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Choosing the Right AI Orchestration Provider: A Simple Reality Check

Choosing the Right AI Orchestration Provider: A Simple Reality Check

By Chris Newell
Founder & President

Most organizations didn’t design a complex AI strategy.  It just… happened.

One team uses ChatGPT. Another deploys AI in the contact center. Sales adopts copilots. Finance follows. IT tries to keep up.

Fast forward 12 months and the result look familiar to many CIO’s:
• Multiple AI models across the business
• Overlapping tools and vendors
• Rising and unpredictable costs
• Limited governance and security controls
• Unclear ROI and business outcomes

This is exactly  the problem AI orchestration is meant to solve.  As the market matures, many companies seem to be focusing on which AI model they’re using instead of who controls how AI works. 

Why AI Orchestration Matters Now

AI orchestration is not about picking the “best” model. It’s about creating a control layer that ensures AI is used consistently, securely, and cost-effectively across workflows, teams, and vendors.

Choosing the right AI orchestration provider has become a foundational decision for organizations looking to scale AI responsibly.

Here are the baseline criteria IT leaders should evaluate:

1.Vendor & model neutrality
If your orchestration platform locks you into a single ecosystem, that’s not orchestration, that’s dependency. True AI orchestration supports multiple models and providers, allowing your organization to:

      • Avoid vendor lock-in
      • Swap models as performance, pricing, or compliance needs change
      • Future-proof your AI strategy

      Flexibility at the orchestration layer is what enables long-term leverage.

      2. True workflow control (not just prompts)

        Prompt management alone is not orchestration. Enterprise-grade orchestration platforms support:

        • Multi-step AI decision flows
        • Confidence thresholds and validation rules
        • Human-in-the-loop approvals
        • Conditional routing based on business logic

        This is how AI moves from experimentation to production-ready workflows.

        3. Governance by design
        Security, auditability, and data controls can’t be afterthoughts. Effective AI orchestration embeds governance directly into workflows, including:

        • Data access controls
        • Usage policies by role or function
        • Audit trails for AI decisions
        • Alignment with internal and regulatory requirements

        For senior IT leaders, governance-by-design is what enables scale without increasing risk.

        4. Cost & outcome visibility
        If you can’t see which AI workflows create value (or driving unnecessary spend), you can’t scale responsibly.  The right orchestration platform provides:

        • Visibility into AI usage and costs by workflow
        • Insight into business outcomes and ROI
        • The ability to optimize or retire low-value use cases

        This transparency is essential for both IT leadership and executive stakeholders.

        The Role of MCP Servers in AI Orchestration

        One concept that’s becoming increasingly important is MCP servers. 

        In simple terms, an MCP  server defines the rules for AI:

        • What data AI can access
        • Where the data comes
        • How it’s used across different AI tools and workflows. 

        MCP servers help keep business logic consistent even as AI tools and models evolve..

        Do You Need to Run MCP Servers Internally?

        Not necessarily.

        Many  organizations(specially in early stages) choose to outsource the MCP layer to their AI orchestration provider.

        That approach can make sense when:

        • Speed, human capital, management and cost matters more than ownership
        • Internal AI governance is still forming
        • You’re validating ROI before scaling
        • Talent and operational overhead are constrained

        The key takeaway: ownership is optional; control is not.

        Control is the strategy.

        The companies that succeed with AI won’t be the ones chasing the newest model, they’ll be the ones that decide how AI decisions are made, governed, and measured across the business.

        How Bluewave Helps

        At Bluewave, we help IT leaders cut through the AI noise.

        We work with organizations to:

        • Evaluate and select AI orchestration platforms
        • Design governance-first AI architectures
        • Balance speed, flexibility, and long-term control
        • Align AI investments with measurable business outcomes

        Whether you’re experimenting with AI or preparing to scale enterprise-wide, having the right orchestration strategy makes all the difference.

        Ready to bring clarity and control to your AI ecosystem?

        Contact Bluewave to get expert guidance on AI orchestration, governance, and cost optimization and make sure your AI strategy is built to scale, not sprawl.

        Overwhelmed by Threats? Here’s Why AI Needs to Be in Your Cybersecurity Stack

        Overwhelmed by Threats? Here’s Why AI Needs to Be in Your Cybersecurity Stack

        By Kirsty-Katie Welch
        Client Advisor

        Today’s cybersecurity teams are drowning.

        Ransomware attacks are more frequent and destructive. Phishing schemes have grown smarter, blending into inboxes with chilling accuracy. Zero-day exploits hit faster than teams can patch. And rule-based security tools (once the go-to defense) are now flooding SOCs with false positives and missing stealthier attacks entirely.

        In this landscape, human teams alone can’t keep up. And that’s the problem.

        The Case for AI in cybersecurity

        Artificial Intelligence (AI) is no longer a nice-to-have for security teams, it’s a lifeline.

        By integrating AI and machine learning into your cybersecurity framework, you can shift from being overwhelmed by alerts to getting ahead of threats. AI doesn’t just make cybersecurity better; it makes it faster, more scalable, and more accurate than any human-only approach can offer.

        Let’s explore the specific problems plaguing modern cybersecurity and how AI solves them.

        Problem #1: you can’t detect what you can’t see

        Solution: AI-Powered anomaly detection

        Traditional tools rely on known threat signatures. But attackers are constantly innovating, using tactics that evade signature-based detection.

        AI changes the game by learning what “normal” looks like across your systems (user behavior, network activity, device interactions) and flags anything unusual in real-time. That means you’re alerted to strange logins or data exfiltration attempts before they turn into breaches.

        Problem #2: Too much noise, not enough signal

        Solution: Intelligent Threat Correlation

        Most SOCs are flooded with alerts that never turn into incidents, burning out analysts and delaying real threats from being addressed.

        AI correlates data across logs, endpoints, cloud environments, and third-party threat feeds to connect the dots and prioritize real risks. The result? Fewer false positives and a faster path to meaningful response.

        Problem #3: Response time is too slow

        Solution: Automated incident response

        Every second counts during a cyberattack. But when humans have to manually investigate and respond, containment takes too long.

        AI-driven tools like SOAR (Security Orchestration, Automation, and Response) and XDR (Extended Detection and Response) can trigger automated actions the moment an incident is detected, isolating endpoints, revoking credentials, or deploying patches without waiting for human intervention.

        Problem #4: Phishing emails keep slipping through

        Solution: AI email & phishing protection

        Phishing tactics have evolved. Attackers now use AI themselves to craft more believable, targeted messages.

        AI-enhanced email security doesn’t just scan for bad links, it understands intent. By analyzing the content, context, and recipient behavior, AI can flag and block phishing attempts before users even see them.

        Problem #5: Malware is evolving faster than signatures

        Solution: Predictive Endpoint Protection

        Today’s malware often bypasses antivirus tools using obfuscation, lateral movement, or fileless techniques.

        AI-powered EDR (Endpoint Detection and Response) platforms can detect these behaviors, even if the malware has never been seen before. Instead of waiting for threat signatures, AI looks for suspicious activity patterns across devices.

        Real-World Payoffs

        Organizations that adopt AI-enhanced cybersecurity see benefits almost immediately:

        • Threats detected in milliseconds, not hours or days
        • Reduced alert fatigue and false positives
        • Proactive threat hunting, guided by machine learning insights
        • Predictive defense, preventing exploits before they happen

        Tools That Lead the Way

        Some top AI-enhanced platforms making waves in the industry:

        • CrowdStrike Falcon
        • Palo Alto Networks Cortex XDR
        • Microsoft Defender for Endpoint
        • SentinelOne
        • Darktrace
        • Arctic Wolf (MDR)

        Each offers different strengths, but all leverage AI to deliver smarter detection, automation, and faster response.

        Final Word: The time to adopt AI is now

        Cybersecurity threats aren’t slowing down, and your response can’t either. If your team is stretched thin, constantly reacting instead of predicting, or missing threats entirely, it’s time to integrate AI.

        Because in today’s cyber landscape, staying ahead means thinking faster than the attacker and that’s exactly what AI was built for.

        Need help figuring out where to start?
        At Technology Navigation, we help organizations choose and implement the right AI-powered cybersecurity tools for their needs. Reach out to our team for expert guidance and future-proof your security strategy.

        SASE Meets ZTNA 2.0 and AI – The Next Evolution

        SASE Meets ZTNA 2.0 and AI – The Next Evolution

        By John Wircher
        Director of Client Engagement

        Organizations are racing to modernize and secure their networks and workforces.  Secure Access Service Edge (SASE) is an attractive framework for converging networking and security into a single solution.  But as threats evolve,,,,, so does SASE and it is not surprising that we’re now entering AI driven, identity centric SASE.  This is being referred to as “SASE 2.0.”

        Past and even some present SASE deployments often meant blending SD-WAN and cloud security from multiple vendors. What we are seeing with suppliers is the next phase is all about AI and proactive continuous monitoring and response. AI and ML are becoming native to modern SASE platforms. This isn’t just about threat detection. We’re seeing SASE platforms that use AI to:

        • Predict and prevent breaches
        • Automate incident response 
        • Prioritize critical business traffic
        • Self-heal network performance issues

        This is creating a shift from reactive management to proactive infrastructure.

        Under the SASE 2.0 framework, legacy and static firewalls are no longer enough. SASE 2.0 uses real time identity, device posture, user behavior, and location data to make access decisions. This new thought process of context aware, Zero Trust approach ensures that access is dynamic and not a “one size fits all”.  

        So, what’s the difference between ZTNA and ZTNA 2.0:

        FeatureZTNA ZTNA 2.0
        Access granularityPer applicationPer application + activity within apps
        Trust evaluationAt loginContinuous and context-aware
        Threat preventionMinimalBuilt-in and ongoing
        Device context awarenessLimitedRequired and dynamic

        The next evolution of SASE isn’t just about merging security and networking, it’s about making them intelligent, proactive and adaptive. Understanding where SASE is headed can help future proof your architecture. 

        If you’re exploring how to adapt your architecture to meet the demands of SASE 2.0 and beyond, our team at Technology Navigation is here to help. We work closely with organizations to cut through the noise, evaluate the right-fit solutions, and build secure, future-ready infrastructures. Let’s talk about how you can take a smarter, more strategic approach to networking and security. Reach out to start the conversation.

        Taming AI Chaos: The Power of Orchestration in a Siloed World

        Taming AI Chaos: The Power of Orchestration in a Siloed World

        By Chris Newell
        Founder & President

        AI adoption is no longer just a talking point, it’s a necessity. But as organizations deploy AI tools across disparate departments and systems, a new layer of complexity has emerged… disconnected systems, siloed data, and overlapping workflows.  This is where AI Orchestration is becoming essential, bringing structure, coordination, and efficiency to increasingly fragmented environments.  

        But equally important as AI Orchestration, is designing the orchestration with offramps for human intervention.  Not every process can… or quite frankly should be fully automated.  When the confidence level of an AI Orchestration driven action falls below a dictated threshold, systems must be able to pause and elevate the decision to a human and not hallucinate.  Organizations can either make this a steadfast rule of human intervention for that particular workflow or use this opportunity to “teach” the AI Orchestration solution to follow the elevated human decision going forward.  This ensures oversight, maintains accuracy, and preserves trust across critical workflows.

        AI Orchestration is the coordination of multiple AI technologies, workflows, data sources, and applications to function across an organization with business outcomes in mind.  This orchestration helps each AI tool and process to work together, adapt to workflows, and drive ROI.  

        Where traditional AI automation stops at the task level, AI Orchestration enables end-to-end process workflows and centers around business outcomes, unifying technologies like:

        • LLMs 
        • Email workflows and associated integrations
        • ML applications 
        • CRM applications (e.g., Salesforce and HubSpot)
        • UCaaS meeting transcriptions & voice recordings 
        • Data analytics (e.g., Power BI, Tableau, Snowflake)
        • Marketing analytics (e.g., Salesforce Marketing Cloud, Oracle Marketing, Heap) 
        • Contact Center (CCaaS) analytics 
        • Accounting and finance applications (e.g., ADP, NetSuite)

        Organizations often deploy AI tools in silos marketing uses one platform, finance another, and operations yet another. AI orchestration bridges these divides, creating workflows that span departments and their associated tools, reducing friction and increasing faster and more predictable outcomes.

        While AI orchestration can automate and optimize complex processes, human judgment remains essential. 

        AI doesn’t ALWAYS replace people, it enhances their ability to make faster, more informed decisions. It’s important to begin with a clearly defined baseline process, understanding that not all workflows will reach full automation. In fact, many may plateau at 30% automation, requiring human intervention for exceptions, strategy, or ethical oversight. True orchestration is about balancing intelligent automation with human insight.

        Final Thoughts

        AI Orchestration is more than a buzzword, it’s becoming the backbone of operational agility in modern enterprises. As organizations evolve, those that invest in orchestration will be best positioned to unlock the full potential of AI, reduce complexity, and deliver outcomes.

        Ready to harness the full power of AI? Contact Technology Navigation today to explore how our experts can help you simplify, scale, and succeed with AI orchestration.

        IT has enough on its plate. How does MetTel take the hassle out of MDaaS

        Mobility services
        Mobility services

        By Chris Newell
        Founder & President

        Let’s be honest: no IT professional dreams of managing wireless and mobility solutions. No one wakes up thinking, “You know what I’d love to do today? Negotiate data plans, track down lost phones, and spend hours on hold with carrier support.”

        Yet, IT teams everywhere are expected to handle these headaches while also cutting costs, securing devices, and somehow keeping up with Johnny from Sales, who just dropped his phone in an airport toilet… again.

        At Technology Navigation, we constantly evaluate solutions that simplify IT operations and drive cost savings. That’s why we took a closer look at MetTel’s Mobile Device as a Service (MDaaS), and it’s a solid solution for businesses looking to take mobility management off their plate.

        MDaaS is more than just pooling devices and data plans; it’s a fully managed, end-to-end solution that eliminates the complexity of dealing with multiple carriers and vendors while reducing costs. Here’s what stood out to us:

        • Cost optimization: Cross-carrier access and pooling ensure businesses only pay for what they need.
        • Device lifecycle management: From procurement to secure disposal, MetTel handles every stage.
        • 24/7/365 support: reliable customer service that keeps businesses connected.
        • AI-powered automation: 98% of orders are automated for faster, more efficient deployments.
        • Security & compliance: Mobile Device Management (MDM) and secure recycling protect enterprise data.

        As a leading Mobile Virtual Network Operator (MVNO) in the U.S., MetTel leverages partnerships with the nation’s largest carriers to provide carrier flexibility. Their recognition in the Gartner Magic Quadrant for Managed Network Services, for the fifth year in a row,  further reinforces their leadership in the space.

        And for those who like to sleep soundly knowing their old devices won’t become a security risk (or end up in a landfill), MetTel’s eco-friendly device recycling is a major plus.

        If mobility management has become an unwanted side quest for your IT team, we’re here to help you explore whether MDaaS is the right fit. Contact us to learn more.