not adapt now risk being left behind.
“Embracing an AI-First strategy is crucial to remain competitive and efficient, and it enables automation of routine activities, predictive maintenance and incident prevention, intelligent knowledgebase creation, enhanced security monitoring with automated remediation, and even automated financial reporting and insights that drive cost efficiencies.”
– Bijo Chacko, Senior VP of Cloud, Gen AI & Cybersecurity at Visionet Systems
Visionet, a leading IT firm that redefines the landscape of digital innovation and business transformation, has been redefining the AI-First approach with panache, delivering superior digital experiences, enterprise modernization, cutting-edge Data & AI applications, and managed IT services. Its tailored solutions leverage the synergy of digital, data, and cloud technologies, ensuring they meet the dynamic needs of its clients with unparalleled relevance and efficiency.
The company has entered into strategic collaborations with technology giants like Microsoft, AWS, and Salesforce, which has amplified its offerings, blending its deep technical expertise with profound industry insights. This partnership model has enhanced its service delivery, ensuring cost-effectiveness and operational excellence, which fuels its clients’ growth trajectories.
To gain fresh insights on how to start, scale and lead a business with AI at the core, we spoke to Visionet Systems’ Bijo Chacko, who’s currently serving as the Senior VP of Cloud, Gen AI & Cybersecurity at the company. A technology leader with over 20 years of experience in cloud and infrastructure services, Bijo oversees the global delivery of cloud transformation and infrastructure modernization initiatives at Visionet Systems. Prior to Visionet, he had a 12-year stint at Mindtree in senior delivery roles and before that he was with 7Strata, Citibank, and Sify Technologies. His expertise spans cloud platforms (AWS, Azure, Kubernetes), solution architecture, and global service delivery, helping enterprises scale efficiently and adopt cloud-first strategies.
In this SME Channels exclusive, Bijo reveals why an AI-First approach is no longer optional; the inner matrixes of Visionet’s much vaunted AI Now Campaign; what drives Visionet’s AI-first architecture; what sets apart AI-first companies; and much more. Edited excerpts…
Why is an AI-First approach no longer optional?
AI has become the operating system of modern enterprises, making an AI-First approach no longer a choice but a necessity. Delaying adoption means falling behind in an environment where customer expectations have shifted dramatically. Users today demand copilot-like experiences, natural language interfaces, and multimodal interactions across their workflows.
From a managed service provider perspective, we in Visionet believe that embracing an AI-First strategy is crucial to remain competitive and efficient. It enables automation of routine activities, predictive maintenance and incident prevention, intelligent knowledgebase creation, enhanced security monitoring with automated remediation, and even automated financial reporting and insights that drive cost efficiencies.
Ultimately, this approach unlocks higher client satisfaction and productivity, ensuring that organizations not only keep pace with industry changes but also lead the way in innovation.
How Visionet’s AI Now Campaign is helping to achieve the goal of being an AI-First company?
In Visionet, the AI Now campaign is designed to transform access into action, equipping every team with the tools, patterns, and guardrails needed to deliver value on a weekly basis.
By providing company-wide sandbox environments, teams across HR, Finance, and Delivery can safely experiment with agents, trial cutting-edge models, and build retrieval-augmented generation (RAG) pipelines without friction. Reusable building blocks in the form of blueprints, prompts, UI components, and evaluation scripts allow product and delivery teams to launch new solutions in hours rather than months. This effort is further supported by a GenAI technology fabric that enables plug-and-play upgrades, seamless orchestration, and policy-driven design.
To ensure adoption is measured and meaningful, the campaign emphasizes observability—tracking adoption, quality, cost, and risk through data rather than anecdotal feedback. Bootcamps and showcases round out the initiative, with structured training programs and live projects that have already equipped hundreds of developers with the skills and confidence to operationalize AI effectively.
Apart from the in-house AI Lab, what all contributes towards Visionet’s AI-first architecture?
Creating an AI-First architecture requires more than just an AI Lab; it needs a strong foundation, governance, and empowered teams. AI-First needs a runway (platform), traffic rules (governance), and pilots (pods and squads), and we have built all three.
Our GenAI Technology Fabric and LLMOps provide a multi-cloud, multi-model environment with LLM gateways, observability, caching, cost controls, feedback loops, and a lifecycle workbench to take ideas from proof of concept to production, ensuring that speed and safety go hand in hand. The multi-LLM gateway model supports OpenAI, Claude, and Llama, enabling model-switching to avoid vendor lock-in. For instance, when Claude 3.7 was launched, Visionet integrated it into existing agents within just 72 hours with the help of our plug-and-play architecture.
How can an AI-First approach help organizations balance innovation with governance and responsible AI practices?
An AI-First approach enables organizations to balance innovation with governance and responsible AI practices by starting fast and scaling safely, while treating governance as a product rather than mere paperwork.
Firstly, governance is embedded by design, with policy packs, control libraries, guardrails, model stores, and data governance built into the delivery platform so that every pilot inherits the right controls from the outset, including classification, prompt and output filtering, red-teaming hooks, lineage, and audit trails. Secondly, a shift-left governance model ensures that every use case passes through a three-stage gate—beginning with ideation and security screening, moving into prototype and testing, and concluding with a pre-production audit that includes formal risk scoring and SME sign-off for sensitive domains.
Thirdly, responsible AI practices extend to bias and fairness checks through representational and statistical validation, while retrieval-augmented generation layers and curated connectors improve truthfulness and minimize hallucinations. In addition, safety and security are strengthened with prompt policies, AI red-team validation, network segmentation, and encryption or tokenization, supported by human oversight with escalation options for risky or ambiguous interactions.
Furthermore, continuous assurance is provided via LLMOps dashboards that monitor token spend, latency, accuracy, user satisfaction, and semantic drift, with automated alerts prompting retraining or prompt updates. Finally, immutable accountability is achieved through version control and audit trails that preserve evidence to answer the auditor’s key question: “Why did the agent return X on date Y?” Taken together, these practices ensure enterprises can innovate rapidly while staying safe—the true essence of responsible AI.
What sets AI-first companies apart from their competitors?
AI-First companies distinguish themselves through compounding speed, trust at scale, and measurable business outcomes.
By maintaining a reusable platform of accelerators, connectors, governance templates, and observability tools, they ensure that each new use case is a matter of configuration rather than starting from scratch, enabling delivery timelines measured in weeks instead of months. Their ability to manage AI risk systematically, with policy frameworks, registries, and continuous monitoring, builds trust across stakeholders and scales responsible AI practices across the enterprise.
Most importantly, they link AI initiatives to clear business outcomes, whether reducing time-to-resolution in IT service delivery, improving capacity forecasting, enhancing security operations, or optimizing financial efficiency. They embed adoption and ROI tracking into their operating cadence, ensuring that each initiative not only delivers technical success but also tangible business value.
By scaling beyond pilots with structured blueprints and platforms that serve multiple functions such as IT, HR, and customer service, AI-First companies create a culture of rapid innovation that is safe, trusted, and outcome-oriented, putting them far ahead of competitors still experimenting at the margins.
WHAT’S AN AI-FIRST APPROACH?
An AI-first approach is a strategic business model in which artificial intelligence is the foundational and primary driver of an organization’s products, operations, and decision-making. Unlike a traditional “AI-inside” model, where AI is merely added to existing features, an AI-first company is built from the ground up to leverage AI’s capabilities for a sustained competitive advantage.
Prominent examples of companies with an AI-first mindset include Google, Duolingo, and Shopify.
Benefits of an AI-first approach
- Enhanced agility and competitiveness: An AI-first approach allows companies to predict market changes and react with unprecedented speed.
- Superior operational efficiency: AI-powered workflows and automation lead to significant productivity gains and reduced operational costs by automating manual tasks.
- Accelerated innovation: By automating repetitive tasks, AI frees up human employees to focus on creative, high-value problem-solving and developing new products and business models.
- Intelligent user experiences: AI can create hyper-personalized, adaptive interfaces and recommendations, leading to higher customer engagement, satisfaction, and retention.
- Data-driven decision-making: AI analyzes large, complex datasets in real-time, enabling faster, more accurate decisions based on data-backed insights rather than intuition.
