SAN FRANCISCO and HANGZHOU, China, March 11, 2026 /PRNewswire/ -- As AI reshapes the digital world through cognitive and generative capabilities, a new frontier emerges: Physical AI — an intelligent system that perceives, reasons through, and acts in the physical world.
Today, Bota launches SAION AI, the first Physical AI platform for biomanufacturing.
SAION AI is not limited to in silico design. Instead, it is a full-stack Physical AI platform integrating cognition, orchestration, and closed-loop execution through end-to-end experimentation, and continuously optimizing biological discovery and biomanufacturing.
SAION AI is built on a three-layer architecture: Cognition, Orchestration, Execution.
Powered by large language models , the platform unifies scientific reasoning with real-world experimental execution. This architecture enables seamless orchestration from biological system understanding to laboratory experiments, forming a self-optimizing closed loop for biomanufacturing.
Cognition: Multi-Scale Biological Understanding
The Cognition Layer is built on data from Bota's Cell2Cloud Biofoundry, integrating tens of millions of experimental data points, millions of scientific publications and patents, and public biological databases. Combined with leading AI4Science models, SAION AI develops multi-scale understanding across the gene–protein–cell–fermentation continuum, enabling systematic design and data-driven scientific decisions.
Orchestration: Intelligent Research Coordination
The Orchestration Layer centers on the Agent harness orchestration engine, powered by the LLM reasoning to coordinate multi-agent collaboration, tool invocation, and end-to-end scientific workflows.
The Layer decomposes complex objectives into structured tasks and integrates 316 specialized scientific tools, enabling dynamic routing and automated research workflows with fault tolerance.
Execution: Autonomous Experimental Operation
Through Bota's proprietary Biological Protocol Language, SAION AI converts experimental designs into standardized instructions that directly drive laboratory hardware, feeding real-world data back into the Cognition Layer, enabling continuous model improvement and R&D acceleration.
Real-world Performance
SAION AI has demonstrated SOTA performance across multiple life science AI benchmarks, validating its capabilities as an AI Scientist system.
Key Results
- Literature Comprehension: 70.7% on LitQA+SuppQA — surpassing leading general-purpose models
- Sequence Reasoning: 88.2% on SeqQA across DNA, RNA and protein tasks.
- Genetic Engineering: 84.9% on gene editing and cloning benchmarks.
- Scientific Discovery: 89.6% on BAIS-SD benchmark.
Real-world validation confirms SAION AI can autonomously complete full research — from literature review to wet-lab assembly — with >90% accuracy.
Toward Autonomous Biomanufacturing
SAION AI's launch moves biomanufacturing beyond traditional trial-and-error experimentation toward an intelligent engineering discipline, where AI and physical laboratories interact to accelerate discovery and industrial scale-up.
SAION AI DEMO
Contact us: bota.pr@bota.bio
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Bota Launches SAION AI -- Physical AI Platform for Biomanufacturing
Bota Launches SAION AI -- Physical AI Platform for Biomanufacturing
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TEANECK, N.J., March 11, 2026 /PRNewswire/ -- Cognizant (Nasdaq: CTSH) released new research showing that companies pursuing AI adoption overwhelmingly prefer IT services firms - such as "AI Builder" firms, a new services model defined by designing and building custom, full stack AI solutions - to deliver real enterprise value from AI.
The research, based on a quantitative study of 600 AI decision makers and qualitative interviews with 38 senior executives, finds that organizations rank custom solutions and flexible engagement models as the most important factor when selecting an AI partner, ahead of pricing and time to value. Pricing and proven AI case studies remain important, but rank below capabilities that enable AI to be embedded directly into business operations and value chains.
At the same time, enterprises cite generic, off-the-shelf AI solutions as a leading reason to reject an AI provider, along with lack of industry-specific expertise, inability to integrate into existing technology stacks, and inadequate support and maintenance. According to the research, the top three challenges organizations face in enterprise AI adoption are regulatory and compliance concerns, difficulty demonstrating return on investment and lack of clear AI strategy and vision.
"AI success is not about deploying isolated models—it's about engineering intelligence into the enterprise with purpose-built solutions," said Ravi Kumar S, CEO of Cognizant. "The most trusted path to an AI future is working with an AI Builder—one that brings deep industry context, systems engineering expertise, and operational accountability. At Cognizant, we focus on building the bridge from AI experimentation to measurable enterprise value."
Key findings from the study include:
Enterprises face a "messy middle" in scaling AI: AI builders can create the bridge to enterprise value -- solving complex, real-world problems:
- 63% of enterprises report moderate-to-large gaps between their AI ambitions and current capabilities.
- The biggest barriers to scaling AI are operational and organizational:
- 33% cite regulatory and compliance challenges
- 31% struggle to demonstrate ROI
- 27% report shortages in talent
- 27% report inadequate data readiness
AI investment is long term, not experimental: Enterprises are committing sustained capital to AI, signaling long-term infrastructure building rather than speculative investment:
- 84% of enterprises maintain formal AI budgets
- 91% expect AI budgets to grow in the next two years
- 50% anticipate double-digit increases in AI budgets over the next two years
- 52% are already investing $10M or more annually on AI initiatives
AI is augmenting human workforces, not replacing them: Enterprise leaders are not forecasting workforce collapse, they're forecasting redesign of workflows for human-AI collaboration.
- Across 13 enterprise functions, the highest expected level of full automation is only 20% (in sales)
- Even in customer service, where 76% of leaders expect workflows to become AI-dominant, only 9% believe they will be fully automated.
In qualitative interviews conducted as part of the research, enterprise leaders said "out‑of‑the‑box" AI is inadequate; they want tailored solutions AI builders can develop and tune.
A Vice President in the UK banking sector shared, "A lot of vendors come in thinking that the off-the-shelf solutions they have would fit our needs, but often enough they find that that's not the case. And it takes them a number of years, more than they planned, and a lot of money, both from us … to get those software working. And these are not just AI software."
A US-based insurance industry CIO stated, "It depends on where I'm inserting this particular ingredient in our value. And so sometimes I want a builder and an engineer, sometimes I want an integrator, sometimes I want an activator. Because they're playing more of a coordinating function—a weaving, stitching-together function."
Together, these research insights underscore a clear shift in enterprise expectations: from experimenting with AI tools to partnering with AI Builders that can design, build, integrate, and operate AI systems at scale— in alignment with client governance, security, and risk‑management frameworks and with lasting business impact.
These findings align with recent remarks by Babak Hodjat, Chief AI Officer at Cognizant, who noted that enterprises are far from being able to rely on AI "out of the box." In interviews with Fortune and Reuters, Hodjat emphasized that while agentic and generative AI systems are advancing rapidly, organizations still need significant help engineering, integrating, governing, and operating these systems in ways that support client safety, reliability and governance requirements within complex enterprise environments.
AI decision makers rated IT services firms like AI builders highest in their ability to assist with their AI adoption (ahead of SaaS providers, cloud providers, AI model companies, AI startups and management consultancies). The research also finds that IT services firms are trusted across the AI adoption lifecycle—especially in ongoing management of AI-enabled systems, but also in AI strategy, custom AI solution development, increasing organizational productivity and scaling AI across the enterprise. IT services firms have a 23% trust advantage over management consultancies in AI adoption. While management consultancies benefit from strong brand recognition, they are seen as less credible in hands-on AI implementation.
About the Research
Cognizant's research findings are based on quantitative research conducted in November 2025 with 600 AI decision makers, and qualitative interviews conducted in October 2025 with 38 business and technology leaders in the United States, Germany, Singapore and Australia with AI decision making responsibility. The full report can be found here: How ai is reshaping business & empowering workforces | Cognizant
About Cognizant
Cognizant (NASDAQ: CTSH) is an AI builder and technology services provider, building the bridge between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, realize tangible returns and keep global enterprises ahead in a fast-changing world. See how at www.cognizant.com or @cognizant.
For more information, contact:
U.S.
Name: Gabrielle Gugliocciello
Email: gabrielle.gugliocciello@cognizant.com
Europe / APAC
Name: Sarah Douglas
Email: sarah.douglas@cognizant.com
India
Name: Vipin Nair
Email: Vipin.Nair@cognizant.com
TEANECK, N.J., March 11, 2026 /PRNewswire/ -- Cognizant (Nasdaq: CTSH) released new research showing that companies pursuing AI adoption overwhelmingly prefer IT services firms - such as "AI Builder" firms, a new services model defined by designing and building custom, full stack AI solutions - to deliver real enterprise value from AI.
The research, based on a quantitative study of 600 AI decision makers and qualitative interviews with 38 senior executives, finds that organizations rank custom solutions and flexible engagement models as the most important factor when selecting an AI partner, ahead of pricing and time to value. Pricing and proven AI case studies remain important, but rank below capabilities that enable AI to be embedded directly into business operations and value chains.
At the same time, enterprises cite generic, off-the-shelf AI solutions as a leading reason to reject an AI provider, along with lack of industry-specific expertise, inability to integrate into existing technology stacks, and inadequate support and maintenance. According to the research, the top three challenges organizations face in enterprise AI adoption are regulatory and compliance concerns, difficulty demonstrating return on investment and lack of clear AI strategy and vision.
"AI success is not about deploying isolated models—it's about engineering intelligence into the enterprise with purpose-built solutions," said Ravi Kumar S, CEO of Cognizant. "The most trusted path to an AI future is working with an AI Builder—one that brings deep industry context, systems engineering expertise, and operational accountability. At Cognizant, we focus on building the bridge from AI experimentation to measurable enterprise value."
Key findings from the study include:
Enterprises face a "messy middle" in scaling AI: AI builders can create the bridge to enterprise value -- solving complex, real-world problems:
- 63% of enterprises report moderate-to-large gaps between their AI ambitions and current capabilities.
- The biggest barriers to scaling AI are operational and organizational:
- 33% cite regulatory and compliance challenges
- 31% struggle to demonstrate ROI
- 27% report shortages in talent
- 27% report inadequate data readiness
AI investment is long term, not experimental: Enterprises are committing sustained capital to AI, signaling long-term infrastructure building rather than speculative investment:
- 84% of enterprises maintain formal AI budgets
- 91% expect AI budgets to grow in the next two years
- 50% anticipate double-digit increases in AI budgets over the next two years
- 52% are already investing $10M or more annually on AI initiatives
AI is augmenting human workforces, not replacing them: Enterprise leaders are not forecasting workforce collapse, they're forecasting redesign of workflows for human-AI collaboration.
- Across 13 enterprise functions, the highest expected level of full automation is only 20% (in sales)
- Even in customer service, where 76% of leaders expect workflows to become AI-dominant, only 9% believe they will be fully automated.
In qualitative interviews conducted as part of the research, enterprise leaders said "out‑of‑the‑box" AI is inadequate; they want tailored solutions AI builders can develop and tune.
A Vice President in the UK banking sector shared, "A lot of vendors come in thinking that the off-the-shelf solutions they have would fit our needs, but often enough they find that that's not the case. And it takes them a number of years, more than they planned, and a lot of money, both from us … to get those software working. And these are not just AI software."
A US-based insurance industry CIO stated, "It depends on where I'm inserting this particular ingredient in our value. And so sometimes I want a builder and an engineer, sometimes I want an integrator, sometimes I want an activator. Because they're playing more of a coordinating function—a weaving, stitching-together function."
Together, these research insights underscore a clear shift in enterprise expectations: from experimenting with AI tools to partnering with AI Builders that can design, build, integrate, and operate AI systems at scale— in alignment with client governance, security, and risk‑management frameworks and with lasting business impact.
These findings align with recent remarks by Babak Hodjat, Chief AI Officer at Cognizant, who noted that enterprises are far from being able to rely on AI "out of the box." In interviews with Fortune and Reuters, Hodjat emphasized that while agentic and generative AI systems are advancing rapidly, organizations still need significant help engineering, integrating, governing, and operating these systems in ways that support client safety, reliability and governance requirements within complex enterprise environments.
AI decision makers rated IT services firms like AI builders highest in their ability to assist with their AI adoption (ahead of SaaS providers, cloud providers, AI model companies, AI startups and management consultancies). The research also finds that IT services firms are trusted across the AI adoption lifecycle—especially in ongoing management of AI-enabled systems, but also in AI strategy, custom AI solution development, increasing organizational productivity and scaling AI across the enterprise. IT services firms have a 23% trust advantage over management consultancies in AI adoption. While management consultancies benefit from strong brand recognition, they are seen as less credible in hands-on AI implementation.
About the Research
Cognizant's research findings are based on quantitative research conducted in November 2025 with 600 AI decision makers, and qualitative interviews conducted in October 2025 with 38 business and technology leaders in the United States, Germany, Singapore and Australia with AI decision making responsibility. The full report can be found here: How ai is reshaping business & empowering workforces | Cognizant
About Cognizant
Cognizant (NASDAQ: CTSH) is an AI builder and technology services provider, building the bridge between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, realize tangible returns and keep global enterprises ahead in a fast-changing world. See how at www.cognizant.com or @cognizant.
For more information, contact:
U.S.
Name: Gabrielle Gugliocciello
Email: gabrielle.gugliocciello@cognizant.com
Europe / APAC
Name: Sarah Douglas
Email: sarah.douglas@cognizant.com
India
Name: Vipin Nair
Email: Vipin.Nair@cognizant.com
** This press release is distributed by PR Newswire through automated distribution system, for which the client assumes full responsibility. **
Cognizant Research Shows Plug-and-Play AI is a Myth
