Five Things Your Consulting Partners Might Not Be Telling You About Your Surging AI Demand

By James Briggs, CEO & Founder, AI Collaborator, Inc.

February 26, 2025

Since attending CES last month, our team has engaged with AI and IT leaders from more than 50 enterprise firms to discuss AI’s impact on their organizations. We have discovered that a profound change is underway as AI demand explodes, leaving many scrambling to implement and scale AI solutions effectively.

Many organizations initially turn to the same big consulting firms they’ve relied on during previous tech disruptions to navigate this shift. While these firms provide valuable insights, they often leave critical aspects of AI demand management unaddressed.

Here’s what they’re not telling you:

1. Your AI Demand Is Outpacing Their Playbook

Big consulting firms rely on established frameworks built for traditional IT and digital transformations. The problem? AI is evolving at an unprecedented pace, and their methodologies aren’t keeping up. While they may offer best practices, they often lack the agility to provide cutting-edge AI strategies that align with real-time AI models and capabilities advancements.

2. They Overlook Grassroots AI Demand in Your Organization

Big consultancies and significant tech giants focus on securing large contract engagements for “strategic initiatives.” However, much of today’s AI demand originates from grassroots initiatives—often led by middle managers—that don’t meet the threshold for C-suite or IT executives to justify consulting engagements.

Yet, these grassroots initiatives present significant opportunities to drive ROI by improving workflows, reducing inefficiencies, and automating repetitive tasks. The managers championing these initiatives are closest to business operations and seek AI to enhance employee satisfaction and boost productivity.

Big consulting firms frequently overlook this bottom-up AI adoption, which has the potential to transform how enterprises deploy AI at scale.

3. They Push Preferred Vendors—Not the Best Fit for You

Many large consultancies have long-standing technology and AI vendor partnerships that shape their recommendations. As a result, their “independent” advice is often biased toward solutions aligned with their business interests rather than your specific needs. You may be missing out on more innovative or cost-effective AI solutions simply because they fall outside of their pre-approved vendor ecosystem.

4. Your Internal Teams Could Move Faster—If Properly Enabled

Many enterprises assume AI adoption requires large-scale consulting support. However, your internal teams could execute AI initiatives faster and at a lower cost with the right enablement strategy.

By investing in AI democratization strategies that expand AI access beyond strategic projects and priority initiatives—reaching underutilized areas of your organization through innovative providers offering access to a global AI marketplace—you can build a more sustainable and scalable AI strategy without excessive reliance on external consultants.

5. Agentic AI Will Challenge the Need for Large Consulting Engagements

One of the most significant disruptions consulting firms aren’t discussing enough is the rise of AI agents (Agentic AI) and the potential impact on their business. These autonomous AI systems can automate and optimize many strategic advisory functions currently handled by consulting firms.

As AI agents advance, enterprises can leverage them for AI demand management, solution evaluation, and even decision-making—reducing the need for prolonged and expensive consulting engagements.

The Bottom Line

AI demand is surging, but relying solely on traditional consulting approaches may slow progress and limit competitive advantage. To stay ahead, enterprises must challenge conventional wisdom, seek diverse AI solution providers, and empower internal teams to drive AI transformation. AI is rewriting the rules of enterprise technology—make sure you’re playing by the new ones, not the old ones.

AI Collaborator, Inc., is your partner in AI Innovation. Committed to helping enterprises build AI-Agility™, our marketplace approach provides on-demand access to AI solutions and services.

AI-Agility™ enables organizations to augment internal capabilities and responsibly scale AI innovation to meet evolving business needs.

As AI steps in to contribute as the main driving force for innovation in the years to come, the emergence of Large Language Models in the last five years presents a crucial opportunity to enhance and streamline processes in organizations, both internally and externally, as communication remains the centerpiece for successful outcomes in these two sides of enterprise operations.

Language is the fundamental medium through which humans are able to share information, align on objectives, and collaborate effectively. Since companies are nothing more than people working together towards a shared goal, language is the raw material that makes up the connection on top of which this goal can be aligned, shared and pursued. 

Internal 

Shared understanding, collective sensemaking, motivation, negotiation, and conflict resolution; those are all building blocks upon which one drives operations in an enterprise environment. There are quite a few LLM use cases for enhancing those building blocks of operational work, some are:

Information dissemination:

• Generate personalized and targeted communications, such as company updates, policy changes, or announcements, and distribute them to relevant employees.

Knowledge management:

• Integrate into knowledge management systems to assist with information retrieval, document summarization, and question-answering.
• Help employees quickly find relevant information from existing documents, policies, or best practices, improving overall organizational knowledge sharing.

Collaboration and coordination:

• Facilitate virtual meetings, transcribe discussions, and generate meeting minutes or action items, improving collaboration and coordination among teams.
• Assist in task planning, project management, and team coordination by generating reminders, schedules, and progress updates.

External

The importance of language and communication extends beyond just the internal workings of an organization – it is equally critical for the external interfaces and interactions a company has with its clients and other stakeholders. How a company communicates with its customers can make or break those vital relationships.

Leveraging the power of Large Language Models, organizations can now automate and scale up their communication capabilities in ways that were previously unimaginable, such as:

Personalized marketing content:

• Generate personalized and engaging marketing content, such as social media posts, email campaigns, and website copy, tailored to the preferences and interests of individual customers or target segments.

Client-facing reports:

• Assist in the creation of high-quality, professional-looking client-facing reports by automating the drafting, formatting, and polishing of these documents.
• Synthesize complex data and information into clear, concise, and compelling narratives that effectively communicate key insights and recommendations to clients.
• Generate executive summaries, visualizations, and other supporting materials to enhance the overall quality and impact of client reports.

Engaging in real-time customer service interactions:

• Chatbots or virtual assistants provide real-time customer service, answering common questions, addressing concerns, and guiding customers through various processes.
• Analyze the context and intent of customer inquiries and provide personalized and empathetic responses, improving the overall customer experience.
• Route complex or escalated customer issues to the appropriate human representatives, ensuring efficient and effective resolution.

By freeing up human resources from repetitive communication tasks, Large Language Models enable employees to focus on higher-value, strategic work that requires uniquely human skills.

Conclusion

As AI continues to drive innovation in the years ahead, the emergence of Large Language Models presents a crucial opportunity for enterprises to enhance and streamline their operations, both internally and externally. Language is the fundamental medium through which organizations align on objectives, collaborate effectively, and engage with customers. By leveraging the power of LLMs, companies can automate and scale up a wide range of communication-centric processes – from personalized employee communications and knowledge management to generating client-facing reports and powering real-time customer service. This frees up human resources to focus on higher-value, strategic work that requires uniquely human skills. Ultimately, the integration of Large Language Models into enterprise operations can drive significant gains in efficiency, productivity, and the overall quality of communication – positioning organizations for greater success in an increasingly AI-powered future.

Introduction

The European Union’s recent legislative milestone, the AI Act, stands as a seminal piece of regulation in the rapidly evolving landscape of artificial intelligence (AI).

Amidst growing concerns over AI’s societal, ethical, and privacy implications, the Act emerges as a pioneering attempt to harmonize the development, deployment, and use of AI systems across member states, setting a global precedent.

The Importance of the EU AI Act

For developers and businesses, the Act provides a regulatory framework, reducing uncertainty and fostering an environment conducive to innovation and growth. By categorizing AI applications into different risk levels, it allows for a nuanced approach that supports technological advancement while ensuring ethical and safe practices.

The Act aims to protect fundamental rights and personal liberties, ensuring that AI systems are used in a manner that respects privacy, non-discrimination, and consumer rights. This boosts consumer confidence in AI technologies, fostering a more trusting relationship between users and technology providers.

As the first of its kind, the Act is poised to set a global benchmark for responsible, ethical AI use, influencing not only European policy but also encouraging similar legislative initiatives worldwide.

Areas of Impact and Objectives

The Act targets areas where AI’s consequences are most profound, including privacy, safety, and fundamental human rights. High-risk AI systems must now meet requirements regarding transparency, accuracy, and security, ensuring they operate as intended and respect individuals’ rights. The Act also sets out to prohibit specific uses of AI that are deemed unacceptable, such as manipulative subliminal techniques, social scoring, and indiscriminate surveillance.

Conclusion

The EU AI Act represents a critical step forward in the global discourse on AI and its societal impacts. By prioritizing safety, ethics, and fundamental rights, the EU sets a precedent that other regions may follow, potentially leading to a global framework for AI governance.

Regulatory effort around emerging technologies such as AI can be challenging given the constant evolving applications and use cases. It is crucial that regulations are able to accurately assess risks without stifling innovation. As the Act moves towards full implementation, its real-world effects on innovation, privacy, and ethical standards will be closely watched, potentially shaping the future of AI regulation worldwide.

Based in Rio de Janeiro, Brazil, João holds a Ph.D. in Statistical Physics from the Brazilian Center for Physical Research (CBPF), where he investigated fluctuation relations, thermostatistics, and turbulent time series with applications in Biology and Finance. During his research, he honed his coding skills, which prompted him to seek a professional path as a data scientist.

In 2018, João won the HackingRio competition (the biggest hackathon in Latin America) in the CleanTech Cluster, where he founded a startup focused on developing solutions for traceability and optimization of industrial waste. This experience in developing and deploying enterprise-ready data solutions motivated him to continue down the startup path, eventually leaving to become the Chief Data Officer at another Rio-based startup, where he partnered with AI Collaborator to co-develop cutting-edge Causal Inference methodologies for a client initiative.

While executing the project, Joao learned more about AI Collaborator and the value we brought to our clients. Motivated by the opportunity to build a global gateway marketplace for enterprises, giving access to innovative AI startups, João signed on as our new Head of AI.

At AI Collaborator, we are thrilled to welcome João as our Head of AI. His impressive background in statistical physics, coding, and startup experience, as well as his passion for building innovative AI solutions, make him an invaluable addition to our team. We are excited to work with João to continue delivering cutting-edge AI solutions to our clients and to build a global gateway marketplace for enterprises. Together, we are committed to providing large enterprises access to the on-demand AI resources they need to build their AI-Agility™.