AI vs. Experts: Consulting’s Fight for Survival

Offering expert insights is undergoing a massive transformation, driven by the relentless advance of technology. Will traditional consulting firms survive the rise of AI-powered knowledge platforms and on-demand expertise?

The way we access and consume expert knowledge is changing, and the pace is only accelerating. Here’s how I see things developing over the next few years, based on my experience building and scaling knowledge management systems for Fortune 500 companies.

1. The Rise of AI-Powered Knowledge Platforms

AI is no longer a buzzword; it’s the engine driving the future of expert insights. We’re seeing the emergence of platforms that can analyze vast amounts of data, identify patterns, and generate actionable recommendations – all without human intervention.

For example, Glean AI Glean AI is already being used to help companies synthesize information from disparate sources. Imagine this on steroids, capable of not just summarizing data, but also providing strategic advice based on real-time market conditions.

Pro Tip: Don’t fear AI; embrace it. Learn how to use these tools to augment your own expertise and become a more effective advisor.

2. Personalized Learning and Development

The days of generic training programs are numbered. Employees in 2026 expect personalized learning experiences tailored to their specific needs and skill gaps. Adaptive learning platforms Adaptive learning platforms, powered by AI, are making this a reality.

These platforms use algorithms to assess an individual’s knowledge level, identify areas for improvement, and deliver customized content. Think of it as having a personal tutor for every employee.

To set up a basic personalized learning path using a platform like Pathgather (now part of Degreed) Degreed, you would:

1. Create a skills assessment using the “Skill IQ” feature.
2. Based on the assessment results, configure personalized learning paths using the “Pathways” feature.
3. Track employee progress and adjust learning paths as needed.

Common Mistake: Implementing personalized learning without clearly defined learning objectives. Ensure that each learning path is aligned with specific business goals.

3. The Democratization of Expertise

Traditionally, access to expert insights has been limited to those who could afford expensive consultants or had connections to industry leaders. Blockchain is changing that.

Blockchain-based platforms are emerging that allow individuals to verify and showcase their expertise, regardless of their educational background or professional affiliations. These platforms use cryptographic techniques to create tamper-proof records of skills, experience, and accomplishments.

Consider Credly Credly, which issues digital credentials that can be verified on the blockchain. In the future, we’ll see more sophisticated platforms that use blockchain to create decentralized reputation systems, where individuals can earn recognition for their expertise based on peer reviews and real-world performance.

4. The Rise of the “Expert Network as a Service”

The traditional expert network model, where companies pay hefty fees to access a limited pool of experts, is being disrupted by the rise of “Expert Network as a Service” (ENaaS) platforms. These platforms offer on-demand access to a vast network of experts, at a fraction of the cost of traditional consulting firms. For many companies, this shift is about embracing technology to drive growth.

Gerson Lehrman Group (GLG) GLG is a well-established player in this space, but we’re seeing the emergence of new platforms that are leveraging AI and machine learning to match clients with the right experts more efficiently.

Here’s what nobody tells you: these platforms are incredibly competitive for experts. You need to have a strong online presence and a compelling value proposition to stand out from the crowd.

5. The Convergence of Data and Expertise

In the future, expert insights will be inseparable from data. The most valuable advisors will be those who can combine their domain expertise with the ability to analyze and interpret data. This also speaks to why tech won’t replace experts, but rather augment their abilities.

We’re seeing the emergence of platforms that integrate data analytics tools with expert networks, allowing clients to access both data and expertise in one place. For example, AlphaSense AlphaSense combines AI-powered search with access to expert insights, providing users with a comprehensive view of their industry.

I had a client last year who was struggling to understand the impact of a new regulation on their business. We used AlphaSense to analyze the regulatory text, identify key stakeholders, and connect with experts who had experience navigating similar regulations. The result? The client was able to develop a proactive compliance strategy and avoid costly penalties.

6. Case Study: Streamlining Compliance with AI-Powered Insights

Let’s look at a hypothetical but realistic scenario. A large financial institution, “GlobalBank,” based in Atlanta, Georgia, faced increasing regulatory scrutiny regarding anti-money laundering (AML) compliance. The cost of maintaining compliance was spiraling out of control, with over $5 million spent annually on manual reviews and investigations.

GlobalBank partnered with RegTech Solutions, a company specializing in AI-powered compliance solutions. RegTech implemented its “AML Insight” platform, which uses machine learning to analyze transaction data, identify suspicious activity, and generate automated reports.

Here’s a breakdown of the implementation:

1. Data Integration: RegTech integrated AML Insight with GlobalBank’s core banking systems, including transaction data from the Fulton County branch and international transfers processed through their Buckhead office.
2. AI Training: The AI model was trained on GlobalBank’s historical transaction data, as well as publicly available information from the Financial Crimes Enforcement Network (FinCEN) FinCEN and other regulatory bodies.
3. Workflow Automation: AML Insight automated the initial screening of transactions, flagging only those with a high probability of being suspicious. This reduced the number of transactions requiring manual review by 70%.
4. Expert Oversight: A team of AML compliance officers at GlobalBank reviewed the flagged transactions and conducted further investigations as needed. The platform provided them with access to relevant information, such as customer profiles, transaction histories, and regulatory guidance.

The results were significant. GlobalBank reduced its AML compliance costs by 40%, saving over $2 million annually. The number of false positives decreased by 60%, freeing up compliance officers to focus on more complex investigations. The bank also improved its ability to detect and prevent money laundering, reducing its exposure to regulatory penalties.

One limitation of this approach is the reliance on historical data. If the AI model is not continuously updated with new data and regulatory guidance, it may become less effective over time. (It’s an ongoing process, not a one-time fix.)

7. The Blurring Lines Between Consulting and Technology

The traditional consulting model, where firms send teams of consultants to client sites to conduct analysis and provide recommendations, is becoming increasingly outdated. In the future, consulting will be more about providing access to technology-enabled solutions and less about providing human labor. To avoid becoming outdated, it’s crucial to avoid fatal flaws and scale faster with technology.

We’re seeing the emergence of companies that are blurring the lines between consulting and technology, offering platforms that combine expert insights with data analytics, workflow automation, and other technological capabilities.

Companies like McKinsey McKinsey and Boston Consulting Group (BCG) are already investing heavily in technology, acquiring data analytics firms and developing their own proprietary platforms. The future of consulting is about providing clients with access to these platforms, rather than simply providing them with advice.

The future of offering expert insights is bright, but it requires a willingness to embrace change and adapt to new technologies. By focusing on personalization, democratization, and data integration, we can create a world where expert knowledge is accessible to everyone.

The shift towards AI-driven expertise isn’t just a trend; it’s a fundamental change. To thrive, professionals need to actively develop their skills in data analysis and AI integration, becoming interpreters of insights rather than just providers of information.