How To Evaluate AI Consulting Partners for BI Modernization With AI Agents

As more enterprises invest in modernizing their outdated business intelligence (BI) applications to better enable AI-supported decision-making, employ self-service analytics capabilities and implement intelligent automation solutions, they are struggling with evaluating AI consulting companies. Many consulting firms offer similar descriptions of themselves related to AI, analytics, and digital transformation. More importantly, organizations face a challenge defining which consulting companies are capable of implementing AI agents for BI and which firms predominantly repackage their legacy BI services with different jargon.

This document offers a practical 10-point checklist that brings structure for business and analytics leaders to utilize in identifying the right AI consulting firm to partner with for BI modernization. The checklist does not evaluate the vendors by providing a ranking; instead, it emphasizes key factors such as the vendor’s strength of client references, supporting evidence, capabilities, methodology, and business factors.

Perceptive’s POV

Perceptive Analytics has experienced many BI Modernization initiatives that were successful due to a combination of trusted data, streamlined reporting processes, AI-enabled decision support systems, and requisite governance structures that allowed organizations to scale analytics with confidence.

Successful BI Modernization programs typically do not succeed due to technology alone; rather they also include elements supporting BI Modernization such as trusted data sources, automated report generation by AI agents, anomaly detection of BI data by AI agents, answering business-related questions in a natural language through the use of AI agents, and proactively determining trends. Organizations must be careful regarding firms that focus on AI capabilities without properly addressing many factors required to successfully implement successful BI Modernization programs such as data quality, adequate data governance, user adoption, and the ability to maintain BI applications over a long time (i.e., long-term maintainability). A successful BI Modernization program should result in reduced analyst workloads, increased analyst / decision-maker decision speed, and create future-ready analytics solutions that change as the organization changes.

What Good Looks Like: Outcomes From AI-Led BI Modernization

Documented BI Modernization Case Studies and Outcomes

Business Outcomes should always be the FIRST CRITERIA to measure a Consulting partner’s performance in relation to Data Warehouse Modernization. Consulting Partners should have documented proof that BI modernization leads to improved reporting efficiencies, increased reports use, and/or faster decision-making processes for the Business by way of document(s) consisting of:

• Shortened Reporting Cycle Time
• Increased use of Dashboards
• Automated Insights
• Accelerated Executive Decision-making

Questions to consider when determining whether or not to move forward with a consulting partner are:

• What Business Outcomes were realized?
• How was success measured?
• What are the business metrics that improved after deployment?

Signs that a consulting partner has successfully achieved the above objectives are:

• Before and After performance metrics
• Quantified Efficiency gains
• Business Outcomes achieved vs. Technical Outputs Achieved

For instance, the Perceptive Analytics Unified Business View initiative provides proof of the benefits organizations will reap from consolidating disparate reporting methods and creating a centralized dominant Analytics Environment with one source of truth for all company-wide departments.

Additionally, the same concept applies to the Perceptive Analytics Executive Marketing Dashboard. This Automated Executive Reporting improves visibility into Campaign Performance, Customer Acquisition metrics and Marketing Effectiveness to allow for better Decision Making for Executives.

A recent McKinsey State of AI report indicated that organizations that have been successful in embedding Analytics and AI within their Decision-Making processes do not view AI as an independent technology initiative; instead they view AI as being fully integrated into their Operational processes.

Depth of AI Agent Expertise for BI Workflows

Business intelligence (BI) is a different expertise from artificial intelligence (AI). Business intelligence requires AI agents with specific combinations of analytics, reporting, automation, and governance capabilities.

When evaluating potential AI tools for business intelligence, you should be looking for the following features:

• Natural Language Querying
• Automated Key Performance Indicator (KPI) Monitoring
• Narrative Insight Generation
• Anomaly Detection Systems

The following questions may be helpful in identifying suitable AI agents for your BI application:

• Can AI Agents Provide Explanations for Performance Drivers?
• Can AI Agents Identify Problems on a Proactive Basis?
• How Do AI-Initiated Insights Get Verified?

The following evaluation signals can help you identify suitable AI tools:

• Demonstrated AI-Enabled Reporting Workflows
• AI Technology Embedded within BI Environments
• Evidence of Business Adoption Beyond Pilot Programs

Advanced AI Agents Support More than Just Conversational Interfaces; They Automate Repetitive Analyses, Identify Unusual Business Conditions, Create Narrative Summaries and Help Users Comprehend the Factors Affecting Performance.

Industry and Domain Experience

Successful modernisation of BI requires more than mere technical expertise.

Important elements to seek

• Industry experience
• Business Process Knowledge
• Industry KPI familiarity
• Regulatory awareness when required

Questions to Consider

• How many BI projects completed within our industry?
• What KPIs do you normally measure?
• How do you deal with industry’s pain points?

Signs of Evaluation

• Industry-specific case studies
• Domain experts are part of project teams
• Demonstrated knowledge of operations workflow

Perceptive’s domain experts partner with analytical professionals to help ensure a BI environment is developing against true business goals. Domain experts enable many organisations in health care, finance, manufacturing, and retail sectors, to leverage the domain knowledge needed to deliver analytics that drive real business value.

Comparing Pricing Models and Service Offerings

Methodologies for Data Quality, Governance, and Change Management

BI modernisation initiatives that do not have an established method to achieve their results experience a significant failure rate due to the lack of data governance initiatives as part of the process.

Things to look for:

• Data governance framework
• Data quality monitoring
• User adoption strategy
• Change management process

You should ask:

• How do you keep your data quality?
• Who provides the business definition?
• How will the users be trained?

Indicator of evaluation:

• Formal governance methodology
• Continual monitoring of quality
• Executive sponsorship style

DAMA defines governance, quality, metadata, and architecture management as the four foundational disciplines necessary for successful enterprise analytic programmes.

Perceptive Analytics has built automated checks, validation routines, and quality controls into their reporting environment so their customer base can have more confidence that they can trust their analytical results, and to reduce the amount of time they spend reconciling manually.

Technology Stack and AI/BI Platform Alignment

When making a decision about technology it should have more of a connection to your company’s needs than what vendors are offering.

Look for the following when evaluating a technology:

• Cloud Native Architectures (e.g., Azure, AWS, Google Cloud) BI Solutions(Examples: Power Bi , Tableau ,Looker)
• Is The architecture AI Ready
• Flexibility to integrate

Questions you might ask yourself while evaluating a vendor solution:

• How does this solution fit into our total solution ecosystem?
• What BI solutions are supported by this vendor?
• What is the potential for the vendor’s architecture to provide support well into the future?

Evaluation signals:

• Vendor / product neutrality(Having vendor-neutral solutions)
• Multi-Platform experience
• Scalable design

Common examples of BI modernization environments include but are not limited to: Power BI, Tableau, Looker, Snowflake, Databricks, Azure, AWS, and Google Cloud respectively.

Perceptive Analytics focuses on developing “future-ready” architecture which meets the evolving needs of a company or agency while reducing ongoing regular operation/ maintenance requirements for their internal technical analyst employees to have time to spend creating value-added insight versus just maintaining the reporting infrastructure.

Pricing Models and Commercial Flexibility

Pricing structures among consulting firms can differ greatly. There are a number of things to consider when investigating pricing.

• Find as much transparency in pricing as you can.
• Look for flexible engagement models.
• Seek out managed services.
• Look for clear support structures.

Some suggested questions to consider are:

• What costs will arise post-implementation?
• How are change requests handled?
• What types of support can I expect?

Indicators of a firm’s pricing structure may include:

• Fixed-fee project work
• Time and materials flexibility
• Clearly defined support agreements

Firms should always assess total cost of ownership rather than focusing on just the implementation cost. All charges incurred after the store opens can make a huge difference in the long-term economics of the store, including licensing costs and requirements, support requirements, platform maintenance and costs associated with monitoring the AI model.

What Client Reviews and Ratings Reveal About Risk

Service Catalog Completeness

BI Modernization: Beyond implementation

What to Consider

Services to consider include:

• Strategy & assessment
• Roadmap creation
• Artificial intelligence (AI) integration
• Managed analytics
• User enablement programs

Questions to Ask

• What services are available after go-live?
• How will knowledge transfer occur?
• What optimization support is available?

Benchmarking Indicators

• End-to-end solutions
• Long-term support options
• Continuous improvement mechanisms

Perceptive Analytics works with companies from beginning to end of the analytics life cycle, including readiness assessments, reporting automation, AI powered dashboards, and ongoing optimization efforts.

Client Reviews, References, and Ratings

Both reviews and references are valuable ways to verify that you’ve done due diligence. When researching a vendor, look for:

• Public case studies, customer references, and long-term client relationships
• Descriptions of measurable results

Questions to ask include:

• Can I contact a recent customer?
• What obstacles did you encounter while delivering the solution?
• What did the company learn from that experience?

Evaluation factors include:

• Business impact measured by quantifying the results
• Transparent metrics for success (e.g., using a graph to document your progress).
• Industry-specific solutions and references (examples include two projects completed by Perceptive Analytics).

An excellent example of an advanced analytics solution is Perceptive Analytics’ Data-Driven Forecasting solution, which demonstrates how to improve sales planning and forecasting accuracy using predictive data.

Another example of an advanced analytics solution is Perceptive Analytics’ Real-Time Review Insights, which shows how automated solutions have reduced manual data entry and made it easier for people to access actionable information.

Most effective references will be based on measurable business results achieved from using a vendor’s product(s) rather than simply by implementing their technology(s).

Security, Compliance, and Responsible AI Practices

There are new governance and compliance requirements due to the introduction of AI agents.

What to look for:

• Responsible AI policies
• Security controls
• Compliance frameworks
• Model governance procedures

Questions to ask:

• How is the use of AI outputs monitored?
• What controls do we have in place to prevent the use of AI inappropriately?
• As the use of AI expands, how is compliance achieved for agencies using AI?

Evaluation Indicators:

• Formal governance frameworks
• Auditability mechanisms
• Human-in-the-loop processes or methods

The NIST AI Risk Management Framework provides a framework for identifying, assessing, mitigating, and monitoring AI-related risks across the full lifecycle of AI systems:

In addition, the OECD AI Principles build upon the NIST AI Risk Management Framework by highlighting critical areas – transparency, accountability, robustness, and human-centered AI governance.

With this new level of governance and compliance becoming required as AI products are being used in business intelligence applications and environments, both frameworks are becoming increasingly relevant.

Operating Model Fit and Co-Delivery Approach

The final evaluation criterion is operational compatibility.

What to look for

• Collaborative delivery models
• Knowledge transfer plans
• Internal capability development
• Flexible engagement structures

Questions to ask

• How will responsibilities be shared?
• How much internal staffing is required?
• What happens after deployment?

Evaluation signals

• Strong stakeholder engagement
• Transparent communication
• Sustainable operating models

At Perceptive Analytics, we believe successful BI modernization should leave organizations more self-sufficient over time. Solutions should require minimal maintenance, support future growth, and provide analysts with tools that accelerate analysis rather than create additional administrative work.

Checklist: How To Shortlist Your BI Modernization Partner

Refer to the following 10-item checklist for evaluating an AI consulting firm for Modernizing Business Intelligence (BI):

• Proven track record of success with Modernizing BI
• Expertise as an AI Agent
• Experience in the industry/domain
• Governance and Data Quality
• Alignment with Technology Stack
• Transparency in Pricing
• Complete Service Offering
• Strong Client References/Reviews
• Responsible AI and Security Practices
• Operating Model and Co-Delivery

Your organization should review and score each of the firms consistently across these ten categories; and request supporting evidence for all claims made.

Conclusion

Choosing an AI consulting firm to help with the modernization of your company’s BI, will require more than evaluating the firm’s branding and/or their claims of expertise in AI. Your strongest partner will possess a proven record of experience with BI modernization, expertise as an AI Agent, have strong governance, have experience in your industry, and will be able to produce tangible evidence of business improvement and growth. By completing the 10-point process, you will be able to create more objective criteria for your Request For Proposal (RFP) to reduce your implementation risk and to identify potential partners who will provide sustainable business value to your organization.

Next Steps

• Download our BI Modernization Partner Evaluation Checklist and compare AI consulting firms side-by-side.
• Request a BI Modernization Readiness Review from Perceptive Analytics in order to evaluate your current analytics environment, identify opportunities for BI Modernization, and establish more objective criteria when selecting a partner prior to issuing the RFP.

Contact us here