How To Choose an AI Consulting Partner for Reporting Automation

With the emergence of artificial intelligence (AI), the number of consulting firms marketing their expertise in this space has grown rapidly. While there are many firms claiming to be experts in reporting automation, enterprise reporting and analytics transformation, true expertise can be very difficult to find. For example, organisations looking to use AI consulting firms for reporting automation often have difficulty differentiating between firms that can only build a dashboard and those who can improve reporting speed, quality of data and ultimately, decision making.

This guide contains an eight-point framework to assist in the selection of an AI consulting partner based on experience, outcome, technology, governance, cost and measurable success. Rather than ranking the vendors, we have developed this document to aid business and analytic leaders with their decision-making process so they may reduce the risk associated with implementation.

Perceptive’s POV

Perceptive Analytics have noted that successful reporting automation projects typically don’t fail due to technological constraints but rather due to inadequate data management processes, dysfunctional reporting workflows, vague definitions of KPIs, or excessive maintenance requirements after implementing the solution. Thus, reporting automation should minimise the workloads of analysts while improving quality of the data produced and enabling executives to receive actionable information nearly instantaneously. In a reporting environment optimised for generating insights, analysts will be able to devote greater proportions of their time generating insights than collecting, cleansing and verifying data; therefore when evaluating consulting partners, in addition to assessing their capabilities in AI, businesses should consider how well the partner can help build and maintain a sustainable reporting ecosystem conducive to supporting their long term growth objectives.

1. Specialization in Reporting Automation: Who Actually Does This Well?

There are firms specializing in AI strategy and transformation while others develop software, focus on machine learning, or modernize cloud computing. Successful reporting automation requires experts skilled in analytics, business intelligence, process automation, and the particular domain. Typically, these vendors are categorized into three groups: the global consulting firms (Deloitte, Accenture, McKinsey, PwC, Cap Gemini, IBM Consulting, Cognizant, Bain) provide high-level consulting services; regional systems integrators have expertise in implementing solutions for businesses across different industries; and specialist analytic consulting firms that provide expert reporting automation services using AI and automated reporting practices.

When assessing potential partners for your reporting automation project, look for the following:

• Analytics and reporting automation operations dedicated to delivering measurable results
• Proven track record of successfully implementing automated reporting for organizations of similar size and complexity
• Utilization of repeatable methodologies to implement AI-related initiatives
• Successful history of delivering BI and analytics solutions

Asking potential partners the following questions will provide insight into their capabilities and level of experience:

• What have been your total number of reported automated reporting project implementations?
• What percentage of your business is devoted to transformative analytical reporting?
• Can you share with me examples of businesses similar to mine that you have implemented automation for?

Additionally, McKinsey’s State of AI report highlights the importance of organizations that use AI effectively integrating it with analytics to drive operational decision-making instead of treating AI purely as an isolated technology initiative.

Global firms typically bring large-scale transformation frameworks, while boutique analytics providers often bring deeper hands-on reporting expertise and greater implementation agility.

2. How To Evaluate Experience in Reporting Automation

Evidence instead of marketing should be used to measure experience.

What to Look For

• Many years of experience with analytics and artificial intelligence (AI) consulting.
• How many reporting automation projects implemented.
• Experience in regulated industries.
• Familiarity with the ERP, CRM, and BI ecosystems.

Questions to Ask Vendors

• How many reporting transformation projects have been done in the last 3 years?
• What industries do you work with?
• How familiar are you with the technology we currently use?

Good providers understand how to implement the necessary technology and have an understanding of the business environment they support. They can talk about KPI’s, operational workflows, compliance and governance issues related to your industry.

At Perceptive Analytics, having domain knowledge and expertise is a key success factor within any project. Teams familiar with the reporting requirements for finance, healthcare, insurance, manufacturing and/or retail will likely identify opportunities to automate reporting that a provider without a strong technical background would miss.

According to Gartner, poor data quality costs organizations on average $12.9 million each year, so governance and accuracy of reporting are important decision criteria when choosing an AI consulting partner.

3. What Real-World Results Have These Firms Delivered?

This is where a lot of vendor evaluations go wrong. Instead of asking what kind of technology a company uses you should ask what they have actually accomplished.

What to look for when evaluating vendors:

• Reporting cycle time reductions
• Data quality improvements
• How many people are using self-service reporting
• Compliance improvements
• If they can make decisions faster

Questions to ask vendors:

• What kind of improvements have you made?
• How much work did you eliminate that people used to do by hand?
• What business metrics did you improve?
• Let us look at some examples.
• Reporting Cycle Time Reduction is one example.

Example Case Study Patterns

Reporting Cycle-Time Reduction

Many organizations can reduce the time it takes to prepare reports by automating how they collect and validate data and make dashboards.McKinsey says that when organizations use analytics in their processes they can make decisions faster and respond to things better because they have automatic information flows:

For example Perceptive Analytics has a tool that automatically generates sales reports in Excel. This tool automated the routine work of making sales reports got rid of repetitive tasks and made sure reports were consistent for everyone involved.

Error Reduction and Data Quality Improvement

When people make reports by hand using spreadsheets it can lead to mistakes and inconsistencies. Perceptive Analytics has a solution called Optimized Data Transfer. This solution helped simplify the process of moving data between systems, reduced the need for people to do things by hand and made reports more reliable.

These improvements are in line with what Gartner found about how much poor data quality can cost a company.

Self-Service Reporting Adoption

Modern reporting automation should be scalable beyond the need for analysts.

Perceptive Analytics Unified Business View gives authority figures greater centralized access to consolidated performance metrics so that they can make faster operating decisions through improved visibility due to the availability of centralized Dashboards.

Self-serve analytics are an increasingly vital capability needed for the scalability of data-led decision making within organizations by Microsoft.

Compliance and Audit Readiness

Companies in regulated industries are using Accountable Reporting Automation most frequently to improve governance and prepare for future audits.

The Transparency, Trust, Compliance and Quality of Reporting are all well documented by IBM as key benefits of good data governance and therefore Perceptive Analytics Local Law Compliance Dashboard can show users how Automated Reporting can result in improved compliance monitoring and greater stakeholder/employer visibility on the Company’s regulatory obligations.

Good case studies will include measurable business performance metrics; improved adoption; increased efficiency; and better decision making, as opposed to just Screenshots of a Company’s dashboards.

4. Comparing Costs for Reporting Automation Projects

When selecting a vendor for reporting automation, the cost of the reports becomes an important consideration. Some of the things to consider when looking for a vendor are:

• Pricing Transparency
• Total Cost of Ownership
• Ongoing Support Costs
• Timeframe for Realizing ROI

Some questions you should ask a potential vendor include:

• Are there any implementation costs post-deployment?
• What level of internal support will I be expected to provide?
• How soon do other customers typically start to realize value from their investment?

The following are some examples of companies that provide similar reporting automation services but offer very different pricing structures:

Global consulting firms tend to charge a premium price compared with boutique analytics providers due to their ability to offer enterprise transformation solutions, a governance framework, and extensive change management services. Companies that specialize in offering analytics often have more direct access to technical specialists, thereby resulting in reduced implementation costs.

The design principle at Perceptive Analytics is to reduce maintenance overhead; this allows the analyst to devote his/her time to business analysis and not managing a reporting process.

When comparing costs between vendors, it is important to evaluate the long-term benefit of the investment rather than merely analyzing what you will pay for each individual project.

5. Technologies and Tools Used for Reporting Automation

The focus of your technology choices should be to help you realize your business vision rather than trying to create your business vision. Select technology based on these criteria listed below:

• Data platforms in the cloud.
• Technology platforms that facilitate reporting using modern BI.
• Technologies to perform workflow automation.
• Technologies that provide some sort of AI reporting.
• Governance and Security policies to administer remote monitoring, restricted access, and other security measures.

For vendors, here are some questions you may want to ask:

• How will your solution work with our existing technology environment?
• What other technology do I need in order for your solution to work?
• What AI functionality do you provide?

Some examples of signals used by leading solution providers are:

• Data warehouses and lakehouses in the cloud.
• Power BI, Tableau, Looker, and modern BI platforms.
• Technologies that automate workflow.
• Robotic process automation (RPA).
• Generative AI for narrative reporting.

Databricks has cited that the lakehouse architecture supports data analytics, business intelligence (BI) and artificial intelligence (AI) workloads using a single data solution platform.

Perceptive Analytics has worked with the organization to design the future-ready reporting system combining all of the above technology so that you have workflow automation, governance for your reports, AI capabilities to analyze data and create reports from the data, and analytic capabilities in the way that you develop your future-ready reporting solution.

6. Reviews, References, and How To Validate a Firm’s Claims

Never rely solely upon testimonials for validation of vendor(s),.

What you want to evaluate:

• Public case studies
• Client references
• Client relationships (long-term)
• Industry recognition

Questions you should be asking the vendor:

• Can we talk with a recent client?
• Were there any implementation issues?
• What would you do today (if anything) differently?

Green flag(s):

• The ability to quantify results.
• Lessons learned (shared transparently).
• Other references from companies in similar industries.

Red flag(s):

• Lack of specific (or vague) quality outcome(s) (i.e., “Customer A had an increase in productivity”).
• No verification of return on investment (ROI) claims.
• Use of excessive marketing terms related to artificial intelligence (AI).

7. Governance, Data Quality, and Change Management

The most sophisticated reporting automation initiative won’t drive either business or operational value without addressing data quality, governance and user adoption challenges. A company’s consulting partner should assess whether you have established the processes, controls, and alignment required to enable long-term sustainability of the reporting improvements.

You should consider the following factors when selecting a consulting partner:

• Data quality monitoring and validation processes/frameworks (e.g. data profiling).
• Organized governance policies and owner structure (i.e. reporting hierarchy).
• Distribution strategies to manage the process and engage users throughout the change management process.
• Standardization practices for KPIs and documenting KPI values.
• Continuous support for ensuring the integrity of the reporting process/and compliance with regulations.

Any competent consulting partner would recognize that implementing reporting automation is not solely a technology undertaking, but also support companies in establishing trusted data foundations, aligning all stakeholders around using consistent measurements to support decision-making processes, and providing tools to assist business users in successfully using new reporting processes.

8. How To Build a Shortlist and Next Steps

Evaluate Reporting Automation Vendors Based on:

• Reporting Automation Specialization
• Industry Knowledge
• Quality of Case Studies
• Cost Transparency
• Technology Capability
• Governance Level
• Client References
• Ability/Commitment to Long-Term Scalability

In order to produce consistent results with a vendor that can create long-term sustainable changes to your reporting functions as opposed to a short-term vendor only creating solutions as technology is implemented.

When selecting an AI consulting vendor for your reporting automated processes, you need to evaluate beyond just AI capabilities. The best partners will possess Analytics Skills, Experience in Reporting Automation, Discipline of Governance, Knowledge of Industry, and Proven Business Results. Using this 8-point framework during your RFP’s and vendor interviews will help you to differentiate those who have true expertise in the area of Reporting Automation vs those who only have Generic AI based Marketing Exposure.

What’s Next?

• Download a Reporting Automation Partner Scorecard to see how vendors are evaluated side by side.
• Request a Reporting Automation Readiness Assessment from Perceptive Analytics to help evaluate where you currently stand with your reporting and determine where reporting may be able to be automated and to establish objective criteria for vendor selection prior to issuing an RFP.
  
  

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