How to Choose an AI Consulting Partner for Strategy and Automation

As businesses transition from trial-and-error iterations of AI to enterprise-wide applications of the technology, executives must make an important choice about their consulting partner. Consultants offer clients methods to transform their businesses through AI initiatives, but their capabilities, ultimately, the results of those initiatives will differ from one consultant to another.

The challenge for CIOs, Chief Data Officers (CDOs), Heads of Transformation (HoT), and others in business leadership potentially lies more in figuring out which brand of consulting will help them achieve their desired outcome than it does with simply identifying a name-brand consulting firm. Therefore, it is critical for decision-makers to find a consulting partner that has the ability to align investments in AI with overall business strategy and objectives, to provide effectively automated processes, to manage risk, and to produce measurable value to the organization.

This guide is intended to highlight points of comparison and evaluation when comparing AI consulting firms for strategy and automation initiatives of your company.

Perceptive’s POV

At Perceptive Analytics we’ve noticed that companies tend to focus on evaluating AI technologies for longer than they have focused on evaluating the implementation partner who will be assisting them through their AI initiatives. However, there are many instances of AI failure due to issues with strategic planning, governance, adoption readiness, data quality and process design rather than problems related to AI technology itself.

Successful AI consulting engagements will include both strategic planning and practical execution. Through these engagements, organizations are able to identify opportunities for higher ROI investment, redesign business processes, implement governance frameworks, and develop operations that support the ongoing adoption of an AI system.

1. Define What a Proven Track Record in AI Strategy Actually Looks Like

A number of consultancy businesses assert to be specialists in developing AI strategy but the majority of people that deliver a strategic consultancy service do not directly correlate that into business results.

When determining if a firm has a proven history of delivering through their consulting services, look for evidence of successful delivery of the advisory consultancy service provided rather than just the thought leadership component.

Some of the signs are;

• Enterprise AI Programs have gone from Strategy to Execution
• Tangible results resulting in an increase in revenue, decrease in cost or productivity improvements
• The firm has experience with establishing artificial intelligence governance frameworks and operational models
• The firm has experience with implementing similar type of solutions in your industry
• A reference from one or more executives that can speak to the long-term impact

Leading businesses within the consulting community (e.g., McKinsey, BCG, Deloitte, Accenture, IBM Consulting & PwC), have shifted their focus from delivering stand-alone strategy engagements to delivering full end-to-end AI transformations.

For example, McKinsey has recently outlined their approach to AI consulting focusing on using operating model redesign to scale AI, creating new capability for organizations to create measurable value and to advance AI;

Deloitte focuses on ensuring organizations implement responsible artificial intelligence governance/controls, are workforce ready and will have integration with their overall transformation initiatives.

Partners that are best positioned to align AI strategy with execution and adoption are those that are leading with these types of engagements.

2. Identify Common Patterns in Enterprise AI Strategy Services

Despite the different ways consulting organisations approach their offerings, the majority of the enterprise AI strategy engagements have several common elements to them.

Identifying AI opportunity assessment

Most consulting organisations will begin with identifying high-value use case opportunities, and then prioritising those opportunities based on business impact, feasibility, and organisational readiness.

For example, BCG uses a value-based prioritisation framework to help organisations identify which potential projects will provide the highest level of measurable returns on investment:

Data and technology readiness assessment

Almost all consulting companies assess the following things:

• Quality of data, including access to data.
• Existing technology stack.
• Integration requirements.
• Security, compliance and safety.

If any of these foundations of data and technology are weak, then the potential for successful AI implementation can be significantly diminished.

Governance and responsible AI

Consulting organisations have recently placed a significant amount of focus on responsible AI.

IBM Consulting and PwC have put considerable effort into developing governance frameworks to mitigate issues such as transparency, accountability, bias and compliance with regulations.

Change management and adoption

Successful implementation of AI requires much more than simply deploying technology. Increasingly, the majority of the leading consulting firms are incorporating both of the following tenets into their consulting services:

• Workforce enablement
• Stakeholder alignment
• Training methodologies
• Measurement of adoption

Without organisational buy-in and engagement, the vast majority of AI initiatives fail to produce their expected outcomes.

Development of roadmap

Finalising a consulting engagement usually results in the development of a phased roadmap that maps to business priorities and also measures success using established key performance indicators (KPIs).

Perceptive Analytics utilises a similar outcome-oriented process by helping organisations connect forecasting, analytical, and decision support initiatives directly to operational/financial objectives. For example, its forecasting solutions help organizations improve planning accuracy using historical sales patterns, market signals, and predictive analytics.

3. Compare Approaches to Business Process Automation

Organizational automation is by far the most common reason companies seek AI consulting firms. Yet, approaches to automation are vastly different.

Task-Based Automation

Many firms concentrate solely on the automation of repeatable tasks through robotic process automation (RPA). Although task automation yields efficiency gains, it does not lead to transformational results when process improvements have not been made.

Process Redesign

A more advanced form of consulting involves analyzing the process and then applying automation to it.

For instance, Accenture integrates process optimization, AI, analytics, and workflow management tools in order to automate whole business processes, rather than just certain tasks.

Intelligent Automation

Today’s advanced consulting firms incorporate:

• AI algorithms.
• Workflow automation.
• Process discovery.
• Prediction modeling.
• Continuous monitoring.

The result is that the organization is able to both optimize operations and automate decision-making.

When assessing an organization’s potential for automation, some things to consider when speaking to vendors are:

• Their methodology for finding automation opportunities.
• The way they choose where to invest.
• The KPIs they track to measure success.
• Exception handling and governance.

Perceptive Analytics has supported automation initiatives focused on reducing manual effort and improving operational efficiency. Examples include automating purchase-order workflows and optimizing data-transfer processes to reduce bottlenecks and improve reporting speed.

4. Decode Testimonials and Case Studies Like an Analyst

Case studies frequently prove to be one of the most powerful resources offered by consulting companies. But they must be assessed critically.

Effective case studies should have:

• Business problem formulation.
• Implementation process description.
• Measurable outcomes.
• Adoption statistics.
• Business impacts.

The most believable case studies show how success was achieved, what organizational changes had to occur, and what were success criteria.

Red Flags to Look Out for

• Beware of case studies that:
• Pay more attention to technology than outcomes.
• Do not have any KPIs involved.
• Are concerned exclusively about pilots.
• Include fuzzy language without factual background.

Perceptive Analytics has a collection of relevant examples concerning customer analytics, forecasting, and decision-making in which business impacts still matter.

Customer analytics services offered by Perceptive Analytics enable businesses to analyze customers’ behaviors, segmentations, and retention factors; and unified reporting solutions help business leaders get accurate data faster.

It is important for effective references to reveal improvements in forecasting, customer retention, operations, or decision-making effectiveness.

5. Understand the Hidden Risks of Choosing the Wrong AI Consulting Firm

An AI consulting project may fail even if the technology functions as intended.

Strategy Without Implementation

Certain consulting firms have a knack for strategizing, while others cannot execute. Organizations may find themselves receiving many ideas and advice without having a way to implement those strategies.

Vendor Dependence

The reliance on proprietary systems, architecture, or platforms may be an issue in the future. Ask the vendor about their portability and maintenance policies.

Governance Issues

With the increasing complexity of regulatory issues around AI, organizations need to make sure that governance works. Governance missteps can lead to compliance, reputation, and operational problems. Further reading can be found in the NIST AI Risk Management Framework.

Lack of knowledge transfer

Organizations need to be able to operate without depending solely on consulting firms indefinitely.

Consulting firms with a good track record can offer:

• Documentation.
• Training.
• Development of internal capabilities.
• Implementation strategies.
• ROI Unrealistically High

Do not believe unrealistic promises from consulting firms. The best consulting firms set realistic goals and monitor performance.

6. Evaluate Value for Money Beyond Project Cost

The least expensive proposal often does not provide the highest value for a business. When evaluating consulting firms, look beyond initial costs to determine their overall economic impact on your business. You should consider the following when evaluating each consulting firm:

• Service offering.
• Knowledge of the industry.
• Ability to leverage and develop reusable tools.
• Ability to provide governance.
• Ability to provide change management support.
• Ability to provide post-implementation support.
• Ability to build capabilities in-house.

Consulting firms typically offer one or more models for pricing their services:

• Fixed fees.
• Time/materials contracts.
• Managed service contracts.
• Prices based on agreed outcomes.

Ultimately, the value of consulting services should be based on return on capital, adoption, scalability and sustainability over the long term.

7. Questions to Ask During Vendor Evaluation

By establishing a structured evaluation process to identify legitimate capability versus marketing claims, ask your potential consulting partners:

• What percentage of AI Strategy engagements has successfully gone into production deployment?
• How do you measure the business outcomes and ROI that your clients can realize from your services?
• What is your approach to intelligent automation?
• How do you handle governance and requirements for responsible AI?
• What knowledge transfer mechanisms do you provide?
• Can you provide references of client organizations that are similar to ours?
• How do you prevent vendor lock-in for your clients?
• What will happen if you do not meet your clients’ adoption goals?

Be especially observant to the level of specificity of response. A well-established consulting firm usually has a number of detailed examples readily available; while lesser-experienced firms will normally be less specific and more generic in their response.

8. A Practical Checklist for Shortlisting AI Consulting Partners

As you are comparing potential AI consulting providers, use the following checklist for evaluating AI Consulting Providers:

1. Demonstrated experience in providing enterprise-wide AI solutions that have resulted in measurable, successful outcomes.
2. Experience with business process automation: (i.e., ability to redesign and optimize processes, not just automate tasks).
3. Industry experience providing AI services in your specific type of business and under your regulatory structure.
4. Governance and Risk Management capabilities: responsible AI, compliance with government regulations and industry standards; security; and change management.
5. Case studies with validated outcomes and references from previous customers.
6. Commercial Transparency: provide clear fee structures, deliverables and success measurement criteria.
7. Capability Transfer: provide training, documentation and internal enablement to the client’s employees after implementation.
8. Long-Term Value Creation: demonstrate that the AI consulting company’s focus is on the sustainable creation of value through the long-term application of their services, rather than the short-term delivery of service for the purpose of receiving immediate payment.

Conclusion

As a business, you are looking for a partner in artificial intelligence (AI) consulting. This is a general business decision, not simply a technology one. The best firms provide both the ability to think strategically and implement what they have planned, as well as automated processes to help clients achieve their goals, governance structures that guide organizations in successfully achieving their objectives, and ultimately providing a long-term return on investment (ROI) through developing scalable AI capabilities rather than isolated AI projects.

In evaluating potential consulting partners, an organization should rely on evidence and results and how well the consulting partner aligns with the organization’s desired business outcomes regardless of whether the organization chooses to use a large global consulting firm or a small, specialized analytics consulting firm.

Use the structure above when determining which AI consulting partners to work with and to develop your Request for Proposals (RFPs) and your AI roadmap so you can select the consulting partner who is best prepared to assist you with your strategic and automation goals.

Next steps: Download our AI Consulting Partner Evaluation Checklist or contact Perceptive Analytics for assistance developing your AI Roadmap, identifying automation opportunities, and determining vendor selection criteria prior to making a substantial investment in AI consulting.

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