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Forbes
06-08-2025
- Health
- Forbes
Why Patients Must Remain The Focus As AI Revolutionizes Healthcare
Ankit Agarwal, Chief Technology Officer, Palco. Smart, scalable and people-centered solutions should be at the heart of everything we do in healthcare. Before the digital revolution transformed healthcare, paper-based systems and traditional contact centers were the foundation of most operational workflows. However, today's demands and fast-paced environment have rendered these manual processes unsustainable, inefficient and prone to risk. Having spent my career leading digital innovation across a range of healthcare organizations—including managed care organizations, Medicaid programs and self-directed care providers—I've always been fascinated by how tools can improve outcomes, reduce friction and give individuals more control over their care. But innovation, including technologies like AI, is only meaningful when it serves people. Let's look at how we can work to deliver future-ready healthcare systems with AI that put people at the center. The Manual Challenge Historically, managing paper records has required significant time and labor, leading to frequent delays, lost productivity, escalating administrative costs and quality issues. Similarly, legacy contact centers face limitations with high agent turnover, long wait times and outdated tools. These factors not only impact efficiency but also drastically take away from the overall patient experience. As a study from the National Library of Medicine comparing paper-based with electronic patient records found, 'Inconsistencies between a patient's electronic and paper-based medical record can lead to significant problems for the health care staff in daily practice.' The modern consumer expects fast, personalized support across multiple channels. In fact, as reported by The Lund Report, 30% of patients have left an appointment due to long waits, and 20% have switched doctors for the same reason. Traditional methods are also often vulnerable to data loss, security risks, limited accessibility and service inconsistencies that may hinder both communications and compliance. These challenges highlight an urgent need for smarter, scalable solutions in healthcare operations. The New Age Of AI AI has quickly evolved from a niche technology to a transformative force in healthcare. What began with automating simple tasks like appointment scheduling or form processing has now expanded into sophisticated applications such as predictive analytics, natural language processing and real-time clinical decision support. But the concern is that AI could cause healthcare to lose the human touch. To solve this, humanized AI seeks to use AI to create a blended approach that enhances rather than replaces humans. This idea is founded in empathy and context awareness to create systems that support patients and providers in more intuitive, effective ways. Dr. Jonathan H. Chen, assistant professor of medicine at Stanford, sums up the idea nicely: "'What is a computer good at? What is a human good at?' We may need to rethink where we use and combine those skills and for which tasks we recruit AI." Organizations are using humanized AI to create scalable, more intentional experiences for their teams and patients. For example, NRC Health created an AI engine, Huey, to keep the human experience at the center of healthcare. From AI-powered chatbots delivering 24/7 support to predictive models that flag high-risk patients before emergencies occur, these tools can reshape healthcare delivery. Humanized AI solves some of healthcare's most persistent operational burdens, such as streamlining patient intake systems, verifying data in real time, reducing administrative workload and providing multilingual support, which enables human teams to focus on more complex and personal interactions. As Dr. Rachel Callcut, associate professor of surgery at the University of California, San Francisco, explains in a recent report from GE Healthcare and the MIT Technology Review, "By focusing on areas that patients, providers or systems are invested in addressing, we have set the stage for more rapid adoption and dissemination of AI.' A Phased Approach to Transforming Care What excites me most about AI in healthcare isn't just the technology itself—it's the potential to remove friction from people's lives. Leading the implementation of humanized AI at my organization was both an exciting and humbling experience. From the beginning, our goal was clear: to automate repetitive, paper-based workflows and contact center processes so we could improve efficiency and reduce human error. Throughout the process, however, I learned some valuable lessons: The first is to engage early and often. We initially thought we had accounted for every business need—but our team quickly flagged key issues we hadn't considered. It's, therefore, crucial to involve subject matter experts and frontline team members in the earliest stages of planning. Second, always have a backup plan. We pushed forward with an aggressive implementation timeline but overlooked the fact that AI systems operate on preset logic and improve over time. The lesson here is simple: Build a plan B to ensure business continuity while AI systems are learning. Finally, implement in phases. Rolling everything out at once sounds efficient, but we learned that a phased approach gave us the space to incorporate real-time feedback and refine our plan for long-term success. A the GE Healthcare and MIT Technology Review report cited above found, '80% of business and administrative healthcare professionals believe AI will make them more competitive providers.' I agree with this. AI tools can transform how patients experience care. But it's not about replacing people or implementing AI at the expense of the patient experience. It's about making healthcare more responsive, more human and more efficient. Not someday, but today. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
22-07-2025
- Business
- Yahoo
TCN Unveils How AI-Powered Solutions Redefine Contact Center Efficiency
ST. GEORGE, Utah, July 22, 2025 /PRNewswire/ -- TCN, a leading provider of cloud-based contact center solutions, today announced its comprehensive strategy and enhanced product suite leveraging artificial intelligence (AI) to empower contact centers to achieve unprecedented levels of efficiency, compliance, and customer satisfaction. TCN's approach simplifies AI implementation, making the power of advanced technology accessible and impactful for businesses of all sizes. "AI is and will continue to shape how businesses are organized, managed, operated and optimized," said Jesse Bird, chief technology officer at TCN. "While AI will grow to be a key component of your contact center, it is an enhancement to your current software, not a replacement. As you incorporate more AI into your system, you must learn where it can be used and then decide where it should be used." Elevating Your Contact Center with AI: Three Key Use Cases TCN's AI-driven solutions address core pain points within contact centers, providing more efficient and reliable solutions that augment, rather than replace, human expertise. Our key use cases include: Agent Augmentation: AI enhances individual agent performance by providing real-time coaching, automating call summaries, and delivering next-best action guidance. This empowers agents to handle interactions more efficiently and effectively. Reporting and Decisioning: AI transforms operational insights through intelligent risk segmentation, optimizing resource allocation, and automating compliance checks to ensure regulatory adherence and minimize risk. Automated Routine Tasks completion: By automating repetitive inquiries and common transactions, TCN's AI-driven solutions improve overall operational efficiency and reliability, freeing human agents to focus on complex, high-value customer needs. TCN's AI-Enhanced Product Suite AI is deeply embedded across TCN's flagship platform, TCN Operator, bringing advanced capabilities to every facet of contact center operations. Businesses can now take advantage of AI across a wide range of TCN solutions: Agent Assist: Guides agents with AI by generating suggested follow-up schedules and real-time response suggestions to enhance interactions. Chat: Optimizes processes using AI chatbots to manage diverse customer inquiries and provide instant support. Email: Employs AI to automate customer service by managing frequently asked questions and processing transactions. Inbound Solutions: Leverages AI voicebots to automate phone interactions, enabling natural conversations and decreasing customer hold times. Interactive Voice Response (IVR): Automates call routing with AI, helping customers navigate IVR, improving access and cutting consumer hold times. List Management Services: Automatically analyzes import headers for field types with AI, saving time and reducing errors across all CRM data storage. Outbound Solutions: Optimizes agent efficiency with AI that monitors hold queues and provides real-time alerts when parties become available. Predictive Dialer: Runs tests and gets feedback before launching a campaign with the help of AI, analyzing settings and potential results. SMS: Automates customer service with AI, handling tasks from FAQs to transactions, freeing agents for complex tasks. Workforce Management: Enhances strategy with AI-powered forecasting, enabling optimal staffing levels and lowering labor expenses. Workforce Optimization: Uses AI to auto-evaluate conversations to identify compliance risks and summarize agent interactions. "Implementing AI into your contact center may seem complex, but TCN makes it simple," added McKay Bird, marketing director for TCN. "Our goal is to help businesses easily reap all the benefits of AI, from optimized efficiency to enhanced customer experiences, without worrying about the technicalities. We invite businesses to connect with us to explore how TCN's AI solutions can best address their unique challenges and elevate their contact center." To learn more about how TCN's AI-powered solutions can transform your contact center operations, visit About TCN TCN is a global leader in cloud-based contact center solutions for accounts receivable management (ARM), healthcare providers, enterprises, contact centers and BPOs. TCN's comprehensive suite includes omnichannel solutions, automation, predictive dialers, IVR, Click2Pay, compliance solutions and real-time analytics, driving operational efficiency and customer satisfaction. TCN promises immediate access to the latest TCN Operator platform, facilitating seamless scalability. With a commitment to excellence and a dedication to meet evolving business needs from start to finish through industry-leading customer service, TCN continues to redefine the contact center landscape. For further details, visit View original content to download multimedia: SOURCE TCN, Inc.
Forbes
14-07-2025
- Business
- Forbes
Consulting In The Age Of Enterprise AI
Noah Ohrner, Chief Technology Officer at Kenley. As a co-founder and chief technology officer of a company that builds AI tools for consultants, from supporting desktop research to generating slide decks, I have witnessed the transformative impact of artificial intelligence (AI) within the consulting industry. This shift is creating a quiet revolution, reshaping the competitive landscape and empowering emerging firms to challenge traditional industry leaders. The Shift In Consulting Power Dynamics Until recently, the incumbents enjoyed economies of scale rooted in armies of analysts. AI flattens the landscape. Large language models can mine public reports, internal slide decks and statistical data sets in minutes, then generate first-pass insights that once required days of effort. According to a 2025 survey of 300 professional service workers (paywall), 95% now use Generative AI monthly, and for them, 14% of model outputs require no rework at all. This productivity step-change neutralizes a historical advantage of mega-firms. A hundred-person firm can wield the same analytical firepower that a thousand-person firm needed a decade ago, while preserving the intimacy and contextual acuity. In my experience, AI enables a significant reduction in the time required to deliver pricing strategy and due diligence projects. Why Mid-Market Firms Are Poised for Success Startups move quickly but struggle with client trust; behemoths enjoy trust but move slowly. I see mid-sized consultancies as sitting in a Goldilocks zone. They possess enough brand equity and sector depth to reassure clients yet remain unencumbered by decades of legacy processes. AI can accentuate those advantages in three ways: 1. Margin-neutral price flexibility. Automatic proposal drafting, data ingestion, and benchmarking collapse non-billable hours, freeing margin that can be redeployed as fee discounts or reinvested in service upgrades or tooling. 2. Time-to-insight as a differentiator. For strategy decisions tied to volatile markets—think foreign exchange exposure or energy procurement—speed outranks polish. Firms armed with domain-tuned LLM agents can iterate scenarios overnight, where manual workflows once took weeks. 3. Hyper-specialization without overhead. A reusable prompt library, chained to vertical knowledge graphs, lets a 12-person pricing-only team rival the depth of an incumbent's pricing practice. The usage metrics are already visible. Thomson Reuters' 2025 professional-services report posits that Gen AI will be central to all professional services organizations within the next five years, if not sooner. Doing AI Right: Beyond Generic Tools The consulting workflow—diagnose, model, recommend, package—contains domain-specific constraints that consumer chatbots ignore. A model that autocompletes poetry is useless if it hallucinates revenue figures in a buy-side diligence. Many firms still reach for general-purpose tools instead of deploying AI built for their specific workflows. I think that's a mistake. Successful AI programs do three things differently: 1. Embed AI inside the native toolchain. Instead of hopping between ChatGPT and Excel, analysts can use work streams to coordinate project insights, preserving provenance and audit trails. 2. Constrain generation to firm-approved standards. Templates enforce everything from slide masters to the lexical choices that signal risk levels; the model should not be able to invent metrics or rewrite disclaimers. 3. Surface source-of-truth metadata. Each generated cell or bullet should link back to the underlying dataset, document and expert interview transcript so senior reviewers can trace reasoning. A recent survey reveals that users of specialized GenAI tools note far fewer concerns about unreliable outputs (21%) than generic-tool users (30%). I expect this differential to increase exponentially with new tools on the market. Technical Imperatives: Data Access Control and Quality Assurance Consultancies must walk a tightrope: Leverage their collective know-how while never undercutting client confidences. The foundation is a dual-zone data architecture. Publicly shareable research and anonymized benchmarks live in an open vector store, while client-sensitive materials reside in encrypted, tenant-isolated stores. Role-based access control (RBAC) gates retrieval functions so that a consumer LLM cannot accidentally cross-pollinate projects. Quality loops are equally critical. Every generated artifact should enter a review queue where consultants can grade relevance, factual accuracy and stylistic adherence. These human-in-the-loop scores can then feed nightly, fine-tuning jobs that harden system performance. I don't consider governance as optional; the General Data Protection Regulation (GDPR), SOC compliance and upcoming EU AI Act provisions will impose traceability, explainability and bias-mitigation requirements by default. Firms that treat these safeguards as design inputs will move faster than rivals forced to retrofit later. Additionally, parallel investment is needed in observability. Token-level logs, latency metrics and guardrail trigger rates reveal drift long before it surfaces in client meetings. Firms can leapfrog vulnerabilities by adopting a "zero-copy" pattern—models come to the data, not vice versa—reducing both breach probability and regulatory friction. Consolidation: Battling Tool Sprawl Decision fatigue creeps in when consultants juggle a dozen unintegrated AI assistants, each with its own prompt syntax, data connectors and permission model. A unified, end‑to‑end platform collapses those seams. Analysts stay in a single interface, queries chain across shared memory and outputs flow into the same governance and audit layer. That consolidation compounds productivity: less context‑switching, fewer data exports and one learning curve instead of ten. I find that security hardens as well. Every additional vendor widens the blast radius for a breach; consolidating onto one stack limits credential exposure and sharpens monitoring. Attack-surface math is unforgiving: ten vendors with a 0.5% annual breach probability yield an aggregate risk of around 5%; one vendor cuts that to 0.5%. When client NDAs carry eight‑figure penalties, that delta is meaningful. For risk officers, a single‑tool architecture can not just be convenient; it can be an insurance policy. Embracing The New Competitive Landscape Taken together, these dynamics point to a consulting market that scales non-linearly with firms' AI sophistication. Expect the capability curve to flatten as premium insight becomes accessible to mid-market clients who once defaulted to DIY analysis or freelancers. Meanwhile, enterprise buyers will scrutinize vendors on governance maturity as closely as on sector credentials. For firms that collaborate closely with AI providers and integrate domain-specific tools, the next five years represent a once-in-a-generation land-grab opportunity. For clients, the upside is clear: faster delivery, deeper specialization and pricing models aligned to value. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
18-06-2025
- Business
- Forbes
A Journey Of Trust: How Businesses Can Leverage Task-Driven Agentic AI
Sal Visca, Chief Technology Officer, Vertex. While GenAI tools continue to be broadly leveraged across all industries, we've seen a change lately not only in the tools being used but also in how the workflows and processes are being re-imagined. Some experts describe a broad-based conversion from GenAI to Agentic AI; however, both continue to establish their ongoing presence and increasing importance in the technology landscape. The paradigm shift we've noted is the development and deployment of targeted, task-oriented, autonomous AI agents within enterprise workflows and processes. What does this mean? We often talk about the concept of 'Jobs To Be Done' (JTBD). In any given workflow, you can break down the tasks that need to happen for the job to be completed. Similar to how one can delegate work to a subordinate employee, some tasks can now be delegated to an agent. Agentic AI fits naturally into that framework: specific, purpose-built tasks that can be completed by AI models operating within a defined domain. The next layer is orchestration: Imagine you're managing a team and assigning tasks—'You handle this; you handle that.' Now, imagine some of those teammates are AI agents. You have to think about how to orchestrate work between people and agents, as well as between agents themselves. It gets kind of surreal and futuristic to imagine a hybrid team made up of humans and AI agents—but for better or worse, that's the playing field that is quickly developing around us. As is often said in different ways, our current generation of leaders may be the last to manage people-only teams. AI agents are now our new teammates. If you're a business leader thinking about agentic AI, it's best to start by understanding what your specific tasks and workflows are, and then thinking about how to apply AI agents to support them. In our world at Vertex, we're focused on indirect tax lifecycle management and workflows. If you're in a tax department, you need to make sure your ERP and e-commerce systems are calculating the correct amount of tax based on where an item is bought, how it's bundled, the jurisdictional rules and so on. For example, if I buy an energy drink in Miami, the tax I pay may vary depending on whether I buy it at an upscale supermarket or a convenience store. The tax department needs to ensure that these taxability rules are applied correctly, that transaction data is collected and reported and that payments are submitted to the appropriate government tax authorities. When we map the end-to-end workflow, we look at all the JTBD—everything from data intake to compliance reporting. Then we ask, 'Where can an AI agent step in and take on some part of the job?' We've found that agents are particularly effective for tasks that are tedious, error-prone or involve sorting through large datasets. For example, discrepancy management—looking through data to find mismatches—is as close to a perfect use case as you can get. Agents can reconcile transactions by comparing inputs, outputs and transformations, something that traditionally involved a human manually sifting through spreadsheets, often to a point of becoming blurry-eyed and making mistakes or overlooking important details Another great use case: smart categorization. Say you're a retailer with a massive product catalog. Rather than regularly going through your ever-changing inventory to determine how each item needs to be taxed, you can have a smart agent continually analyze and read all the metadata about those products and use generative AI to fill in gaps and map each product to the right taxability category. It not only categorizes and enriches the data, but also learns and improves over time. The optimal use cases are jobs that must be done but that people dislike doing; these are exactly the kind of repetitive, detail-heavy tasks AI agents can do well, and without complaints. We're also working on supporting an ecosystem of AI agents. We are already seeing environments where AI agents can connect and communicate cross-functionally or across organizations to complete tasks. This approach will have similarities to orchestrating Application Programming Interfaces (APIs) and microservices, but now will include built-in intelligence for dynamic decisioning based on the context and data. We'll see these agents coordinating within and across companies in a very purposeful, controlled way. This is the next frontier. Of course, people have varying degrees of comfort with this notion of hybrid Human and AI teams. On one hand, there is general excitement about what the technology can do—how it can reduce workloads and improve efficiency and productivity. On the other hand, there are human and societal concerns about what it means to work alongside AI agents. Ethical considerations abound. One of the big risks is the potential lack of control and even lack of visibility of what the AI agents are doing and how they accomplish the tasks. We are now embedding intelligence into systems, but users may not fully understand how it's all working. Anthropic, one of the leading AI companies, recently published several articles and papers exploring how their large language models arrive at answers—and the concept of 'chain of thought.' It's about tracing the logic path, which ties into the concept of Explainable AI, or XAI. We care deeply about the idea of explainability. Right now, AI is often a black box. These models are ingesting the entirety of human knowledge—devouring massive amounts of content throughout the expansive internet, online books, databases and knowledge sources, all the while continuously learning from the trillions and trillions of daily interactions with humans. It's not always apparent how they generate a certain answer. With XAI, users can at least get a high-level breakdown—'Here are the five steps the model took to deconstruct and understand your question and arrive at an answer. Here's the citation from the Florida tax code that supports this decision.' It's not just about transparency; it's about the logic steps, data sources and decision lineage. You need to know what happened and be able to reverse it, if needed. That becomes even more critical with agentic AI. These tools aren't just generating answers—they're taking actions. So we must implement strict governance: clear boundaries, strong audit trails, stage gates and checkpoints where users can approve actions before they're executed. We also talk a lot about the 'trust journey.' We want to help our customers build trust in AI by starting small, 'dialing up' their use of AI-driven tools slowly. As customers see value and gain confidence that the AI is helping, and also understand the steps taken to solve problems, they'll become more willing to involve these tools further in their processes and workflows. I like to describe this progression as going from co-pilot to autopilot. Eventually, you get to a point where you're comfortable 'authorizing' the AI agent: 'Go ahead and take care of it—just let me know when it's done and show all the steps taken in the process.' Today, many companies are experiencing tension: They may be hesitant to get fully onboard with AI agents, but they also feel immense pressure not to be left behind. The reality is that this new technology is massively disruptive. It's not just a business shift; it's a societal one. There are real ethical and social implications we still need to navigate. One thing we emphasize is 'human in the loop.' Even with an autonomous vehicle, you should still always be able to grab the steering wheel. You should know it's signaling right before it turns. But if you imagine where technology is going, it's not hard to see a scary scenario: an autonomous system without a steering wheel, one where you can't intervene or override. Once the human is completely out of the loop, that's where it could become dangerous without the right guardrails. We have to stay on that continuum—constantly balancing value versus risk. For us, visibility and control are non-negotiable. That means we need dashboards with built-in system observability—the ability to see what's going on inside these intelligent systems. It's about turning the black box into something more transparent. I often joke that it's like elementary school math classes, where simply providing the right answer isn't enough. Teachers remind us to 'show our work' to get full marks. And that's our goal with agentic AI: Show your work! Show the steps that lead us to this answer. This type of transparency and explainability is necessary to build the trust and confidence for Agentic AI to be a foundational technology in all the software solutions that are inextricably embedded into our lives and the world around us. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Tahawul Tech
13-06-2025
- Business
- Tahawul Tech
Opinion: TeKnowledge CTO on the Enterprise AI Execution Gap
Mahmood Lockhat, Chief Technology Officer at TeKnowledge, discusses the execution gap faced by many organisations when it comes to AI and how their new partnership with aims to address this in this exclusive op-ed. Introduction: The AI Execution Challenge There's no question about it, Artificial Intelligence (AI) is transforming how businesses operate, improve customer interactions, and foster innovation. Yet, despite recognizing AI's vast potential, many organisations struggle to fully integrate and scale it within their daily operations. This 'execution gap' is exactly what the partnership between TeKnowledge and aims to bridge. In today's rapidly evolving digital landscape, enterprises face a fundamental conundrum: while AI technologies have reached unprecedented sophistication, the gap between AI ambition and real- world execution is still a reality that many enterprises are faced with. While we're surrounded by headlines about AI breakthroughs, many of us in enterprise technology are facing a frustrating reality in what I call 'The Pilot Paradox' running impressive demos that never translate into enterprise-wide transformation. We have brilliant AI pilots running in isolation, executive teams asking when we'll see real business impact, which needs to be delivered at scale. The partnership between and TeKnowledge isn't just another vendor, partner announcement it's a recognition that successful AI transformation requires more than just great technology. It needs a fundamentally different approach. Why Most AI Transformations Fail: The Missing Dimensions After years of watching AI implementations succeed and fail, I've learned that technology is just one piece of the puzzle. The organisations that truly succeed with AI get five critical dimensions right: Technology is the foundation, but it's not enough on its own. You need a platform that can evolve from simple chatbots to sophisticated multi-agent orchestrations. has cracked this code with their AI Agents Operating System, moving enterprises from guided intelligence through to full autonomy while maintaining human oversight. Data Readiness is where most projects stumble. Your AI is only as good as your data, and enterprise data is messy scattered across systems, trapped in silos, mixing structured databases with unstructured documents. TeKnowledge's expertise in data strategy and integration becomes crucial here, ensuring your AI agents have access to clean, relevant, and contextual information. Cybersecurity can't be an afterthought anymore. We're not just protecting data; we're securing AI models against adversarial attacks, preventing bias, and ensuring our AI agents don't become security vulnerabilities. TeKnowledge's cybersecurity practice understands these unique challenges and builds protection into every layer of the AI stack. Governance separates successful AI implementations from chaos. As we move toward super-agents managing multiple AI systems, we need frameworks that ensure accountability, transparency, and regulatory compliance. This isn't just about policies—it's about embedding governance into the platform architecture itself. Digital Skills might be the biggest gap of all. Your people need to understand how to work alongside AI agents, how to prompt them effectively, and how to maintain and evolve these systems over time. TeKnowledge's comprehensive skilling programs ensure your teams can grow with your AI capabilities. A Strategic Convergence: Platform Intelligence Meets Expert Services The partnership between a global market leader, expert in conversational, enterprise AI, and TeKnowledge, a leader in technology services including AI, customer experience, and cybersecurity, combines our strengths to help businesses scale AI smoothly and effectively. brings to this partnership a mature, agent-first platform that has evolved beyond the traditional chatbot paradigm. has pioneered the transition from guided intelligence to full autonomy through their AI Agents Operating System, spanning from reliable conversational bots to sophisticated autonomous agents capable of human-like interaction. offers a user-friendly yet powerful AI orchestration platform designed to boost productivity, efficiency, and customer service. Their technology easily understands what users want, communicates clearly with different systems, and performs tasks independently. This flexibility helps businesses integrate solutions into their existing systems, adapting quickly to changing technology. provides a comprehensive solution stack which is built around 3 major pillars: AI for Service – Enhances customer experience through intelligent virtual assistants, agent support, and contact centre optimisation. AI for Work – Increases employee productivity via workplace assistants (HR, IT, internal ops) and enterprise workflow automation. AI for Process – Automates end-to-end business processes with advanced AI-driven orchestration. Their platform addresses the three areas where businesses need AI most: reimagining work (helping employees be more productive), reimagining service (creating better customer experiences), and reimagining process (automating complex workflows). But here's what matters, they've built this with enterprise realities in mind. TeKnowledge complements by providing strategic guidance, advisory and precise execution to ensure AI projects deliver real business value. With over 6,000 experts across 19 global hubs, TeKnowledge makes sure AI implementations are strategic, measurable, and adaptable. We also stress and recognize the importance of being prepared with quality data, strong cybersecurity, effective governance, and digital skills, viewing these as essential for successful AI transformations beyond just technology. Creating Real, Lasting Impact What makes this partnership different is our shared focus on human-centric AI. Both TeKnowledge and believe AI should simplify interactions, improve business operations, and deliver clear, measurable benefits, such as improved efficiency, exceptional customer experiences, and innovative solutions. Gartner predicts that by 2028, 95% of businesses will integrate generative AI into their daily operations, a significant increase from just 15% in 2025. This rapid growth emphasises the need for businesses to strategically adopt AI. Gartner also notes the rapid advances in multimodal and agentic AI, promising to transform automation and improve user interactions significantly. The partnership helps organisations stay ahead of these trends. Similarly, Boston Consulting Group (BCG) emphasises the crucial role of CEOs and leadership in successfully implementing AI, warning that disconnected AI solutions—or 'AI islands'—can limit potential benefits. This partnership addresses these issues directly by providing an integrated, scalable, and well-governed AI ecosystem, enabling businesses to fully realise their AI investments. The Partnership Synergy: Providing End-to-End Solutions Here's why this partnership excites me as a CTO it addresses the complete AI transformation challenge, not just the technology piece. From Fragmentation to Integration: The partnership addresses the AI Chaos which many enterprises are facing today, the overwhelming array of hyperscalers, open source vendors, Agent AI, Intelligent Virtual Assistant solutions, CCaaS and enterprise ecosystems that create decision paralysis. By combining unified platform with TeKnowledge's structured implementation methodology, enterprises gain a clear path from concept to capability to business value Skills That Scale: TeKnowledge's skilling programs mean your people can actually use and evolve the AI capabilities. This isn't just about using AI, it's about building internal capabilities to innovate with AI continuously. Security by Design: With AI models becoming potential attack vectors, TeKnowledge's cybersecurity expertise ensures protection is built in from the start. We understand AI-specific threats like prompt injection, data breach and leakage, model poisoning, adversarial attacks, and bias exploitation. Data That Works: TeKnowledge helps organisations get their data ready for AI, not just accessible, but clean, contextual, and structured for use effectively within enterprise AI orchestration. Governance That Scales: As you move from single-purpose chatbots to orchestrated super-agents, governance becomes critical. The partnership provides frameworks that ensure accountability while enabling innovation. The Super-Agent Future: What's Coming Next In the coming year, we will witness the rise of what I call 'Super-agents' Powerful AI operating systems that coordinate multiple AI agents simultaneously. These advanced systems will significantly enhance organisational decision-making and problem-solving capabilities, guiding enterprises smoothly from basic intelligence to partial or full autonomy, always within human oversight. Imagine having a team of highly skilled professionals tirelessly working round the clock, resolving issues, streamlining processes, and executing transactions seamlessly, without downtime or interruptions. The growth in AI agent autonomy will revolutionise industries by optimising networks, enhancing fraud detection, streamlining patient care, personalising customer experiences, boosting employee productivity, and automating routine tasks. This shift allows employees to focus on more strategic, higher-value tasks, leading to faster decision-making, better performance, and accelerated business operations across all sectors, from product development, marketing to sales and customer support. We're also heading towards a future defined by AI-first customer experiences. AI will significantly improve customer interactions through complete, end-to-end orchestration, providing a personal assistant experience across sectors like banking, travel, healthcare, and retail, offering assistance, solving problems, and enabling seamless transactions around the clock, without us even noticing we're interacting with AI. offers the platform for this AI operating system future, enabling organisations to build, maintain, measure, and manage AI agents, systems, and orchestration. While provides the orchestration platform for these super-agents, TeKnowledge ensures organisations have the expertise and support to implement, govern, and evolve these sophisticated AI ecosystems responsibly with real business value. Why This Partnership Matters Now As we navigate the AI revolution, the question isn't whether to adopt AI, it's how to do it effectively at scale. The and TeKnowledge partnership provides a proven path that addresses the complete transformation challenge. For technology leaders, this partnership offers what we've all been looking for: a comprehensive approach that combines mature platform capabilities with the strategic services, security expertise, data readiness, governance frameworks, and skills development necessary for sustainable AI transformation. The reality is that successful AI transformation requires both technological sophistication and human expertise. It needs platform intelligence and strategic services. It needs innovation and governance. It needs automation and human insight. This partnership delivers all of that; not as separate services you have to coordinate, but as an integrated approach designed to help enterprises move from AI ambition to AI impact. And in a world where the pace of AI innovation continues to accelerate, having partners who can help you navigate both the technical and human dimensions of transformation isn't just valuable, it's essential. The future belongs to organisations that can harness AI's transformative potential while maintaining the human-centred approach that drives real business value. The and TeKnowledge partnership provides exactly that foundation for success. Image Credit: TeKnowledge
