What is the difference between AI and automation, and why is this distinction crucial for consultants?
Further Information on FAQ
utomation primarily deals with rule-based, repetitive tasks that follow linear logic (if-then statements) and require minimal human intelligence. It is often a more cost-effective and energy-efficient solution for straightforward processes. AI, on the other hand, is designed for tasks requiring reasoning, decision-making, learning, and prediction. It handles complex scenarios, can generate new content, and adapts over time.
This distinction is crucial because it allows us to recommend the most appropriate and cost-effective solutions. Misapplying AI to a task that could be handled by simpler automation can lead to unnecessary complexity, higher costs, and ultimately, client dissatisfaction. As consultants, we act as “human GPS systems”, guiding our clients to understand when a task requires AI’s transformative capabilities versus when it simply needs efficient automation. Most common Automation tools are Zapier, Make and n8n for SMEs, other tools such as Power Automate tend to be for larger corporate organisations.
How are client assessments conducted, and what is their primary purpose in AI consulting?
Client assessments are the crucial first step in AI consulting, designed to gain a comprehensive understanding of a company’s current operations, technology stack, and pain points. The process typically involves four stages:
- Preparation: Gathering initial information and customising assessment questions based on the client’s industry and potential needs.
- Structuring: Tailoring the assessment to specific company levels (company, department, team, individual) and their unique situations.
- Running: Conducting interviews with key personnel to capture insights on workflows, frustrations, efficiency gaps, and decision-making processes. Consultants listen for complaints, manual interventions, and areas that require significant data checking or info gathering.
- Finalising (Synthesising and Reporting): Compiling all collected information into a “Key Findings Report.” This report provides clarity, identifies opportunities and risks, and lays out a strategic roadmap, helping the client understand the why and how of AI adoption.
The primary purpose of an assessment is to provide clarity, simplify complex concepts, cut through market noise, and demonstrate the potential for saving time and money through AI implementation. It helps set realistic expectations regarding costs and timelines, serving as an essential diagnostic step before diving into detailed workflow design or implementation. At Anatomic Consulting we use a hybrid methodology which we call the Anatomic Approach.
How much will AI implementation cost for my business?
What can AI actually do for my business, and what shouldn’t I expect?
AI is a powerful tool, but it’s important to set realistic expectations. AI is not replacing humans; instead, it is replacing specific “activities and actions”, as well as some “decision-making and reasoning”.
Clients often come with “unrealistic expectations”, asking broad questions like “which AI will grow my business?” or “generate leads.”
It is our job to clarify that AI is a tool, similar to how a mechanic uses tools to fix a car – it requires assessment and a breakdown of the problem. AI can lead to competitive differentiation, unlock hyper-personalisation and automation, accelerate decision-making, create unified visibility across departments, improve AI model performance, reduce operational waste, enable advanced analytics and forecasting, and fuel scalability and growth.
As consultants, we can provide “examples of what’s possible” for a client’s industry or problem and then help determine “what’s practical” for their specific needs. It’s crucial for consultants to remember that AI-generated content always requires human review and refinement.
What are the different levels of AI implementation, and how do they relate to a consultant’s role?
AI implementation can be categorised into three levels of complexity:
- Level One Implementation: Focuses on more foundational tasks, such as setting up basic tools, prompts, simple workflows, co-pilots, chatbots, data management, and integrating technologies. This level also includes ongoing maintenance and testing of systems. Many of these simpler tasks are suitable for consultants to handle directly, filling a gap often overlooked by larger implementation teams.
- Level Two Implementation: Involves more complex aspects like technical workflows, deeper-level applications, and agentic implementations (where AI agents can make more autonomous decisions). This moves closer to development work.
- Level Three Implementation: Represents the highest level of complexity, involving massive development efforts and significant integration into existing company technologies and infrastructures. This typically requires highly specialised developers or dedicated implementation agencies.
As consultants, the primary focus is on strategy, planning, and Level One implementation. For Level Two and Three needs, we may leverage internal or vetted external implementation teams. Our role then shifts to concierging the client and ensuring smooth communication with the implementation team, rather than being directly “in between” the client and the technical experts.
What are the key considerations for data management in AI integration?
A robust data plan is crucial for successful AI integration because “AI is only as good as the data it’s fed” (garbage in, garbage out). Key considerations include:
- Data Quality: Ensuring data is accurate, complete, consistent, timely, and relevant. This involves data profiling and identifying any missing information or quality gaps.
- Data Accessibility and Integration: Understanding where data exists (inventory of data sources, e.g., CRMs, spreadsheets), how it’s collected, stored, and shared, and whether different systems can communicate effectively (e.g., through APIs). This also includes identifying who owns and manages the data.
- AI Readiness: Assessing whether data is structured or unstructured (as different AI models require different formats), if it needs labelling or preparation, and if there’s sufficient data for relevant sample sets to train AI models effectively.
- Security and Compliance: Addressing risks related to data leaks, corruption, loss, and ensuring adherence to regulatory requirements (e.g., GDPR, HIPAA), especially in sensitive industries like medical or finance.
- Bias in Training Data: Recognising and mitigating biases in training data, including social, cultural, and Western-centric biases, to ensure fair and representative AI outputs.
As your consultant, we play a vital role in helping your business define your data objectives, assess your current data state, and develop a roadmap for future data management, often by proposing data flow diagrams and defining data governance needs. Part of our skill set is data science, and we have a trusted third-party who helps us with this.
Why do I need an assessment or strategy phase before implementation?
Skipping a proper assessment or strategy phase is “always a mistake” because it provides a crucial “bigger picture strategy”. No implementation specialist will proceed without proper assessments, much like a doctor conducting a thorough analysis before treatment to avoid malpractice.
The initial assessment defines objectives and creates a strategic roadmap for the client. It helps to clarify opportunities, risks, and leverage points. While clients may initially want to rush directly into implementation, an assessment can be presented as a “taste tester” to build trust and demonstrate value.
It also helps to prevent “scope creep” by clearly defining what is and is not covered in the project. This structured approach helps uncover previously unconsidered use cases and allows the consultant to open up many more areas for future work, thereby increasing the client’s lifetime value.
Will AI replace my employees or compromise my data security?
